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Abstract

Li-rich manganese-based layered oxide cathodes (LMNO) are strong candidates for next-generation high-energy-density lithium-ion batteries thanks to their high specific capacity and low cost — but they face compounding failure modes: irreversible oxygen release during the first cycle, transition-metal migration that drives irreversible layered-to-spinel or layered-to-rock-salt phase transformation, continuous voltage decay from oxygen-loss-induced transition-metal valence reduction, and the poor rate capability inherent to Li₂MnO₃-type components. To address these challenges, this study introduces a Li‑Rich Hybrid Cathode — designated LMDR — designed through an electrode-level physical blending strategy that combines micron-scale Li-rich layered oxide (LMNO) with nanoscale, rigid Ni-based disordered rock-salt cathode (DRX) particles, followed by a low-temperature heat treatment to construct a 3D interstitial network. In this hybrid architecture, LMNO serves as the structural “pillar” providing the primary capacity and layered diffusion scaffold, while DRX particles uniformly embed within LMNO’s inter-particle gaps, forming a percolating network that enhances ion/electron transport and redistributes mechanical stress. This spatial coupling improves Li-rich cathode compaction density and conductivity — key parameters validated using the IEST PRCD1100 powder resistivity and compaction density tester — enabling reversible oxygen redox, suppressing oxygen release and transition-metal dissolution, and mitigating stress accumulation during cycling. The optimized Li-Rich Hybrid Cathode (LMDR-10) delivers higher initial Coulombic efficiency, enhanced cycling stability, reduced voltage decay, and superior rate performance.

📄 Source Paper

First Author: Jianan Hao · Corresponding Authors: Dr. Jinyang Dong, Prof. Yuefeng Su, Assoc. Prof. Lai Chen

Rational Design of Li-Rich Hybrid Cathodes with Stiff Redox-Active Interstitial Networks Enabling Local Environment Engineering for Reversible Oxygen Redox

DOI: 10.1016/j.ensm.2026.105121
| Journal: Energy Storage Materials, 2026, 88, 105121
| Affiliations: School of Materials Science and Engineering, Beijing Institute of Technology; BIT Chongqing Innovation Center; China Electric Power Research Institute; Beijing Institute of Technology, Zhuhai Campus

✓ IEST Instrument acknowledged — IEST Powder Resistivity & Compaction Density Tester (PRCD1100) used in this research

1. Research Background

Li-rich manganese-based layered oxide cathodes (LRM/LMNO) are considered one of the most promising candidates for next-generation high-energy-density lithium-ion batteries, owing to their high specific capacity and low cost. However, LRM materials face multiple challenges in practical application: the characteristic Li-O-Li configuration that enables anionic redox activity also drives irreversible oxygen release, causing first-cycle capacity loss; during deep delithiation, transition metals readily migrate from octahedral sites in the layered structure into tetrahedral sites within the lithium layer, triggering irreversible transformation of the layered phase into spinel or rock-salt phases; oxygen loss further reduces transition-metal valence states, causing continuous voltage decay; and the inherently low ionic conductivity and sluggish oxygen-anion redox kinetics of the Li₂MnO₃ component severely constrain rate performance.

Conventional modification strategies — doping, surface coating, heterostructure design, and crystal-facet engineering — offer some improvement but often require complex precursors and cumbersome processing. More importantly, single-material modification struggles to simultaneously resolve structural instability, ion-transport kinetics, interfacial side reactions, and low compaction density together. Developing electrode-level synergistic design strategies — achieving performance complementarity through physical coupling of different material systems — therefore carries significant scientific and engineering value.

2. Article Overview

Prof. Yuefeng Su, Assoc. Prof. Lai Chen, and Dr. Jinyang Dong’s team at Beijing Institute of Technology have proposed an electrode-level physical blending design strategy: physically mixing micron-scale Li-rich layered oxide cathode (LMNO) with nanoscale, rigid Ni-based disordered rock-salt cathode material (DRX), followed by low-temperature heat treatment, successfully constructing a hybrid cathode (LMDR) with a 3D interstitial network for cathode materials.

In this design, LMNO functions as the structural “pillar” that provides the primary capacity contribution and layered diffusion scaffold, while the additionally introduced DRX particles embed uniformly within the inter-particle gaps of LMNO, forming a percolating network that simultaneously enhances ion/electron transport and redistributes mechanical stress. This spatial synergistic coupling effectively regulates the local chemical and mechanical environment, enabling reversible oxygen redox, suppressing oxygen release and transition-metal dissolution, and mitigating stress accumulation during cycling.

The optimized LMDR-10 cathode demonstrates superior initial Coulombic efficiency, enhanced cycling stability, reduced voltage decay, and excellent rate performance. This work, titled “Rational Design of Li-Rich Hybrid Cathodes with Stiff Redox-Active Interstitial Networks Enabling Local Environment Engineering for Reversible Oxygen Redox,” was published in Energy Storage Materials (2026, 88, 105121).

3. Article Highlights

3.1 Structure, Morphology, Compaction Density, and Conductivity

Figure 1. LMDR structure, morphology, and compaction density/conductivity test results.

Results show that construction of the LMDR rigid network does not alter the phase composition or lattice parameters of the individual components, and LMDR-10 achieves uniform distribution of all constituents. Compaction density and powder resistance testing show that LMDR effectively increases electrode density while reducing overall resistance — a direct benefit for volumetric energy density and rate capability in the finished cell. The material’s compaction density and powder resistance were tested using the Powder Resistivity & Compaction Density Tester (PRCD1100) from IEST Instrument.

Figure 2. Official acknowledgments excerpt from Energy Storage Materials (2026) — Beijing Institute of Technology research team acknowledges IEST Instrument’s support for compaction density and conductivity testing.

3.2 Electrochemical Performance

Figure 3. (a) The cycling performance of all samples at 1 C between 2.0–4.6 V; The dQ/dV curves during discharging process of (b) LMNO, (c) LMDR-5, (d) LMDR-10, and (e) LMDR-15; GITT curves and the calculated Li+ diffusion coefficients of (f) LMNO, (g) LMDR-5, (h) LMDR-10, and (i) LMDR-15; (j) Average discharge voltage profiles of all samples at 1 C between 2.0–4.6 V; (k) Rate performance of all samples.

Electrochemical testing results show that after 300 cycles at 1C, LMDR-10 exhibits improved capacity retention and reduced voltage decay relative to pristine LMNO. dQ/dV analysis indicates that LMDR-10 effectively suppresses the irreversible transformation of the layered phase into the spinel phase. GITT testing confirms that LMDR-10 increases the lithium-ion diffusion coefficient. LMDR-10 also shows improved rate capability across increasing current densities.

3.3 Oxygen Redox Behavior: DEMS, In-Situ EIS, and Soft X-Ray Absorption Spectroscopy

Figure 3. DEMS of O2 and CO2 of (a) LMNO and (b) LMDR-10; In situ EIS of (c) LMNO and (d) LMDR-10 during charging process; (e) The fitting results of Rct and Rs during charging process; Normalized soft XAS spectra at selected voltage during the initial and second cycle of O K-edge of (f) LMNO and (g) LMDR-10; (h) Intensity ratio of OKL/OKH from the actual test results.

DEMS (differential electrochemical mass spectrometry) shows that oxygen redox reversibility is improved in LMDR-10. In-situ EIS reveals that interfacial side reactions are effectively suppressed in LMDR-10. O K-edge soft X-ray absorption spectroscopy analysis shows that the TM-O framework structural stability is enhanced in LMDR-10. Together, these results confirm that the LMDR design achieves a stabilized surface structure and regulated anionic redox activity, thereby improving overall electrochemical stability.

3.4 Multiscale Post-Cycling Characterization

Figure 5. Nyquist plots of different samples at (a) initial cycle and (b) 100th cycle; Raman spectra after 100 cycles of (c) LMNO, (d) LMDR-5, (e) LMDR-10, and (f) LMDR-15; Atomic force microscopy (AFM) images showing DMT modulus of (g) LMNO and (h) LMDR-10 after cycling; XPS spectra after 100 cycles of O 1 s in (i) LMNO and (j) LMDR-10; Contour plots of the Mn K-edge WT-EXAFS spectra of cycled (k) LMNO and (l) LMDR-10.

Multiscale characterization of cycled electrodes provides further insight into LMDR-10’s advantages: phase transformation in LMDR-10 is significantly suppressed, stress distribution is more uniform, lattice oxygen signal intensity is higher after cycling, and Mn-O and Mn-Mn bond strengths are higher relative to pristine LMNO. Laboratory-scale thick-electrode testing further confirms that LMDR’s advantages persist even at higher areal loading — an important indicator for practical cell-level scale-up.

3.5 COMSOL Simulation: Li-Ion Concentration and Stress Distribution

Figure 6. (a) Simulation diagram of Li-ion concentration distribution for LMNO and LMDR; (b) Simulation diagram of stress distribution for LMNO and LMDR.

COMSOL simulation further demonstrates that the LMDR rigid network produces more uniform lithium-ion concentration and mechanical stress distribution during charge-discharge cycling — significantly mitigating particle microcrack formation and electrode cracking.

LMDR-10 vs Pristine LMNO: Key Performance Metrics

Table 1. Summary comparison of pristine LMNO and LMDR-10 hybrid cathode across compaction density, electrochemical, spectroscopic, and simulation results reported in this study.

4. Conclusion

This work, through an electrode-level design strategy, successfully integrates micron-scale Li-rich manganese-based layered oxide cathode with nanoscale rigid disordered rock-salt cathode material in a synergistic manner, constructing a cathode material (LMDR) built around a 3D interstitial network for cathode materials. The resulting LMDR-10 demonstrates multiple advantages: high initial Coulombic efficiency, excellent long-term cycling stability, significantly suppressed voltage decay, and outstanding rate performance.

This work offers mechanistic insight into how inter-particle phase coupling and local environment engineering can synergistically stabilize redox chemistry in Li-rich layered oxides, providing a scalable electrode-design paradigm for developing high-energy-density, long-life Li-rich cathode materials.

5. Original Article

Jianan Hao, et al. Rational Design of Li-Rich Hybrid Cathodes with Stiff Redox-Active Interstitial Networks Enabling Local Environment Engineering for Reversible Oxygen Redox. Energy Storage Materials, 2026, 88, 105121.

6. FAQs

6.1 What is a Li-rich manganese-based layered oxide cathode and why is it difficult to commercialize?

A Li-rich manganese-based layered oxide cathode (LRM/LMNO) is a high-capacity, low-cost cathode material considered a leading candidate for next-generation high-energy-density lithium-ion batteries. Its characteristic Li-O-Li local configuration enables anionic (oxygen) redox activity that boosts specific capacity beyond conventional transition-metal-only redox. However, this same structural feature drives irreversible oxygen release during early cycling, causing first-cycle capacity loss. Deep delithiation also promotes transition-metal migration from layered octahedral sites into tetrahedral sites in the lithium layer, triggering irreversible transformation toward spinel or rock-salt phases — which, combined with oxygen-loss-driven transition-metal valence reduction, causes continuous voltage decay and severely limits rate capability due to the low ionic conductivity of the Li₂MnO₃ component.

6.2 What is a 3D interstitial network for cathode materials and how does it stabilize Li-rich cathodes?

A 3D interstitial network for cathode materials, as demonstrated in the LMDR hybrid cathode design, is formed by physically embedding nanoscale rigid particles — in this case, a Ni-based disordered rock-salt (DRX) cathode material — uniformly within the inter-particle gaps of a micron-scale layered cathode host (LMNO). This creates a percolating three-dimensional network that provides additional ion and electron transport pathways beyond the host material’s own diffusion channels, while also mechanically redistributing stress across the electrode during cycling. By regulating both the local chemical environment (suppressing oxygen release and transition-metal dissolution) and the local mechanical environment (reducing stress concentration and microcrack formation), this interstitial network stabilizes redox chemistry without requiring complex doping or coating chemistry.

6.3 Why is a Ni-based disordered rock-salt cathode used as the rigid interstitial component instead of a coating or dopant?

A Ni-based disordered rock-salt cathode (DRX) was selected as a nanoscale, rigid, redox-active particle rather than an inert coating or a simple dopant because it serves multiple simultaneous functions that single-material modification approaches cannot achieve together. As a physically distinct, mechanically stiff particle, it structurally supports the softer layered LMNO framework and redistributes mechanical stress during lithiation/delithiation. Because it is itself redox-active and disordered rock-salt materials are known for reasonable ionic conductivity despite their disordered cation arrangement, it also contributes meaningfully to ion/electron transport rather than acting as dead weight. This combination — mechanical reinforcement plus functional redox and transport contribution — is difficult to achieve with a conventional surface coating or lattice dopant, which typically address only one failure mode (interfacial stability or bulk doping effects) rather than the coupled mechanical-chemical degradation pathways in Li-rich cathodes.

6.4 How does compaction density testing validate the LMDR hybrid cathode design?

Compaction density testing directly validates whether physically blending nanoscale DRX particles into the LMNO host actually improves electrode-level packing efficiency, rather than simply diluting capacity with an inactive filler. In this study, compaction density and powder resistance were measured using the IEST PRCD1100 Powder Resistivity & Compaction Density Tester, showing that the LMDR hybrid effectively increases electrode density while simultaneously reducing overall powder resistance — a combination that directly benefits volumetric energy density (through higher active material packing per unit electrode volume) and rate capability (through lower electronic resistance) in the finished cell. This measurement is essential because a design that improved cycling stability but reduced compaction density would trade one performance metric for another rather than achieving genuine, scalable improvement.

6.5 Does the LMDR hybrid cathode design work in thick electrodes, or only in thin laboratory coin cells?

This study specifically tested laboratory-scale thick-electrode configurations at higher areal loading — a critical validation step, since many cathode modification strategies that work well in thin, low-loading coin cells fail to translate to the higher loadings required for practical, energy-dense cell formats. The results confirmed that LMDR’s advantages persist even at higher areal loading, suggesting the 3D interstitial network design is not merely a thin-electrode artifact but provides genuine mechanical and transport benefits that scale toward more industrially relevant electrode configurations. This is an important signal for the design’s potential scalability, though further validation at full pouch-cell or cylindrical-cell format would be the next logical step toward commercial application.

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Abstract

The core difference between the two-probe and four-probe method for battery electrode resistance testing is what each method actually measures. The 2 point probe (two-point probe) method measures total through-plane resistance — the composite of contact resistance, coating layer resistance, and current collector resistance in series — making it reliably sensitive to coating quality differences in both low- and high-resistance electrodes. The 4 point probe (four-point probe) method applies current and measures voltage through separate in-plane contacts, meaning that for low-resistance electrodes, electrons predominantly bypass the coating through the metal foil (path 2), and the measured resistance approaches the foil baseline — making the coating invisible to the measurement. For battery electrode resistance testing of cathode, anode, and dry-process film electrodes, the 2 probe method is the appropriate choice.

1. Introduction: Why Electrode Resistance Method Selection Matters

Lithium-ion battery resistance is one of the key indicators for evaluating battery performance. Its magnitude directly influences capacity, cycle life, and safety. Factors affecting lithium-ion battery resistance include electrode materials, formulation, electrolyte, coating uniformity, and intrinsic electrode resistance. Electrode resistance reflects both the performance of the electrode active material and the quality of its coating formulation.

By analyzing electrode resistance test results with the correct measurement method, manufacturers can optimize the coating process and material formulation to enable rapid evaluation of material systems, and quickly identify, classify, and eliminate electrodes with excessively high resistance before they enter cell manufacturing. The choice between the two-point probe method and the four-point probe method for battery electrode resistance is not interchangeable — the two methods measure fundamentally different physical quantities, and selecting the wrong one produces data that cannot differentiate coating quality from foil contribution.

Figure 1. (a) Schematic of the 2 point probe (two-point probe) configuration; (b) Schematic of the 4 point probe (four-point probe) configuration for battery electrode resistance measurement.

In the 2 point probe configuration, the terminal contacts are placed at the vertical ends of the electrode stack, and an AC voltage signal is applied to collect current and calculate resistance. The result is the series sum of contact resistance, coating (active material layer) resistance, and current collector resistance — the total through-plane resistance of the electrode composite. In the 4 point probe configuration, current contacts and voltage-sensing contacts are placed separately on the sample surface, and voltage is measured between the inner contacts while current flows through the outer contacts — eliminating contact resistance but also redirecting current in-plane through whichever path offers lowest resistance.

2. Experimental Equipment and Test Methods

2.1 Test Equipment

The IEST BER2500 Battery Electrode Resistance Tester was used for all measurements. The instrument supports both 2 point probe and 4 point probe configurations with a 14 mm electrode diameter measurement fixture and an applied pressure range of 5–60 MPa. The MRMS software automatically records electrode thickness, resistance, resistivity, and conductivity at each test condition.

Figure 2. (a) Appearance of the IEST BER2500 Battery Electrode Resistance Tester; (b) BER2500 structural diagram showing electrode fixture and probe configurations.

2.2 Samples

Seven sample types were tested: cathode electrode cathode-1 (low resistance), cathode electrode cathode-2 (high resistance), anode electrode anode-1 (low resistance), anode electrode anode-2 (high resistance), binder-free pure membrane samples (dry process, single layer and double layer), pure aluminum foil, and pure copper foil.

3. What Is the Difference Between Two Probe and Four Probe Method for Battery Electrodes?

3.1 Cathode Electrode Resistance: 4 Point Probe vs 2 Point Probe

For cathode electrode resistivity tests (Figure 3), a striking divergence emerges between the two methods when applied to low-resistance electrodes:

• Four-point probe method (4 point probe): The resistivity of low-resistance cathode-1 measured by 4-probe (2.1×10⁻⁶ Ω·cm) differed by only approximately one order of magnitude from pure aluminum foil (2.884×10⁻⁵ Ω·cm) — the coating and foil resistivities are so close that the 4-probe measurement cannot reliably distinguish the coating contribution. For high-resistance cathode-2, the 4-probe method yielded 1.3316 Ω·cm, clearly elevated above the foil.

• Two-point probe method (2 point probe): The resistivity of cathode-1 measured by 2-probe (1444.94 Ω·cm) was several orders of magnitude greater than the aluminum foil baseline (0.370026 Ω·cm) — a clear, unambiguous separation of coating from foil at all resistance levels. Both cathode-1 and cathode-2 showed resistivity values substantially higher than their respective foil baselines.

Figure 3. (a) Four-point probe cathode electrode resistivity test — cathode-1 approaches foil resistance, making coating indistinguishable; (b) Two-point probe cathode electrode test — coating clearly differentiated from foil baseline at both low and high resistance levels.

3.2 Anode Electrode Resistance: 4 Point Probe vs 2 Point Probe

Analogous trends were observed for anode electrode resistance (Figure 4). The four-point probe method could not distinguish the influence of the coating layer when testing low-resistance anode samples — the measured resistance closely approached the copper foil baseline — while it did show elevated values for high-resistance anode samples. The two-point probe method, by contrast, clearly revealed resistivity differences between coatings and pure copper foil for both low-resistance and high-resistance anode samples.

Figure 4. (a) Four-point probe anode electrode resistivity test — low-resistance anode coating is indistinguishable from foil; (b) Two-point probe anode electrode test — coating clearly differentiated from copper foil at all resistance levels.

3.3 Pure Membrane (Binder-Free Film) Resistance

For binder-free pure film samples coated using the dry process, both methods were compared (Figure 5). The 4 point probe measured resistivity of the single-layer film (0.27 Ω·cm) is nearly identical to that of the double-layer film (0.26 Ω·cm) — values far above both aluminum foil (2.884×10⁻⁵ Ω·cm) and copper foil (1.832×10⁻⁵ Ω·cm), because without a metallic current collector substrate, the in-plane current path is also through the film, and the 4-probe result is genuinely the film resistivity. The 2 point probe measured resistivity of single-layer film (1.27 Ω·cm) and double-layer film (1.23 Ω·cm) are closely comparable but consistently higher than the 4-probe values — reflecting the additional contact resistance term captured by the 2-probe method.

This result explains why the four-probe method does produce reliable resistivity data when measuring self-standing film electrodes without a foil substrate — the only case where the metallic bypass path (path 2) is absent.

Figure 5. Pure membrane (binder-free, dry-process film) resistivity measurement by 4 point probe vs 2 point probe. Single-layer: 0.27 Ω·cm (4-probe) vs 1.27 Ω·cm (2-probe). Double-layer: 0.26 Ω·cm vs 1.23 Ω·cm. Without a foil substrate, the 4-probe method accurately measures film resistivity.

4. Mechanism: Why the 4 Point Probe Fails for Low-Resistance Electrodes on Foil

The equivalent circuit analysis of both probe configurations (Figure 6) explains the divergent results:

Two-point probe circuit (Figure 6a): Current and voltage terminals are at the vertical (through-plane) ends of the electrode stack. The measurement result is the total series sum of: contact resistance (R_contact) + coating layer resistance (R_coating) + current collector (foil) resistance (R_foil). Because the coating resistance of most battery electrodes is far higher than the foil resistance, the total measured resistance is dominated by the coating — making the 2-probe method inherently sensitive to coating quality and batch variation.

Four-point probe circuit (Figure 6b): Applied current enters through two outer surface contacts and can flow via three paths — path 1 (through the coating layer vertically), path 2 (horizontally through the metallic current collector), or path 3 (diagonal combinations). For low-resistance electrodes where the foil resistance is comparable to or lower than the coating resistance, electrons predominantly flow through path 2. The voltage measured between the inner contacts therefore reflects primarily the foil resistance — not the coating. This is why the 4-probe result for cathode-1 (2.1×10⁻⁶ Ω·cm) was close to pure aluminum foil (2.884×10⁻⁵ Ω·cm). Only when the coating resistance is very high (cathode-2: 1.3316 Ω·cm) does path 1 carry appreciable current, making the 4-probe measurement coating-sensitive.

Furthermore, because the absolute resistance values measured by the 4-probe method are very small (often below 1 Ω for foil-backed electrodes), extremely high requirements are imposed on instrument precision, measurement range, and pressure control stability — making reliable, repeatable data difficult to obtain under practical laboratory conditions.

Figure 6. Equivalent circuit diagrams. (a) Two-point probe: current passes through the full vertical stack — contact resistance + coating + foil in series. (b) Four-point probe: current divides into three paths; for low-resistance electrodes, path 2 (through foil) dominates, masking coating contribution.

2 Probe vs 4 Probe: Method Comparison Summary for Battery Electrodes

Table 1. 2 probe vs 4 probe method comparison for battery electrode resistance measurement — BER2500 test data across cathode, anode, and pure film samples

5. Summary

This study demonstrates that the two-point probe method is the appropriate choice for measuring electrode resistance in lithium-ion battery cathodes and anodes. The 2-probe method measures total through-plane resistance — capturing the composite of contact, coating, and foil contributions — and reliably differentiates coating quality from foil baseline at all resistance levels. The four-point probe method, while eliminating contact resistance, causes current to bypass the coating through the metallic foil in low-resistance electrodes, rendering the coating contribution invisible. Only when the electrode coating resistance substantially exceeds the foil resistance (typically high-resistance or degraded samples) does the 4-probe method become coating-sensitive — and even then, the small absolute values demand high instrument precision and pressure control stability. For production screening, process optimization, and material formulation evaluation of foil-backed battery electrodes, the 2 point probe method provides more reliable, actionable resistance data.

6. References

[1] Hiroki Kondo et al. Influence of the Active Material on the Electronic Conductivity of the Positive Electrode in Lithium-Ion Batteries. Journal of The Electrochemical Society, 2019,166 (8) A1285-A1290

[2] B.G.Westphal et al. Influence of high intensive dry mixing and calendering on relative electrode resistivity determined via an advanced two point approach. Journal of Energy Storage 2017, 11, 76–85

[3] Nils Mainusch et al. New Contact Probe and Method to Measure Electrical Resistances in Battery Electrodes. Energy Technology.2016, 4, 1550-1557

7. FAQs

7.1 What is the difference between two probe and four probe method for battery electrode resistance?

The key difference between the two-probe and four-probe methods for battery electrode resistance is what each measurement captures. The two-point probe method applies current and voltage at the vertical (through-plane) ends of the electrode stack, measuring the total series resistance of contact layers, active material coating, and metallic current collector. The four-point probe method places current and voltage contacts separately on the electrode surface, routing current in-plane and eliminating contact resistance — but this also allows current to bypass the coating through the highly conductive metallic foil. For low-resistance electrodes on aluminum or copper foil, the four-probe measurement therefore approaches the foil baseline, making the coating contribution invisible. The two-probe method is sensitive to the coating at all resistance levels and is the recommended choice for battery electrode quality control.

7.2 When should I use 2 probe vs 4 probe for electrode resistance measurement?

Use the 2 probe (two-point probe) method for electrode resistance measurement whenever the electrode consists of an active material coating on a metallic foil substrate — which describes the vast majority of lithium-ion battery cathode and anode electrodes. The 2-probe method’s through-plane measurement captures coating resistance independently of the foil’s in-plane conductivity, reliably distinguishing low- and high-resistance coatings at all levels. Use the 4 point probe method when measuring the intrinsic resistivity of freestanding electrode films without a foil substrate, or when comparing material-level resistivity of powders and pure films where contact resistance elimination is critical and no metallic bypass path exists.

7.3 Why does the 4 point probe method fail for low-resistance battery electrodes?

The four-point probe method fails for low-resistance battery electrodes because the measurement current divides between three parallel paths at the electrode surface: path 1 through the active material coating, path 2 through the highly conductive metallic current collector (aluminum or copper foil), and diagonal combinations. When electrode coating resistance is low (comparable to or only slightly higher than the foil’s in-plane resistance), electrons predominantly travel through path 2 — the metallic foil — bypassing the coating entirely. The measured voltage reflects primarily the foil resistance rather than the coating. For cathode-1, the 4-probe result (2.1×10⁻⁶ Ω·cm) was within one order of magnitude of pure aluminum foil (2.884×10⁻⁵ Ω·cm), providing no usable coating quality information. The 2-probe method avoids this problem by directing current through the full electrode thickness, forcing it to pass through the coating.

7.4 What does the two-point probe method measure in a battery electrode?

The two-point probe method measures the total through-plane resistance of a battery electrode — the series sum of contact resistance at both electrode faces, active material coating resistance, and current collector (foil) resistance. Because the active material coating typically has resistivity several orders of magnitude higher than the metallic foil, the total resistance measured by the 2-probe method is dominated by the coating contribution. This makes two-probe resistivity measurement directly sensitive to coating formulation, conductive additive loading, calendaring conditions, and batch-to-batch material variation — the parameters that battery manufacturers need to monitor and control in cathode and anode electrode production.

7.5 How do 2-probe and 4-probe resistivity values compare for the same battery electrode?

For the same battery electrode, the 2-probe resistivity value is always higher than the 4-probe value — because the 2-probe measurement includes contact resistance and measures only through-plane current flow, while the 4-probe measurement eliminates contact resistance and allows in-plane bypass through the foil. For a binder-free pure film sample (no foil), this study measured 1.27 Ω·cm (2-probe) vs 0.27 Ω·cm (4-probe) for the single-layer film — a factor of approximately 4.7× difference attributable to the contact resistance term. For foil-backed low-resistance electrodes, the divergence is even more dramatic: the 4-probe value can approach the foil baseline while the 2-probe value clearly shows the coating contribution.

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Abstract

The MacMullin number (also spelled McMullin number; symbol Nm, defined as Nm = τ/ε = σ/σ_eff) quantifies effective ionic transport resistance inside porous battery electrodes — the ratio of electrode tortuosity τ to porosity ε, equivalently the ratio of bulk electrolyte conductivity σ to effective ionic conductivity σ_eff. A lower MacMullin number means less impeded Li⁺ transport and directly predicts better electrolyte rate performance. Nm is measured from symmetric-cell electrochemical impedance spectroscopy (EIS): ionic resistance R_ion is extracted from the Nyquist plot (3× the difference between low-frequency and high-frequency real-axis intercepts), then Nm = R_ion·A·ε·σ/d. In this study, three LiPF₆ electrolyte formulations (0.8M, 1.0M, 1.6M in EC:DMC:EMC = 3:5:2) were evaluated using the IEST EIC1400 tortuosity tester. The Nm rank (Formula 2 < Formula 1 < Formula 3) matched the 10C battery capacity retention rank exactly (93.92% / 90.55% / 89%), validating MacMullin number as a fast, reliable screening metric for electrolyte rate performance without requiring full-cell long-term testing.

1. Preface

This paper presents a rapid, practical method to evaluate lithium-ion electrolyte performance by linking electrolyte rate capability with electrode sheet tortuosity and the McMullin number. Combining symmetric-cell electrochemical impedance spectroscopy (EIS) with electrode tortuosity measurement enables quantification of how pore microstructure (pore size, throat diameter, connectivity) and electrolyte properties (viscosity, wettability) govern ion transport and battery rate behavior. The approach accelerates electrolyte R&D by providing an early screening metric that correlates with full-cell rate tests.

2. Introduction: Why Electrode Tortuosity Matters for Electrolyte Rate Performance

The electrolyte is the ionic highway inside a lithium-ion cell. Fast charge and discharge requires rapid Li⁺ transport through: (1) the bulk liquid phase, (2) desolvation and crossing the SEI membrane, and (3) porous electrode pathways to active particles. The microstructure of electrode coatings — especially pore connectivity and geometric complexity — determines the actual path Li⁺ must follow. This geometric impediment is captured by electrode tortuosity (τ), commonly defined as the squared ratio of actual transport path length to straight-line coating thickness: τ = (L′/L)².

Because direct porosity measurement is often complex in production settings, the MacMullin number (Nm = τ/ε) — the tortuosity-to-porosity ratio — is widely used as a convenient, physically meaningful descriptor of effective ion transport resistance inside porous electrodes. (Note: some literature variants spell it McMullin number or mcmullin number; all refer to the same concept, named after R. A. MacMullin.)

For a battery to achieve excellent rate performance, the electrolyte needs high lithium-ion transport capacity, and the speed of that transport is directly related to electrolyte performance.^[1] As shown in Figure 1, the charging process involves four stages: (1) solvated lithium forms and diffuses under concentration and potential gradients; (2) at the SEI interface, solvated Li⁺ undergoes desolvation; (3) desolvated Li⁺ transports through the SEI membrane; and (4) Li⁺ diffuses through the active material body to form an intercalation compound.^[2,3]

Fig. 1. (a) Schematic of lithium-ion battery charging process — four stages governing electrolyte rate performance: solvation/diffusion, SEI desolvation, SEI transport, active material intercalation; (b) Energy barrier profile at each stage^[3,4]

3. Principle: From EIS to MacMullin Number

The effective ionic conductivity in a porous electrode, σ_eff, links bulk electrolyte conductivity σ, porosity ε, and electrode tortuosity τ through the Bruggeman-type relation that underpins the MacMullin number:

Rearranged, the MacMullin number equals Nm = τ/ε = σ/σ_eff. Practically, σ_eff is obtained from EIS on a symmetric cell (two identical electrodes separated by the same porous coating):

1. Assemble a symmetric cell and run EIS (frequency sweep, e.g., 100 kHz to 0.01 Hz).

2. Fit the low-frequency intercepts to extract ionic resistance R_ion of the electrode coating from the Nyquist plot.

3. Compute σ_eff from geometry: σ_eff = d / (R_ion·A), where d is coating thickness and A is electrode area.

4. Calculate MacMullin number: Nm = σ/σ_eff (with σ from bulk electrolyte conductivity measurement).

This electrochemical route captures not only electrode geometry but also real wetting and electrolyte microphysics (viscosity, surface tension, solvation effects), making Nm a robust predictor of in-cell rate behavior — and a faster alternative to full-cell rate cycle tests for initial electrolyte screening.

4. Test Conditions & Methods

4.1 Test Equipment

Symmetric cell assembly and EIS testing used the IEST EIC1400 Electrode Sheet Tortuosity Tester & Separator Ion Conductivity Tester (Figure 2), which integrates four battery assembly fixtures and provides four-channel simultaneous EIS testing. Specifications: pressure range 0–20 kg; frequency range 100 kHz–0.01 Hz.

Fig. 2. IEST EIC1400 Electrode Sheet Tortuosity Tester — (a) instrument appearance; (b) four-channel symmetric-cell assembly fixtures for MacMullin number measurement

Pouch cell battery performance was evaluated using standard charge-discharge equipment. All three electrolyte formulations were tested with the same cathode and anode electrode sheets in assembled pouch cells.

4.2 Test Samples

Electrodes: LiCoO₂ (LCO) cathode electrode / graphite anode electrode.

Electrolytes:

• Formula 1: 0.8M LiPF₆, EC:DMC:EMC = 3:5:2

• Formula 2: 1.0M LiPF₆, EC:DMC:EMC = 3:5:2

• Formula 3: 1.6M LiPF₆, EC:DMC:EMC = 3:5:2

4.3 Testing Process

Symmetric cells were assembled inside a glovebox using the EIC1400 fixture: the electrode-separator-electrode stack was loaded into the assembly jig, 5 kg pressure applied for approximately 10 minutes for wetting stabilization, then EIS was initiated via the instrument software. Ionic resistance R_ion was extracted from the fitted Nyquist plot, and the MacMullin number was calculated automatically by the software.

Battery rate testing: Soft-pack cells assembled with each electrolyte formulation were cycled at 0.5C, 1C, 3C, 5C, and 10C to measure rate-dependent capacity retention.

4.4 Calculation of the McMullin Number

Where: τ = electrode tortuosity; R_ion = ionic resistance (Ω) from EIS; A = electrode area (cm²); ε = electrode porosity; σ = bulk electrolyte conductivity (S/cm); d = electrode coating thickness (cm). Because porosity measurement is complex, the McMullin number Nm = τ/ε (combining tortuosity and porosity into a single, measurable parameter) is used as shown in Equation (2):

From the symmetric-cell Nyquist plot (Figure 3), R_ion is determined as follows: extend the low-frequency line segment until it intersects the real (X) axis; the difference between this intersection and the high-frequency real-axis intercept, multiplied by three, equals the coating’s ionic resistance R_ion. This R_ion is substituted into Equation (2) to obtain the electrode sheet McMullin number.

Fig. 3. Symmetric-cell EIS Nyquist plot — low-frequency intercept minus high-frequency intercept, multiplied by 3, gives ionic resistance R_ion for MacMullin number calculation

5. Analysis Of Results

Fig. 4. EIS impedance profiles — cathode symmetric cells: formula 1 (a1), 2 (a2), 3 (a3); anode symmetric cells: formula 1 (b1), 2 (b2), 3 (b3). Fitted to extract R_ion and calculate electrode sheet McMullin number

EIS was performed for each electrolyte formulation, using the same cathode or anode electrode sheet assembled into symmetric cells. The ionic resistance of each electrode was extracted by fitting the impedance profile, then Equation (2) was used to calculate the electrode McMullin number. Results are shown in Fig. 5.

Fig. 5. McMullin number for cathode(LiCoO₂) and anode(graphite) electrode sheets in three electrolyte formulations — Nm rank: Formula 2 < Formula 1 < Formula 3

The McMullin numbers of both cathode and anode electrode sheets rank as Formula 2 < Formula 1 < Formula 3. This ordering reflects the combined effect of electrolyte physical properties on effective ionic transport — not just electrode geometry. When electrode tortuosity is measured electrochemically, the effective conductivity obtained reflects actual wetting conditions: viscosity and surface tension differences between formulations produce different wettability, and if electrolyte fails to penetrate into nanopore spaces, the effective ion transport path lengthens — increasing the apparent MacMullin number and hindering Li⁺ shuttle between electrodes, ultimately affecting rate performance, discharge capacity, and service life. The LiPF₆ salt concentration changes the electrolyte’s viscosity and surface tension, which in turn determines wettability. At 1.6M (Formula 3), the higher viscosity increases Nm; at 1.0M (Formula 2), the optimal concentration minimizes Nm and maximizes effective transport.

Table 1. Charging and discharging capacities of different battery formulas at various C-rate

Fig. 6. Rate-capacity retention curves for three electrolyte formulations at 0.5C–10C — Formula 2 (lowest Nm) achieves best 10C retention (93.92%); Formula 3 (highest Nm) shows lowest retention (89%)

Figures 5 and 6 and Table 1 demonstrate that the MacMullin number rank directly correlates with the rate performance rank of soft-pack batteries assembled with the same electrode sheets but different electrolytes. At 10C, the capacity retention is 90.55% (Formula 1), 93.92% (Formula 2), and 89% (Formula 3) — exactly matching the Nm rank (F2 < F1 < F3). This confirms that the electrode sheet McMullin number, measured by EIS tortuosity testing, can predict battery rate performance — enabling rapid electrolyte formulation screening without full cycle testing for each candidate.

6. Practical Implications for Electrolyte and Electrode R&D

• Rapid electrolyte screening: Measuring MacMullin number via symmetric-cell EIS provides a fast, low-material-consumption way to rank electrolyte candidates before committing to full-cell rate tests. This can shorten electrolyte development cycles significantly.

• Process optimization — wetting issues: Differences in McMullin number across electrolyte concentrations or formulations can reveal wetting problems (e.g., poor penetration into electrode nanopores) that are addressable by adjusting electrolyte viscosity, adding wetting agents, or tuning liquid-fill process parameters.

• Electrode design feedback: If MacMullin number remains high despite a well-optimized electrolyte, the bottleneck is electrode geometry — optimize electrode porosity, particle size distribution, or calendering pressure to reduce tortuosity.

• Battery simulation inputs: Nm serves as a compact, physically meaningful input parameter for porous-electrode models and multi-physics battery simulations to predict rate capability and internal resistance without full-cell experiments.

7. Limitations & best practices

• Geometry accuracy: Accurate coating thickness (d) and electrode area (A) measurements are critical when computing σ_eff; calibrate thickness measurement regularly.

• Wetting interpretation: Electrochemical Nm reflects real wetting as well as geometry; a high McMullin number could result from incomplete electrolyte wetting into nanopores rather than from geometric tortuosity alone. Complement with FIB-SEM or X-ray CT imaging when geometric versus wetting contributions need to be separated.

• Reproducibility conditions: Maintain consistent test temperature and assembly pressure across measurements; both influence ionic conductivity and therefore the calculated MacMullin number.

8. Summary

This study assembled symmetric cells and pouch cells with three LiPF6 electrolyte formulations and demonstrated a clear correlation between electrode sheet McMullin number (𝑁𝑚) — measured by EIS-based tortuosity testing — and battery rate performance at 0.5C to 10C. The MacMullin number rank (Formula 2 < Formula 1 < Formula 3) matched the rate capacity retention rank, confirming that 𝑁𝑚 from symmetric-cell EIS can serve as a reliable, fast, non-destructive screening metric for electrolyte development. Beyond electrolyte screening, the electrode tortuosity test can be applied to study the effects of electrode formulation, porosity, active material particle morphology, separator type, and manufacturing process parameters on lithium-ion battery performance.

9. References

[1] Li N, Chen Z P, Ren W C, et al. Flexible graphene-based lithium ion batteries with ultrafast charge and discharge rates[J]. Proceedings of the national academy of sciences of the United States of America, 2012, 109 (43): 17360-17365.

[2] Yamada Y, Furukawa K, Sodeyama K, et al. Unusual stability of acetonitrile-based superconcentrated electrolytes for fast-charging lithium-ion batteries [J]. Journal of the American chemical society, 2014, 136(13): 5039-5046.

[3] Caiwl, Yao Y X, Zhu G L, et al. A review on energy chemistry of fast-charging anodes [J]. Chemical society reviews, 2020, 49 (12): 3806-3833.

[4] Yin ZG, Wu NN, Cao MH, et al. Progress of electrolyte for fast-charging lithium-ion batteries[J]. New Energy Progress, 2024, 12(2): 216-226.

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Abstract

Lithium plating on graphite anodes during fast charging or low-temperature operation presents a major safety and lifetime risk for lithium-ion batteries. This study integrates in-situ dynamic electrochemical impedance spectroscopy (DEIS) with high-precision thickness monitoring to resolve the dynamic evolution of lithium plating in commercial pouch C/LiFePO₄ cells. Lithium plating follows a reproducible three-stage pattern detectable by dynamic electrochemical impedance spectroscopy (DEIS): Stage I — normal graphite lithiation (Rct,a decreases linearly, no Li deposits); Stage II — lithium nucleation and growth (Rct,a declines acceleratedly, particulate deposits confirmed by SEM); Stage III — dendrite growth (Rct,a reaches a near-steady plateau, metallic Li and thick SEI accumulate, irreversible capacity loss accelerates). In-situ thickness measurement (dT/dQ) provides complementary macroscopic evidence of plating severity — DEIS detects plating onset earlier, while dT/dQ better reflects deposit morphology. Together they form a multidimensional descriptor for lithium plating state in C/LFP cells under fast charging and low-temperature operation.

📄 Source Paper

Ying Lin, Wenxuan Hu, Meifang Ding, Yonggang Hu, Yufan Peng, Jinding Liang, Yimin Wei, Ang Fu, Jianrong Lin, Yong Yang

Unveiling the Three Stages of Li Plating and Dynamic Evolution Processes in Pouch C/LiFePO4 Batteries

DOI: 10.1002/aenm.202400894
| Journal: Advanced Energy Materials (2024)
| Institutions: Xiamen University

✓ Equipment: IEST Silicon-Based Anode Swelling Screening System(RSS1400) · IEST In-Situ Cell Swelling Testing System(SWE2100) used in this research

1. Research Background

Lithium plating — metallic Li deposition on graphite — occurs under extreme conditions such as fast charging and sub-ambient temperatures, and can form electrically isolated “dead lithium,” accelerate SEI buildup, and produce dendrites that risk short circuits and thermal runaway. Existing detection methods often identify plating onset but lack resolution to describe its subsequent morphological evolution. This study applies a multidimensional, in-situ methodology combining DEIS and sub-micron thickness monitoring to (1) detect plating onset with high SOC resolution, (2) classify deposition states, and (3) link plating stages to capacity fade and safety risk metrics in commercial LFP pouch cells.

2. Work Overview

Recently, Professor Yong Yang team at Xiamen University comprehensively studied the evolution process of Lithium plating on graphite surfaces in graphite/LiFePO4 pouch batteries under harsh conditions (low temperature/room temperature fast charging) using a combined analysis method of in-situ dynamic electrochemical impedance spectroscopy (DEIS) and thickness measurements. This work expands the application of impedance spectroscopy and thickness measurement in detecting Li plating. Researchers found that the anode charge transfer resistance R_ct,_a exhibits a three-stage variation pattern as Li plating progresses. Combined with mass spectrometry titration (MST) and scanning electron microscopy, they confirmed that these three stages correspond to different Li plating evolution processes: non-plating, lithium nucleation & growth, and dendrite growth. The study also extensively analyzed the effects of lithium plating and different lithium deposition states on battery capacity degradation. This research titled “Unveiling the Three Stages of Li Plating and Dynamic Evolution Processes in Pouch C/LiFePO4 Batteries“ was published in the prestigious journal “Advanced Energy Materials”.

3. Experimental Overview

3.1 Cell Chemistries and Preparation

Commercial pouch cells with graphite anodes and LiFePO₄ cathodes (C/LiFePO₄) were used as the primary test platform. To decouple electrode contributions in thickness signals, LTO (zero-strain anode material) was used in dedicated LTO//LFP and LTO//Graphite assemblies — providing independent reference measurements of each electrode’s volume expansion.

3.2 In-situ instrumentation and measurement strategy

• DEIS (dynamic EIS): A continuous AC perturbation (50 kHz → 5 Hz) was applied during charging. Low-frequency data were de-emphasized to obtain EIS spectra every 33 seconds (<1% SOC resolution). DRT (distribution of relaxation times) analysis extracted the negative-electrode charge transfer resistance Rct,a as a dynamic descriptor of plating state.

• Thickness monitoring:The IEST RSS1400 provided high-resolution (0.1 µm) thickness traces for rapid electrode decoupling experiments. The IEST SWE2100 recorded real-time pouch cell thickness under controlled preload (285 kg) and temperature (0°C) to quantify the dT/dQ metric that serves as the macroscopic plating indicator.

• Ex-situ validation: Mass spectrometry titration (MST) quantified “dead lithium” and SEI species at each stage. SEM and optical microscopy characterized deposited lithium morphology at representative plating stages.

Figure 1. Schematic of the in-situ DEIS and thickness measurement device used for lithium plating detection experiments — electrochemical workstation integrated with the IEST in-situ swelling tester — and the thickness-based method for detecting lithium plating onset.

3.3 Test Matrix

Cells were charged at multiple C-rates (0.1C, 0.2C, 0.5C at 0°C and room-temperature high-rate tests) to induce a spectrum of plating behaviors ranging from plating-free graphite lithiation through nucleation to extensive dendrite growth. Incremental capacity analysis (ICA) and differential OCV (dOCV) were used to cross-validate plating indicators against the DEIS and thickness signals.

4. Key results

Figure 2. Negative electrode EIS Nyquist plot and DRT analysis at different SOC and temperatures — showing R_ct,a evolution that underlies the three-stage lithium plating classification.

Figure 3. Decoupling of graphite and LiFePO₄ thickness expansion behaviors using LTO zero-strain reference assemblies, enabling accurate electrode-level thickness attribution during lithium plating experiments.

4.1 DEIS Identifies a Three-Stage R_ct,a Evolution During Charging

DEIS/DRT analysis of R_ct,a revealed a reproducible three-stage profile under plating-prone conditions — low temperature (0°C) and high C-rate charging in commercial LFP pouch cells:

• Stage I — Linear decrease:R_ct,a decreases gradually, consistent with normal graphite lithiation kinetics. No Li deposits detected at this stage.

• Stage II —Accelerated decline (nucleation/growth):R_ct,a falls more rapidly. DRT indicates the emergence of a plating-related relaxation mode consistent with nascent Li nucleation and small aggregate growth on the graphite surface.

• Stage III —Plateau (dendrite growth):R_ct,a reaches a near-steady plateau despite continued charging, interpreted as a surface increasingly dominated by metallic Li coverage and dendritic morphology. The plateau reflects the transition from mixed insertion-plating to plating-dominated electrochemistry.

Three-Stage Lithium Plating Summary

Table 1. Three-stage lithium plating classification in commercial C/LFP pouch cells, validated by DEIS R_ct,a in-situ thickness dT/dQ, MST, and SEM (Advanced Energy Materials, 2024).

In conclusion, both impedance and thickness measurements can detect the process of lithium plating, but they provide different types of information and potential physical significance. They complement each other and cross-validate findings. Electrochemical information (R_ct,a) and structural information (dT/dQ) together form multidimensional descriptors to accurately determine the state of lithium deposition, facilitating a more comprehensive understanding of the evolution process of lithium plating.

Figure 4. EIS Nyquist plot and DRT of LFP soft-pack batteries during charging at different C-rates, demonstrating how the three-stage lithium plating pattern emerges under more severe charging conditions.

Figure 5. Cell voltage, negative electrode charge transfer resistance R_ct,a/capacity, and thickness variation during lithium plating in LFP pouch cells — the three-stage pattern is visible across both electrochemical and mechanical signals.

4.2 Thickness Metrics (dT/dQ) Complement Electrochemical Detection

• At low rates (0.1C, 0.2C) and 0°C, dT/dQ remained below the empirically established plating threshold, consistent with no-plating classification by DEIS in most cases.

• At 0.5C and higher SOC, dT/dQ exceeded the plating threshold after approximately 1,200 mAh of charge at 0°C — providing a macroscopic signature of extensive lithium accumulation consistent with Stage III.

• DEIS detected lithium plating onset earlier than thickness methods, whereas dT/dQ better reflected the physical severity and bulk morphology of deposited lithium. The two modalities are complementary: electrochemical sensitivity (R_ct,a) and structural sensitivity (dT/dQ) together enable more accurate staging than either method alone.

Figure 6. ICA (incremental capacity analysis) and dOCV (differential open circuit voltage relaxation) cross-validation of the DEIS method for lithium plating detection in LFP pouch cells.

4.3 Ex-Situ Validation Links Stages to Physical Deposits and Capacity Loss

MST and SEM analyses on cells disassembled at representative R_ct,a stages confirmed direct correspondence between the electrochemical classification and physical lithium deposition state:

• Stage I: No detectable metallic Li; normal SEI composition with no abnormal organic components.

• Stage II: MST-quantified active Li deposits present; early SEI organic growth initiated; SEM reveals particulate and clustered deposits on the graphite surface.

• Stage III: Abundant metallic Li and thick SEI layers confirmed; dendritic features observed by SEM; significant irreversible capacity loss attributed to dead lithium and extensive SEI formation.

Figure 7. (a) Mass spectrometry titration results of graphite electrode sheets charged to three different stages of R_ct,a variation. (b) Capacity loss attributed to “dead lithium” and different organic SEI components. (c) Scanning electron microscopy and optical images of three representative cells at different stages of R_ct,a variation. (d) Schematic illustration of lithium plating behavior on graphite surface during Stages II/III of R_ct,a variation.

Figure 8. Schematic of the in-situ DEIS and thickness measurement method monitoring the lithium plating process in LFP pouch cells, with boundaries delineating Stage I, II, and III of lithium plating occurrence.

4.4 Cycle-to-Cycle Advancement of Lithium Plating

In continuous cycling experiments, Stage II and Stage III onset occurred at lower SOC in the second cycle compared to the first. The researchers attributed this to residual lithium on the graphite surface from the first discharge cycle — reducing the nucleation barrier for subsequent Li plating and thereby causing premature plating onset in later cycles. Three batteries were then cycled within SOC ranges corresponding to each plating stage. EMF-based analysis of SOH and Active Lithium Loss (LLI) confirmed that early onset of Stage II and III significantly accelerated capacity fade, with dendrite growth (Stage III) producing the most rapid and irreversible capacity loss. This underscores the critical importance of detailed analysis and monitoring of Li plating evolution processes to advance the application of lithium-ion batteries under extreme conditions.

Figure 9. (a) Variation of R_ct,a across three cycles, and (b) starting SOC of Stage II/Stage III during these cycles. (c) Discharge capacity variation with cycle number for three batteries cycled within different SOC ranges. Under equivalent total charge throughput, changes in (d) State of Health (SOH) and (e) Active Lithium Loss (LLI) of batteries.

5. Discussion: Practical Implications for BMS and Cell Design

5.1 Complementarity of DEIS and Thickness Monitoring

DEIS (R_ct,a) is highly sensitive to interfacial electrochemical changes and detects lithium plating initiation earlier than macroscopic thickness methods — making it directly applicable to early-warning BMS integration. In-situ thickness (dT/dQ) provides a robust measure of deposit volume and morphology that correlates with safety risk and irreversible capacity loss — more reflective of the physical severity of plating than impedance alone. Combining these modalities produces multidimensional descriptors (electrochemical + structural) that enable reliable staging of lithium plating and improved prognostics across fast-charging and low-temperature operating regimes.

5.2 Applications for BMS Charging Protocols

• Early detection: Dynamic EIS-derived R_ct,a inflection signatures can serve as real-time precursors for adaptive BMS strategies — triggering dynamic charge current reduction or thermal management interventions before Stage II onset.

• Stage-based thresholds: The R_ct,a inflection boundaries mapped vs. C-rate and SOC define operational zones (safe graphite lithiation, nucleation risk, dendrite growth) directly useful for cell-level charge protocol optimization under LFP low-temperature charging conditions.

• Aging management: Monitoring the cycle-to-cycle advancement of Stage II/III onset SOC provides a prognostic indicator of cumulative active lithium loss that can inform SOH models and maintenance schedules.

6. Conclusion

This work demonstrates a robust, in-situ methodology to detect, stage, and quantify lithium plating in commercial LFP pouch cells by combining DEIS (dynamic impedance via R_ct,a) and high-precision thickness monitoring (IEST RSS1400, IEST SWE2100). The reproducible three-stage R_ct,a evolution — (I) graphite lithiation, (II) Li nucleation/growth, (III) dendrite growth — corroborated by dT/dQ, MST, and SEM, provides a physically validated framework for staging lithium deposition in C/LFP cells. Without precise monitoring of lithium plating evolution states, Li plating can become uncontrollable, leading to rapid capacity degradation or catastrophic capacity drops. This work provides novel insights into the complex phenomenon of Li plating in commercial cells and contributes to the development of next-generation BMS strategies and potential applications involving graphite–lithium hybrid negative electrodes.

7. Original Article

Ying Lin, Wenxuan Hu, Meifang Ding, Yonggang Hu, Yufan Peng, Jinding Liang, Yimin Wei, Ang Fu, Jianrong Lin, Yong Yang. Unveiling the Three Stages of Li Plating and Dynamic Evolution Processes in Pouch C/LiFePO₄ Batteries. Advanced Energy Materials. 2024, 2400894.

8. FAQs

8.1 What is lithium plating in LFP batteries and why is it dangerous?

Lithium plating in LFP batteries refers to the deposition of metallic lithium on graphite anode surfaces instead of intercalation into the graphite lattice — a process that occurs primarily during fast charging or low-temperature operation when the lithiation rate exceeds the graphite’s intercalation capacity. Lithium plating is dangerous because: (1) deposited metallic Li forms “dead lithium” after electrical isolation, permanently consuming active lithium inventory; (2) repeated SEI rupture and regeneration on fresh plated surfaces consumes electrolyte and increases cell resistance; (3) dendritic lithium growth can penetrate the separator and cause internal short circuits, thermal runaway, and fire. A 2024 Advanced Energy Materials study from Xiamen University identified three reproducible stages of lithium plating evolution detectable by DEIS in commercial C/LFP pouch cells.

8.2 What are the three stages of lithium plating identified by DEIS?

DEIS (Dynamic EIS) monitoring of the negative-electrode charge transfer resistance (Rct,a) reveals three reproducible stages in C/LFP pouch cells during fast or low-temperature charging. Stage I: Rct,a decreases linearly — normal graphite lithiation with no metallic Li deposits. Stage II: Rct,a declines acceleratedly — Li nucleation and growth begin; particulate deposits confirmed by SEM and MST. Stage III: Rct,a reaches a near-steady plateau — extensive dendritic lithium growth dominates; thick SEI forms; irreversible capacity loss (dead lithium + LLI) accelerates. Each stage boundary is validated by mass spectrometry titration (MST) and scanning electron microscopy (SEM) ex-situ analysis.

8.3 How does LFP battery charging below 0°C cause lithium plating?

LFP battery charging below 0°C causes lithium plating because low temperatures substantially increase the charge transfer resistance (Rct,a) at the graphite anode, slowing Li-ion intercalation kinetics. When the applied charge current exceeds the rate at which Li ions can intercalate into graphite at sub-zero temperatures, Li ions instead deposit as metallic lithium on the graphite surface — initiating Stage II (nucleation) and, at higher C-rates or SOC, Stage III (dendrite growth). In the Xiamen University study, cells charged at 0.5C at 0°C showed dT/dQ exceeding the plating threshold after approximately 1,200 mAh of charge — a condition that would not cause plating at room temperature at the same C-rate.

8.4 How does lithium plating affect capacity fade in LFP batteries?

Lithium plating in LFP batteries accelerates capacity fade through three interconnected mechanisms. First, metallic Li deposits that become electrically disconnected from the graphite (”dead lithium”) permanently remove active lithium from the electrochemical cycle — quantifiable as active lithium loss (LLI) using EMF-based analysis. Second, the repeated rupture and regeneration of SEI film on freshly exposed plated lithium surfaces consumes electrolyte and increases cell impedance. Third, the reduced nucleation barrier from residual plated lithium on the graphite surface causes Stage II and Stage III onset to advance to lower SOC in subsequent cycles — creating a self-reinforcing acceleration of degradation. Cells cycled within Stage III (dendrite growth) SOC ranges showed the fastest capacity loss and highest LLI among the three stage-based test groups in the Xiamen University study.

8.5 Can in-situ thickness measurement detect lithium plating in pouch cells?

Yes. In-situ thickness measurement (quantified as dT/dQ — the derivative of cell thickness with respect to charge capacity) provides a macroscopic indicator of lithium plating in pouch cells. When lithium deposits on the graphite surface rather than intercalating, the cell thickness increases beyond the normal graphite expansion curve, producing a detectable dT/dQ anomaly that exceeds an empirically established plating threshold. However, DEIS (dynamic EIS) detects plating onset earlier than thickness methods. The IEST RSS1400 (0.1 µm resolution) and IEST SWE2100 (real-time thickness under controlled preload) systems were used in this study to provide thickness data complementary to DEIS impedance signals — together enabling more accurate lithium plating staging than either method alone.

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Abstract

Battery gas analysis for lithium iron phosphate (LFP) cells quantifies the gas species and volumes produced during abnormal operation such as overcharge and overdischarge. LFP stands for lithium iron phosphate (LiFePO₄), the cathode material used in LFP cells, which are the dominant format for new energy vehicles and grid-scale energy storage due to their safety advantages over high-nickel NCM chemistries. In this study, LFP cells were overcharged to 5.0 V and overdischarged to 0 V while the IEST GVM2200 in-situ battery gassing and volume analyzer recorded real-time volume changes. Gas chromatography (GC-2014C, TCD/FID detectors) then identified gas species from the electrochemical cell headspace. Key findings: overcharge produces a complex gas mixture dominated by H₂, CO, and CO₂ (from electrolyte oxidation and moisture-related reactions) plus light hydrocarbons; overdischarge yields primarily H₂ with lower levels of oxidative products. Volume inflection points — near 110% SOC during overcharge, below ~0.4 V during overdischarge — provide early, non-destructive indicators of battery gassing onset without requiring disassembly.

1. Introduction: LFP Cell Working Principles and Gassing Risk

Lithium iron phosphate (LFP) cells — which typically use LiFePO₄ with an olivine crystal structure coated on aluminum foil as the cathode, and graphite coated on copper foil as the anode — have become the preferred choice for new energy vehicles and energy storage power stations due to their superior safety profile, long cycle life, and low material cost.

During charging, Li⁺ ions migrate from the LiFePO₄ particle surface into the electrolyte, pass through the separator, and intercalate into the graphite lattice on the anode side, forming a series of intercalation compounds (LiCₓ). Electrons flow from the aluminum foil through the external circuit to the copper foil, maintaining charge balance. As Li⁺ ions are extracted from the cathode, the material transforms from LiFePO₄ to Li_1−xFePO₄. During discharge, the process reverses: Li⁺ deintercalates from the graphite anode, moves through the electrolyte and separator, and re-embeds into the LiFePO₄ lattice. Figure 1 illustrates the LFP cell working principle.

Figure 1. LFP cell working principle schematic — Li⁺ intercalation between LiFePO₄ cathode (Al foil) and graphite anode (Cu foil); electron flow through external circuit^[1]

Both overcharge and overdischarge cause severe damage to LFP cells. Overcharging can trigger lithium plating and gas generation; overdischarging can cause copper dendrite formation and gas evolution. Both mechanisms accelerate performance degradation and may initiate thermal runaway. Understanding battery gassing under these abuse conditions — gas species, volumes, and timing — is essential for LFP cell safety design and failure analysis.

This study uses an IEST GVM in-situ gassing and volume analyzer combined with gas chromatography to perform real-time battery gas analysis of LFP cells during controlled overcharge and overdischarge tests, with the goal of quantifying gas evolution, identifying gas species, and correlating gas production with electrochemical events.

2. Experimental Equipment and Test Methods

2.1 Instruments

GVM2200 — In-Situ Battery Gassing & Volume Analyzer (IEST)

• Temperature range: 20 °C – 85 °C

• Dual-channel synchronous testing (two cells simultaneously)

• Real-time logging of cell volume, temperature, current, voltage, and capacity

Gas Chromatography (GC-2014C) for gas analysis battery:

• Samples taken (1 mL) from the cell headspace inside an inert glovebox.

• Detectors: TCD (thermal conductivity) and FID (flame ionization) to cover H₂, CO, CO₂, CH₄, C₂H₆, C₂H₂, and other hydrocarbons.

Figure 2. IEST GVM2200 In-Situ Battery Gassing & Volume Analyzer — 20–85°C, dual-channel, real-time battery gas volume and cell swelling monitoring

Gas Chromatography (GC-2014C) for electrochemical cell headspace gas analysis:

• 1 mL headspace gas samples collected inside an inert glovebox

• Detectors: TCD (thermal conductivity detector) and FID (flame ionization detector) to cover H₂, CO, CO₂, CH₄, C₂H₆, C₂H₂, and other hydrocarbons

2.2 Overcharge & Overdischarge Protocols

• Cells preconditioned at 2.5 V, held 2 hours.

• Cell A (overcharge): 0.5C (1.5 A) CCCV to 5.0 V, cutoff current 0.2 mA, then hold.

• Cell B (overdischarge): 0.5C (1.5 A) CC discharge to 0 V, then hold.

• Initial mass (m₀) recorded; GVM2200 records continuous volume changes while GC samples headspace gas at the end of each abuse test.

2.3 Test Method

Each cell was weighed (m₀) and placed into the GVM2200. Parameters including cell ID and sampling frequency were configured in MISG software, which automatically recorded volume change, temperature, current, voltage, and capacity throughout the test. For electrochemical cell headspace gas analysis, 1 mL of gas was extracted in a glovebox and analyzed with the GC-2014C using TCD and FID detectors (Figure 3).

Figure 3. Gas species detectable by TCD and FID detectors — H₂, CO, CO₂, CH₄, C₂H₆, C₂H₂ and hydrocarbons, covering the full battery gassing spectrum for LFP overcharge and overdischarge analysis

3. Volume Change and Gassing Behavior Analysis

3.1 Volume Change During Normal Charging: Graphite Staged Lithiation

As shown in Figure 4, the volume and voltage change curve of the lithium ion from the positive electrode during the normal charging stage of the charging cell, the voltage increases, as the voltage increases, and the cell volume increases, and the process of graphite can reach 10%^[2]。Graphite negative electrode is a typical phased interlayer lithium embedding process. After lithium ion is embedded, the layer keeps the plane. The graphite layer and the embedded layer are arranged in parallel, and every third layer, 2 and one layer are regularly embedded to form Li-C interlayer compounds (LiCx) with different phases such as 3,2 and 1.The initial stage is stage 4, and the state of each three layer of lithium ion is called stage 3, which corresponds to Li_0.3C₆Compounds, with a relative lithium concentration of 33.33%.Each two layers of lithium embedding is stage 2, corresponding to Li_0.5C₆, The relative concentration was 50%.After the graphite is completely embedded with lithium, the LiC is formed₆Compound, one lithium ion embedded in the middle of every six hexagonal carbon atoms, is a 100% relative lithium-embedded concentration^[2].

As shown in Figure 5 shows the change of the negative state in the normal charging stage of LFP cell. The above lithium embedded stage is in a completely ideal state. The actual lithium embedded state inside graphite is more complex, which is often a mixture of multiple stages.The volume change of the corresponding cell charging stage is mainly related to the structural phase change caused by the negative electrode lithium embedding^[5], In the initial stage of charging, with the increase of lithium embedded, the volume of graphite lattice expands, forming the expansion curve with the larger slope of the first stage, the lattice size of graphite changes least between x=0.2 and 0.6, and the expansion appears a platform curve; LiC₆The layer spacing of the phase is significantly greater than that of Li_0.5C₆each other.equal LiC₆The maximum slope for the increase of the corresponding thickness change occurs in the presence of the phase.

Figure 4. LFP cell volume change during normal charging — staged graphite lithiation (stage 4 → stage 1, LiC₆) produces characteristic volume increase with inflection points at each phase transition

Figure 5(1). Graphite anode staging during normal LFP cell charging^[2] — phase sequence stage 4 → stage 3 (Li_0.3C₆, 33%) → stage 2 (Li_0.5C₆, 50%) → stage 1 (LiC₆, 100%)

Figure 5(2). Graphite volume expansion vs relative Li concentration^[5] — plateau region (x = 0.2–0.6) and maximum slope at LiC₆ formation

3.2 Overcharge and Overdischarge Volume Signatures

• Overcharge (to 5.0 V): Volume versus voltage curves show a clear inflection point near the overcharge onset (~110% SOC), followed by sustained volume growth even as voltage holds at the overvoltage plateau. Visible pouch bulging occurs, indicating gas generation and irreversible swelling — classic battery gassing from electrolyte decomposition and moisture-driven reactions (Figure 6a).

• Overdischarge (to 0 V): Volume is stable through most of the discharge, but an inflection appears below approximately 0.4 V; prolonged low-voltage conditions drive continued volume increase and mild bulging (Figure 6b), consistent with reductive side reactions at the anode-current collector interface.

These volume inflection signatures enable rapid, non-destructive detection of battery gassing onset and provide precisely timed triggers for GC headspace sampling — the core advantage of combining in-situ GVM monitoring with post-test gas chromatography for battery gas analysis.

Figure 6. LFP cell volume change during (a) overcharge to 5.0 V — inflection at ~110% SOC confirms battery gassing onset; (b) overdischarge to 0 V — inflection below ~0.4 V from reductive gas evolution

4. Electrochemical Cell Headspace Gas Analysis (GC Results)

4.1 Gas Composition Overview

Headspace samples taken after overcharge and overdischarge were analyzed by GC with TCD and FID detection. Major findings for LFP cell battery gas analysis:

• Hydrogen (H₂): Highest proportion in both conditions — likely from trace water decomposition and side reactions at elevated potential or temperature. Moisture impurities in electrodes or electrolyte catalyze H₂ evolution.

• Carbon oxides (CO, CO₂): Detected in substantial quantities during overcharge, confirming oxidative decomposition of carbonate-based electrolyte solvents (EC, DMC, EMC, DEC). CO₂ is a well-established product of carbonate electrolyte oxidation at high potentials.

• Light hydrocarbons (CH₄, C₂H₆, C₂H₂): Present in smaller but measurable amounts, reflecting radical-pathway electrolyte fragmentation and possible SEI degradation. Presence of C₂H₂ may indicate severe electrolyte breakdown.

Overall, the battery gas analysis confirms that overcharge generates a richer mixture of oxidative decomposition gases (CO, CO₂, hydrocarbons) in addition to H₂, while overdischarge yields primarily H₂ and lower levels of oxidation products. GC spectra and quantitative comparisons are shown in Figures 7–8 and Table 1.

4.2 Interpretation of Gas Origins

• H₂: May originate from water desorption/decomposition on electrodes and catalytic hydrogen evolution at high potentials. Literature suggests water desorption under vacuum at ~350 K with activation energy ~1.3 eV; in cells, localized heating and catalysis accelerate H₂ production.

• CO / CO₂: Oxidative decomposition of carbonate solvents (EC, DMC, EMC, DEC) at high potentials generates COx species. Overcharge to very high voltages amplifies solvent oxidation and CO₂ formation.

• Hydrocarbons / C₂ species: Radical pathways and electrolyte fragmentation produce small hydrocarbons; presence of C₂H₂ may signal severe electrolyte breakdown.

Overall, gas analysis battery results confirm that overcharge produces a richer mixture of oxidative decomposition gases (CO, CO₂, hydrocarbons) in addition to H₂, while overdischarge yields significant H₂ and lower levels of oxidation products.

Figure 7. GC headspace gas analysis results — (a) overcharge: H₂, CO, CO₂, CH₄, C₂H₆, C₂H₂ from electrolyte oxidation; (b) overdischarge: H₂-dominant with lower CO/CO₂, consistent with reductive reactions

Table 1. Gas species comparison: LFP cell overcharge vs overdischarge

Figure 8. Total gas production comparison — LFP cell overcharge (more diverse oxidative gas mixture) vs overdischarge (primarily H₂)

4.2 Gas Origins: Mechanistic Interpretation

• H₂: Originates from water desorption and decomposition on electrode surfaces and catalytic hydrogen evolution at high potentials. Literature indicates water desorption from graphite surfaces at ~350 K with activation energy ~1.3 eV; in cells, localized heating and catalysis at the electrode-electrolyte interface accelerate H₂ generation.^[3,4]

• CO / CO₂: Oxidative decomposition of carbonate solvents (EC, DMC, EMC, DEC) at high potentials produces CO and CO₂. Overcharge to 5.0 V significantly exceeds the electrochemical stability window of standard electrolytes, amplifying solvent oxidation and CO₂ formation.

• C₂H₂ and other hydrocarbons: Radical fragmentation pathways during extreme electrolyte breakdown produce trace C₂ species. Detection of C₂H₂ is a marker of severe electrolyte degradation beyond normal operating conditions.

5. Implications for LFP Cell Safety and Battery Gas Diagnostics

• Early battery gassing detection: Real-time volume monitoring with the GVM series provides immediate indicators of gas generation onset through volume inflection signatures, enabling timely intervention during testing and manufacturing validation. Combining volume signals with targeted GC sampling yields robust, quantitative battery gas analysis results.

• Moisture control: Minimizing residual moisture in electrodes and electrolyte is critical to reducing H₂ evolution. Strict material drying protocols and dry-room humidity control directly reduce the dominant gas hazard in both overcharge and overdischarge scenarios.

• Electrolyte design: Electrolyte formulations with improved oxidative stability and high-voltage-stable additives reduce CO/CO₂ formation under overcharge conditions. LFP cells, while safer than high-Ni NCM chemistries, still experience electrolyte solvent oxidation at extreme states.

• Failure forensics: Quantitative gas species data combined with precise timing relative to SOC/voltage from the GVM allows root-cause analysis — distinguishing thermal runaway precursors from purely electrochemical decomposition events and supporting pack-level safety system design.

6. Summary

Combining in-situ battery gassing volume monitoring (GVM2200) with GC electrochemical cell headspace gas analysis provides a powerful gas analysis battery workflow for LFP cell abuse studies. Key outcomes:

• Overcharge to 5.0 V produces a mixed gas dominated by H₂, CO, and CO₂, plus light hydrocarbons — consistent with electrolyte oxidation and moisture-related reactions at extreme potentials.

• Overdischarge to 0 V yields significant H₂ production, reflecting moisture-related and reductive side reactions at low potential.

• Real-time volume trends provide precise timing for GC sampling and support rapid safety validation, R&D diagnostics, and quality control for LFP-based systems.

This combined approach helps engineers and researchers understand battery gassing mechanisms, improve cell formulations, and design safer batteries and packs.

7. References

[1] Zheng Zhikun Cheng.Research on lithium iron phosphate energy storage overcharged thermal runaway and gas detection safety early warning [D].Zhengzhou University.

[2] Reynier Y, Yazami R, Fultz B, et al. Evolution of lithiation thermodynamics with the graphitization of carbons[J].Journal of Power Sources, 2007, 165(2):552-558.

[3] Yang L, Chen H S, Song W L, et al. Effect of Defects on Diffusion Behaviors of Lithium-Ion Battery Electrodes: In Situ Optical Observation and Simulation[J].ACS Applied Materials & Interfaces, 2018, 10(50).

[4] Kajiura H, Nandyala A, Bezryadin A. Quasi-ballistic electron transport in as-produced and annealed multiwall carbon nanotubes[J].Carbon, 2005, 43(6):1317-1319.

[5] H.Michael, F.Iacoviello, T.M.M.Heenan, A.Llewellyn，J.S.Weaving, R.Jervis, D.J.L.Brett, and P.R.Shearing. A Dilatometric Study of Graphite Electrodes during Cycling with X-ray Computed Tomography[J]Journal of the Electrochemical Society, 2021,168: 010507.

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Abstract

Calendering pressure is the primary process lever for controlling graphite anode electrode compaction density and through-thickness electrical resistivity in lithium-ion battery manufacturing. In this study, four graphite electrode batches were produced at increasing calendering pressures, achieving compaction densities of 1.35, 1.5, 1.6, and 1.65 g/cm³ (Electrodes 1 to 4). Electrode compression testing with the IEST BER2500 (5–60 MPa, 5 MPa steps, 15 s hold) shows that higher calendering pressure reduces maximum, reversible, and irreversible deformation — with diminishing returns as pressure increases — and produces more stable electrode resistivity response under subsequent compression. Lower-pressure electrodes (Electrodes 1 and 2) exhibit greater thickness reduction and elastic rebound during testing because their inter-particle contacts and particle-to-current-collector interfaces are not yet fully consolidated by calendering. Higher-pressure electrodes (Electrodes 3 and 4) show near-stable thickness and resistivity from the start of the test, as their pre-consolidated structure leaves little room for further compression-induced improvement.

1. Background: Why Electrode Calendering Pressure Matters

The calendering process is a critical step in lithium-ion battery electrode manufacturing. By applying pressure between rollers—through predefined gap or force control—at optimal speed and temperature, this step ensures electrodes achieve their target compaction density. Calendering aims to increase volumetric capacity, improve electronic conductivity, and enhance overall electrochemical performance, making precise calendering pressure control essential for both cell energy density and cycle life.

In 2022, Zhang et al.^[1] conducted a systematic micro- and macro-scale study of electrode calendering by combining Discrete Element Method (DEM) simulation with experimental trials. Their work, supplemented by the Heckel equation for compression modeling, confirmed that electrode deformation involves particle crushing, secondary particle fusion, binder network compression, and current collector surface deformation. The study also established that improved electronic conductivity after calendering stems from two simultaneous effects: enhanced internal conductive pathways between active material particles and improved tightness of contact between the coating and the current collector surface.

Building on this foundational research, we used an IEST BER2500 electrode resistance analyzer to test the conductive properties of graphite anode electrodes calendered at four different pressures (yielding four different compaction densities). Simultaneously, the instrument’s compression fixture was used to analyze mechanical compression behavior. This combined approach enables rapid, quantitative evaluation of how calendering pressure affects both the electrical and mechanical state of the electrode sheet — without requiring full cell fabrication.

Figure 1. Schematic force-displacement curve of an electrode sheet during calendering — green: experimental results; gray: DEM simulation results. Shows progressive stages of particle rearrangement, elastic deformation, plastic deformation, and fracture^[1]

2. Experimental Equipment and Test Methods

2.1 Experimental Equipment

Tests were conducted using the IEST BER2500 Electrode Resistance Analyzer (Figure 2). This instrument applies pressures from 5 to 60 MPa on a 14 mm diameter electrode sample, simultaneously measuring resistance, resistivity, conductivity, and electrode thickness — enabling combined electrode compression testing and resistivity characterization in a single instrument run.

Figure 2. (a) BER2500 appearance; (b) BER2500 structural diagram — 14 mm probe, 5–60 MPa, simultaneous resistance and electrode compression measurement

2.2 Sample Preparation and Testing

2.2.1 Sample Fabrication

Four graphite anode electrode sheets were prepared with the same slurry formulation but calendered at increasing pressures, yielding four compaction density levels:

• Electrode 1: lowest calendering pressure → compaction density 1.35 g/cm³

• Electrode 2: medium-low calendering pressure → compaction density 1.5 g/cm³

• Electrode 3: medium-high calendering pressure → compaction density 1.6 g/cm³

• Electrode 4: highest calendering pressure → compaction density 1.65 g/cm³

Compaction density was calculated by the cutting-thickness-weighing method. Higher calendering pressure consistently yielded higher compaction density, as expected from the fundamental powder compression mechanism.

2.2.2 Testing Procedure

The BER2500 steady-state test mode was used with the following parameters: pressure range 5–60 MPa, 5 MPa steps, 15 s hold at each step, followed by stepwise unloading back to 5 MPa. At each pressure point, electrode thickness and resistance were recorded simultaneously. The initial 5 MPa point was used as the benchmark for normalized thickness deformation calculations (stress-strain curves). This loading-unloading protocol separates elastic (reversible) deformation from plastic and fracture (irreversible) deformation for each electrode.

3. Data Analysis and Discussion

3.1 Electrode Compression Behavior Under Applied Pressure

Figure 3 shows the stress-strain (compression performance) curves for the four electrode sheets under the 5–60 MPa loading-unloading cycle, and Table 1 summarizes the key deformation metrics.

Key finding: Maximum deformation, reversible deformation, and irreversible deformation all decrease as calendering pressure (and compaction density) increases — following the order Electrode 1 > 2 > 3 > 4. However, the rate of decrease slows at higher pressures, indicating diminishing returns from further calendering.

This trend reflects the powder compaction mechanisms within the electrode coating. During initial calendering at low pressure, powder particles rearrange and slide into denser packing — the dominant mechanism that closes inter-particle voids. At higher calendering pressure, particles undergo elastic then plastic deformation once their yield stress is exceeded. Electrodes that have already passed through these stages at high calendering pressure have less compressible structure remaining, so they show smaller total deformation and less elastic rebound (spring-back) when compressed again during testing.

Figure 3. Stress-strain compression curves for four electrode sheets (Electrodes 1–4, compaction densities 1.35, 1.5, 1.6, 1.65 g/cm³) — maximum, reversible, and irreversible deformation all decrease with increasing initial calendering pressure

Table 1. Deformation characteristics of graphite electrodes

3.2 Thickness and Resistivity Response to Applied Pressure

During lithium-ion battery electrode manufacturing, the calendering process primarily compresses electrode thickness while keeping areal mass nearly constant — reducing coating thickness, increasing compaction density, and improving particle-to- particle and particle-to-current-collector adhesion. During roll-pressing, particles form micro-indentations (”pits”) on the current collector surface, increasing the contact area and mechanical adhesion between coating and current collector.

Figures 4 and 5 show the thickness change and electrical conductivity curves for all four electrodes during the stepwise BER2500 pressure test:

• Thickness behavior: Electrodes 1 and 2 (lower calendering pressure, lower compaction density) start thicker and exhibit more significant thickness reduction under test pressure, as well as greater elastic rebound on unloading. Electrodes 3 and 4 (higher calendering pressure, ≥1.6 g/cm³) are denser, deform less under the same test pressure, and show less elastic rebound — because their structure is already highly consolidated.

• Resistivity behavior: The resistivity of Electrodes 1 and 2 changes more significantly with applied test pressure than Electrodes 3 and 4. This occurs because Electrodes 3 and 4 already have tightly consolidated inter-particle contacts and coating-to-current-collector interfaces from high-pressure calendering, leaving little room for further pressure-induced conductivity improvement during the test. Notably, the absolute resistivity values of the lower-pressure electrodes may appear lower under test pressure due to their larger thickness reduction — resistivity is geometry-dependent, and significant thickness compression can numerically lower apparent resistivity even if intrinsic conductivity changes are modest. This highlights that electrode resistivity assessment must account for the test geometry and report both thickness and resistance together.

Figure 4. Thickness vs pressure curves for four electrode sheets (5–60 MPa, loading and unloading) — Electrodes 1 and 2 show larger thickness reduction and elastic rebound; Electrodes 3 and 4 show stable, near-constant thickness

Figure 5. Electrical conductivity vs pressure for four electrode sheets — Electrodes 1 and 2 show larger conductivity variation with applied test pressure; Electrodes 3 and 4 show stable response, as pre-existing high-pressure calendering has already consolidated inter-particle and coating-current-collector contacts

4. Practical Guidance for Process Engineers on Calendering Pressure

Based on the measured behavior, the following process guidelines apply to graphite anode electrode calendering:

• Target compaction density by calendering pressure: Higher calendering pressure reliably delivers higher compaction density (1.35 → 1.65 g/cm³ in this study), which reduces subsequent compressibility and stabilizes electrode thickness under load. Compaction densities ≥1.6 g/cm³ for graphite anodes produce near-stable structures that are robust to downstream assembly pressure.

• Account for test geometry when evaluating electrode resistivity: Test geometry and transient thickness change during flat-plate compression can mask or reverse apparent conductivity trends between electrodes at different compaction densities. Always report both thickness and resistance together — not resistivity alone — to avoid misleading comparisons.

• Balance volumetric energy density against ion transport: Higher calendering pressure increases volumetric energy density by raising compaction density, but over-calendering collapses inter-particle pore connectivity, restricting electrolyte wetting and lithium-ion transport. The optimal calendering pressure must be co-optimized with slurry formulation and conductive-additive content for the target C-rate and cycle-life requirements.

5. Summary

This study demonstrates the effective use of the BER2500 electrode resistance analyzer to differentiate the compression and conductive properties of graphite anode electrodes produced under varying calendering pressures. The methodology clearly reveals how initial calendering pressure dictates an electrode’s mechanical resilience and its electrical resistivity response to subsequent compression testing.

The choice of optimal calendering pressure in actual production must be carefully tailored to the specific electrode formulation and cell design targets. The right balance maximizes battery volumetric energy density while ensuring robust mechanical integrity, efficient electronic conduction, and adequate porosity for ion transport — collectively determining cell performance and cycle life.

6. References

[1] Zhang J, Huang H, Sun J. Investigation on mechanical and microstructural evolution of lithium-ion battery electrode during the calendering process[J]. Powder Technology, 2022, 409: 117828.

[2] BG Westphal et al. Influence of high intensive dry mixing and calendering on relative electrode resistivity determined via an advanced two point approach. Journal of Energy Storage 2017, 11, 76–85

[3] Yang Shaobin, Liang Zheng. Principles and applications of lithium-ion battery manufacturing process[M]. Chemical Industry Press, 2020.

7. FAQs

7.1 How does calendering pressure affect electrode compaction density?

Calendering pressure and electrode compaction density have a positive, but progressively diminishing, relationship. As calendering pressure increases, electrode coating density rises because pressure forces particle rearrangement (filling inter-particle voids), elastic deformation (reversible shape change), plastic deformation (permanent densification), and ultimately particle fracture. In this study, compaction densities ranged from 1.35 g/cm³ (lowest calendering pressure) to 1.65 g/cm³ (highest), calculated by the cutting-thickness-weighing method. However, the density increase per unit pressure decrement becomes smaller at higher pressures — reflecting that most void filling and rearrangement occurs early, and further densification requires increasingly large energy input for diminishing density gain. This diminishing-returns behavior is visible in the stress-strain curves (Figure 3) as the gap between adjacent electrode curves narrows at higher compaction densities.

7.2 What is electrode compression testing and what does the BER2500 instrument measure?

Electrode compression testing characterizes how a calendered battery electrode sheet deforms mechanically under applied uniaxial load — quantifying the elastic (reversible) and plastic/fracture (irreversible) components of deformation as a function of applied pressure. The IEST BER2500 electrode resistance analyzer performs this characterization in a combined measurement: it applies a defined pressure protocol (in this study, 5–60 MPa in 5 MPa steps with 15 s hold per step, followed by stepwise unloading) to a 14 mm diameter electrode sample while simultaneously recording electrode thickness and resistance at each step. This enables stress-strain curve generation and resistivity measurement in a single test run, linking mechanical and electrical properties to calendering process parameters — without full-cell fabrication.

7.3 What is the difference between reversible and irreversible electrode deformation, and why does it matter for calendering?

During electrode compression testing (and during actual calendering), total deformation has two components. Reversible (elastic) deformation is the thickness recovery that occurs when the applied load is released — the electrode springs back. This spring-back is undesirable in calendering because it means the final electrode is thicker and less dense than the in-calender measurement suggests, requiring higher roll pressure to achieve the target compaction density. Irreversible (plastic and fracture) deformation is the permanent thickness reduction that remains after unloading — the densification that actually stays. For electrode design, the goal is to maximize irreversible (permanent) densification while controlling elastic rebound. In this study, both reversible and irreversible deformation decrease as calendering pressure increases (1 > 2 > 3 > 4), because highly calendered electrodes have already consumed most of their deformation capacity, leaving a more dimensionally stable structure.

7.4 What is the relationship between calendering pressure and electrode through-thickness resistivity?

Calendering pressure improves through-thickness (vertical) electrode resistivity by two mechanisms: tightening inter-particle contacts within the active material coating (reducing contact resistance between active material, conductive additives, and binder), and improving the contact area between the coating and the current collector (as active material particles create micro-indentations in the metal foil surface). However, interpreting the measured resistivity of different-pressure electrodes requires care: resistivity is calculated from resistance and geometric dimensions, and a thicker, less-calendered electrode compressed during testing will show a numerically lower apparent resistivity due to thickness reduction — even if the intrinsic conductivity change is modest. To avoid misleading comparisons, electrode resistance and thickness should always be reported together when comparing electrodes with different pre-calendering histories.

7.5 What is DEM simulation in electrode calendering research?

Discrete Element Method (DEM) simulation is a computational technique that models the individual mechanical interactions between particles — each represented as a discrete object with defined contact laws for normal force, tangential force, and failure — rather than treating the electrode coating as a continuum. In calendering research (such as Zhang et al. 2022, referenced in this article), DEM is combined with Heckel compression modeling and experimental force-displacement curves to identify which deformation mechanism dominates at each stage of calendering pressure. DEM simulation reveals the sequence of particle rearrangement, elastic deformation, plastic deformation, and particle fracture — showing how each stage contributes to the final compaction density and identifying pressure thresholds beyond which fracture dominates. These computational insights complement experimental BER2500 electrode compression testing by providing mechanistic explanations for the observed stress-strain behavior.

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Abstract

The stress-lens electrochemical sintering (SLES) strategy, reported by Tianze Xu, Qingdong Gao and co-authors from Tianjin University and Tsinghua University in Advanced Materials (DOI: 10.1002/adma.74090), transforms the detrimental ~300% volume expansion of silicon anodes in all-solid-state batteries into a constructive electrochemical sintering force. By depositing amorphous silicon at the high-curvature pore openings of a porous carbon host, the pore geometry concentrates lithiation-induced stress precisely at interparticle contacts — a mechanism validated using the IEST SPFT2000 Single-Particle Mechanical Testing System. The resulting selectively sintered Si network delivers ~100% capacity retention after 100 cycles in half-cells and maintains over 60% capacity after 700 cycles in NCM532-based full cells, fundamentally resolving the conflict between mechanical stability and ionic transport in high-capacity alloy anodes.

📄 Source Paper

Tianze Xu^1,2,3, Qingdong Gao^1,2,3, Quan-Hong Yang^1,2,3,8*, Shichao Wu^1,2,3,8*, Ziyun Zhao^1,7* et al.

Stress-Lensed Electrochemical Sintering Enables Fast and Stable Lithium-Silicon Alloy Chemistry in All-Solid-State Batteries

DOI: doi.org/10.1002/adma.74090
| Journal: Advanced Materials
| Institutions: Nanoyang Group, Tianjin University of Advanced Carbon and Energy Materials Laboratory, Tsinghua University,

✓ IEST SPFT2000 Single Particle Mechanical Properties Test System used in this research

1. The Silicon Anode Dilemma in All-Solid-State Batteries

All-solid-state batteries (ASSBs) offer a transformative pathway toward safer, higher-energy-density energy storage. Among candidate anode materials, silicon stands out with a theoretical specific capacity of ~3,579 mAh g⁻¹ — nearly ten times that of graphite. However, the lithiation of silicon to form LiₓSi alloys induces a volumetric expansion exceeding 300%, which triggers cascading failure mechanisms at the electrode level.

Electrochemical Sintering — The stress-driven fusion of adjacent active-material particles during electrochemical cycling. In silicon anodes, repeated lithiation/delithiation creates mechanical stress that drives atomic rearrangement at particle contacts, forming sintered necks that can either restore (if controlled) or disrupt (if uncontrolled) ionic connectivity.

This volume change manifests in three interconnected degradation modes: (1) particle pulverization — repeated alloying/de-alloying generates internal stress that fractures individual Si particles; (2) solid–solid interface disconnection — the expanding and contracting Si network loses physical contact with the solid electrolyte, creating dead zones where ion transport is blocked; and (3) electrode-level structural collapse — cumulative particle rearrangement and fracture lead to macroscopic electrode cracking and loss of percolation. These problems are fundamentally exacerbated by an intrinsic electrochemical process: during cycling, neighboring Si particles undergo electrochemical sintering, gradually fusing together. While controlled sintering could theoretically restore ionic connectivity, uncontrolled sintering produces oversized agglomerates that magnify stress concentrations and accelerate electrode fracture.

2. Static Constraints Cannot Resolve Dynamic Evolution

Conventional approaches to managing Si volume expansion rely on static mechanical constraints — primarily carbon encapsulation and rigid ceramic coatings. These methods operate on the principle of physical confinement: a mechanically rigid shell (typically carbon or oxide) surrounds each Si particle, physically limiting its outward expansion.

Two representative strategies illustrate the fundamental limitation:

• Non-electrochemical sintering (NES): A continuous carbon layer fully encapsulates each Si particle, physically separating adjacent Si domains. While this prevents uncontrolled agglomeration, the carbon interlayer introduces a substantial barrier to interparticle Li⁺ transport, producing poor rate capability and high interfacial resistance.

• Carbon-coating delayed sintering: A thin carbon shell temporarily suppresses sintering, but after repeated cycling the shell fractures under cumulative stress, exposing bare Si surfaces that then undergo uncontrolled agglomeration — ultimately producing the very electrode cracking the coating was designed to prevent.

These limitations reflect a deeper design principle: static electrode architectures are fundamentally mismatched to the dynamic behavior of high-volume-change alloy anodes. What is needed is a mechanism that guides — rather than suppresses — the dynamic evolution of the silicon structure during cycling.

3. Introducing the Stress-Lens Electrochemical Sintering (SLES) Strategy

Stress-Lens Electrochemical Sintering (SLES) — A geometry-guided strategy in which amorphous silicon is conformally deposited at the high-curvature pore entrances of a porous carbon host. During lithiation, these constricted pore openings act as “stress lenses” that concentrate volumetric expansion stress precisely at interparticle contact points, lowering the atomic diffusion barrier and inducing selective, local electrochemical sintering to form a continuous, percolating Si network while preserving internal voids for volume buffering.

Professor Yang Quanhong’s team at Tianjin University, in collaboration with Dr. Zhao Ziyun at Tsinghua Shenzhen International Graduate School and co-authors, introduced a conceptual shift: instead of preventing electrochemical sintering, use geometric design to control where and how it occurs. The SLES strategy deposits amorphous silicon into the high-curvature pore entrance regions of a porous carbon matrix. During the initial lithiation cycles, these geometric constrictions act as stress lenses — concentrating the mechanical expansion stress at precisely the locations where interparticle sintering is desired.

This focused mechanical energy locally reduces the atomic diffusion barrier for Si rearrangement, promoting the formation of robust, selective sintered connections between adjacent Si domains while maintaining internal porosity to accommodate future volume changes. The outcome is a self-organized, percolating Si network that is simultaneously mechanically robust and ionically conductive — a feature that static encapsulation strategies cannot achieve.

4. Four Design Paradigms of Silicon Anodes

Figure 1. Four design paradigms of Si anodes for all-solid-state batteries, organized by mechanical constraint type (static vs. dynamic) and sintering control (suppressed vs. guided). Quadrant I (SLES, this work): Dynamic stress guidance — porous carbon pore openings act as stress lenses to induce selective electrochemical sintering, achieving both mechanical stability and fast Li⁺ transport. Quadrant II (Non-electrochemical sintering): Static constraint by full carbon coating that physically isolates particles but blocks interparticle Li⁺ conduction. Quadrant III (Carbon-coating delayed sintering): Static constraint that only temporarily inhibits sintering; shell fracture at later cycles triggers uncontrolled agglomeration. Quadrant IV (Uncontrolled pure-Si sintering): No constraint — severe agglomeration and electrode cracking under unrestrained volume expansion.

Figure 1 presents four design paradigms for silicon anodes, comparing static constraint versus dynamic stress‑guided approaches. The first quadrant shows the stress‑lens‑induced selective sintering architecture of this work: porous carbon pore openings act as stress-lensed, inducing directional sintering between particles and simultaneously achieving mechanical stability and fast Li‑ion conduction. The second quadrant depicts a non‑electrochemical sintering structure, where carbon layers isolate silicon particles to buffer expansion, but these layers suffer from poor ionic conduction and uneven stress distribution. The third quadrant corresponds to a carbon‑coating structure that delays excessive sintering—it only suppresses sintering temporarily; after cycling, the carbon layers crack and particle agglomeration blocks ion‑transport pathways. The fourth quadrant shows pure silicon with uncontrolled sintering: without constraint, silicon severely agglomerates, intensified strain causes electrode fracture, and long‑range conductive networks completely collapse. This comparison directly demonstrates that conventional approaches cannot simultaneously satisfy both stability and kinetics, highlighting the novelty of the present strategy.

5. Synthesis and Multi-Scale Structural Characterization

Figure 2. Synthesis route and structural characterization of the SLES architecture. (a) Schematic comparison of Si-SLES (Si deposited at pore entrances) versus Si-NES (continuous outer carbon coating). (b) Spherical aberration-corrected STEM with Si/C EDS mapping confirming uniform Si distribution at pore openings in SLES versus a continuous carbon film in NES. (c) N₂ adsorption-desorption isotherms and pore size distribution showing the high-curvature ink-bottle pore structure of the porous carbon host. (d) Small-angle X-ray scattering (SAXS) confirming that both SLES and NES retain internal void volume for Si expansion buffering.

The SLES composite was synthesized by selecting a porous carbon host with a high density of ink-bottle-shaped pores — narrow neck openings with wider internal cavities. Amorphous silicon was deposited via chemical vapor deposition, with process parameters tuned to concentrate Si selectively at the pore neck regions rather than filling the internal cavities. The control sample (Si-NES) was prepared by applying an additional full-surface carbon coating, producing a continuous outer carbon shell that physically isolates all Si domains.

Nitrogen adsorption analysis confirmed that the SLES sample retained a substantial population of open micropores after Si deposition — direct evidence that the internal void volume was preserved. In contrast, the NES sample showed near-complete closure of micropores after the outer carbon coating step. This structural difference is critical: the retained internal pores in SLES provide the physical space needed to accommodate the ~300% Si volume expansion during lithiation, preventing the stress buildup that drives uncontrolled agglomeration and electrode fracture.

6. Stress-Lens Effect and Selective Electrochemical Sintering Mechanism

Figure 3. Multi-technique validation of the stress-lens sintering mechanism. (a) Finite element simulation showing lithiation-induced stress concentrated at the high-curvature pore opening region in SLES versus uniformly distributed stress in NES. (b) AFM force-distance curves and Young’s modulus mapping after cycling: SLES interfaces reach 49 GPa modulus with low plastic deformation; NES interfaces show only 10.9 GPa with substantial plastic deformation. (c) Cross-sectional FESEM with EDS before and after one cycle: SLES particles exhibit mutual fusion with minimal cracking; NES particles retain clear boundaries with extensive cracking. (d) FIB-SEM 3D tomographic reconstruction showing the continuous percolating Si network in SLES versus isolated, disconnected particles in NES.

Finite element simulation (Figure 3a) provided the first direct evidence of the stress-lens mechanism: when the Si domain at a pore opening undergoes lithiation, the geometrical constraint of the narrow pore neck concentrates the hoop stress by a factor of more than 5× compared to an unconstrained Si surface. This localized stress concentration drives Si–Si bond rearrangement and interfacial neck formation precisely at the pore opening — the location where interparticle sintering is desired.

AFM mechanical characterization after cycling (Figure 3b) revealed a striking contrast: the SLES sintered interface exhibited a Young’s modulus of 49 GPa, nearly five times the 10.9 GPa measured for the NES interface. The SLES interface also showed significantly lower plastic deformation, indicating that the sintered structure is both stiffer and more resilient to repeated mechanical cycling.

FIB-SEM 3D reconstruction (Figure 3d) provided the most direct visual evidence: the SLES electrode showed a continuous, three-dimensionally percolating Si network with interconnected domains, while the NES electrode consisted of isolated particles with extensive cracking. The carbon coating in NES, rather than protecting the structure, acts as a barrier that prevents the formation of the very sintered connections needed for stable long-range ion transport.

7. Interface Mechanical Reliability and Lithium-Ion Transport

Figure 4. Mechanical reliability and Li⁺ transport of SLES versus NES interfaces. (a) FEM stress distribution at different lithiation states: SLES shows <0.5 GPa interparticle stress difference versus 59.5 GPa for NES. (b) Single-particle indentation fracture test: SLES sintered agglomerate sustains 15.7 mN fracture force (4.6× NES at 3.4 mN). (c) KPFM surface potential mapping showing uniform potential distribution across SLES particle boundaries versus sharp potential drops at NES boundaries. (d) GITT, DC polarization, EIS, and DRT analysis confirming higher Li⁺ diffusion coefficient and lower interfacial resistance for SLES, with stable impedance over cycling.

The mechanical and transport synergy of the SLES design was quantified at the single-particle level. Finite element simulation of stress distribution during lithiation (Figure 4a) showed that the SLES electrode maintains an interparticle stress difference of less than 0.5 GPa — meaning that stress is uniformly distributed across the sintered network. In contrast, the NES electrode exhibits a stress difference exceeding 59.5 GPa, concentrated at the carbon coating interfaces where Li⁺ diffusion is blocked, creating mechanical hot spots that drive coating fracture and particle isolation.

Single-particle testing— a critical measurement for quantifying the mechanical integrity of the sintered interface — was performed using the IEST SPFT2000 Single-Particle Mechanical Testing System. This system applies a controlled compressive load to individual agglomerated particles via a flat diamond indenter, recording the force-displacement curve with ±0.01 mN force accuracy and 10 nm displacement resolution until the particle fractures. The results show that the SLES sintered agglomerate sustains a fracture force of 15.7 mN — 4.6 times higher than the 3.4 mN measured for the carbon-coated NES control. This quantitative difference directly explains why the NES structure suffers from rapid capacity fade: the carbon coating creates a mechanically weak interface that fractures under the stresses of normal cycling, exposing fresh Si surfaces that undergo uncontrolled sintering and exacerbating electrode degradation.

Complementing the mechanical data, KPFM surface potential mapping (Figure 4c) demonstrated that SLES particle boundaries exhibit uniform surface potential distribution with no measurable potential drop, indicating low interfacial resistance. The NES sample, conversely, showed sharp potential drops of 50–80 mV at each particle boundary — direct evidence that the carbon interlayer acts as a resistive barrier to Li⁺ transport. GITT, DC polarization, and EIS-DRT analysis (Figure 4d) confirmed that the Li⁺ diffusion coefficient in SLES is an order of magnitude higher than in NES, and the interfacial impedance remains stable over cycling without the continuous growth that characterizes the NES control.

8. Precise Single-Particle Mechanical Quantification with IEST SPFT2000

Figure 5. Single-particle mechanical characterization of SLES and NES Si anode particles using the IEST SPFT2000. (left) Schematic of the Single-Particle Mechanical Testing System, (right) Representative force-displacement curves: SLES sintered agglomerate (red) sustains 15.7 mN fracture force with a characteristic multi-stage fracture profile; NES carbon-coated particle (blue) fractures at 3.4 mN with a single brittle fracture event.

The SLES design’s mechanical advantage was quantitatively established through single-particle compression testing on the IEST SPFT2000, an instrument specifically designed for mechanical characterization of individual battery material particles in the 5–50 μm size range, compliant with Chinese national standard GB/T 43091-2023. Key specifications of the system include a force measurement accuracy of ±0.01 mN (0–100 mN range) and ±0.05 mN (0–500 mN range), displacement resolution of 10 nm, and integrated optical imaging at up to 1200× magnification for real-time particle visualization during compression.

Testing was performed on individual agglomerates of the SLES and NES electrodes after one formation cycle. Each particle was centered using the system’s automated XY displacement stage, and a controlled compressive load was applied at a constant displacement rate of 0.5 μm s⁻¹ while the force-displacement curve and optical image were recorded synchronously. The SLES sintered agglomerate exhibited a fracture force of 15.7 mN with a characteristic multi-stage force-displacement profile — initial elastic deformation, followed by micro-cracking events, then final catastrophic fracture. This profile is consistent with a well-sintered, interconnected network structure where mechanical load is distributed across multiple sintered necks before final failure. The NES control, in contrast, showed a single brittle fracture event at only 3.4 mN, with no evidence of load redistribution — consistent with a structure of isolated particles weakly bonded through a brittle carbon coating.

The 4.6× improvement in fracture force provides a direct quantitative explanation for the cycling stability difference between the two architectures: the SLES sintered network resists the mechanical stress of repeated lithiation without fracturing, preserving the percolating ion-transport network over hundreds of cycles. This measurement capability — enabled by the IEST SPFT2000 — turns a qualitative structural hypothesis into a quantifiable mechanical design parameter, providing a methodology that can be extended to other high-volume-change electrode materials such as tin and lithium metal.

9. Electrochemical Performance in Half-Cells and Full Cells

Figure 6. Electrochemical performance of SLES Si anode in sulfide-based ASSBs. (a) Half-cell cycling at 0.2C (100 cycles): SLES retains near-100% capacity; NES retains only 20.6% with continuous decay. (b) dQ/dV contour maps: SLES shows stable polarization without increase over 100 cycles; NES shows progressive polarization growth indicative of increasing interfacial resistance. (c) Rate capability test: SLES delivers 81% capacity retention at 1 mA cm⁻²; NES shows negligible capacity above 0.5 mA cm⁻². (d) Benchmarking against reported Si-based ASSBs: this work achieves the best combination of cycle life and rate performance. (e) Full cell with NCM532 cathode and Li₃InCl₆ electrolyte: 700 cycles with >60% capacity retention.

The SLES silicon anode was evaluated in ASSB half-cells using a Li–In alloy counter electrode, Li₆PS₅Cl solid electrolyte, and a stack pressure of 50 MPa. The results are unambiguous: the SLES anode retains nearly 100% of its initial capacity after 100 cycles at 0.2C, with stable Coulombic efficiency above 99.5% throughout. The NES control, under identical conditions, retains only 20.6% of its initial capacity, with continuous capacity fade from the first cycle onward. The dQ/dV analysis (Figure 6b) reinforces this contrast: the SLES cell shows stable peak positions and intensities over 100 cycles, indicating consistent lithiation/delithiation kinetics and negligible overpotential growth. The NES cell shows progressive peak broadening and shifting, consistent with increasing interfacial resistance and loss of active material contact.

Rate capability testing (Figure 6c) demonstrates that the SLES structure does not compromise high-rate performance for stability. At a current density of 1 mA cm⁻² (corresponding to approximately 1C rate), the SLES anode retains 81% of its low-rate capacity. Even at higher current densities, the SLES anode maintains more than half of its capacity, while the NES anode delivers negligible capacity above 0.5 mA cm⁻². Critically, when the SLES electrode was tested in liquid electrolyte cells, the advantage over NES disappeared — confirming that the SLES strategy is specifically optimized for the all-solid-state configuration, where solid–solid interface stability is the dominant failure mode.

10. Practical Full-Cell Validation: 700 Stable Cycles with NCM532

To demonstrate practical viability, the SLES Si anode was paired with a commercial NCM532 cathode in a full-cell configuration using Li₃InCl₆ as the solid electrolyte. The cell maintained over 60% capacity retention after 700 cycles, with stable voltage profiles and no evidence of sudden failure. This performance places the SLES design among the very best reported for silicon-based all-solid-state full cells, particularly considering that most prior reports are limited to <200–300 cycles before capacity drops below 60%.

The 700-cycle stability is especially significant because it demonstrates that the SLES structure — built in situ during the first cycles — remains mechanically and electrochemically stable over extended cycling. The sintered Si network does not undergo progressive structural degradation; instead, the initial cycling establishes a stable configuration that persists for hundreds of subsequent cycles. This self-limiting characteristic is a direct consequence of the geometry-guided sintering mechanism: once the sintered network is established, the stress concentrations at pore openings dissipate, and the driving force for further sintering diminishes — preventing the uncontrolled over-sintering that would produce oversized agglomerates and electrode fracture.

11. IEST–NanoYang Joint Laboratory at Tianjin University

The collaborative achievement described in this study builds upon a long-standing research partnership between the Nanoyang research group (led by Professor Yang Quanhong at Tianjin University) and IEST Instrument. On March 23, 2026, this partnership was formalized through the establishment of the IEST–NanoYang Joint Laboratory for Advanced Battery Materials Characterization at Tianjin University. The joint laboratory integrates Nanoyang’s frontier mechanism research in carbon-based energy materials with IEST’s precision characterization instruments, providing a dedicated platform for developing and validating next-generation battery material characterization methodologies.

For context on this collaboration, see the official announcement: IEST Instrument and Nanoyang at Tianjin University Establish Joint Laboratory for Advanced Battery Research.

The joint laboratory is strategically positioned to serve as both a showcase for advanced testing instrumentation in the northern China market and as a critical support platform for translating fundamental scientific discoveries into industrial-scale battery solutions. The SLES study represents a direct outcome of this academia-industry synergy: Nanoyang’s deep understanding of carbon material geometry and interfacial electrochemistry, combined with IEST‘s precision single-particle mechanical testing capability, produced the quantitative mechanical evidence that distinguishes SLES from prior static-constraint approaches.

12. Conclusions and Outlook

This work demonstrates that electrochemical sintering — conventionally regarded as a detrimental side reaction in silicon anodes — can be harnessed as a constructive process through precise geometric design at the microscale. The SLES strategy uses porous carbon with high-curvature pore openings as stress concentrators that selectively direct lithiation-induced mechanical stress to particle contact points, inducing controlled, local electrochemical sintering that builds a continuous, mechanically robust, and ionically conductive Si network while preserving internal void volume for volume expansion buffering.

Key quantitative achievements include:

• Interface modulus: 49 GPa (SLES) vs. 10.9 GPa (NES) — a 4.5× improvement in interfacial stiffness.

• Interparticle stress uniformity: <0.5 GPa (SLES) vs. 59.5 GPa (NES) — stress is uniformly distributed across the sintered network.

• Single-particle fracture force: 15.7 mN (SLES) vs. 3.4 mN (NES) — a 4.6× improvement, measured on the IEST SPFT2000.

• Half-cell capacity retention: ~100% after 100 cycles at 0.2C.

• Rate capability: 81% capacity retention at 1 mA cm⁻².

• Full-cell stability: >60% capacity retention after 700 cycles with NCM532 cathode.

The “geometry-guided dynamic evolution” design philosophy demonstrated here has broad applicability beyond silicon anodes. The same principle of using geometric constrictions to concentrate and direct mechanical stress during electrochemical cycling can be extended to other high-volume-change anodes for all-solid-state batteries, including tin (Sn) and lithium metal, offering a generalized pathway toward stable, high-energy-density solid-state energy storage.

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Abstract

Silicon anodes undergo approximately ~300% volume expansion during lithiation — a fundamental challenge that silicon-carbon (Si-C) composite designs partially mitigate but cannot eliminate. In a 50-cycle in-situ swelling study on pouch cells with 3 wt.% and 5 wt.% silicon content, cumulative silicon carbon anode swelling reached 8.8% and 11.2% total thickness increase, respectively. Irreversible swelling thickness — a direct indicator of SEI accumulation and active lithium loss — diverges significantly between the two formulations after cycle 35, with the higher silicon content cell showing markedly greater degradation. Critically, silicon anode swelling percentage correlates directly with capacity retention: as swelling accumulates, capacity fade accelerates, confirming that silicon anode volume expansion and capacity decay share a common root cause in SEI rupture-regeneration cycles and particle pulverization.

1. Why Does Silicon Anode Volume Expansion Limit Si-C Battery Cycle Life?

Rising energy density demands in electric vehicles are pushing battery anode development beyond the practical limits of graphite, whose theoretical capacity ceiling of 372 mAh/g has largely been reached by commercial cell designs. Silicon offers a compelling alternative — with a theoretical capacity of 4,200 mAh/g and a low lithiation potential of approximately 0.4 V — but its deployment at scale is constrained by one fundamental problem: silicon anode volume expansion of approximately 300% during lithiation and delithiation.

This silicon anode swelling drives a destructive cycle at the particle level. As silicon expands and contracts with each charge–discharge cycle, the SEI film on the particle surface cracks and reforms continuously. Each reformation consumes active lithium and electrolyte, accumulates irreversible side reaction products inside the cell, and progressively increases internal resistance and polarization. Particle pulverization further reduces the active surface area available for lithium intercalation, accelerating capacity fade.

Silicon-carbon composite anodes address these issues by embedding silicon within a carbon matrix that buffers volume change, maintains electronic conductivity, and partially stabilizes the SEI. However, the degree of swelling suppression depends critically on silicon content — a relationship that requires direct, in-situ quantification rather than inference from electrochemical data alone. This study uses the IEST SWE2110 in-situ swelling analyzer to directly measure and compare silicon carbon anode swelling percentage in Si-C pouch cells with 3 wt.% and 5 wt.% silicon content across 50 full charge–discharge cycles.

2. Experimental Equipment and Test Methods

2.1 Test Equipment

All swelling measurements were performed using the SWE2110 In-Situ Cell Swelling Testing System (IEST) in constant pressure mode at 5.0 kg, continuously monitoring cell thickness throughout 50 charge–discharge cycles. The MISS software package automatically records thickness, thickness variation, temperature, current, voltage, and capacity at each sampling interval for subsequent analysis.

Figure 1. IEST SWE2110 In-Situ Cell Swelling Testing System used for silicon carbon anode swelling percentage measurement.

2.2 Test Cell Specifications and Charge–Discharge Protocol

Two silicon-carbon composite anode pouch cell formulations were tested — one with 3 wt.% silicon content and one with 5 wt.% silicon content — prepared by the same process to isolate the effect of silicon loading on swelling behavior. Cell specifications and the charge–discharge protocol are summarized in Tables 1 and 2.

Table 1. Technical specifications of the LCO/Si-C cell Information of cell

Table 2. Battery testing multi-step protocol and sampling configuration

2.3 Cell Thickness Swelling Test Process

Put the cell to be tested into the corresponding channel of SWE2110, open the MISS software, set the cell number and sampling frequency and other parameters corresponding to each channel, the software will automatically read the thickness of the cell, thickness variation, and test temperature during the charging and discharging process , current, voltage, capacity and other data for subsequent comparative analysis.

3. How Does Silicon Content Affect Anode Swelling Percentage Over 50 Cycles?

Set the in-situ swelling analyzer (SWE2110) to the constant pressure mode (the pressure value is 5.0kg), and monitor the thickness change of different proportion of silicon-carbon system (silicon content is 3 wt.% and 5 wt.% respectively) soft core under long cycle (50 cycle), the results are shown in Figure 2. Through the initial thickness normalization, it can be found that with the increase of the number of cycles, the thickness swelling curves of both are also rising, and the higher the silicon content, the more obvious the swelling growth.

3.1 Total Silicon Carbon Anode Swelling: 8.8% vs 11.2% After 50 Cycles

Compared with the initial state, 3wt.% after 50 cycles And 5 wt.% The thickness swelling percentage of the silicon content cell is 8.8% and 11.2% respectively, indicating that both of them have accumulated a lot of side reaction products after long cycle, resulting in the continuous increase of the total volume of the cell. Due to the serious volume swelling of the silicon particles in the cathode during the lithium intercalation process, the active material particles will be broken and pulverized, and the existing SEI film on the particle surface will be destroyed, while the exposed new silicon particle surface will further react with the electrolyte to form a new SEI film. This repeated rupture and regeneration of the SEI film will not only accumulate many side reaction products and make the total volume of the cell expand continuously, but also easily cause the internal resistance and polarization of the cell to increase continuously, and finally aggravate the capacity attenuation of the cell ^[2,3].

Figure 2. Normalized thickness swelling curves for 3 wt.% and 5 wt.% Si-C anode cells over 50 charge–discharge cycles. Total silicon carbon anode swelling after 50 cycles: 8.8% (3 wt.%) and 11.2% (5 wt.%).

Table 3. Comparative swelling and degradation parameters for 3 wt.% vs 5 wt.% silicon carbon composite anode cells across 50 cycles.

3.2 Irreversible Swelling Thickness Diverges After Cycle 35

Beyond the total swelling curves, each cycle’s irreversible swelling thickness was extracted by subtracting the discharge-phase thickness contraction from the charge-phase swelling — a quantity that approaches zero in a fully reversible system but accumulates as the SEI grows and silicon particles fragment. Figure 3 shows this parameter for both cell groups across 50 cycles.

Before cycle 35, the irreversible swelling accumulation rates of both groups are nearly identical, indicating that at low cycle numbers, the 2 wt.% difference in silicon loading does not yet produce distinguishable degradation signatures. After cycle 35, however, the 5 wt.% Si cell shows markedly accelerated irreversible swelling growth — consistent with a threshold effect in which a critical fraction of silicon particles have fractured sufficiently to expose fresh silicon surfaces at a rate that overwhelms the electrolyte’s capacity to form stable SEI. This late-cycle acceleration of silicon anode swelling at higher silicon content directly foreshadows the capacity fade pattern observed in Section 4.

Figure 3. Irreversible swelling thickness as a function of cycle number for 3 wt.% and 5 wt.% Si-C anode cells. The two groups track closely until cycle 35, after which the 5 wt.% cell shows significantly accelerated irreversible swelling accumulation.

4. Correlation Between Silicon Carbon Anode Swelling and Capacity Decay

Swelling thickness and capacity retention data extracted cycle-by-cycle reveal a mechanistically linked co-evolution pattern that differs in detail between the two silicon content levels.

4.1 Swelling–Capacity Relationship Across the Cycle Life

Figure 4 plots the per-cycle swelling thickness alongside the capacity retention rate for both cell groups. In the early cycle regime (cycles 1–35), total swelling thickness rises while capacity retention falls — both driven by ongoing SEI accumulation, active lithium consumption, and progressive reduction of active silicon surface area. In the late cycle regime (after cycle 35), the thickness swelling curve flattens for both groups, reflecting the fact that particle pulverization, electrolyte depletion, and reduced active lithium concentration collectively limit how much lithium intercalation — and therefore volume change — can still occur. Critically, capacity fade continues during this late-cycle period even as swelling stabilizes, demonstrating that the two metrics are correlated but not proportional: structural damage accumulated in early cycles continues to degrade electrochemical performance after macroscopic swelling has plateaued.

The 5 wt.% Si cell shows greater swelling magnitude and more severe capacity decay than the 3 wt.% cell throughout, confirming that silicon carbon anode swelling percentage is a quantitative predictor of long-cycle degradation severity — and that silicon content is the primary lever controlling both.

Figure 4. Correlation between silicon carbon anode swelling thickness and capacity retention rate for 3 wt.% and 5 wt.% Si-C cells over 50 cycles. Swelling plateaus after cycle 35; capacity fade continues.

4.2 dQ/dV Analysis: Increased Polarization and Phase Transition Suppression

Differential capacity (dQ/dV) curves before and after the 50-cycle test provide complementary electrochemical evidence for the structural changes driving capacity decay. Figure 5 compares the 1st-cycle and 50th-cycle dQ/dV curves for both cell groups.

In both the 3 wt.% and 5 wt.% Si-C cells, the entire dQ/dV profile shifts to the right after 50 cycles — a characteristic signature of increased internal polarization consistent with thickened SEI films, reduced ionic conductivity, and elevated charge transfer resistance. The characteristic peaks at 3.72 V and 3.81 V — associated with specific phase transformation reactions — are substantially reduced in intensity and area at cycle 50, indicating that these reactions no longer contribute their full capacity at the end of life. Several minor characteristic peaks present in the 1st-cycle curve are absent entirely at cycle 50, reflecting the irreversible suppression of specific phase change reactions as electrode microstructure degrades. Both effects are more pronounced in the 5 wt.% Si cell, consistent with its higher silicon anode swelling percentage and faster SEI accumulation rate.

Figure 5. dQ/dV curves at cycle 1 (red) and cycle 50 (black) for (a) 3 wt.% and (b) 5 wt.% Si-C anode cells. Right-shift of the full curve indicates increased polarization; reduced peak intensity at 3.72 V and 3.81 V indicates suppressed phase transformation capacity.

5. Degradation Mechanisms in Silicon Carbon Composite Anodes

The combined swelling and electrochemical data are consistent with four interconnected degradation pathways that collectively govern silicon carbon battery swelling and capacity fade, as illustrated schematically in Figure 6^[4]:

• Particle cracking and active material isolation: The ~300% silicon anode volume expansion per particle generates mechanical stress that fractures silicon particles over repeated cycles. Fractured fragments may lose electronic contact with the carbon matrix, permanently removing that material from electrochemical participation.

• Continuous SEI formation and active lithium loss: Each fracture event exposes fresh silicon surface that immediately reacts with electrolyte to form new SEI. This process consumes both active lithium and electrolyte irreversibly, reducing the lithium inventory available for capacity and contributing to the cumulative thickness increase observed in Figure 2.

• Impedance growth and increased polarization: SEI thickening at the silicon particle surface raises electrode impedance and slows lithium-ion transport across the interface — reflected directly in the dQ/dV right-shift observed at cycle 50. The progressive impedance increase alters the electron and ion transport characteristics of the electrode layer and is a primary driver of voltage polarization loss.

• Electrode porosity changes: The combination of electrode volume swelling and continuous SEI formation alters the pore structure of the electrode, reducing electrolyte access to interior active material and further degrading rate capability and capacity utilization in later cycles.

Figure 6. Schematic of the four primary degradation mechanisms in silicon-based anodes: particle cracking, SEI rupture–regeneration, impedance growth, and porosity change.^[4]

Strategies to address silicon anode volume expansion and improve Si-C composite cycling performance include: (1) reducing silicon particle size to nano-scale to lower absolute expansion per particle; (2) synthesizing nanostructured silicon electrodes (e.g., hollow or porous silicon architectures) that accommodate volume change internally; (3) controlling the charge potential window to avoid formation of crystalline Li–Si alloys, which generate larger volume excursions than amorphous lithiation products; (4) using self-healing binders that maintain contact between active material and conductive network through volume changes; and (5) substituting silicon oxide (SiOₓ) for crystalline silicon, since SiOₓ undergoes a smaller specific volume change during lithiation/delithiation.

6. Summary

In-situ swelling measurements using the IEST SWE2110 swelling analyzer over 50 charge–discharge cycles establish a quantitative relationship between silicon anode swelling percentage, irreversible thickness accumulation, and capacity decay in silicon-carbon composite anode pouch cells. Key findings:

• Total silicon carbon anode swelling after 50 cycles: 8.8% at 3 wt.% Si vs 11.2% at 5 wt.% Si — a 27% relative difference driven by the ~300% silicon anode volume expansion per particle and its associated SEI rupture–regeneration cycle.

• Irreversible swelling thickness accumulates similarly in both groups until cycle 35, after which the 5 wt.% Si cell diverges significantly — indicating a threshold effect in late-cycle silicon particle fragmentation.

• Capacity fade is directly correlated with cumulative swelling, but continues even after the swelling curve plateaus in late cycles, reflecting structural damage that outlasts the macroscopic expansion signature.

• dQ/dV analysis confirms increased internal polarization and suppressed phase transformation reactions at cycle 50 in both formulations, with more severe effects in the higher silicon content cell.

These results confirm that silicon content is the primary controllable variable governing silicon carbon battery swelling and long-cycle capacity retention, and that in-situ swelling characterization provides essential input for Si-C composite anode optimization and material selection.

7. References

[1] M. Ashuri, Q.R. He and L.L. Shaw, Silicon as a potential anode material for Li-ion batteries: where size, geometry and structure matter. Nanoscale 8 (2016) 74–103.

[2] S. Chae, M. Ko, K. Kim, K. Ahn and J. Cho, Confronting issues of the practical implementation of Si anode in high-energy lithium-ion batteries. Joule 1 (2017) 47-60.

[3] X.H. Shen, R.J. Rui, Z.Y. Tian, D.P. Zhang, G.L. Cao and L. Shao, Development on silicon/carbon composite anode materials for lithium-ion battery. J. Chin. Cream. Soc. 45 (2017) 1530-1538.

[4] I. Choi, J.L. Min, S.M. Oh and J.J. Kim, Fading mechanisms of carbon-coated and disproportionated Si/SiOx negative electrode (Si/SiOx/C) in Li-ion secondary batteries: Dynamics and component analysis by TEM. Electrochim. Acta 85 (2012) 369-376.

8. FAQs

8.1 What is the silicon anode swelling percentage in lithium-ion batteries?

Silicon anodes undergo approximately 300% volume expansion during full lithiation — compared to roughly 10% for graphite — because silicon alloys with lithium to form Li₃.₇₅Si at full charge. In silicon-carbon composite anodes, the carbon matrix buffers this expansion, but cumulative silicon anode swelling percentage at the cell level still reaches 8.8% to 11.2% total thickness increase after 50 cycles at 3 wt.% and 5 wt.% silicon loading, respectively, as measured by in-situ swelling analysis.

8.2 How does silicon content affect the silicon carbon anode volume expansion percentage?

Higher silicon content directly increases silicon carbon anode volume expansion percentage. In-situ swelling measurements on 3 wt.% and 5 wt.% Si-C composite cells under identical cycling conditions show that after 50 cycles, the 3 wt.% Si cell accumulates 8.8% total thickness swelling while the 5 wt.% Si cell reaches 11.2% — a 27% relative increase. The divergence is most pronounced in the irreversible swelling component after cycle 35, where the higher silicon content cell shows accelerated SEI accumulation consistent with a critical fraction of fractured silicon particles exposing reactive fresh surface.

8.3 Why does silicon carbon anode swelling increase with cycle number?

Silicon carbon anode swelling increases with cycle number primarily because silicon particle volume expansion (~300%) fractures particles repeatedly, exposing fresh silicon surface that reacts with electrolyte to form new SEI film. Each SEI formation event consumes active lithium and electrolyte irreversibly, accumulating solid side-reaction products inside the cell that increase total thickness. This cycle of SEI rupture and regeneration is self-reinforcing: more fractures produce more fresh surface, accelerating both swelling accumulation and active lithium loss.

8.4 Is there a direct correlation between silicon anode swelling percentage and capacity decay?

Yes, silicon anode swelling percentage and capacity decay are directly correlated but not proportionally so throughout the cycle life. In the first 35 cycles, both swelling accumulation and capacity fade advance together as SEI growth consumes active lithium. After cycle 35, the swelling curve flattens as particle fragmentation and electrolyte depletion limit further expansion, but capacity fade continues — reflecting structural damage that outlasts its macroscopic swelling signature. The higher silicon content cell shows greater swelling and faster capacity decay at every stage, confirming silicon loading as the primary variable governing both.

8.5 How can silicon carbon battery swelling be reduced to improve cycle life?

Reducing silicon carbon battery swelling requires addressing the root cause — the ~300% silicon anode volume expansion per particle. Proven strategies include: reducing silicon particle size to nano-scale (lower absolute expansion); using porous or hollow nanostructured silicon architectures that accommodate expansion internally; substituting silicon oxide (SiOₓ) which has smaller volume change than crystalline silicon during lithiation; controlling the charge potential window to prevent crystalline Li–Si alloy formation; and using self-healing binders to maintain electronic contact through volume changes. Lower silicon content formulations (e.g., 3 wt.% vs 5 wt.%) also show measurably lower swelling accumulation and better capacity retention over 50 cycles.

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This article was originally published at: Silicon Carbon Anode Swelling Percentage and Capacity Decay: A 50-Cycle In-Situ Comparative Study first appeared on IEST Instrument | World-leading Innovative Lithium Battery Tester Solution Provider

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Abstract

Pouch cell swelling during electrochemical cycling is non-uniform: expansion force peaks in the center of the cell and is substantially weaker at the heat-sealed edges and tab regions. To measure pressure in pouch cell surfaces with spatial resolution, a thin-film pouch cell swelling pad — a conformal grid of pressure sensors placed beneath the cell — records synchronous local force traces at each position throughout charge and discharge. In a representative test on a wound pouch cell cycled at 0.75C, peak expansion force reached 125.3 kg at full charge and dropped to 56.9 kg at end of discharge, with edge zones showing higher rates of force change despite lower absolute values — a signature of corner-stress accumulation and potential lithium plating sites.

1. Introduction

Pouch cells are widely used in electric vehicles, portable electronics, and energy storage systems due to their high energy density and flexible packaging. During electrochemical cycling, electrode volume changes, gas evolution, and heat generation cause macroscopic deformation — most visibly as pouch cell swelling in the thickness direction. Uneven lithium distribution and internal structural features produce spatially non-uniform stresses across the pouch surface. Accurately monitoring these local changes is essential for understanding failure modes, improving safety, and optimizing cell and pack designs.

This study uses a Battery Pressure Mapping Sensor Measurement System(BPD) together with an in-situ swelling analyzer to measure pressure in pouch cell surfaces during charge–discharge, producing a high-resolution map of expansion force that reveals where the pouch expands most and why.

Figure 1. Simulation of lithium concentration and stress strain distribution corresponding to lithium-ion pouch cells when they are fully charged.

2. Test Information

2.1 Test Equipment

All measurements were performed using two complementary instruments:

• In-situ swelling analyzer, model SWE2110 (IEST) — applies controlled clamping forces from 50 N to 10,000 N while recording real-time cell thickness changes throughout the charge–discharge cycle.

• Battery Pressure Mapping Sensor Measurement System(BPD) — a conformal pouch cell swelling pad consisting of a thin-film sensor array placed beneath the pouch cell. The pad divides the cell surface into multiple measurement zones; each sensor logs a synchronous local force trace, enabling spatially resolved expansion force mapping across the full cell surface.

Figure 2. IEST SWE series in-situ swelling analyzer — the platform used to apply controlled clamping pressure and synchronously record pouch cell expansion force during cycling.

2.2 Test Parameters

The Pouch Cells Information is Shown in Table 1

Table 1. Basic technical specifications of the pouch cell Information of cell

2.3 Test Protocol

Pouch cells were placed in the SWE2110 test chamber in constant-gap mode. The charge–discharge sequence was as follows: 60-minute rest → CC charge at 0.75C (cut-off current 0.05C) → 10-minute rest → CC discharge at 0.75C (cut-off voltage 3.0 V). The BPD swelling pad simultaneously recorded local expansion force at each sensor zone throughout three full cycles.

3. Results and Analysis

3.1 Overall Expansion Force vs. Charge–Discharge Cycle

Figure 3 shows the maximum expansion force curve synchronized with cell voltage over three complete cycles. As lithium ions extract from the positive electrode and insert into the negative electrode during charging, the graphite anode expands and expansion force rises continuously. Peak expansion force at full charge reached 125.3 kg. During discharge, lithium ions return to the positive electrode, the anode structure contracts, and pressure falls progressively to 56.9 kg at end of discharge. The force profile is highly repeatable across all three cycles, confirming measurement stability under constant-gap clamping conditions.

Figure 3. Cell voltage and expansion force variation curve over three charge–discharge cycles (0.75C). Peak force: 125.3 kg (full charge); end-of-discharge force: 56.9 kg.

Table 2. Key expansion force and test parameters from the BPD + SWE measurement campaign.

3.2 Spatial Mapping: Center-Heavy Stress Distribution

To analyze expansion force variation across the cell surface, the pouch is divided into a grid of small sensor zones as shown in Figure 4. Each zone corresponds to one thin-film sensor in the pouch cell swelling pad, and all zones log synchronous force traces throughout cycling.

Figure 4. Grid subdivision of the pouch cell surface into individual sensor zones for spatially resolved expansion force measurement.

Figure 5 presents heatmap visualizations of expansion force across all zones as SOC increases during charge and decreases during discharge. The color scale correlates directly with local expansion force magnitude — darker regions indicate higher force. As SOC rises, expansion force increases substantially in the center region of the cell, while edge zones remain comparatively weaker. This center-heavy stress distribution is attributable to two factors: (1) the heat-sealed edge of the aluminum-plastic pouch film itself acts as a mechanical constraint, limiting thickness expansion at the periphery; and (2) the geometry of the wound electrode stack concentrates lithiation-induced strain in the central electrode layers. At the same time, the change of expansion force will also be affected by the glue at the end of the pouch cells and the thickness of the tab.

Figure 5. Expansion force heatmap across pouch cell surface zones as a function of SOC. Darker color = higher local expansion force. Center zones dominate; edge zones remain consistently lower in absolute force.

3.3 Edge and Corner Effects: Higher Rate of Expansion Change

Figure 6 compares the spatially resolved expansion force distribution in the zero-charge and full-charge states of the second cycle. While absolute expansion force is lower at the edges than in the center, the rate of expansion change — the increase from zero-charge to full-charge — is highest at edge and corner positions. This elevated rate-of-change at corners is consistent with stress accumulation associated with the wound electrode geometry, where electrode layers are bent through tighter radii at the corners, increasing local mechanical strain. This is also a location associated with elevated lithium plating risk during fast charging, and the expansion force signature provides a non-destructive indicator of that risk.

Figure 6. Relative expansion force distribution in zero-charge and full-charge states (second cycle). Edge and corner zones show the highest rate of expansion change despite lower absolute force values.

3.4 Zone-Level Force Traces and Tab-Region Anomaly

Figure 7 shows the expansion force curve of some selected small area units. Judging from the absolute value of the expansion force curves at different positions, the absolute value of the expansion force is the smallest at edge positions 1, 5, and 11,This may not be flat with the initial surface of the cell, so the initial force is less at the edge, Especially at the position 11 close to the tab, there is basically no obvious change in expansion force detected during the charging and discharging process, indicating that this position is basically not in contact with the pressure sensor. The uneven stress distribution in each area of the battery may also be related to the deformation process inside the winding cell. In the constant gap mode, the thickness of the battery expands during charging, and the expansion of the thickness forms a force on the clamp. Maintaining a constant gap between the clamps is equivalent to exerting a certain pressure on the battery. Under the pressure, wrinkles and curls may occur inside the cell. As shown in Figure 8, the stress on each area is not uniform.

Figure 7. Expansion force traces for selected individual sensor zones. Edge positions 1, 5, and 11 show lowest absolute values; position 11 (tab-adjacent) registers near-zero force change throughout cycling.

The non-uniform stress distribution observed across zones also reflects the internal deformation mechanics of the wound cell. Under constant-gap clamping, cell thickness increases during charging exert a reaction force on the clamps. The resulting compressive load can induce internal wrinkles and electrode curling, particularly where the electrode stack lacks lateral support — further redistributing local stress in a pattern that deviates from simple center-to-edge gradients.

Figure 8. Schematic of internal deformation in a wound pouch cell under constant-gap clamping, illustrating electrode wrinkle and curl formation that drives non-uniform surface stress distribution.

4. Practical Considerations & Best Practices

• Size and position the pouch cell swelling pad to ensure full cell surface contact; account for tab height and edge thickness variations that can create sensor gaps at peripheral zones.

• Calibrate pad sensors under known loads and conduct repeatability checks before each test campaign to remove baseline offsets caused by initial gaps, surface creases, or pouch film non-uniformities.

• Combine swelling heatmaps with thermal imaging data to correlate localized heat generation with mechanical expansion — corner zones showing high rate-of-change in expansion force alongside elevated temperature are candidate lithium plating sites.

• For production-line screening, a fast BPD pad measurement stage can flag units exhibiting abnormal local stress patterns — such as asymmetric center–edge ratios or anomalous tab-zone signals — before final assembly.

5. Summary

Using the IEST SWE series in-situ swelling analyzer together with the BPD Battery Pressure Distribution Measurement System, this study demonstrates how to measure pressure in pouch cell surfaces with full spatial resolution during cycling. The pouch cell swelling pad approach quantitatively maps local expansion force across the entire cell surface in real time, revealing three consistent findings: (1) expansion force peaks in the center at 125.3 kg and falls to 56.9 kg at end of discharge; (2) absolute force is lower at the heat-sealed edges, but the rate of expansion change is highest at corners — indicating stress accumulation and potential lithium plating risk; and (3) the tab-adjacent zone shows near-zero force change, reflecting a mechanical contact gap rather than true zero expansion. This non-destructive characterization approach provides battery engineers with a direct, spatially resolved tool for stress analysis, failure investigation, and cell design optimization.

6. References

[1] Yanan Wang, Hua Li, Zheng Kun Wang, Chen Lian, Zongfa Xie. Factors affecting stress in anode particles during charging process of lithium-ion battery, Journal of Energy Storage, 43(2021)103214.

[2] Anna Tomaszewska, Zhengyu Chu, Xuning Feng, et al. Lithium-ion battery fast charging: A review, eTransportation, 1 (2019) 100011.

[3] Yong Kun Li, Chuang Wei, Yumao Sheng, Fei Peng Jiao, and Kai Wu. Swelling Force in Lithium-Ion Power Batteries，Ind. Eng. CHem. Res，2020, 59, 27, 12313–12318.

[4] Ali M Y, Lai W J, Pan J. Computational models for simulations of lithium-ion battery cells under constrained compression tests, Journal of Power Sources, 2013, 242:325-340.

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Abstract

Graphite lithiation—the process by which lithium ions intercalate into graphite anode layers during battery charging—proceeds through multiple ordered stage transitions that directly determine the rate and safety of fast charging. A 2026 JACS study by researchers at Nanjing University and CATL‘s 21C Innovation Lab has, utilizing an optical electrochemical imaging platform with high spatiotemporal resolution developed in collaboration with IEST Instrument, for the first time, directly visualized these phase transitions within single commercial natural graphite particles under real electrochemical conditions. The central finding is a previously unreported stochastic nucleation–confined propagation mechanism governing the early lithiation stages (1L→3L and 3L→2 transitions), contrasting sharply with the classic shrinking-core model observed in the final 2→1 stage. Critically, the rate-limiting step shifts between these stages: interfacial Li-ion transport dominates early transitions, while solid-state diffusion becomes the bottleneck in the final stage—a unified kinetic framework that resolves decades of academic debate and directly informs fast-charging anode design.

📄 Source Paper

Haoran Li, Ben Niu, Xinyue Wang, Mai Wang, Jia Gao, Yuyang Lu, Weiyi Liu, Jiao Gao, Xing-hua Xia, Jinding Liang, *Wei Wang*

Visualization of Stochastic Nucleation and Confined Propagation in Graphite Lithiation

DOI: 10.1021/jacs.6c03598
| Journal: Journal of the American Chemical Society
| Institutions: Nanjing University; CATL 21C Innovation Lab

✓ IEST Instrument acknowledged — Single Particle Electrochemical Performance Testing System(SPEC1000) used in this research

1. Background: What Makes Graphite Lithiation the Bottleneck for Fast Charging?

Graphite remains the dominant commercial anode material in lithium-ion batteries, largely because of its high theoretical capacity, low intercalation potential, and established manufacturing ecosystem. However, the phase transition dynamics of graphite during lithiation present a fundamental barrier to fast charging—one that has resisted resolution for decades.

The staging phenomenon in graphite refers to the ordered, sequential intercalation of lithium ions between specific graphene layer spacings, producing a series of well-defined crystallographic phases (stages) with distinct lithium stoichiometries and optical reflectivities. Each transition between adjacent stages—1L→3L, 3L→2, and 2→1—occurs at a characteristic electrochemical potential and involves the nucleation and growth of a new phase domain.

Figure 1. Optical response of a commercial natural graphite particle during electrochemical lithiation, captured by the single-particle optical electrochemical imaging platform. The time-resolved contrast changes directly reflect phase domain nucleation and boundary propagation across successive stage transitions.

For decades, the identity of the rate-limiting step in graphite lithiation has been contested. Early literature attributed slow lithiation kinetics to solid-state diffusion of Li⁺ ions through the graphite bulk. More recent studies, however, have implicated interfacial Li-ion transport—the passage of lithium ions across the graphite–electrolyte interface—as the primary kinetic bottleneck. Reconciling these conflicting views has proved difficult, because the relevant processes are inherently heterogeneous and non-equilibrium at the particle scale.

Conventional diagnostic techniques have not been capable of resolving this question. GITT and PITT are electrochemical methods that estimate ion transport parameters through voltage relaxation analysis but provide only ensemble-averaged, spatially unresolved information. X-ray diffraction (XRD) probes bulk crystallographic phase fractions but lacks the spatial resolution to observe individual phase domains within a single particle. Synchrotron-based and electron microscopy techniques offer higher resolution but impose stringent environmental constraints that preclude long-duration, in operando observation under realistic electrochemical conditions.

The core challenge, therefore, was to develop a platform capable of directly visualizing the phase transition dynamics of graphite during lithiation in real time, at the single-particle level, without disturbing the electrochemical environment—resolving which process limits lithiation kinetics and whether that bottleneck is uniform across all stages of intercalation.

2. Technical Platform: Single-Particle Electrochemical Imaging at Near-Diffraction-Limit Resolution

Single-particle electrochemical imaging is defined here as an in operando optical microscopy technique that correlates the spatially resolved optical reflectivity of an individual electrode particle with its local electrochemical state during active charge or discharge, enabling direct observation of phase nucleation, boundary propagation, and domain evolution at sub-micron resolution.

The research team—in collaboration with IEST Instrument—co-developed a dedicated single-particle optical electrochemical imaging platform that addresses three critical technical challenges simultaneously:

2.1 Active 3D Drift Correction

Long-duration electrochemical cycling experiments—spanning days to weeks—are fundamentally compromised by thermal and mechanical drift in the imaging stage. The platform incorporates a hardware-level, three-dimensional drift correction module that performs real-time active compensation in both the x–y focal plane and the z-axis. This ensures that phase domain evolution data collected over extended cycling periods remains spatially registered and quantitatively comparable, enabling rigorous analysis of local structural dynamics within individual particles.

2.2 Optically Compatible Half-Cell

A custom-designed, optically transparent half-cell was fabricated to maintain full compatibility with high-numerical-aperture optical microscopy while replicating the electrochemical behavior of commercial coin cells. The electrochemical response of the optically compatible half-cell was validated to be essentially identical to that of standard coin-cell assemblies, confirming that observations made in the imaging system accurately represent behavior under practical battery operating conditions.

2.3 High Spatiotemporal Resolution

The imaging system achieves spatial resolution approaching the optical diffraction limit, enabling clear discrimination of sub-micron-scale phase domains within individual graphite particles. Temporal resolution at the second scale allows continuous tracking of phase boundary nucleation and propagation from initiation through completion across all stage transitions. Together, these capabilities provide the first complete, real-time dynamic record of graphite lithiation at the single-particle level.

Figure 2. Schematic of the single-particle optical electrochemical imaging platform co-developed by the research team and IEST Instrument, including the optically compatible half-cell design that replicates commercial coin-cell electrochemistry.

3. Discovery: How Stochastic Nucleation Governs Early-Stage Graphite Phase Transitions

Direct imaging of individual commercial natural graphite particles during lithiation revealed a phase transition behavior in the early intercalation stages (1L→3L and 3L→2) that had not been previously reported at the single-particle level.

Stochastic nucleation, in the context of graphite lithiation phase transitions, refers to the asynchronous, spatially random initiation of new phase domains at discrete sites within a single particle, rather than the uniform or deterministic nucleation expected from classical phase-transition models. Each nascent domain forms independently, without apparent coordination with neighboring domains.

Confined propagation refers to the subsequent spatial evolution of each stochastically nucleated phase domain, in which the phase boundary advances within a spatially restricted region of the particle rather than sweeping continuously across the entire particle interior. Each domain propagates within its own bounded territory, resulting in a mosaic of coexisting phase domains within a single graphite particle during the transition.

In the 3L→2 stage transition specifically, the imaging data show multiple discrete phase domains nucleating asynchronously across the graphite particle surface. Rather than a single phase front advancing uniformly from one end of the particle, each domain independently expands within a spatially confined region. The result is a mosaic microstructure of coexisting 3L and stage-2 domains that persists throughout the transition.

Figure 3. Single commercial natural graphite particle exhibiting stochastic nucleation–confined propagation behavior during the 3L→2 phase transition. Multiple phase domains nucleate asynchronously and propagate within spatially restricted regions. Scale bar: 5 μm.

This stochastic nucleation–confined propagation mode contrasts sharply with the behavior observed in the final 2→1 stage transition.

The shrinking-core model describes a phase transition in which a single phase front advances progressively inward from the particle surface toward its center, consuming the parent phase in a geometrically regular, spatially correlated manner. This classical model predicts a single contiguous phase boundary that moves continuously across the particle.

In the 2→1 lithiation stage, the imaging data reveal behavior consistent with the shrinking-core model: a single, coherent phase front advances across the graphite particle in a spatially correlated, continuous manner—the canonical behavior that had been assumed to govern all stages of graphite lithiation in earlier literature. The stark contrast between the two regimes—stochastic in early stages, shrinking-core in the final stage—is made visually explicit by the single-particle imaging platform at a 5 μm scale resolution.

Figure 4. Direct comparison of phase transition mechanisms in a single commercial natural graphite particle. upside: 3L→2 stage transition exhibiting stochastic nucleation and spatially confined phase boundary propagation. Below: 2→1 stage transition exhibiting the classic shrinking-core phase front. Scale bar: 5 μm.

Phase Transition Behavior Comparison Across Graphite Lithiation Stages

Table 1. Summary of phase transition characteristics and stage-dependent rate-limiting mechanisms identified by single-particle electrochemical imaging of commercial natural graphite (JACS 2026, DOI: 10.1021/jacs.6c03598).

4. Stage-Dependent Rate-Limiting Mechanisms: From Interfacial Transport to Solid-State Diffusion

The distinct phase transition behaviors observed across graphite lithiation stages reflect fundamentally different rate-limiting steps—the slowest elementary process that controls the overall lithiation rate at each stage.

A rate-limiting step in an electrochemical reaction refers to the elementary kinetic process whose activation energy or transport coefficient is sufficiently low (relative to competing processes) that it controls the overall reaction rate. In graphite lithiation, identifying the rate-limiting step at each stage is essential for designing targeted interventions to accelerate charging.

To distinguish between interfacial control and solid-state diffusion control, the research team conducted systematic kinetic analyses at multiple current densities. The methodology exploited the fact that interfacial-transport-limited processes and solid-state-diffusion-limited processes respond differently to changes in applied current: the characteristic signatures of nucleation frequency, domain growth rate, and phase front morphology shift predictably under each control regime.

The results establish a clear, stage-dependent kinetic picture:

• In early lithiation stages (1L→3L and 3L→2): The stochastic nucleation frequency and the spatial extent of confined propagation domains are strongly sensitive to current density, in a manner consistent with interfacial Li-ion transport as the dominant rate-limiting step. The slow passage of lithium ions across the graphite–electrolyte interface—governed by interfacial desolvation energy, SEI film conductivity, and local surface site availability—determines how rapidly new phase domains can be initiated and sustained.

• In the final lithiation stage (2→1): The phase front velocity and overall transition kinetics are dominated by the rate at which Li⁺ ions diffuse through the already-lithiated graphite bulk to reach unreacted stage-2 material. Solid-state diffusion through the dense, high-lithium-content graphite lattice becomes the rate-limiting bottleneck—consistent with the shrinking-core phase front geometry, which is the expected spatial signature of bulk-diffusion-controlled reactions.

Figure 5. Schematic of the stage-dependent rate-limiting mechanism established by single-particle electrochemical imaging. Interfacial Li-ion transport governs the 1L→3L and 3L→2 transitions; solid-state diffusion governs the 2→1 transition.

This finding resolves the long-standing debate by demonstrating that both proposed mechanisms are correct—but each applies to a different lithiation stage. The the lithium intercalation dynamics in natural graphite do not follow a single, universal rate-limiting mechanism; rather, the controlling step shifts dynamically as intercalation proceeds, and any single-mechanism interpretation of bulk electrochemical measurements will necessarily be incomplete.

5. Implications for Fast-Charging Graphite Anode Design

The unified kinetic framework established by this study provides concrete, stage-specific guidance for overcoming the graphite anode fast charging bottleneck—a challenge that has constrained fast-charging performance in commercial lithium-ion batteries.

Table 2. Stage-dependent design strategies for fast-charging graphite anodes derived from the unified kinetic framework (JACS 2026).

For early-stage transitions governed by interfacial transport, the most effective interventions target the graphite–electrolyte interface: electrolyte composition, SEI film chemistry, and surface coatings that reduce the desolvation energy barrier for Li⁺. For the final 2→1 transition, strategies that shorten the diffusion path length—such as particle size reduction, introduction of fast-diffusion grain boundaries, or design of heterostructured electrode particles—offer the greatest kinetic benefit. At the cell-engineering level, charging protocols that are dynamically adapted to the time constants of each stage transition can reduce total charging time without triggering lithium plating at the anode surface.

Furthermore, the single-particle optical electrochemical imaging methodology demonstrated in this study offers a generalizable platform for the kinetic characterization of other intercalation-based electrode materials—including layered oxide cathodes, silicon–carbon composite anodes, and sodium-ion electrode materials—wherever multi-stage phase transitions govern rate performance.

6. IEST SPECT1000: Standardizing Single-Particle Electrochemical Characterization

Building on the co-development work underlying this JACS study, IEST Instrument has standardized and productized the single-particle optical electrochemical imaging technology as the SPECT1000 Single-Particle Electrochemical System—a dedicated instrument for high-throughput, multi-parameter electrochemical characterization of battery electrode materials at the single-particle scale.

The SPECT-1000 integrates an optical microscopy subsystem, a precision position control stage, an optically compatible electrochemical cell, and unified control software into a single platform. The system is designed to support systematic measurement of multiple electrochemical performance parameters in parallel—including reaction activity, kinetic rate constants, phase transition potentials, C-rate performance, spatial heterogeneity distribution, and volume expansion—under operando conditions that faithfully replicate real battery environments.

Key technical capabilities of the SPECT1000 system include:

• High-fidelity operando optical imaging at near-diffraction-limit spatial resolution, enabling direct observation of real-time internal dynamics within actual battery electrode systems

• Extended continuous measurement stability, maintaining quantitative comparability of imaging data across multi-day charge/discharge test campaigns through active 3D drift correction

• Multi-parameter electrochemical compatibility, supporting simultaneous measurement of reaction kinetics, phase transition characteristics, rate capability, spatial heterogeneity, and volumetric expansion at the single-particle level

Figure 6. Operational principle and system schematic of the IEST single-particle electrochemical testing system

7. Conclusion

Real-time single-particle electrochemical imaging of commercial natural graphite has revealed that the phase transition mechanism in graphite lithiation is fundamentally stage-dependent rather than uniform. Early lithiation stages (1L→3L and 3L→2) proceed via stochastic nucleation of asynchronous, spatially discrete phase domains whose subsequent confined propagation is controlled by interfacial Li-ion transport. The final 2→1 stage follows the classical shrinking-core model, governed by solid-state Li⁺ diffusion.

This unified kinetic framework, established through the optically compatible half-cell imaging platform co-developed by Nanjing University, CATL, and IEST Instrument, provides the first mechanistic basis for stage-specific fast-charging optimization of graphite anodes—and opens a new in operando visualization approach to the broader field of intercalation electrode kinetics.

8. Original Article

Visualization of Stochastic Nucleation and Confined Propagation in Graphite Lithiation. Haoran Li, Ben Niu, Xinyue Wang, Mai Wang, Jia Gao, Yuyang Lu, Weiyi Liu, Jiao Gao, Xing-hua Xia, Jinding Liang,* Wei Wang* J.Am. Chem. Soc. 2026, DOI: 10.1021/jacs.6c03598

9. FAQs

9.1 What is stochastic nucleation in graphite lithiation?

Stochastic nucleation in graphite lithiation refers to the asynchronous, spatially random initiation of new lithium-rich phase domains at discrete sites within a single graphite particle during intercalation. Unlike deterministic or uniform nucleation, stochastic nucleation produces multiple independent phase domains that form without coordination, resulting in a heterogeneous mosaic microstructure within a single particle. This mechanism was directly observed for the first time in the 3L→2 and 1L→3L stage transitions of commercial natural graphite using single-particle optical electrochemical imaging (JACS 2026, DOI: 10.1021/jacs.6c03598).

9.2 Why does graphite lithiation bottleneck fast-charging in lithium-ion batteries?

The graphite anode fast charging bottleneck arises from the kinetic limitations inherent in the staged lithium intercalation process. During fast charging, lithium ions must both cross the graphite–electrolyte interface (interfacial transport) and diffuse through the graphite bulk (solid-state diffusion). A 2026 JACS study shows that different stages of graphite lithiation are limited by different processes: early transitions (1L→3L, 3L→2) are limited by interfacial ion transport, while the final 2→1 transition is limited by solid-state diffusion. Exceeding the kinetic capacity of either step leads to lithium plating—a safety and degradation risk that constrains achievable charging rates.

9.3 How do phase transition dynamics differ across graphite lithiation stages?

Phase transition dynamics in graphite lithiation differ fundamentally between early and late intercalation stages. In early stages (1L→3L and 3L→2), phase transitions proceed via stochastic nucleation—asynchronous initiation of multiple discrete phase domains—followed by spatially confined propagation of each domain within a restricted region of the particle. In the final 2→1 stage, a single coherent phase front advances progressively inward in a shrinking-core pattern governed by solid-state diffusion. These contrasting behaviors reflect different underlying rate-limiting mechanisms and were directly visualized at the single-particle level in a commercial natural graphite particle by Nanjing University and CATL researchers (JACS 2026).

9.4 What is confined propagation in graphite phase transition, and why does it matter?

Confined propagation in graphite phase transition describes the spatially restricted advance of a stochastically nucleated phase domain within a limited region of the graphite particle, rather than a continuous sweep across the entire particle cross-section. Confined propagation results in the coexistence of multiple phase domains within a single particle during the transition—a state invisible to ensemble techniques such as XRD or GITT. Understanding confined propagation matters for fast-charging design because it reveals that interfacial kinetics, not bulk diffusion, control early lithiation stages, directing engineers toward interface-targeted electrolyte and SEI engineering rather than particle size reduction as the primary optimization lever for those stages.

9.5 How can single-particle electrochemical characterization advance fast-charging battery design?

Single particle electrochemical characterization—such as the in operando optical imaging technique demonstrated in JACS 2026—enables direct, spatially resolved measurement of phase transition kinetics, nucleation behavior, and rate-limiting steps within individual electrode particles under realistic electrochemical conditions. This capability resolves mechanistic ambiguities that ensemble methods cannot distinguish and provides stage-specific kinetic parameters that can directly inform charging protocol design (e.g., current ramping strategies tailored to each lithiation stage’s time constant), electrolyte formulation (targeting interfacial transport bottlenecks in early stages), and particle architecture design (targeting solid-state diffusion bottlenecks in the final stage). IEST Instrument’s SPECT1000 system translates this methodology into a standardized research platform for electrode material kinetic characterization.

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Abstract

CVD silicon-carbon anode (chemical vapor deposition Si-C anode, also called gas-phase or vapor-deposition silicon-carbon) deposits nano-silicon into a porous carbon scaffold via silane decomposition at elevated temperature — the most commercially promising approach to high-capacity silicon-based anodes because it achieves nanoscale silicon distribution without wet milling. Scaling CVD silicon-carbon anode production reliably requires fast, non-destructive screening at the powder level before electrode and cell fabrication. This application note describes a three-stage anode powder testing workflow: (1) powder resistivity and compaction density measured by the IEST PRCD3100 (10–350 MPa sweep), (2) electrode sheet resistance measured by the IEST BER2500 (5 MPa, six locations per electrode), and (3) coin-cell electrochemical performance — tested across eight batches from two CVD process routes (Process A: GA-1 to GA-4; Process B: GB-1 to GB-4). Key result: anomalous powder batches (GA-4 with high resistivity; GB-2 with unusually low resistivity) mapped directly to anomalous coin-cell performance, validating powder-level anode testing as a fast QC gate.

1. Background: Why CVD Silicon-Carbon Anodes Need Better Batch Screening

CVD silicon-carbon anode (chemical vapor deposition Si-C anode, also called gas-phase or vapor-deposition silicon-carbon) deposits nano-silicon into a porous carbon scaffold via silane decomposition at elevated temperature — the most commercially promising approach to high-capacity silicon-based anodes because it achieves nanoscale silicon distribution without wet milling. Scaling CVD silicon-carbon anode production reliably requires fast, non-destructive screening at the powder level before electrode and cell fabrication.

Silicon-based anodes offer theoretical capacity (~4,200 mAh/g) nearly ten times that of graphite (372 mAh/g), but massive volume expansion during lithiation (~300%) leads to particle pulverization, structural degradation, and continuous SEI reformation — causing rapid capacity fade and low initial coulombic efficiency.^[1] Research has pursued nano-structuring, compositing, carbon coating, silicon oxidation, alloying, pre-lithiation, and pre-magnesiation to address these challenges.^[2]

Two silicon-carbon material categories have reached commercial scale:

• Nano-silicon-carbon (grinding route): Silicon is milled to ~100 nm and composited with carbon. Problems include particle agglomeration, persistent volume expansion, and limited cycle performance. Carbon-coating (”carbon-coated silicon” with a fruit-shell-like protective shell) reduces fracture risk but balances between capacity, Coulombic efficiency, rate performance, and cycle life according to the silicon/carbon ratio.^[3]

• Silicon oxide-carbon (SiOx route): Lower volume expansion (~118%) and better cycling than pure Si, but significant first-cycle irreversible capacity from Li₂O and Li₄SiO₄ formation results in first Coulombic efficiency of only ~70%. Pre-lithiation or pre-magnesiation raises this to 86–90% but adds cost. Commercial SiOx anodes typically deliver 450–500 mAh/g.^[4]

CVD silicon-carbon (vapor deposition Si-C) has emerged as the next-generation route: silane is thermally decomposed inside a porous carbon scaffold in a rotary kiln or fluidized bed reactor, depositing nano-scale silicon directly into the scaffold’s pores. This avoids the agglomeration problems of milled nano-Si and achieves uniform silicon distribution at the nanometer scale. Three industrialization challenges remain for CVD silicon-carbon anodes:

• Porous carbon scaffold selection: Different porous carbons must be matched with appropriate graphite blends to achieve target battery-level performance.

• Deposition equipment: Rotary kilns are prone to non-uniform silicon deposition and incomplete carbon coverage; silane utilization is also lower. Fluidized beds achieve more uniform deposition and higher silane utilization but require high-airtightness, high-pressure equipment — a scale-up challenge.

• Deposition process consistency: Mass production requires extreme uniformity across hundreds of kilograms of feedstock, multiple furnace temperature zones, and controlled cavity partial pressures; optimal silicon residence time in the deposition zone must be established empirically.

These industrialization challenges mean that anode powder testing — specifically, measuring batch-to-batch variation in powder resistivity and compaction density before electrode fabrication — is essential for CVD silicon-carbon anode process control. This study demonstrates a validated powder-to-cell screening workflow for exactly this purpose.

2. Testing Methodology: Anode Powder Testing Workflow

2.1 Test Equipment

Two key instruments from IEST Instrument were utilized:

• PRCD3100 Powder Resistivity & Compaction Density Meter: Applies pressure up to 5 tonnes while simultaneously measuring powder resistivity, conductivity, and compaction density. Generates the full pressure-property curve needed to compare CVD silicon-carbon anode powder batches across the full compression range (Figure 1).

• BER2500 Electrode Resistance Tester: 14 mm diameter probe, pressure range 5–60 MPa. Measures resistance, resistivity, conductivity, and compaction density of calendered electrode sheets in a single-point or multi-point test (Figure 2).

Figure 1. IEST PRCD3100 Powder Resistivity & Compaction Density Meter — schematic and two testing principles (two-probe and four-probe) for anode powder testing

Figure 2. IEST BER2500 Electrode Resistance Tester — (a) appearance; (b) structural diagram; 14 mm probe, 5–60 MPa, measures silicon-carbon anode electrode resistance and resistivity

2.2 Experimental Process

1. Materials: Two sets of CVD silicon-carbon anode powder were prepared via different CVD process routes (Process A and Process B), each comprising four consecutive production batches: GA-1 to GA-4 (Process A) and GB-1 to GB-4 (Process B).

2. Powder-level anode testing (PRCD3100): A stepwise pressure profile was applied to each powder sample (6–200 MPa). Resistivity and thickness were recorded at each pressure hold, generating resistivity and compaction density curves as a function of applied pressure.

3. Electrode preparation and testing: Powders were used to prepare slurry and coat single-sided electrodes (labeled JA-1 to JA-4 and JB-1 to JB-4). A consistent slurry formulation was used for all batches (Table 1). The BER2500 was used in single-point mode (5 MPa, 15 s hold) to measure electrode resistance at six locations per sample, and the coefficient of variation (COV) was calculated to quantify spatial uniformity.

4. Cell assembly and testing: Electrodes were assembled into coin cells for standard electrochemical performance evaluation.

Table 1. Slurry mixing formula for JA and JB groups Materials Silicon Carbide Gas Phase SP CNT LA133 JA Group 94% 1% 1% 4% JB Group 94% 1% 1% 4%

3. Results and Discussion

3.1 Powder Resistivity and Compaction Density: Process Route and Batch Discrimination

Figures 3 and 4 show the resistivity and compaction density trends for all eight CVD silicon-carbon anode powder batches under increasing pressure.

Key Observations:

• Resistivity decreased with applied pressure for all samples, reflecting reduced inter-particle contact resistance as particles are forced into closer contact.

• Process B powders (GB series) consistently showed lower resistivity than Process A (GA series), indicating superior intrinsic electronic conductivity — attributable to more uniform silicon deposition and better carbon coverage in the CVD process.

• Batch consistency was pressure-dependent. Below 100 MPa, both process routes showed significant batch-to-batch variation, driven by differences in particle contact geometry and morphology. Above 100 MPa, GB batches converged closely while GA batches still showed discernible differences — indicating that Process B achieves more uniform silicon deposition within and between batches.

• Compaction density trends were similar for both process routes, though Process A powders were slightly less dense. This suggests a more porous particle structure from Process A — which can accommodate more silicon but also contributes to higher resistivity, since silicon’s intrinsic conductivity is low.

Figure 3. Powder resistivity vs pressure for GA-1 to GA-4 (Process A) and GB-1 to GB-4 (Process B) — Process B shows lower, more consistent resistivity; GA-4 exhibits anomalously high resistivity

Figure 4. Powder compaction density vs pressure (PRCD3100, 0–200 MPa) — Process A powders are slightly less dense; GA-4 shows anomalously low compaction density consistent with an out-of-specification deposition batch

3.2 Electrode Resistance: Powder Trends Preserved and Amplified

Figure 5 shows electrode resistance and resistivity for JA and JB electrode series. The electrode-level measurements directly preserve the powder-level trend: JA electrodes (from GA powders, Process A) show consistently higher resistance and resistivity than JB electrodes (from GB powders, Process B), even though both series used an identical slurry formulation with the same conductive agent loading. This demonstrates that the active material’s own conductivity and morphology independently influence electrode conductivity, beyond the contribution of added conductive agents.

More significantly, the COV of JA electrode resistance is larger than that of JB — mirroring the larger batch-to-batch resistivity variation seen in GA powder (Figure 3). This amplification effect occurs because non-uniform powder particles are more difficult to disperse evenly during slurry preparation, and uneven dispersion produces spatially variable electrode sheet resistance after coating and drying. The BER2500‘s six-location sampling protocol per electrode makes this spatial COV directly visible as a production-quality signal.

Figure 5. Electrode resistance and resistivity results (BER2500, 5 MPa, 15 s hold, six locations per sample) — JA electrodes show higher resistance and larger COV than JB; the powder-level trend is preserved and amplified at the electrode level

3.3 Coin-Cell Performance Correlation: Powder Anomalies Map to Cell Anomalies

Table 2. Battery discharge data comparison for JA and JB batches Batch Number JA-1 JA-2 JA-3 JA-4 JB-1 JB-2 JB-3 JB-4 Charge Capacity
mAh/g 1958.83 1989.82 1950.70 1710.96 1925.04 1798.10 1896.48 1870.01 Discharge Capacity
mAh/g 2096.65 21334.10 2094.98 1924.95 2061.89 1952.51 2040.86 2003.80 Initial Effect 93.41% 93.24% 93.11% 88.88% 93.36% 92.09% 92.92% 93.29%

The above-mentioned electrodes were prepared into buckles and electrical performance tests were conducted. As can be seen from Table 2, provided the final performance link. The JA cells generally delivered higher charge capacity than JB cells, possibly due to a higher silicon content in Process A material.

Crucially, outliers in cell performance could be traced back to powder properties:

• JA-4 showed anomalous coin-cell performance. Its source powder GA-4 exhibited abnormally high resistivity (Figure 3) and low compaction density (Figure 4) — both out-of-specification signals relative to GA-1, GA-2, and GA-3. These powder-level flags were detectable before electrode fabrication, meaning the batch anomaly could theoretically have been identified and quarantined at the anode powder testing stage.

• JB-2 showed lower capacity than the other JB cells. Its source powder GB-2 exhibited notably lower resistivity — potentially indicating lower silicon content than specification, reducing stored capacity.

It can be seen from the above analysis that the resistivity and compaction density of silicon carbon powders of different processes and batches have a relatively good correlation with the final battery performance. This correlation makes the detection of parameters such as resistivity and compaction density of active particle powder an effective means of predicting final battery performance. By accumulating a certain amount of powder characterization test data during production, we can establish this correlation, allowing us to quickly identify batch anomalies and predict final cell performance. Therefore, the detection of parameters such as resistivity and compacted density of active granular powder is of great significance for improving production efficiency and product quality.

4. Conclusion

This case study demonstrates an effective multi-scale workflow for monitoring CVD silicon-carbon anode production batch stability. Using the PRCD3100 and BER2500 instruments, we rapidly characterized the electrical and physical properties of powders and electrodes from two CVD process routes across eight production batches.

Process B produced silicon-carbon anode material with better conductivity, higher compaction density, and superior batch-to-batch consistency compared to Process A. These powder-level advantages translated directly to more consistent electrode resistance and predictable cell performance. Anomalies in final cell data were traceable to anomalies in initial powder characterization data. Anode powder testing using powder resistivity, compaction density, and electrode resistance monitoring provides a powerful, rapid screening method for CVD silicon-carbon anode quality control—invaluable for both R&D process optimization and production-line batch release decisions.

5. References

[1] Q. Liu, Y. Hu, X. Yu, et al. The Pursuit of Commercial Silicon-Based Microparticle Anodes for Advanced Lithium-Ion Batteries: A Review. Nano Research Energy. 2022, 1: e9120037.

[2] Hu B, Jiang S, Shkrob I A, Zhang J, Trask S E, Polzin B J, et al. Understanding of prelithiation of poly(acrylic acid) binder: Striking the balances between the cyclingperformance and slurry stability for silicon-graphite composite electrodes in Li-ion batteries[J].Journal of Power Sources.2019,416(1):125-131.

[3] WANG W，KUMTA P N. Nanostructured hybrid silicon/carbon nanotube heterostructures: reversible high-capacity lithium-ion anodes［J］.ACS Nano，2010，4（4）：2233-2241.

[4] Shi H,Zhang H,Li X,et al. In-situ fabrication of dual coating structured SiO composite as high-performance lithium ion battery anode by fluidized bed chemical vapor deposition[J]. Carbon,2020,168:113-124

[5] CABELLO M，GUCCIARDI E，HERRÁN A，et al. Towards a high-power Si@graphite anode for lithium ion batteries through a wet ball milling process［J］.Molecules，2020，25（11）：2494.

6. FAQs

6.1 What is a CVD silicon-carbon anode and how does it differ from other silicon-based anode types?

A CVD silicon-carbon anode (chemical vapor deposition Si-C anode) uses silane gas thermal decomposition inside a porous carbon scaffold to deposit nano-scale silicon directly within the carbon’s pore network. This is fundamentally different from the two other major silicon-carbon anode types: the grinding nano-Si route (where silicon is mechanically milled to ~100 nm then composited with carbon, suffering from agglomeration and limited cycle life), and the SiOx (silicon oxide-carbon) route (where SiO reacts during first lithiation to form inert buffer phases, reducing expansion to ~118% but causing ~30% first-cycle capacity loss). CVD silicon-carbon avoids wet milling and achieves more uniform nano-silicon distribution within the carbon scaffold, producing better rate performance and cycle stability — but requires specialized deposition equipment (rotary kiln or fluidized bed) and tight process control to maintain batch-to-batch consistency in silicon loading and distribution.

6.2 What anode powder testing methods are used to evaluate silicon-carbon anode powder batches?

The recommended anode powder testing workflow for CVD silicon-carbon material combines two instruments. First, a powder resistivity and compaction density tester (such as the IEST PRCD3100) applies a controlled pressure sweep (e.g., 10–350 MPa in steps) to the powder, recording resistivity/conductivity and compaction density at each pressure point. This generates a pressure-property profile that reliably distinguishes process routes and flags batch anomalies (abnormally high resistivity, abnormally low or high compaction density) before any electrode coating begins. Second, after electrode preparation, an electrode resistance tester (such as the IEST BER2500) measures sheet resistance at multiple locations per electrode, quantifying spatial uniformity via coefficient of variation (COV). In this study, both instruments together provided a fast, non-destructive screening gate that correlated directly with coin-cell performance outcomes.

6.3 How does powder resistivity correlate with silicon-carbon anode electrode and battery performance?

In this study, powder resistivity measured at the PRCD3100 level correlated directly with electrode resistance at the BER2500 level, and both correlated with final coin-cell performance. Process B (GB series) powders consistently showed lower resistivity, which translated to lower electrode resistance in the JB electrode series and more consistent cell performance across batches. Outlier batches (GA-4 with anomalously high powder resistivity; GB-2 with unusually low resistivity) both produced anomalous cell-level results. This powder-to-cell correlation enables early detection of batch anomalies: by setting powder resistivity and compaction density specification limits based on accumulated historical data, production teams can flag and quarantine out-of-specification batches before the more expensive electrode and cell assembly steps.

6.4 What are the main challenges in scaling CVD silicon-carbon anode production?

Three categories of challenge dominate CVD silicon-carbon anode scale-up. First, porous carbon scaffold selection: the scaffold determines both silicon loading capacity and the graphite blend ratio needed for target battery performance; different porous carbon morphologies require different process parameters. Second, deposition equipment: rotary kilns tend to produce non-uniform silicon deposition and have lower silane gas utilization efficiency; fluidized beds achieve more uniform deposition at higher silane utilization but require high-airtightness, high-pressure reactors, creating a scale-up engineering challenge. Third, process consistency at scale: mass production requires tight control of feedstock mass (hundreds of kilograms per batch), furnace temperature uniformity across multiple zones, cavity partial pressures, and deposition residence time — all of which are more difficult to control at scale than in lab-scale trials. These challenges make batch-to-batch resistivity and compaction density monitoring via anode powder testing an essential process quality-control tool.

6.5 Why measure electrode resistance at multiple locations, and what does COV indicate?

Measuring electrode resistance at multiple locations per sheet (six points per sample in this study, using the BER2500 at 5 MPa with a 15 s hold) provides two distinct pieces of information. The average resistance characterizes the overall conductivity level of the electrode. The coefficient of variation (COV) — standard deviation divided by mean, expressed as a percentage — quantifies spatial uniformity within the sheet. A high COV indicates that the active material is unevenly distributed across the electrode, which typically leads to non-uniform current distribution during battery cycling, creating localized over-lithiation or under-lithiation zones that accelerate degradation. In this study, JA electrodes (from Process A powders with larger batch-to-batch variability) showed consistently higher COV than JB electrodes — because non-uniform powder particles are harder to disperse uniformly during slurry mixing, and this non-uniformity is amplified in the coated electrode.

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Abstract

Powder material compaction density for battery electrode materials is defined as the mass of powder after compression divided by the compressed volume. It differs from true density (no pores), particle density (intraparticle pores only), loose-fill density (no applied pressure), and vibration density (vibration only) by the presence of external compressive load: compaction density > vibration density > loose-fill density. Electrode compacted density = areal coating mass ÷ (calendered electrode thickness − current collector thickness). Key factors affecting measured compaction density are particle size and distribution, particle morphology, true density, mold diameter, applied pressure, hold time, and whether thickness is measured under load or after unloading. Automated battery powder compaction tooling such as the IEST PRCD series integrates pressing, thickness measurement, and calculation in one system, improving repeatability over manual tablet-press methods and supporting multi-pressure profiling, unloading (rebound) tests, and continuous compression (Heckel analysis) in a single instrument.

1. Why Battery Powder Compaction Density Matters

The performance and safety of lithium-ion batteries are fundamentally governed by the properties of their constituent electrode materials. Among these, the characteristics of cathode and anode active powders are paramount. A key metric linking powder properties to cell performance is electrode compaction density—the density achieved after the electrode coating is calendered under a defined pressure. This parameter directly influences energy density (how much active material fits per unit volume), internal resistance (porosity controls electrolyte access and ion transport), and cycle life (excessive compaction collapses electrolyte pores; insufficient compaction wastes volumetric capacity).

Accurately assessing powder compaction density and understanding the underlying powder compression behavior are therefore critical for material selection, process optimization, and quality control throughout battery manufacturing. This article provides a systematic analysis of powder compression fundamentals—including density definitions, compression stages, the Heckel equation, and testing protocols—and offers practical guidance for reliable compaction density evaluation of cathode and anode battery powder materials.

2. Fundamental Density Definitions for Battery Powders

In lithium-ion battery manufacturing, the calendering process densifies the coated electrode—essentially compacting a layer of active powder mixed with binder and conductive additives into a defined volume. Understanding powder compression requires a clear hierarchy of density definitions:^[1]

• True density (ρ_t): Mass per unit volume excluding all pores—both intra-particle and inter-particle. Reflects the intrinsic crystallographic density of the pure material. True density imposes the absolute upper limit on achievable compaction density; no real electrode can exceed it.

• Particle (apparent) density: Mass per unit volume including intra-particle pores but excluding inter-particle voids. Relevant for materials with significant internal porosity such as carbon anodes or porous cathode agglomerates.

• Bulk (fill) density: Mass per unit volume including all pores and voids. Subdivided as follows:

  • Loose-fill density: free, unperturbed powder accumulation, no pressure or vibration.

  • Vibration (tap) density: density after mechanical tapping or vibration until volume stabilizes.

  • Compaction density: density achieved after external uniaxial pressure is applied.

The density hierarchy is: True density > Particle density > Compaction density > Vibration density > Loose-fill density. Electrode compaction density is calculated as:

Electrode compaction density = coating areal mass ÷ (calendered electrode thickness − current collector thickness)

At the powder-testing level, compaction density = mass of powder after compression ÷ volume of compressed powder compact. Monitoring this value during materials R&D and incoming quality control provides an effective, fast assessment of batch-to-batch consistency and the impact of process modifications on packing behavior.

3. Understanding Powder Compression Behavior

Powder compression under external load proceeds through sequential, overlapping stages that reflect different deformation mechanisms:^[1]

• Low-pressure rearrangement: Particles slide and rearrange into denser packing configurations. Interparticle porosity decreases but individual particles are undeformed.

• Elastic deformation: As pressure increases beyond rearrangement, particles begin to deform reversibly. Interparticle porosity changes little, but intraparticle pore size decreases. This deformation is fully recovered on unloading (the “rebound” observed in thickness measurements).

• Plastic deformation: At higher pressures, irreversible (permanent) deformation occurs. Intraparticle pore volume decreases further. This component does not recover on unloading.

• Brittle fracture: Brittle particles crack and fragment under sufficient compressive stress, dramatically reducing pore size. Fragmentation is irreversible and can disrupt the electrical contact network in the electrode if it occurs during calendering.

In practice, powder compression is a complex, simultaneous composite of all these mechanisms. The proportion of elastic versus plastic deformation—quantified by the ratio of recovered (rebound) thickness to total compression—is a material fingerprint that reflects particle mechanical properties and informs safe calendering pressure ranges.

Figure 1 illustrates the microstructural evolution of electrode coatings during the roll-pressing (calendering) process, showing how inter-particle voids collapse and the coating densifies under load:

Figure 1. Schematic of microstructure evolution during electrode roll-pressing (calendering): (a) cathode electrode; (b) anode electrode — inter-particle voids collapse and coating densifies as powder compaction density increases ^[2]

The IEST PRCD series powder resistivity and compaction density testers serve over 300 customers in the lithium battery industry and provide three complementary test modes to characterize these behaviors (Figure 2):

Figure 2. IEST PRCD3100 Powder Resistivity & Compaction Density Measurement System — integrated battery powder compaction tooling for cathode and anode powder testing; measures resistivity, compaction density, and compression behavior

Figure 3. (a–b) Unloading test method and rebound thickness results — characterizes elastic recovery and particle fracture onset. (c–d) Steady-state compression test and representative stress-deformation curve — point ① maximum deformation; point ② irreversible deformation; ①−② reversible (elastic) component

The unloading test (Figures 3a–b) applies pressure then fully releases it, measuring the rebound thickness. As pressure increases, rebound thickness initially grows (more elastic deformation stored) then stabilizes—the point where rebound ceases to increase indicates the onset of dominant plastic deformation or particle fracture. This inflection point defines the practical upper pressure limit for calendering without particle damage.

The steady-state compression test (Figures 3c–d) provides the full stress-deformation curve, decomposing total deformation into reversible (elastic) and irreversible (plastic + fracture) components — the standard input for advanced Heckel analysis.

4. The Heckel Equation and Powder Compaction Density Analysis

The Heckel equation is the most widely used semi-empirical model relating applied pressure to the compaction density (relative density) of a powder compact. It was originally developed in pharmaceutical powder research but is directly applicable to battery electrode powders. The void ratio and Heckel equation are expressed as follows:^[4]

Where:

• ρ_b = bulk (fill) density at pressure P

• ρ_t = true density of the material

• D = relative density = ρ_b/ρ_t (the ratio of actual compaction density to true density)

• k = slope of the linear region of the Heckel plot — a measure of powder plasticity. Larger k means a given pressure produces a larger density increase, indicating more plastic (deformable) powder. Harder, more brittle powders have lower k.

• A = Heckel intercept, related to D_A through A = ln[1/(1−D_A)], where D_A is the maximum relative density achievable by particle rearrangement before deformation begins. D_A is closely related to the tap density ratio and the initial packing efficiency of the powder.

The Heckel equation is most reliably applied in the high-pressure, low-void-ratio regime where the log-linear relationship holds. Deviations from linearity at low pressure reflect the rearrangement stage; deviations at very high pressure may reflect particle fracture changing the compaction mechanism. For battery electrode powders, generating a complete Heckel plot from PRCD3100 continuous compression data provides direct insight into the pressure range where each deformation mechanism dominates — informing calendering process design.

5. Key Factors Affecting Measured Battery Powder Compaction Density

Compaction density is not a fixed intrinsic property — it is a measurement-condition-dependent outcome. The main variables are:

• Powder characteristics: Particle size distribution (D₁₀, D₅₀, D₉₀), morphology (spherical vs. irregular), specific surface area, true density, and surface chemistry all influence packing efficiency and deformation behavior. Most battery electrode powders fall in the 0.1–100 µm size range.

• Test fixture and sample mass: Mold diameter, aspect ratio (height-to-diameter), and sample mass affect wall friction and the uniformity of pressure distribution through the compact. Comparing data from different mold sizes requires careful normalization.

• Loading protocol: Applied pressure target, ramp rate, dwell/hold time at each pressure step, and whether thickness is measured under load (gives lower apparent density due to elastic deformation included) or after unloading (gives rebound-affected, higher apparent density). Standard GB/T 24533-2019 specifies the manual method; automated PRCD systems eliminate operator-induced variability.

Benchmarking note: when comparing compaction density data between laboratories, instruments, or publications, always verify mold diameter, measurement point (under load vs. unloaded), hold time, and sample mass — these variables alone can produce apparent differences of 5–15% between identical materials on different equipment.

6. Testing Protocols for Battery Powder Compaction Density

Three test protocols are available on the PRCD series, each suited to a different characterization goal:

• Single-point (unloading) test: Applies one target pressure, holds, releases completely, and measures thickness after unloading. Mimics standard GB/T 24533-2019 manual methods. Provides a single compaction density value at one pressure point — suitable for incoming quality control and rapid batch comparison.

• Multi-point pressure test: Steps through a range of pressures (e.g., 10–200 MPa in 20 MPa steps, 10 s hold per step), measuring compaction density at each step. Generates the full pressure-density profile shown in Figure 4 — enables direct comparison of different materials or formulations across the full calendering pressure range.

• Continuous compression test: Ramps pressure continuously to the target while recording thickness, enabling complete stress-strain and Heckel analysis. Also supports the unloading mode (pressure applied then fully released) to separately quantify elastic recovery and irreversible plastic deformation.

Figure 4. Compaction density vs pressure profiles for different battery powder materials (PRCD3100 multi-point test) — compaction density increases with pressure at different rates depending on particle morphology and true density

7. Summary

Powder compaction density is not a single number — it is the outcome of the material’s intrinsic true density, particle morphology and size distribution, and its mechanical response to applied pressure (elastic rebound, plastic deformation, and fracture). A deep understanding of powder compression behavior, supported by the Heckel equation and automated battery powder compaction tooling such as the IEST PRCD series, enables battery engineers to make informed decisions about material selection, electrode formulation, and calendering process design. Rigorous, repeatable powder-level testing — covering resistivity, compaction density, and compression behavior in a single instrument — is an effective front-end screen that reduces waste and accelerates development timelines for both cathode and anode battery electrode materials.

8. References

[1] Yang Shaobin, Liang Zheng. Lithium-ion Battery Manufacturing Process Principles and Applications.

[2] mikoWoo@Ideal Life. Theory and Process Basis of Lithium-ion Battery Polar Cells.

[3] B K K A , A S A , A H N , et al. Internal resistance mapping preparation to optimize electrode thickness and density using symmetric cell for high performance lithium-ion batteries and capacitors[J]. Journal of Power Sources, 2018, 396:207-212.

[4] SI Guo-ning, HUANG Wan-ting, LI Gen-sheng, XU Fei, CHU Meng-qiu. Application Research of Different Compression Model on Four Powder Excipients Compression[J]. Chinese Pharmaceutical Journal, 2018, 53(23): 2021-2028 https://doi.org/10.11669/cpj.2018.23.009

9. FAQs

9.1 What is compaction density in battery powder materials?

Compaction density (also called “compact density” or “press density”) is the mass of a powder sample divided by the volume it occupies after being compressed under a defined uniaxial pressure. It differs from loose-fill density (no applied pressure) and vibration/tap density (vibration only) by the presence of an external compressive load: compaction density > vibration density > loose-fill density. For battery electrode design, electrode compaction density = areal coating mass ÷ (calendered electrode thickness − current collector thickness), and it directly determines volumetric energy density, electrolyte porosity, ionic transport resistance, and cycle life. The China national standard GB/T 24533-2019 defines the manual measurement method; automated systems such as the IEST PRCD series improve repeatability and enable multi-pressure profiling.

9.2 What is the Heckel equation and how is it used in powder compression analysis?

The Heckel equation is a semi-empirical model that describes how powder compaction density (expressed as relative density D = bulk density / true density) changes with applied pressure P: ln(1/(1−D)) = kP + A. In this equation, k (the slope of the linear region of a Heckel plot) is a plasticity parameter — a larger k indicates more plastic (permanently deformable) powder, where a given pressure increment produces a larger density increase. A (the intercept) is related to D_A, the maximum relative density achievable by particle rearrangement before deformation begins, which is closely related to tap density. The Heckel equation is most reliable at high pressure and low void ratio. For battery electrode powders, fitting PRCD continuous compression data to the Heckel equation identifies the dominant deformation mechanism at each pressure range and informs safe calendering pressure selection.

9.3 What battery powder compaction tooling or equipment is used to measure compaction density?

Battery powder compaction density can be measured with a dedicated integrated powder compaction and measurement system such as the IEST PRCD series. These instruments combine a precision press, a high-resolution in-situ thickness sensor, and automated data acquisition into a single platform, replacing the manual tablet-press approach specified in GB/T 24533-2019. The PRCD series supports three test modes: single-point (mimicking standard methods), multi-point pressure sweep (generating the full pressure-density profile from e.g. 10 to 350 MPa), and continuous compression (for Heckel analysis and unloading/rebound testing). It also measures powder resistivity and conductivity simultaneously, making it a single-instrument solution for cathode and anode powder characterization in materials R&D, process development, and incoming batch quality control.

9.4 What are the stages of powder compression and what does elastic rebound mean in compaction testing?

Powder compression proceeds through four overlapping stages: particle rearrangement (low pressure, reversible), elastic deformation (reversible particle shape change), plastic deformation (permanent, irreversible), and brittle fracture (irreversible particle cracking). In compaction testing, elastic rebound (or “spring-back”) refers to the thickness increase that occurs when the applied pressure is released — the elastic deformation component recovers, while the plastic and fracture components do not. Rebound thickness is measured by the unloading test mode on instruments like the PRCD series: thickness under maximum pressure versus thickness after full unloading. As pressure increases, rebound thickness first grows then stabilizes — the stabilization pressure indicates that elastic particle deformation has reached a plateau and further compression mainly drives plastic deformation or fracture. This rebound analysis is directly relevant to electrode calendering: materials with high elastic rebound spring back after the calender nip, reducing the final electrode density relative to the in-nip density.

9.5 Which characterization tests are relevant for battery powders and what do they measure?

The most relevant powder-level characterization tests for battery electrode materials are: compaction density measurement (how densely the powder packs under calendering-representative pressure), powder resistivity/conductivity measurement (electronic conductivity of the active material, which determines conductive additive requirements), compression behavior testing (elastic and plastic deformation components, rebound thickness, fracture onset — from stress- strain curves and Heckel analysis), particle size distribution (D₁₀/D₅₀/D₉₀ by laser diffraction), and morphology imaging (SEM). Automated integrated instruments such as the IEST PRCD series combine the first three of these in a single instrument, while particle size and SEM are conducted with separate equipment. Together these tests provide the material property data needed for electrode formulation design, calendering process optimization, and batch incoming quality control without requiring full electrode or cell fabrication.

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Hard carbon electrical conductivity measurement is performed using the four-probe method in a controlled powder resistivity tester to eliminate contact resistance artifacts — critical because hard carbon’s lower resistance makes the two-probe method unreliable. In this study, the IEST PRCD3100 applied 10–200 MPa in 20 MPa steps (10 s hold per step) to four hard carbon and four Prussian blue powder samples. Results:

• Hard carbon conductivity rank: HC-1 > HC-4 > HC-2 > HC-3

• Hard carbon compaction density rank: HC-4 > HC-1 > HC-2 > HC-3

• Prussian blue conductivity: Modified PB-2 and PB-4 show substantially better conductivity than unmodified PB-1 and PB-3

• Prussian blue compaction density rank: PB-1 > PB-3 > PB-4 > PB-2 — higher conductivity modification does not necessarily increase packing density

1. Background: Sodium-Ion Battery Materials

Sodium shares similar chemical properties with lithium due to their common group placement in the periodic table, while offering significant advantages in natural abundance and cost. Sodium-ion batteries (SIBs) have attracted considerable research attention as promising alternatives to lithium-ion batteries, owing to their rapid charging capability, superior low-temperature performance, enhanced safety, and compatibility with existing lithium battery manufacturing processes. These attributes position SIBs as strong candidates for next-generation commercial energy storage.

Advances in SIB research have led to notable progress in both cathode and anode materials. Cathode materials primarily include layered oxides, polyanionic compounds, Prussian blue analogues (PBAs), and organic compounds. Anode materials are largely categorized into carbon-based materials, titanium-based compounds, organic electrodes, and alloy-based systems.

Among cathode options, Prussian blue (PB) and its analogues— representative metal-organic framework (MOF) materials—have attracted interest due to their low cost, facile synthesis, and open three-dimensional framework structure. PB-derived nanomaterials retain high surface area, interconnected pores, and tunable pore size distributions, facilitating efficient charge transfer in energy storage systems. By optimizing synthesis conditions such as temperature and atmosphere, PBAs with desirable structural and electrochemical properties can be achieved.^[1] Figure 1 illustrates the Prussian blue crystal structure and its analogues.

Figure 1. Crystal structure of Prussian blue (left) and its analogues (right) — 3D open framework with metal-CN-metal linkages enables Na⁺ intercalation

Figure 2. SEM images of layered oxide cathode materials Na_0.67Ni_0.33Mn_1−xSn_xO₂ with increasing Sn substitution^[2]: (a) x=0; (b) x=0.01; (c) x=0.03; (d) x=0.05^[2]

On the anode side, carbon-based materials—particularly hard carbon— are considered the most practical choice due to their low sodium-insertion potential, high capacity, excellent cycling stability, resource availability, and relatively simple preparation. Hard carbon stands out for its large interlayer spacing (larger than graphite, enabling Na⁺ storage between disordered graphene layers), low cost, tunable synthesis, and the possibility of derivation from renewable precursors. Figure 3 illustrates a typical hard carbon synthesis process and its microstructural characteristics.

Figure 3. Hard carbon synthesis schematic and microstructure characterization — disordered turbostratic carbon structure with large d-spacing enables Na⁺ storage

Evaluating Prussian blue structure, electronic conductivity, and Prussian blue density together with hard carbon density and conductivity at the powder stage is critical for rapid material screening and downstream electrode design. This study evaluates four Prussian blue materials (PB-1 to PB-4) and four hard carbon materials (HC-1 to HC-4) using the PRCD3100 system to measure their electrical conductivity and compaction density across a controlled pressure range.

2. Test Method: PRCD3100 Hard Carbon Conductivity Measurement

The PRCD3100 (IEST) was used to characterize the conductivity and Prussian blue density and hard carbon density properties. The Prussian blue samples(PB-1/PB-2/PB-3/PB-4) were tested in a two-probe mode, while the hard carbon samples(HC-1/HC-2/HC-3/ HC-4) were measured using a four-probe method to improve accuracy. The testing equipment is shown in Figure 4.

Test parameters included an applied pressure range of 10–200 MPa, incremented at 20 MPa intervals, with a 10-second hold at each pressure step.

Figure 4. (a) PRCD3100 appearance; (b) PRCD3100 structural diagram — two-probe for Prussian blue, four-probe for hard carbon; 10–200 MPa, 20 MPa steps, 10 s hold

3. Test Results and Analysis

3.1 Prussian Blue: Conductivity, Density, and Modification Effects

Prussian blue and its analogues feature a three-dimensional open framework that enables efficient sodium-ion intercalation and deintercalation, making them attractive cathode materials for sodium-ion batteries. Although PBAs offer a theoretical specific capacity of approximately 170 mAh/g and good cycling stability, practical application has been limited by structural vacancies, coordinated water molecules, and poor rate capability — all of which reduce specific capacity, impede ionic conductivity, and risk structural collapse during cycling.

Modification strategies have been developed to enhance the physical and electrochemical properties of PBAs. Figure 5 presents resistivity and conductivity results for PB-1 through PB-4. PB-2 and PB-4 are modified versions of PB-1 and PB-3 respectively, and clearly show superior electronic conductivity, confirming that material modification effectively improves electron transport in Prussian blue.

Figure 5. (A) Resistivity and (B) conductivity of four Prussian blue materials (PB-1 to PB-4, two-probe method, 10–200 MPa) — modified PB-2 and PB-4 show substantially improved conductivity over unmodified variants

Compaction testing revealed variability in Prussian blue density across samples. Measured compaction densities followed PB-1 > PB-3 > PB-4 > PB-2 under the test conditions. This is an important observation: higher conductivity from modification does not automatically produce higher packing density — Prussian blue structure and surface chemistry must be considered alongside density when screening cathode powders. Key takeaways for PB cathode material selection:

• Modified PBAs can deliver better electronic conductivity but may alter particle packing behavior and reduce compaction density.

• Powder-level conductivity testing identifies promising PB derivatives before electrode fabrication, saving cell-level screening cost and time.

• Optimal PBA performance requires balancing crystal vacancies, coordinated water content, and conductive additive loading alongside compaction density.

Figure 6. Prussian blue compaction density vs pressure (PB-1 to PB-4) — density rank: PB-1 > PB-3 > PB-4 > PB-2; conductivity improvement via modification does not increase packing density

3.2 Hard Carbon: Conductivity and Density for Sodium-Ion Battery Anodes

Hard carbon is the leading anode choice for sodium-ion batteries. Its disordered, turbostratic carbon microstructure — with interlayer spacings larger than graphite — provides the interstitial sites and nanopore volume needed for Na⁺ storage. Measuring hard carbon electrical conductivity at the powder level is directly relevant to electrode design: lower powder resistivity means fewer conductive additives are needed in the electrode formulation, increasing the volumetric active-material fraction and energy density.

Four-probe resistivity testing (Figure 7) returned a hard carbon conductivity rank of HC-1 > HC-4 > HC-2 > HC-3, with HC-1 showing the best electronic transport. Compaction testing returned hard carbon density rank HC-4 > HC-1 > HC-2 > HC-3, indicating that HC-4 packs most densely under identical pressing conditions — despite ranking second in conductivity. This conductivity-density relationship between HC-1 and HC-4 illustrates a common trade-off in hard carbon material selection: the material with the highest conductivity may not simultaneously deliver the highest compaction density, requiring a balanced assessment of both metrics for electrode applications.

Figure 7. (A) Resistivity and (B) conductivity of four hard carbon materials (four-probe method, 10–200 MPa) — HC-1 shows best conductivity; HC-3 shows lowest

Figure 8. Hard carbon compaction density vs pressure (four-probe, PRCD3100) — density rank: HC-4 > HC-1 > HC-2 > HC-3; HC-4 packs most densely but ranks second in conductivity

4. Discussion: Linking Powder Metrics to Electrode Design

Powder resistivity and compaction density are rapid, low-cost diagnostics that predict key electrode attributes before full electrode fabrication:

• Electronic percolation: Low powder resistivity suggests fewer conductive additives are needed to reach target electrode conductivity, increasing the volumetric active-material loading.

• Volumetric energy density: Higher compaction density under equivalent calendering pressure typically translates to higher volumetric capacity at the electrode level.

• Processing window: Compression and rebound behavior at different pressures informs calendering pressure limits to avoid particle damage while maximizing density.

For Prussian blue cathodes, modifying the structure to reduce coordinated water and stabilize crystal vacancies can improve electronic conductivity but may reduce Prussian blue density if the particle morphology changes. For hard carbon anodes, tuning microstructure and the degree of graphitic ordering optimizes both hard carbon compaction density and sodium storage kinetics. Neither metric alone is sufficient for material selection: HC-1 and HC-4 both present as promising hard carbon candidates—HC-1 for its superior conductivity and HC-4 for its superior compaction density—and the optimal choice depends on the target electrode formulation and rate-capability requirements.

5. Summary

This study used the IEST PRCD3100 powder resistivity and compaction density system to evaluate the conductivity and density properties of four Prussian blue cathode and four hard carbon anode materials for sodium-ion batteries. The two-probe/four-probe dual method configuration — applied according to each material’s resistance range — provides reliable hard carbon conductivity and Prussian blue conductivity data that clearly differentiates sample performance. The results confirm that this powder-level measurement approach is an effective, rapid tool for sodium-ion battery material screening, guiding further optimization toward higher performance and commercial viability.

6. References

[1] Chen J, Wei L, Mahmood A, et al. Prussian blue, its analogues and their derived materials for electrochemical energy storage and conversion – ScienceDirect[J]. Energy Storage Materials, 2020, 25:585-612.

[2] Li J, Risthaus T, Wang J, et al. The effect of Sn substitution on the structure and oxygen activity of Na0.67Ni0.33Mn0.67O2 cathode materials for sodium ion batteries[J]. Journal of Power Sources, 2019, 449:227554.

[3] Yin X, Lu Z, Wang J, et al. Enabling Fast Na+ Transfer Kinetics in the Whole-Voltage-Region of Hard-Carbon Anodes for Ultrahigh-Rate Sodium Storage[J]. Advanced Materials, 2022.

[4] Wu Junda, Zhao Yabin, Zhang Fuming. Research progress on hard carbon materials as anode materials for room temperature sodium-ion batteries [J]. Shandong Chemical Industry, 2019, 488.

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Abstract

Anode particle size and crushing strength are inversely correlated: smaller particles consistently achieve higher crushing strength (MPa, resistance per unit area) while larger particles generate higher absolute crushing force (N, total force to fracture). This counter-intuitive relationship holds across silicon-carbon (SiC), hard carbon (HC), and resin carbon (RC) anode materials prepared by the same process, and was quantified using the IEST SPFT2000 single particle mechanical testing system in accordance with GB/T 43091-2023. The mechanism: smaller particles densify more completely during granulation and sintering, leaving fewer internal micropores, primary cracks, and structural defects — resulting in higher intrinsic crushing strength and greater resistance to fracture under calendar pressure and during lithiation/de-lithiation cycling. At the same particle size (8.5 µm), intrinsic microstructure determines the absolute crushing strength ceiling: SC2 achieves 1,632 MPa, resin carbon RC reaches 968 MPa, while SC1 shows only 339 MPa — confirming that particle size modulates strength within the material’s fixed microstructure-defined range.

1. Introduction

To simultaneously optimize electrode compaction density, slurry processability, conductive network continuity, electrode sheet resistance, electrolyte pore distribution, and the balance between cycle life and fast-charging performance, battery material manufacturers typically prepare silicon-carbon particles across multiple particle size grades — combining large and small particles, or blending silicon-carbon materials with graphite of different particle sizes. The differences in bulk compressive resistance among different particle size grades directly affect roll-press fracture rate and charge/discharge pulverization degree during cycling. This study uses the IEST SPFT2000 Single Particle Force Tester to systematically measure single-particle compression tests across multiple particle size grades for silicon-carbon samples prepared by the same process, establishing the intrinsic relationship between particle size, crushing force, and crushing strength. Simultaneously, hard carbon and resin carbon were tested to establish the particle size — mechanical property relationship across three anode material types, providing experimental data for anode powder granulation, particle size grading control, and formulation blending design.

2. Equipment and Method

2.1 Test Equipment: IEST SPFT2000 Single Particle Force Tester

All single particle mechanical property tests were performed using the IEST SPFT2000 Single Particle Force Tester (Figure 1a), a dedicated instrument for single particle mechanical testing of battery active materials in accordance with Chinese national standard GB/T 43091-2023. The system integrates a high-precision load cell, motorized compression stage, and optical bottom-view imaging system that enables particle centering and diameter verification before each test.

Figure 1. IEST SPFT2000 Single Particle Force Tester: (a) instrument; (b) compression test mode — particle positioned between indenters, load and displacement recorded simultaneously to derive crushing force and crushing strength; (c) optical bottom-view imaging for particle centering and size verification before each test.

2.2 Test Samples

• Silicon-carbon materials: two silicon-carbon samples prepared by the same process, designated SC1 and SC2. Each material was separated into two particle size grades: 4.5 ± 0.5 µm and 8.5 ± 0.5 µm.

• Hard carbon materials: two hard carbon samples prepared by the same process, designated HC1 and HC2. Each separated into 4.5 ± 0.5 µm and 8.5 ± 0.5 µm grades.

• Resin carbon: one resin carbon sample designated RC, separated into 8.5 ± 0.5 µm and 15 ± 0.5 µm grades.

2.3 Test Procedure

Sample solutions were dispersed uniformly and individually dropcast onto glass slides. Five particles were tested per particle size grade for each material. The optical imaging system confirmed particle diameter and centering before each compression test. Crushing force (N) and crushing strength (MPa) were calculated according to the formula in Equation (1).

The compressive strength of the powder is calculated according to Equation (1):

where:

𝑝𝑐 — compressive strength, in MPa;

𝛼 — calculation coefficient, taken as 2.48;

𝐹𝑘 — crushing force, in mN;

𝑑 — particle size (diameter), in 𝜇m.

Key Definitions

Crushing force (N): the minimum external compressive load required to fracture a single particle. Increases with particle size because larger particles have greater load-bearing cross-sectional area and volume.

Crushing strength (MPa): crushing force normalized by the projected cross-sectional area of the particle — a size-independent measure of intrinsic per-unit-area compressive resistance. Allows meaningful comparison of mechanical performance across different particle sizes and material types.

2.1 For the Same Material: Crushing Force Increases with Particle Size

Figure 3 shows the crushing force comparison across three anode material types at different particle sizes. The result is consistent across all materials: crushing force increases with increasing particle size for silicon-carbon SC and hard carbon HC (8.5 µm particles show significantly higher crushing force than 4.5 µm), and for resin carbon RC (15 µm particles show significantly higher crushing force than 8.5 µm).

Figure 2. Crushing force vs. particle size for SC1, SC2 (silicon-carbon), HC1, HC2 (hard carbon), and RC (resin carbon). Crushing force increases with particle size across all three material types: larger particles require significantly higher total force to fracture, because larger cross-sectional area and volume increase effective load-bearing capacity.

Mechanical mechanism: as particle size increases, the particle’s effective load-bearing cross-sectional area and total volume both increase. The critical external force needed to initiate fracture therefore rises proportionally. This explains why large-particle anode materials can withstand higher total roll-press forces during calendering — though this does not imply they are intrinsically stronger per unit area (see Section 2.2).

Table 1. Crushing force and crushing strength vs. particle size — all anode materials (SPFT2000, GB/T 43091-2023)

2.2 For the Same Material: Crushing Strength Decreases with Increasing Particle Size

Crushing strength represents compressive resistance per unit cross-sectional area — a size-normalized, intrinsic measure of particle mechanical performance. Figure 4 shows that crushing strength follows the opposite trend from crushing force: for all three anode material types, larger particle size corresponds to lower crushing strength. Silicon-carbon SC and hard carbon HC both show higher mean crushing strength at 4.5 µm compared to 8.5 µm; resin carbon RC shows higher crushing strength at 8.5 µm compared to 15 µm.

Figure 4. Crushing strength vs. particle size for SC1, SC2, HC1, HC2, and RC. Crushing strength decreases with increasing particle size across all material types. At 8.5 µm: SC2 = 1,632 MPa; RC = 968 MPa; SC1 = 339 MPa — confirming that intrinsic microstructure (carbonization quality, defect density) sets the absolute crushing strength ceiling, with particle size modulating performance within that range.

Why do smaller particles have higher crushing strength? During granulation and sintering of anode materials, smaller particles achieve higher densification. Smaller particle volume means shorter diffusion distances during sintering, more complete pore elimination, fewer residual internal micropores, fewer primary cracks, and lower structural defect density overall. Because crack propagation and fracture initiation in ceramic-like particles originate at internal defects, a particle with fewer and smaller defects requires higher stress to initiate fracture — resulting in higher crushing strength per unit area.

This relationship has a direct practical implication for long-cycle battery formulation design: small-particle anode materials buffer the internal stress generated by lithiation volume expansion more effectively, and are more resistant to the particle fracture and pulverization that causes capacity fade during cycling. This is precisely why long-cycle power battery formulations preferentially incorporate small-particle-grade materials — not because they withstand higher total force, but because they have higher intrinsic per-unit-area compressive resistance that preserves particle integrity during repeated lithiation stress.

2.3 At Equal Particle Size, Intrinsic Material Microstructure Determines the Crushing Strength Ceiling

Comparing crushing strength at the same particle size (8.5 µm) across the three material types reveals that intrinsic microstructure — not particle size — is the dominant factor setting the upper limit of particle mechanical performance:

• SC2 (silicon-carbon): 1,632 MPa — the highest among all tested materials at 8.5 µm, exceeding both hard carbon grades.

• Resin carbon (RC): 968 MPa — intermediate strength at 8.5 µm.

• SC1 (silicon-carbon): 339 MPa — substantially lower than SC2 despite identical particle size and the same stated process, indicating that the specific carbonization conditions, silicon dispersion state, or precursor quality within the “same process” range produce significantly different microstructural outcomes.

• HC1 and HC2 (hard carbon): both fall between RC and SC1 in crushing strength at 8.5 µm.

The SC1 vs. SC2 comparison at the same particle size is particularly instructive: two silicon-carbon materials described as “same process” differ in crushing strength by nearly 5× (339 MPa vs. 1,632 MPa). This confirms that particle size is a secondary modulator of crushing strength — the primary determinant is the intrinsic microstructure established during material synthesis, including carbonization degree, carbon shell continuity, internal porosity, and primary defect density. Particle size can only adjust crushing strength within the range defined by the material’s fixed microstructural properties; it cannot overcome a fundamentally weak microstructure.

This finding has direct relevance to anode particle size optimization for cycling stability: selecting a smaller particle size grade of a microstructurally inferior material may improve its crushing strength relative to its own larger grade, but will not match the performance of a microstructurally superior material at the same particle size. Effective formulation design must address both dimensions — material microstructure and particle size grading — rather than optimizing either in isolation.

3. Conclusions

Using the IEST SPFT2000 Single Particle Force Tester (GB/T 43091-2023), this study systematically establishes the particle size — mechanical property relationships for silicon-carbon, hard carbon, and resin carbon anode materials:

• Crushing force increases with particle size (positive correlation): larger particles have greater effective load-bearing cross-sectional area and volume, requiring higher total external force for fracture. This trend is consistent across silicon-carbon (4.5 µm vs. 8.5 µm), hard carbon (4.5 µm vs. 8.5 µm), and resin carbon (8.5 µm vs. 15 µm).

• Crushing strength decreases with particle size (inverse correlation): smaller particles densify more completely during sintering, leaving fewer internal defects and microcracks, resulting in higher per-unit-area compressive resistance. Small-particle anode materials better resist lithiation-induced internal stress and particle pulverization during cycling — explaining why long-cycle power battery formulations preferentially use small-particle-grade materials.

• Intrinsic material microstructure sets the crushing strength ceiling: at equal particle size (8.5 µm), SC2 achieves 1,632 MPa, resin carbon RC 968 MPa, and SC1 only 339 MPa — confirming that carbonization quality and microstructural density are the primary determinants of particle mechanical performance, with particle size modulating strength within material-defined limits.

Single particle mechanical testing using the SPFT2000 provides a quantitative, standardized method to characterize the particle size — mechanical property relationship in anode powders. This data directly guides anode powder granulation process control, particle size grading classification, and multi-grade formulation blending design — offering a low-cost route to simultaneously improving anode material processability and long-cycle stability. Note: the trends reported here apply to materials prepared by the same process; special formulations or atypical manufacturing processes may produce deviations from these general rules.

4. References

[1] GB/T 43091-2023. Powder Crushing Strength Test Method [S]. China National Standard, 2023.

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Abstract

Silicon anode swelling percentage varies widely by material and modification strategy. In this study, three commercial silicon-carbon (Si/C) anode materials with matched capacity (~5.9 mAh) showed swelling rates of 8.9% (specially modified, low-expansion Si/C, sample B), 33% (sample C, ~3.7× sample B), and 45% (sample D, ~5× sample B) over the final two charge-discharge cycles, measured by IEST In-Situ Silicon-based Anode Swelling Rapid Screening System and confirmed by SEM cross-section thickness measurement. The corresponding swelling thickness values were approximately 4.2 µm, 15.6 µm, and 21 µm. These results show that silicon anode swelling percentage is not an intrinsic, fixed property of “silicon-carbon material” as a category—it depends heavily on particle modification—and that it can be measured rapidly at the electrode level using a coin-cell-format in-situ screening system, without building full pouch or stacked cells.

1. Preface: Why Silicon Anode Swelling Percentage Matters

Silicon anode materials, with their unique advantages of high theoretical capacity (4,200 mAh/g) and abundant resources, are expected to replace widely used graphite negative electrodes as the dominant anode material for next-generation lithium-ion batteries. The most commercially promising silicon-based anodes are silicon-carbon (Si/C) anodes and silicon-oxygen anodes, both offering high specific capacity. However, the alloying/de-alloying mechanism of silicon causes significant structural swelling that damages the pre-existing solid electrolyte interface (SEI) on the silicon surface.^[2] This drives continuous SEI destruction and regeneration during cycling, consuming electrolyte and ultimately causing rapid battery capacity decay. Evaluating a candidate silicon anode material’s swelling percentage—alongside specific capacity, initial efficiency, and cycle efficiency—is therefore essential, not optional, in materials screening.

Conventional swelling evaluation methods require silicon anode material to first be built into a pouch cell or stacked cell, then monitored for in-situ swelling using force structures and high-precision sensors (such as the IEST SWE swelling series). This powder-to-finished-cell pipeline requires a mature cell production line and a long evaluation cycle—a significant bottleneck for materials researchers who need to screen many candidate silicon-carbon formulations quickly.

IEST has developed a four-channel Silicon-Based Anode Swelling In-Situ Screening System (Figure 1) that, modeled on coin-cell assembly, directly measures silicon anode electrode swelling at the electrode level. This eliminates the labor, material, and time costs of building finished cells, enabling fast, low-consumption evaluation of the most important silicon anode performance indicators. The system also supports conventional swelling testing of small pouch cells and stacked cells (100×100 mm), giving it dual-purpose utility across both rapid screening and standard cell-level validation.

Figure 1. IEST Silicon-Based Anode Swelling In-Situ Rapid Screening System (four channels) — measures silicon anode swelling percentage directly at the coin-cell electrode level

2. Swelling Test of Three Different Silicon-Carbon Materials

2.1 Test Sample Information

• Cathode electrode: NCM811, cut into 14 mm diameter discs.

• Anode electrodes: Materials B, C, and D have matched capacity (~5.9 mAh) but different modification methods, cut into 16 mm diameter discs. Material B is a specially modified, low-expansion silicon-carbon anode material from a battery materials company in Ningbo; materials C and D are two common commercial silicon-carbon materials.

• Electrolyte: Commercial electrolyte.

• Separator: PP separator, cut into 18 mm diameter discs.

2.2 Test Information and Process

Table 1. Battery charging and discharging test process

2.3 Analysis of Swelling Results

In a glove box, the three silicon-carbon materials were each assembled into a coin-cell full battery (using the same NCM811 cathode to preserve a single-variable comparison), then rapidly tested using the IEST silicon anode swelling in-situ screening system (Figure 2). All three silicon-carbon materials expand during charging and contract during discharge—consistent with anode swelling during lithium-ion intercalation on charge and anode contraction during de-intercalation on discharge. Although a full cell was assembled, overall cell swelling behavior is dominated by the negative (silicon-carbon) electrode, since cathode expansion and contraction is much smaller than anode swelling.^[3] The inflection points of each material’s swelling curve align closely with the inflection points of its charge/discharge voltage curve, confirming that the swelling curve faithfully tracks lithium-ion intercalation/de-intercalation behavior.

The coin-cell swelling evaluation method also clearly resolves swelling differences between the three silicon-carbon materials. Over the same operating voltage range, material B’s overall swelling is much smaller than that of materials C and D, demonstrating that B’s specialized modification treatment substantially suppresses silicon-carbon anode swelling—reducing the side reactions swelling causes and ultimately improving cycling performance.

Figure 2. Coin-cell in-situ swelling thickness curves for silicon-carbon materials B, C, and D over 3 charge-discharge cycles — dotted line: voltage vs time; solid line: swelling thickness vs time. Material B (modified, low-expansion) shows substantially lower swelling than common commercial materials C and D.

Table 2. The swelling thickness of three silicon-carbon materials per charge and discharge cycle

Table 3. Cell expansion percentage variations across multiple cycles

Tables 2 and 3 quantify swelling thickness and swelling rate per cycle for all three materials. Two key patterns emerge:

• First-cycle swelling is highest: All three materials show greater swelling on the first charge than on the first discharge or on subsequent cycles, with measurable irreversible swelling in the first charge-discharge cycle. This occurs because the negative electrode forms an SEI film during the first charge—producing irreversible swelling on the active particle surface in addition to reversible lithium-intercalation swelling.

• Stabilized silicon anode swelling percentage: Comparing the last two charge-discharge cycles, modified material B shows an average swelling thickness of only ~4.2 µm and a swelling rate of ~8.9%. Material C’s average swelling is approximately 3.7× that of material B, and material D’s is approximately 5× that of material B—demonstrating that B’s modification strategy delivers a clear, quantifiable swelling-suppression effect.

3. Electron Microscope Verification of Silicon-Carbon Electrode Swelling

To validate the coin-cell swelling measurements against an independent method, the fully charged silicon-carbon electrodes were disassembled and their cross-sections measured by scanning electron microscope (Figure 3). After subtracting copper foil thickness, the type B silicon-carbon electrode coating expanded from ~50.81 µm to ~55.45 µm when fully charged—a total swelling of ~4.64 µm, closely matching the average swelling thickness measured by the coin-cell screening method. Materials C and D showed coating thickness expansion of approximately 11.98 µm and 14.65 µm respectively after full charge, consistent with the swelling data from the last two cycles in Table 2.

Whether measured by in-situ coin-cell screening or by disassembly and SEM cross-section measurement, the swelling trend across the three silicon-carbon materials is consistent: D > C > B.

Figure 3. SEM cross-section images of three silicon-carbon electrodes before (Fresh) and after full charge (Full Charged): (a–b) material B; (c–d) material C; (e–f) material D — confirms the coin-cell swelling trend D > C > B

4. Summary

This study rapidly evaluated the swelling of three silicon-carbon materials with different modification conditions using the IEST Silicon-Based Anode Swelling In-Situ Screening System (RSS1400). None of the three silicon-carbon anodes required preparation as a pouch cell or stacked cell—each was assembled directly into a coin-cell format for in-situ swelling thickness measurement, eliminating the cumbersome finished-cell preparation step and substantially improving silicon material swelling-evaluation throughput.

The coin-cell in-situ test results show that specially modified material B’s silicon anode swelling percentage is far lower than common commercial materials C and D. Independent SEM cross-section measurement after full charge confirmed the same trend, validating that the IEST screening system enables direct, accurate evaluation of silicon anode swelling performance at the electrode level—with minimal material consumption and maximum throughput for R&D screening.

5. References

[1] M. Ashuri, Q.R. He and L.L. Shaw, Silicon as a potential anode material for Li-ion batteries: where size, geometry, and structure matter. Nanoscale 8 (2016) 74–103.

[2] X.H. Shen, R.J. Rui, Z.Y. Tian, D.P. Zhang, G.L. Cao and L. Shao, Development on silicon/carbon composite anode materials for lithium-ion battery. J. Chin. Cream. Soc. 45 (2017) 1530-1538.

[3] R. Koerver, W.B. Zhang, L. Biasi, S. Schweidler, A. Kondrakov, S. Kolling, T. Brezesinski, P. Hartmann, W. Zeier and J. Janek, Chemo-mechanical expansion of lithium electrode materials – on the route to mechanically optimized all-solid-state batteries. Energ. Environ. Sci. 11 (2018) 2142-2158.

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Abstract

A PAA binder (poly(acrylic acid)) outperforms conventional CMC/SBR binder in silicon-carbon (Si/C) anode electrodes on two measurable, electrode-level properties: electrical conductivity and compression behavior. This study uses the IEST Electrode Sheet Resistance Tester (BER2500) to conduct a comparative evaluation of binder choice in silicon anode electrode design. We systematically assess the electrical conductivity and compression performance of Si/C anode sheets prepared with CMC/SBR versus PAA binder, quantifying the measurable impact of binder selection on these two critical electrode properties. This study shows that the PAA-bound electrode (SC-PAA) has lower sheet resistance and resistivity than the CMC/SBR-bound electrode (SC-CMC), and exhibits less maximum deformation, less irreversible deformation, and less elastic rebound (spring-back) under compression from 5–60 MPa. The mechanism is structural: PAA’s linear polymer backbone forms “segment-to-surface” hydrogen-bonded contact with active particles, compared to the weaker “point-to-surface” contact typical of SBR latex—producing a more uniform, electrically continuous coating and a more dimensionally stable electrode after calendering. For binder selection in silicon-based anode development, these direct electrode-level measurements provide faster, more actionable data than full-cell cycling alone.

1. The Silicon Anode Promise and the Binder Challenge

As the new energy industry continues to expand, the specific capacity of graphite anodes can no longer meet future demands for battery energy density. In contrast to graphite, silicon offers an ultra-high theoretical specific capacity of 4,200 mAh/g in its fully lithiated state—meaning a silicon anode can store substantially more capacity than a graphite anode at equivalent mass.

However, during cycling, lithium-ion insertion causes massive volume expansion (~300%) in silicon anodes. As lithium ions repeatedly insert and extract, silicon volume continuously changes. This intense volumetric fluctuation produces surface cracking, and propagating cracks lead to electrode fragmentation and silicon particle pulverization. The end result is detachment of active material from the current collector, disruption of the conductive network, continuous capacity loss, and ultimate battery failure.

Three primary strategies currently address this volume-change challenge:^[1–4]

• Structural design of silicon—nanotubes, nanowires, or nanoshells designed to accommodate volume change through engineered porosity

• Silicon-based composite materials—silicon-carbon (Si/C) and silicon oxide-carbon anodes that buffer volume expansion through synergistic material effects

• High-performance binder systems—polymer binders engineered to suppress and accommodate silicon’s volume expansion mechanically

Silicon structural design typically involves high cost and complex processing, largely remaining at laboratory scale. Silicon-based composite materials combined with high-performance binders are therefore the more widely adopted, commercially practical approach—making binder selection for silicon-based anodes a critical, directly actionable engineering decision.

2. PAA vs CMC/SBR: A Mechanistic Comparison for Binder Selection

For silicon-based anodes, PAA binder has become a subject of intense research.^[5,6] As a water-soluble, linear polymer, PAA offers distinct advantages over conventional carboxymethyl cellulose/styrene-butadiene rubber (CMC/SBR) binder systems. The key difference lies in the bonding mechanism: PAA facilitates “segment-to-surface” bonding, creating a more uniform, cohesive network that anchors particles more effectively and yields greater electrode integrity. CMC/SBR, by contrast, typically provides “point-to-point” bonding.

This structural difference between PAA and CMC/SBR binder translates into minimal swelling in carbonate-based electrolytes and excellent structural stability for PAA during cycling. The abundant carboxyl (–COOH) groups in PAA also form strong hydrogen bonds with functional groups on silicon and carbon particle surfaces, promoting a more uniform coating, enhancing adhesion to the current collector, and contributing to a more stable solid electrolyte interphase (SEI)— collectively improving cycle performance for silicon-carbon anode systems.

3. Experimental Methodology: Precision Measurement of Electrode Properties

3.1 Test Equipment

The core instrument is the BER2500 Electrode Sheet Resistance Tester (Figure 1). It accommodates electrode samples with a 14 mm diameter and applies pressure from 5 to 60 MPa, simultaneously recording sheet resistance, resistivity, electrical conductivity, and compaction density in real time.

Figure 1. (a) BER2500 appearance; (b) BER2500 structural diagram — used to compare PAA and CMC/SBR binder performance in Si/C anode electrodes

3.2 Experimental Procedure

Two sets of Si/C anode sheets were prepared with identical formulations and active materials, differing only in the binder system: one with CMC/SBR (labeled SC-CMC) and one with PAA (labeled SC-PAA).

• Electrode Resistance Test: Single-point test mode at a constant pressure of 5 MPa with a 15-second hold. Eight data points were sampled per sheet to obtain average values for thickness, resistance, resistivity, and conductivity.

• Compression Performance Test: Steady-state test mode, ramping pressure from 5 MPa to 60 MPa in 5 MPa steps with a 15-second hold at each step, measuring electrode thickness change and compression behavior across the full pressure range.

4. Results: Electrical Conductivity Advantage of PAA Binder Over CMC/SBR

Comparative data for sheet resistance, resistivity, and thickness are presented in Figure 2. The results clearly show that the Si/C anode sheet prepared with CMC/SBR binder exhibits slightly higher resistance and resistivity than its PAA-bound counterpart.

Figure 2. Comparative resistance, resistivity, and thickness results for SC-PAA vs SC-CMC Si/C anode electrode sheets — PAA binder shows lower resistance and resistivity than CMC/SBR binder

This conductivity advantage in PAA binder is attributable to its bonding mechanism. As illustrated in Figure 3, PAA’s linear structure enables more extensive “segment-to-surface” contact with active material particles compared to the “point-to-surface” contact of SBR latex. This superior adhesion—driven by strong hydrogen bonding with surface hydrated layers—produces a more uniform coating over silicon particles. The high concentration of polar groups (e.g., sodium carboxylate) in PAA also improves bonding to the current collector. This dual effect strengthens the electrical contact network both between active material/conductive agent particles and between the composite coating and the current collector, reducing overall electronic resistance. Research also suggests PAA may participate in SEI formation: its –COOH groups can interact with solvated Li⁺ ions, potentially facilitating desolvation and lithium-ion insertion kinetics at the silicon surface.

Figure 3. Schematic of binder-active particle contact mechanisms — point-to-surface (CMC/SBR), segment-to-surface (PAA), and network-to-surface contact — PAA’s extended contact area explains its conductivity advantage in Si/C anodes

5. Results & Discussion: Superior Compression Performance of PAA Binder

Compression performance test results are shown in Figure 4 and summarized in Table 1. Key metrics—maximum deformation, reversible deformation, and irreversible deformation—were all greater for the SC-CMC electrode than for the SC-PAA electrode.

Figure 4. Compression performance test results for SC-PAA vs SC-CMC Si/C anode electrode sheets, 5–60 MPa — CMC/SBR binder shows consistently greater deformation at every pressure step

Table 1. Deformation characteristics of SC-CMC and SC-PAA cells

This indicates that the CMC/SBR-based electrode is more compressible. A significant downside of this higher compressibility is greater elastic rebound (spring-back) after calendering, which poses challenges for cell assembly and precise control of electrode porosity in silicon anode manufacturing. The difference stems from the inherent mechanical properties of each binder: CMC has a rigid cellulose backbone, imparting stiffness and brittleness. SBR is added as an elastomeric modifier to reduce brittleness and increase maximum strain, but the composite CMC/SBR system still shows different viscoelastic behavior than PAA. The flexible, polyethylene-like backbone of PAA contributes to its lower permanent (irreversible) deformation and reduced rebound under compression.

6. Conclusion: PAA Binder as a Key Enabler for Robust Silicon Anodes

This case study demonstrates the value of precise, electrode-level testing in binder selection for silicon-based anodes. Using the BER2500 system, we quantitatively compared CMC/SBR and PAA binder. The results confirm that PAA binder not only improves electrical conductivity—by enhancing inter-particle and electrode-current collector contact—but also delivers superior compression performance, characterized by reduced elastic rebound after calendering.

These benefits trace directly to PAA’s chemical structure. Its higher carboxyl group content facilitates stronger hydrogen bonding with active materials, promoting a more homogeneous coating. This produces a more stable electrode architecture that better accommodates volume changes during cycling, fosters a more robust and thin SEI layer, reduces interfacial impedance (particularly charge-transfer resistance), and improves lithium-ion diffusion rates. Selecting an advanced binder system like PAA is therefore a critical step in developing high-performance, durable silicon-based lithium-ion battery anodes.

7. References

[1] Kang K, Song K, Heo H, et al. Kinetics-driven high power Li-ion battery with a-Si/NiSix core-shell nanowire anodes[J]. Chemical Science, 2011, 2(6): 1090-1093.

[2] FU Yan-peng,CHEN Hui-xin,YANG Yong . Silicon Nanowires as Anode Materials for Lithium Ion Batteries[J]. Journal of Electrochemistry，2009，15(1): 54-61.

[3] Zhang X, Wang D, Qiu X, et al. Stable high-capacity and high-rate silicon-based lithium battery anodes upon two-dimensional covalent encapsulation[J]. Nature Communications, 2020, 11(1): 3826.

[4] Gendensuren B, He C, Oh E-S. Preparation of pectin-based dual-crosslinked network as a binder for high performance Si/C anode for LIBs[J]. Korean Journal of Chemical Engineering, 2020, 37(2): 366-373.

[5] Magasinski A., Zdyrko B., Kovalenko I., et al. Toward efficient binders for Li-ion battery Si-based anodes: polyacrylic acid[J]. Acs Applied Materials & Interfaces, 2010, 2(11): 3004-3010.

[6] Komaba S., Shimomura K., Yabuuchi N., et al. Study on Polymer Binders for High-Capacity SiO Negative Electrode of Li-Ion Batteries[J]. Journal of Physical Chemistry C, 2011, 115(27): 13487-13495.

[7] Ma Y , Ma J , Cui G . Small things make big deal: Powerful binders of lithium batteries and post-lithium batteries[J].Energy Storage Materials, 2019, 20: 146-175

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Abstract

Formation temperature is a key process parameter in lithium-ion cell manufacturing that directly controls both the chemical composition of the SEI film (solid electrolyte interphase) on the negative electrode and the timing and magnitude of cell volume expansion during formation. Using the IEST GVM2200 in-situ gassing volume analyzer on NCM523/graphite cells across five formation temperatures (25°C, 45°C, 55°C, 65°C, 85°C), this study shows that higher formation temperature causes earlier onset and larger magnitude of cell swelling, sharper phase-transition peaks in differential capacity above 55°C, and a compositional shift in the SEI toward inorganic-rich phases (LiF, Li₂CO₃, RCO₂Li) that are less mechanically compliant. A practical formation temperature window of 45°C–70°C balances reduced cell polarization against preservation of SEI mechanical integrity for NCM523/graphite chemistry.

1. Introduction

Formation is a critical manufacturing step for lithium-ion cells: its primary purpose is to generate a stable SEI film on the negative electrode that electronically isolates the electrode from the electrolyte while still allowing lithium-ion transport.^1,2 The quality of the SEI film formed during this step strongly influences subsequent cycle performance and cell lifetime. Formation reactions also generate gas and induce electrode volume changes; formation temperature is therefore a key process parameter that couples electrochemistry, interphase chemistry, and cell swelling behavior.

This article uses the In-Situ Gassing Volume Monitor Analyzer (GVM) to perform in-situ volume measurements on NCM523/graphite cells at different formation temperatures, and explains how formation temperature influences SEI film composition, SEI mechanical properties, and the timing and magnitude of cell volume swelling during the formation step.

Figure 1. Research progress and timeline of SEI film formation on graphite and lithium metal surfaces¹

2. Experimental Equipment and Test Methods

2.1 Experimental Equipment

Testing used the IEST GVM2200 In-Situ Battery Gassing Volume Analyzer, with a test temperature range of 20°C–85°C and dual-channel capability for simultaneous testing of two cells (Figure 2).

Figure 2. IEST GVM2200 In-Situ Battery Gassing Volume Analyzer — 20°C–85°C test range, dual-channel simultaneous cell testing

2.2 Test Information

Cells with an NCM523/graphite system were used, charged at 0.5C constant current to 4.2 V, with a theoretical capacity of 2,400 mAh.

Figure 3. Test cells — NCM523/graphite system, 2,400 mAh theoretical capacity

2.3 Test Method

Cells were initially weighed (m₀), placed into the instrument channels, and the MISG software was initiated. The software was configured with the corresponding cell ID and sampling frequency parameters, automatically recording data for volume change, temperature, current, voltage, and capacity throughout formation.

3. Formation Temperature Schedule

Five formation temperatures were compared: 25°C, 45°C, 55°C, 65°C, and 85°C, following the process shown in Figure 4(a). For each temperature point, five parallel cells were run using the same formation procedure to produce comparable volume-expansion and differential capacity (dQ/dV) data sets, shown in Figures 4(b) and 4(c). The resulting volume and dQ/dV profiles were used to assess gas evolution, phase-transition behavior, and SEI film evolution as a function of formation temperature.

4. Observations: Timing and Magnitude of Cell Volume Expansion

As formation temperature increased, the onset of measurable cell expansion occurred earlier in the charge profile. Volume growth accelerated and reached a near-stable maximum as cell voltage approached approximately 3.7 V, followed by slight contraction during the constant-voltage stage. Higher temperatures produced larger total gas evolution and earlier expansion onset, indicating that elevated formation temperature both shifts and accelerates the reactions contributing to cell swelling.

Notably, when formation temperature exceeded 55°C, the first phase-transition peak in the differential capacity (dQ/dV) curves became noticeably sharper. This sharpening is consistent with more abrupt electrochemical events and indicates that high-temperature formation drives more intense SEI-related reactions— for example, faster reduction of solvent components and more rapid decomposition pathways at the graphite/electrolyte interface.

Figure 4. Cell formation process: (a) formation protocol, (b) volume swelling curves, (c) differential capacity (dQ/dV) curves at 25°C, 45°C, 55°C, 65°C, 85°C — higher temperature produces earlier, larger volume expansion and sharper phase-transition peaks above 55°C

5. SEI Film Formation: Competing Processes and Temperature Dependence

SEI film formation is governed by two opposing processes: SEI growth (net formation) and SEI dissolution (loss of SEI components into the electrolyte). Experimental evidence shows SEI growth is largely tied to electrochemically induced solvent reduction and is relatively less sensitive to temperature. In contrast, higher temperatures accelerate dissolution of the initially formed SEI components into the electrolyte. As a result, SEI films produced at different formation temperatures possess distinct compositions and morphologies.

At elevated temperatures, organic SEI components dissolve more readily in the organic electrolyte, favoring an SEI film enriched in inorganic species—including LiF, Li₂CO₃, RCO₂Li, and other carbonates—that are less soluble. This compositional shift reduces the mechanical compliance of the SEI film and decreases the electrode’s ability to accommodate volume changes, exacerbating measurable cell swelling and interface instability under high-temperature formation conditions.

Conversely, SEI film formed at lower temperatures tends to be denser and more compact. While a compact SEI may reduce gas-producing side reactions, it can also exhibit lower ionic conductivity and higher polarization; at very low formation temperatures this can limit lithium transport and increase the risk of localized lithium plating. There is therefore a clear trade-off in SEI film formation conditions: low temperature produces a dense but less ionically conductive SEI, while high temperature produces a more inorganic, less mechanically compliant SEI with greater gas evolution.

6. Mechanistic Interpretation: Transport, Viscosity and Interfacial Stress

Formation temperature alters several coupled physical properties that together determine SEI film outcome and cell swelling behavior:

• Electrolyte viscosity and ionic conductivity: Higher temperature reduces electrolyte viscosity and increases ionic conductivity, increasing Li⁺ transport rates and reducing cell polarization during formation.

• Electrode diffusion kinetics: Elevated temperature increases lithium diffusion within active materials, shifting phase boundaries and causing phase-transition peaks to shift to lower voltage (a left shift in the dQ/dV curve).

• Interfacial chemical kinetics: Higher temperatures accelerate side reactions and SEI dissolution, increasing gas production and shifting SEI composition toward inorganic-rich layers that are mechanically stiffer and less able to accommodate electrode volume change.

Together, these effects explain why higher formation temperature generally reduces polarization and can improve some formation-step metrics, yet beyond an optimal range it degrades SEI mechanical integrity and increases both cell swelling and irreversible side reactions.

7. Quantitative Trends and Practical Formation Temperature Window

The in-situ volume data show a clear trend: higher formation temperature leads to earlier and larger volume increases. Excessive formation temperature also correlates with stronger gas evolution and sharper early phase-transition peaks in the dQ/dV curves, particularly above 55°C. Because overly high formation temperature accelerates volatile electrolyte component loss and damages SEI film structure, the industry commonly adopts 45°C–70°C as a practical formation temperature window that balances ionic transport, SEI film quality, and gas evolution control for NCM523/graphite and similar cell chemistries.

8. Practical recommendations for formation process control

• Target a balanced formation temperature: Based on the measured trends, select a formation temperature within 45°C–70°C for NCM523/graphite cells as a starting point, then fine-tune within this window according to electrolyte formulation and electrode porosity.

• Monitor in-situ volume and gas evolution during pilot runs: Real-time volume monitoring (e.g., IEST GVM2200) helps detect excessive gas generation or abrupt SEI-related events, providing direct, quantitative feedback on whether formation temperature adjustments are needed.

• Consider SEI chemistry when adjusting temperature: Higher temperature favors an inorganic-rich SEI film; if a more elastic, organic-rich SEI is desired (for example, for high-expansion anode chemistries such as silicon-containing anodes), moderate formation temperatures combined with electrolyte additive strategies may be preferable.

• Account for volatile electrolyte loss at high temperature: Formation temperatures near the high end of the tested range (e.g., 85°C) can accelerate loss of low-boiling-point electrolyte components and trigger secondary reactions; limit exposure time at high temperature or revise electrolyte formulation accordingly.

9. Summary

Formation temperature strongly controls two intertwined outcomes: the chemical and mechanical character of the SEI film, and the timing and magnitude of cell volume expansion during the formation step. This study, using a controllable dual-channel in-situ gas volume monitor, shows that higher formation temperatures accelerate cell expansion and gas evolution and shift electrochemical phase transitions, while also changing SEI film composition toward inorganic-rich, less mechanically compliant films—particularly above 55°C. For the NCM523/graphite cells tested here, an industry-typical formation temperature window of 45°C–70°C provides a reasonable compromise between reduced polarization and preserved SEI film integrity. These quantitative, in-situ measurements can guide process engineers in optimizing formation temperature together with formation rate, applied pressure, and electrolyte/additive selection.

10. References

[1] Jian Tan, John Matz, Pei Dong, Jianfeng Shen, and Mingxin Ye. A Growing Appreciation for the Role of LiF in the Solid Electrolyte Interphase. Adv. Energy Mater. 2021.DOI:10.1002/aenm.202100046

[2] Wei Wenfei, Zhong Kuan, Jiang Shiyong. Effect of the formation temperature on lithium-ion battery performance[J]. Energy Storage Science and Technology, 2018, 7(5): 908-912.

11. FAQs

11.1 What is formation temperature in lithium-ion battery manufacturing?

Formation temperature is the temperature at which a lithium-ion cell undergoes its first charge cycles (the “formation” step) during manufacturing. This step builds the initial SEI (solid electrolyte interphase) film on the negative electrode surface, which is essential for long-term cycling stability. Formation temperature directly affects SEI film composition, electrolyte viscosity and ionic conductivity, electrode diffusion kinetics, and the rate of gas-generating side reactions. In this study on NCM523/graphite cells, formation temperatures from 25°C to 85°C were compared, with 45°C–70°C identified as a practical working window that balances reduced cell polarization against SEI film mechanical integrity.

11.2 What is the SEI decomposition temperature in lithium-ion batteries, and how does formation temperature affect it?

SEI decomposition in lithium-ion batteries typically becomes significant in the range of approximately 80–120°C during abusive heating (e.g., thermal runaway analysis), which is distinct from but related to the SEI film’s compositional sensitivity to formation temperature. During the formation step itself (typically conducted at 25–85°C), higher formation temperature does not decompose existing SEI but instead changes its growth chemistry: it accelerates dissolution of initially formed organic SEI components into the electrolyte, shifting the final SEI composition toward inorganic-rich species (LiF, Li₂CO₃, RCO₂Li) that have different thermal stability characteristics than organic-rich SEI formed at lower temperature. This connection between formation temperature and SEI composition is one reason formation conditions are considered relevant to a cell’s later thermal stability margin, though formation temperature itself remains well below SEI breakdown/decomposition thresholds.

11.3 How does formation temperature affect lithium-ion battery gas production and cell volume expansion?

Higher formation temperature causes both earlier onset and larger magnitude of cell volume expansion during formation, as measured by in-situ gassing volume analysis. In this study, volume growth accelerated and approached a near-stable maximum as cell voltage neared 3.7 V, with higher-temperature cells reaching this point earlier and with greater total expansion. The mechanism is temperature-accelerated SEI dissolution and side-reaction kinetics: as temperature rises, more solvent decomposition and gas-generating reactions occur per unit time, and the resulting SEI is enriched in inorganic components that are less able to mechanically buffer subsequent volume changes. Formation temperatures above 55°C in this study showed a clear inflection toward stronger gas evolution and sharper differential-capacity phase-transition peaks.

11.4 What SEI film formation conditions produce the most stable SEI for lithium-ion batteries?

There is no single formation temperature that is universally “most stable”— SEI film formation conditions involve a trade-off. Low formation temperature (e.g., 25°C) produces a denser, more compact SEI film with lower gas evolution, but this SEI can exhibit lower ionic conductivity and higher polarization, increasing the risk of localized lithium plating at very low temperatures. High formation temperature (e.g., 85°C) reduces polarization and improves ionic transport during formation, but produces an inorganic-rich, less mechanically compliant SEI with greater gas evolution and earlier, larger cell swelling. Based on the quantitative trends in this study, a formation temperature window of 45°C–70°C is recommended for NCM523/graphite cells as a practical compromise, to be fine-tuned according to specific electrolyte formulation and electrode porosity.

11.5 How is cell volume expansion measured during the formation process?

Cell volume expansion during formation is measured using an in-situ gassing volume analyzer, such as the IEST GVM2200, which monitors a cell’s volume change in real time throughout the formation charge-discharge protocol without disassembling the cell. The cell is weighed, placed into a temperature- controlled instrument channel, and cycled according to a defined formation protocol while the instrument simultaneously records volume change, temperature, current, voltage, and capacity. This in-situ approach is essential for formation-temperature studies because it captures the precise voltage and time at which volume expansion onset occurs and how expansion magnitude evolves throughout the charge profile—data that cannot be obtained from post-formation (ex-situ) thickness or density measurements alone.

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Abstract

Three electrode system — also called a three electrode cell or 3 electrode electrochemical cell — is a laboratory configuration designed to deliver precise control and measurement in electrochemical experiments, consisting of a working electrode (WE), counter electrode (CE), and reference electrode (RE) that separates potential measurement from current flow. Unlike a two-terminal battery, this 3 electrode system lets a potentiostat hold the working electrode at a precisely known potential while the counter electrode alone carries the current — the foundation for advanced battery diagnostics including EIS, CV, GITT, and PITT. In this article we explain what a three-electrode system is, how it’s wired, why researchers use it, and how it powers advanced battery diagnostics such as EIS, CV, GITT and PITT.

1. What is a Three Electrode System / Three Electrode Cell?

A three electrode system is an electrochemical configuration consisting of a Working Electrode (WE), Counter Electrode (CE), and Reference Electrode (RE). Unlike two-terminal setups, the 3 electrode system decouples potential measurement from current flow. This separation eliminates voltage drop errors, ensuring precise control and accuracy in advanced battery diagnostics including EIS, CV, and GITT.

Three electrode system refers to an electrochemical cell architecture in which the current path (WE–CE) and the potential measurement path (WE–RE) are physically separated, allowing independent, simultaneous control of working electrode potential and measurement of cell current.

The system operates through three distinct components — the Working Electrode (WE), the Counter Electrode (CE), and the Reference Electrode (RE) — each with a precisely defined and non-interchangeable role:

• Working Electrode (WE) — the electrode under study where the electrochemical reaction of interest occurs.

• Counter Electrode (CE) — also called the auxiliary electrode; it completes the current path and supplies or balances current.

• Reference Electrode (RE) — a non-polarizable electrode that provides a stable reference potential against which the WE potential is measured.

By separating current control from potential measurement, the three electrode cell allows researchers to independently control the working electrode’s potential while the counter electrode handles the system current, providing unmatched experimental accuracy compared to two-electrode setups.

2. Roles & Requirements of the Working Electrode, Counter Electrode, and Reference Electrode

Each of the three electrodes — the working electrode, counter electrode, and reference electrode — carries a distinct responsibility. Understanding what the working electrode, counter electrode, and reference electrode each do is the starting point for designing a reliable electrochemical experiment.

2.1 Working Electrode (WE)

The WE is the focus of all experiments. It must be chemically inert relative to the electrolyte, present a reproducible surface, and have a controlled geometric area. Common working electrodes include glassy carbon, platinum, gold, conductive oxides (FTO/ITO), and composite battery electrodes prepared as test coupons. All electrochemical signals of interest originate at the working electrode surface.

Working electrode (WE) is defined as the electrode at which the electrochemical reaction under investigation occurs; its potential is precisely controlled and its current response is measured by the potentiostat.

2.2 Counter Electrode (CE)/Auxiliary Electrode

The counter electrode — also termed the auxiliary electrode — must be large, highly conductive, and chemically stable (typically platinum mesh or graphite rod). Its sole function is to counterbalance the electron flow driven through the working electrode, allowing the potentiostat to maintain the target WE potential accurately. The counter electrode carries all the cell current so that the reference electrode branch draws none.

Counter electrode (CE) is defined as the auxiliary electrode that completes the current circuit in a three electrode system; it supplies or absorbs electrons at a rate equal and opposite to the working electrode, playing no role in the potential measurement.

2.3 Reference Electrode (RE)

The reference electrode provides a stable, reproducible voltage reference (common examples: Ag/AgCl, saturated calomel electrode — SCE). Because the RE branch draws negligible current, its equilibrium potential remains constant regardless of what is happening at the working electrode. This allows the potentiostat to measure and control working electrode potential with millivolt-level precision against a known, fixed baseline.

Reference electrode (RE) is defined as a non-polarizable electrode that maintains a stable, known electrochemical potential independent of cell current; it serves as the fixed voltage reference against which working electrode potential is measured in a three electrode system.

3. Why is the Three Electrode System So Important?

In the early days of electrochemical research, experiments relied on two-electrode systems. Although straightforward, the two-electrode setup had significant limitations in measuring and controlling electrode potentials, leading to systematic errors. In the 1920s, electrochemists introduced the reference electrode, thereby creating the 3 electrode system. This innovation substantially improved the precision and reproducibility of electrochemical measurements. The three electrode system delivers two key advantages over two-electrode configurations:

3.1 Precise Potential Control

The reference electrode allows independent measurement and control of the working electrode potential without interference from the current flowing in the counter electrode branch. This independence is particularly critical when studying the kinetics and mechanisms of electrochemical reactions, because even small potential errors introduce exponentially large errors in rate constants via the Butler–Volmer equation.

3.2 Improved Accuracy in Complex Systems

In a two-electrode setup, voltage drops from solution resistance (IR drop) and counter electrode polarization obscure the true working electrode potential. The three electrode cell largely eliminates this ambiguity: the reference electrode provides a stable potential baseline, allowing clear separation and analysis of the different electrochemical processes within the system — including interfacial charge transfer, diffusion layers, and film formation kinetics.

3.3 Two-Electrode vs. Three-Electrode System: Side-by-Side Comparison

Table 1. Methodological comparison of two-electrode and three-electrode testing configurations and their strategic implications for battery researc

4. How a Three Electrode Cell Is Connected — the “Two-Circuit” Concept

A three electrode system is operated with an electrochemical workstation (potentiostat/galvanostat). Conceptually, the three electrode cell forms two distinct circuits running in parallel:

• Potential measurement circuit: high-impedance voltmeter between WE and RE — measures and controls working electrode potential with negligible current draw on the RE.

• Current supply circuit: ammeter and current source between WE and CE — delivers or extracts the current required to maintain the target WE potential.

This “three-electrode, two-circuit” arrangement is the foundation for experiments such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), galvanostatic intermittent titration technique (GITT), and potentiostatic intermittent titration technique (PITT). Without the reference electrode branch to fix the potential reference point, the 3 electrode potentiostat cannot accurately distinguish working electrode kinetics from counter electrode polarization.

Figure 1. Structure of the three electrode system: working electrode (WE), counter electrode (CE), and reference electrode (RE) wiring in a two-circuit potentiostat configuration

5. Practical Tips for Effective Three-Electrode Measurements

• Reference placement: Position the RE tip as close to the WE surface as possible (Luggin capillary geometry) to minimize uncompensated resistance Ru and reduce IR drop error in EIS high-frequency data.

• Counter electrode sizing: Use a CE with surface area at least 5–10× larger than the WE to prevent CE polarization from limiting current supply and distorting WE potential control.

• Surface preparation: Standardize WE polishing (0.05 μm alumina for glassy carbon), rinse sequence, and equilibration time before each run — surface state variability is the leading cause of poor CV reproducibility.

• IR compensation: Apply potentiostatic EIS at open circuit to determine Ru, then apply positive feedback compensation in CV and GITT protocols. Avoid over-compensation — Ru overshoot causes instability oscillations.

• Cell geometry: Use appropriate cell designs (beaker cell for exploratory work, Swagelok or coin-cell adaptors for battery electrodes, T-cell for symmetric electrode studies) matched to the material geometry and electrolyte volume requirements.

In electrochemical research, particularly in battery-related studies, the three electrode system has become the standard laboratory configuration. However, to fully leverage the advantages of the three electrode cell, high-precision electrochemical analyzers are essential. The following sections detail the specific instrument requirements and how the IEST Electrochemical Analyzer addresses each.

Figure 2. Three electrode cell measurement performed with the IEST Electrochemical Analyzer

6. Precise Potential Control in a Three Electrode System

The reference electrode in the 3 electrode system provides a stable potential baseline for precise control of the working electrode. High-precision electrochemical workstations must maintain potential stability even at sub-microampere current levels — the regime critical for studying SEI film formation on lithium-ion battery negative electrode materials.

The SEI formation window on graphite anodes typically spans a narrow potential range (approximately 0.5 V to 0.05 V vs. Li/Li⁺), within which multiple overlapping reduction reactions occur. Resolving these reactions by CV or GITT requires potential accuracy at the microvolt level. The IEST Electrochemical Analyzer controls and measures potential changes with accuracy within ±100 μV, enabling reliable differentiation of SEI formation sub-steps that would be indistinguishable on lower-precision instruments.

Figure 3. Voltage control in a three-electrode setup: setpoint 4.5 V, measured value 4.5 V ± 100 μV

7. Accurate Current Measurement in a Three Electrode Cell

Understanding the dynamics and mechanisms of electrode reactions requires precise measurement of current responses. In CV testing on battery electrode materials, researchers must resolve peak currents that can be as small as a few nanoamperes for thin-film or low-loading test coupons. The IEST Electrochemical Analyzer provides current measurement resolution at the nanoampere (nA) level with overall current accuracy of ±0.01% FS across all measurement ranges.

This precision level matters in practice because CV peak current ratios (𝑖𝑝⁢𝑎/𝑖𝑝⁢𝑐) and peak potential separations (Δ⁢𝐸𝑝) are used to extract diffusion coefficients, rate constants, and reversibility indicators for electrode materials. A current accuracy error of 0.5\% or greater introduces proportional errors in all derived kinetic parameters — making sub-0.05\% measurement accuracy a practical necessity rather than a specification marketing point.

Figure 4. Current control in a three-electrode system: setpoint 100 mA, measured value 100 mA ± 5 μA

8. Low-Noise Characteristics for EIS in Three-Electrode Setup

Electrochemical signals in three electrode cells are frequently very weak — particularly in the sub-nanoampere current range associated with trace reaction monitoring or thin-film electrode characterization. External electromagnetic interference, ground loops between the potentiostat and the cell, and thermal noise at the working electrode input amplifier all degrade signal quality.

The IEST Electrochemical Analyzer‘s EIS module maintains noise floors below the minimum required measurement threshold across the full frequency range of 0.01 Hz to 100 kHz. In EIS measurements, high-frequency noise (above 10 kHz) distorts the semicircle associated with charge-transfer resistance (𝑅𝑐⁢𝑡) and double-layer capacitance (𝐶𝑑⁢𝑙), leading to systematic underestimation of 𝑅𝑐⁢𝑡. The IEST integrated low-noise EIS design resolves these high-frequency features accurately, supporting reliable equivalent-circuit fitting and SEI film characterization in three-electrode cell configurations.

Figure 5. EIS setup steps and impedance spectrum on the IEST Electrochemical Analyzer using a three-electrode configuration

9. Wide Frequency Response Range for Three-Electrode EIS

Electrochemical impedance spectroscopy in a three electrode cell probes processes spanning many decades of frequency:

• High frequency (1 kHz – 100 kHz): Solution resistance (𝑅𝑠), inductive response of leads, and charge-transfer kinetics at the electrode–electrolyte interface.

• Mid frequency (1 Hz – 1 kHz): Charge-transfer resistance (𝑅𝑐⁢𝑡), double-layer capacitance (𝐶𝑑⁢𝑙), and SEI film impedance.

• Low frequency (0.01 Hz – 1 Hz): Solid-state lithium-ion diffusion within active material particles, expressed as the Warburg impedance element.

The IEST Electrochemical Analyzer covers the full 0.01 Hz to 100 kHz frequency range in a single EIS sweep with consistent phase accuracy throughout. This breadth allows a single measurement protocol to capture both the solid-state diffusion information needed for lithium-ion diffusion coefficient (𝐷𝐿⁢𝑖+) calculation and the high-frequency charge-transfer data needed for interfacial kinetic modeling, without requiring separate instruments or cell re-assembly.

Figure 6. Physical and chemical properties of electrochemical systems characterized by EIS across different frequency ranges (0.01 Hz–100 kHz)

10. Multi-Functional Integration: CV, EIS, GITT, and PITT in One Platform

The IEST Electrochemical Analyzer integrates the following electrochemical test techniques in a single instrument, compatible with three electrode cell configurations:

• Cyclic voltammetry (CV) — redox peak identification, reaction reversibility assessment, and electroactive surface area determination.

• Electrochemical impedance spectroscopy (EIS) — interfacial resistance, diffusion parameters, and equivalent-circuit model fitting.

• Galvanostatic intermittent titration technique (GITT) — chemical diffusion coefficient (D_Li⁺) determination during charge/discharge by measuring the transient and equilibrium voltage response to current pulses.

• Potentiostatic intermittent titration technique (PITT) — complementary diffusion coefficient measurement under potential-step control, resolving the current transient decay to quantify lithium solid-state diffusion.

GITT (Galvanostatic Intermittent Titration Technique) is defined as an electrochemical method in which a series of short current pulses are applied to a battery electrode in a three electrode cell, each followed by a relaxation period; the resulting voltage transients are used to calculate the solid-state lithium-ion diffusion coefficient D_Li⁺ at each state of charge.

PITT (Potentiostatic Intermittent Titration Technique) is defined as the potential-step complement to GITT, in which the working electrode is stepped to a series of fixed potentials and the resulting current transient decay is analyzed to extract D_Li⁺ at each state of charge.

GITT (Galvanostatic Intermittent Titration Technique) is defined as an electrochemical method in which a series of short current pulses are applied to a battery electrode in a three electrode cell, each followed by a relaxation period; the resulting voltage transients are used to calculate the solid-state lithium-ion diffusion coefficient D_Li⁺ at each state of charge.

Beyond conventional charge/discharge functions, the IEST Electrochemical Analyzer integrates CV and EIS modules — functionality that previously required separate dedicated electrochemical workstations — into a single hardware platform. This consolidation reduces cell re-assembly steps between test types, eliminating the baseline drift that occurs when a three electrode cell is disconnected and reconnected between instruments.

11. Data Processing and Analysis for Three-Electrode Measurements

The IEST Electrochemical Analyzer provides real-time data processing and multi-dimensional analysis of complex electrochemical data sets generated in three electrode cell experiments. Key capabilities include:

• Automated equivalent-circuit fitting for EIS spectra (Randles circuit and modified variants including SEI film elements).

• GITT diffusion coefficient calculation with automated pulse integration and equilibrium potential extraction.

• CV peak detection, integration for charge quantification, and scan-rate-dependent analysis for determining diffusion- vs. capacitive-controlled contributions.

• Multi-channel synchronized data acquisition for parallel three-electrode experiments, supporting statistical comparison across replicate cells.

Hardware parameters alone are not the primary limiting factor in current electrochemical workstation performance — circuit optimization improves results to a degree, but the more pressing need is intelligent data analysis software that moves beyond raw data display toward automated extraction of physically meaningful parameters. The IEST platform continues development in this direction, with ongoing software updates targeting automated anomaly flagging and parameter trend monitoring across charge–discharge histories.

Need Precision Three-Electrode Measurements?

The IEST ERT Series Electrochemical Analyzer supports three electrode cell configurations with ±100 μV potential accuracy, nA-level current resolution, and an integrated EIS module covering 0.01 Hz to 100 kHz — meeting R&D-grade precision requirements for EIS, CV, GITT, and PITT battery diagnostics.

View the ERT Series →

11. Summary

The three electrode system — comprising the working electrode, counter electrode, and reference electrode — is the foundational laboratory configuration for precision electrochemical research. Although the three-electrode structure is invisible in finished battery cells, it plays a critical role at the material development and characterization stage:

• EIS measurements in a three electrode cell resolve impedance contributions from the SEI film, charge-transfer resistance, and solid-state diffusion — each at a different frequency range.

• CV measurements in a three-electrode setup provide redox reaction mechanisms, peak potentials, and kinetic parameters for electrode materials.

• GITT and PITT measurements in the three electrode system yield chemical potential curves and solid-state diffusion coefficients, enabling material design optimization.

The introduction of the reference electrode into the 3 electrode system in the 1920s transformed electrochemical science by enabling potential-resolved studies of complex interfacial processes. As battery materials become more sophisticated — high-voltage cathodes, conversion anodes, solid electrolytes — the precision demands on three electrode cell measurements only increase. Instruments capable of sub-100 μV potential accuracy, nA current resolution, and integrated multi-technique operation are no longer optional; they are the prerequisite for generating data that supports publication-grade mechanistic conclusions.

13. FAQs About the Three Electrode System

What is a three electrode system and its components?

A three electrode system is an electrochemical configuration used for precise measurements. It consists of a Working Electrode (WE) where the reaction occurs, a Counter Electrode (CE) that completes the current circuit, and a Reference Electrode (RE) that provides a stable potential baseline. The three electrode cell ensures precise control in advanced battery diagnostics including EIS, CV, and GITT.

What is the working principle of a three electrode system?

A three electrode system achieves precise electrochemical measurement by separating the current path (working electrode–counter electrode) from the potential measurement path (working electrode–reference electrode). The reference electrode provides a stable potential reference; the working electrode hosts the reaction of interest; and the counter electrode completes the current loop. This two-circuit design eliminates potential measurement errors caused by current flow — the fundamental problem in two-electrode systems — and is essential for accurate CV and EIS data.

What is the difference between a 2-electrode and a 3-electrode system?

Structure: A two-electrode system has only a working electrode and a counter electrode; a three electrode system adds an independent reference electrode.
Accuracy: A three-electrode setup allows precise control of working electrode potential, avoiding IR drop and CE polarization errors.
Application: Two-electrode setups are suitable for simple battery cycling tests; three-electrode systems are used for reaction mechanism studies, kinetic analysis, EIS, CV, GITT, and PITT.
Measurement: The three electrode cell enables independent measurement of working electrode potential, separating anodic and cathodic processes.

What is the role of the counter electrode (auxiliary electrode) in a three-electrode setup?

The counter electrode — also known as the auxiliary electrode — completes the current path in the three electrode cell. Its function is to supply or absorb electrons at an equal and opposite rate to the working electrode, so the potentiostat can maintain the target WE potential. The counter electrode plays no role in the potential measurement. For high-precision measurements, the counter electrode surface area should be at least 5–10× that of the working electrode to avoid polarization.

Why is a high-precision analyzer required for 3-electrode measurements?

To fully leverage a three electrode system, electrochemical analyzers must provide microvolt-level (±100 μV) potential control and nanoampere-level current measurement (±0.01% FS accuracy). These specifications are critical for resolving minute electrochemical changes — such as SEI formation sub-steps on lithium-ion battery anodes — that occur within narrow potential windows and at sub-microampere current levels during EIS and CV tests. The IEST ERT Series Electrochemical Analyzer meets these precision requirements with an integrated EIS module covering 0.01 Hz to 100 kHz.

What is the difference between the working electrode and the counter electrode?

The working electrode hosts the electrochemical reaction under study and its potential is precisely controlled by the potentiostat. The counter electrode completes the current circuit, supplying or absorbing electrons so the working electrode potential remains stable. Critically, the counter electrode participates in no measurement — all electrochemical information is extracted from the working electrode response measured against the reference electrode. When people search “working electrode vs counter electrode,” the core distinction is: the working electrode is measured; the counter electrode is only a current path.

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This article was originally published at: Entering Electrochemistry | The Secret Weapon of Electrochemical Research—The Three Electrode System first appeared on IEST Instrument | World-leading Innovative Lithium Battery Tester Solution Provider

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Coulombic Efficiency (CE) of a lithium-ion battery is calculated as: CE = Q_D(n) / Q_C(n), where Q_D(n) is the discharge capacity and Q_C(n) is the charge capacity in cycle n. In an ideal battery with no side reactions, CE = 1 (100%). In practice, side reactions irreversibly consume active lithium, making CE < 1, and the deviation from 1 directly determines capacity fade rate and battery cycle life. A CE of 99.95% versus 99.955% produces a 1.97% prediction error in a 500-cycle lifespan model on a 100 Ah cell. The dQ/dV curve (incremental capacity / IC curve) of an LFP battery reveals three distinct phase transition peaks whose areas represent individual capacity contributions; changes in peak height and position across cycles identify active material loss, active lithium loss, and internal resistance increase without full cell teardown. Both measurements require test equipment precision of 0.01% (1/10,000) or better — equipment at 0.05% (1/20,000) accuracy cannot resolve the <0.003 CE differences that distinguish good from poor electrolyte formulations after just 16 cycles.

1. Background

High-precision charge/discharge testing — defined here as measurement accuracy of 0.01% (1/10,000) or better for both current and voltage — is a prerequisite for three specific analytical tasks that conventional 0.1% or 0.05% accuracy equipment cannot support: accurate Coulombic Efficiency measurement for rapid lifespan prediction, detection of charge endpoint slippage (ΔC) for side reaction assessment, and high-resolution 𝑑⁢𝑄/𝑑⁢𝑉 curve analysis for capacity decay factor identification.

As lithium-ion battery demands increase — for longer cycle life, higher energy density, and faster charging — the material and electrolyte improvements being pursued produce performance differences that are often smaller than the measurement noise of conventional battery testers. A tester with 0.05% accuracy produces current and voltage fluctuations that wash out the signals from new material improvements, making it impossible to distinguish between electrolyte formulations or detect early-cycle side reactions that predict long-term behavior.

2. Coulombic Efficiency (CE): How to Calculate It and Why It Predicts Battery Life

2.1 How to Calculate Coulombic Efficiency

Coulombic Efficiency (CE) quantifies the reversibility of each charge-discharge cycle. The calculation is straightforward:

Coulombic Efficiency Formula

CE(n) = Q_D(n) / Q_C(n)

where: Q_D(n) = discharge capacity in cycle n | Q_C(n) = charge capacity in cycle n | CE = 1.0 (100%) means zero irreversible loss; CE < 1 indicates active lithium consumption by side reactions.

In the ideal scenario with no side reactions, CE = 1 (fully reversible cycling, theoretically infinite lifespan). In practice, side reactions at the electrode–electrolyte interfaces — SEI layer growth, electrolyte decomposition, electrode active material dissolution — consume active lithium irreversibly each cycle, making CE < 1. The accumulated per-cycle lithium loss determines total capacity fade over the cell’s life.

2.2 CE-Based Battery Lifespan Prediction Model

Because per-cycle capacity loss is proportional to CE deviation from 1, a CE-based lifespan prediction model provides a mathematically direct path from early-cycle CE measurements to long-cycle capacity prediction. Figure 1 illustrates charge-discharge curves and CE measurement concepts; Figure 2 shows the prediction model framework:

Model A: 𝐶𝑘 =𝛼0 ⋅𝐶⁢𝐸𝑘 +𝛼1

Battery Coulombic Efficiency CE lifespan prediction model formula: 𝐶𝑘 =𝐶0 ×∏𝑘𝑖=1𝐶⁢𝐸𝑖, showing that accumulated per-cycle CE losses determine remaining capacity after 𝑘 cycles, with empirical parameters 𝑎0 (initial capacity) and 𝑎1 (capacity decay coefficient).

The model predicts remaining capacity after 𝑘 cycles as 𝐶𝑘 =𝐶0 ×(𝐶⁢𝐸1 ×𝐶⁢𝐸2 ×⋯ ×𝐶⁢𝐸𝑘), where 𝐶0 is initial capacity and 𝐶⁢𝐸𝑘 is Coulombic Efficiency for cycle 𝑘. The practical implication: a 0.005% error in measured CE translates to a 1.97% prediction error after 500 cycles on a 100 Ah cell (99.95% CE →𝐶500 =77.88 Ah; 99.955% CE →𝐶500 =79.85 Ah). This is why sub-0.001% CE resolution — achievable only with 0.01% or better test precision — is the minimum requirement for accurate lifespan prediction.

2.3 Early-Cycle CE for Rapid Lifespan Screening

The most commercially valuable application of high-precision CE measurement is the ability to predict long-cycle performance from short early-cycle data — replacing weeks of continuous cycling with a few days of high-precision measurement. For example, Figures 1(c) and (d) respectively illustrate the comparison of long-cycle capacity and CE comparison results in the early cycles of batteries prepared using three different electrolytes:

• In long-cycle testing (Figure 1c): electrolyte (VC+VEC+FEC+PS) maintains cycle life to ~500 cycles; the other two formulations fail at ~150 and ~300 cycles respectively.

• In early-cycle CE (Figure 1d, cycles 1–16): the CE of electrolyte (VC+VEC+FEC+PS) stays above 0.999; the other two reach only ~0.998 and ~0.9965 — a maximum difference of 0.003.

• Test precision comparison (Figure 1b): equipment at 0.05% (1/20,000) accuracy produces CE fluctuations of ±0.006 — which completely overwhelms the 0.003 difference between formulations. Equipment at 0.01% (1/10,000) or better reduces fluctuation to within ±0.001, clearly resolving all three formulations after just 16 cycles instead of 500.

Figure 1. High-precision CE testing enables rapid battery life prediction: (a) charge-discharge curves; (b) CE test precision comparison — 1/5,000 accuracy (±0.006 fluctuation) cannot resolve electrolyte differences that require ±0.003 resolution; (c) long-cycle capacity under three electrolyte formulations; (d) early-cycle CE comparison after 16 cycles showing CE of 0.9965, 0.998, and 0.999 — a gap detectable only at 1/10,000 precision or better.

Figure 2. Coulombic Efficiency-based battery lifespan prediction model. With initial capacity C₀ = 100 Ah: at CE = 99.95%, C₅₀₀ = 77.88 Ah; at CE = 99.955%, C₅₀₀ = 79.85 Ah — a 1.97% model accuracy difference from a 0.005% CE measurement error, confirming why 0.01% test precision is required for accurate cycle life prediction.

3. Charge Endpoint Slippage (Δ⁢𝐶): Internal Side Reaction Assessment

Next, let’s introduce the second significance of high-precision current & voltage testing: internal side reaction assessment of batteries. Before delving into detailed application cases, we need to introduce two concepts: Δ_C (Charge Endpoint Slippage) or Ch.End.Cap. (%). Both are derived directly from high-precision charge capacity data:

Charge Endpoint Slippage Definitions

ΔC = Q_C(n+1) − Q_D(n)

Ch.End.Cap.(%) = Q_C(n) / Q_C(1) × 100%

Both parameters characterize cathode-side electrolyte oxidation rate. Lower values = less side reaction = longer cycle life.

As shown in Figure 3(a), Δ⁢𝐶 can be calculated by subtracting the charging capacity of the previous cycle from that of the subsequent cycle, i.e., Δ⁢𝐶 =𝑄⁢𝐶⁢(𝑛 +1)–𝑄⁢𝐷⁡(𝑛); while 𝐶⁢ℎ.𝐸⁢𝑛⁢𝑑.𝐶⁢𝑎⁢𝑝.(%) can be calculated by dividing the charging capacity of the 𝑛th cycle by that of the first cycle, i.e., 𝐶⁡ℎ.𝐸⁢𝑛⁢𝑑.𝐶⁡𝑎⁢𝑝.(%) =𝑄⁢𝐶⁡(𝑛)/𝑄⁢𝐶⁡(1) ×100%. Although these two parameters have different calculation methods, they represent the same significance, both of which can characterize the degree of oxidation reaction occurring in the electrolyte on the positive electrode side. This oxidation reaction continuously consumes the electrolyte and deposits reaction by-products on the surface of the negative electrode material. Over time, this will clog the gaps in the negative electrode material and lead to a drop in battery capacity.

The specific reaction process is illustrated in Figures 3(c) and (d). Figure 3(b) shows the charge endpoint shift of the battery under multiple cycle conditions, indicating that the positive electrode side continuously consumes active lithium in the electrolyte, gradually affecting the battery’s cycle life. Generally, in a stable cycling process, the values of Δ_C or Ch.End.Cap. (%) for mature batteries are relatively small. If the testing accuracy is too low, accurate and effective analysis results cannot be obtained. Therefore, we need high-precision testing equipment for detailed analysis of battery side reactions.

Figure 3(e) also illustrates four aspects of the application of parameters Δ_C or Ch.End.Cap. (%): ① Screening of different electrolyte additives; ② Screening of different cathode electrode materials; ③ Determination of oxidation charge under different potentials; and ④ Study of related material mechanisms.

Figure 3. Charge endpoint slippage Δ⁢𝐶 for internal side reaction assessment: (a) definition (Δ⁢𝐶 =𝑄⁢𝐶⁢(𝑛 +1)–𝑄⁢𝐷⁡(𝑛)); (b) multi-cycle endpoint shift showing ongoing cathode-side electrolyte oxidation; (c–d) oxidation reaction mechanism — electrolyte consumed at cathode, by-products deposited on anode pores; (e) four applications of Δ⁢𝐶 and 𝐶⁢ℎ.𝐸⁢𝑛⁢𝑑.𝐶⁢𝑎⁢𝑝.(%) in battery R&D.

Figures 4(a-c) show the comparison of cycle life of LCO batteries under three different electrolytes. Figure 4(d) extracts the Ch.End.Cap. (%) of the first 16 cycles for comparison, and it is found that the electrolytes with the addition of 1wt% or 2wt% VC have much lower Ch.End.Cap. (%) values compared to the electrolyte without VC addition.

This indicates that the addition of VC can slow down the oxidation rate of the electrolyte on the cathode electrode side, thereby extending the battery’s cycle life. From the long-term cycling results shown in Figure 4(e), it can also be seen that after cycling for 110 cycles, the capacity retention rate of the electrolyte without VC addition has dropped to around 86%, while the capacity retention rate of the electrolytes with the addition of 1wt% or 2wt% VC remains above 94%.

Figure 4. VC additive effect on LCO battery cycle life: (a–c) full cycle life curves; (d) Ch.End.Cap.(%) during first 16 cycles — VC-containing electrolytes show significantly lower charge endpoint slippage; (e) long-term capacity retention — without VC: 86% at 110 cycles; with 1wt% or 2wt% VC: above 94%. Early-cycle Ch.End.Cap.(%) from 16 cycles predicts 110-cycle outcome.

4. LFP dQ/dV Curve Analysis: Identifying Battery Capacity Decay Mechanisms

The dQ/dV curve (also called the incremental capacity / IC curve) and its inverse dV/dQ curve (differential voltage / DV curve) are the third class of high-precision measurement that unlocks refined battery failure analysis. These curves transform the voltage plateau regions of standard charge-discharge profiles into peaks and features that directly identify phase transitions, active material changes, and internal resistance evolution — without destructive cell teardown.

Figure 5. LFP battery dQ/dV (IC curve) and dV/dQ analysis: (a) dQ/dV curve with three phase transition peaks ⑤×II, ②×II, ①×II; (b) corresponding dV/dQ regions Q_A, Q_B, Q_C; (c) multi-cycle dQ/dV comparison — peaks ⑤×II and ②×II stable (no active material loss), peak ①×II decreases (active lithium loss, primary decay mechanism); (d) test precision comparison — 0.05% accuracy buries small phase peaks in noise; 0.01% accuracy resolves them cleanly for failure analysis.

Figure 5 illustrates three key findings from LFP dQ/dV analysis:

• Three-peak structure (Figure 5a): the LFP dQ/dV curve shows three distinct phase transition peaks (⑤×II, ②×II, ①×II) whose areas correspond to the charge capacity of each phase transition stage. The corresponding dV/dQ plot (Figure 5b) maps these to three capacity regions Q_A, Q_B, Q_C.

• Decay mechanism identification (Figure 5c): after cycling, peaks ⑤×II and ②×II remain stable in shape and area — confirming no significant active material loss. Peak ①×II decreases in height — identifying active lithium loss as the dominant capacity decay mechanism. No peak position shift means no significant internal resistance increase.

• Precision requirement (Figure 5d): at 0.05% (5/10,000) accuracy, small phase transition peaks are buried in measurement noise, making failure identification impossible. At 0.01% (1/1,000) or better accuracy, all peaks are clearly resolved — enabling detection of early-stage weak side reactions before they escalate to measurable capacity loss.

5. IEST High-Precision Electrochemical Performance Analyzer: ECT and ERT Series

The IEST ECT and ERT series Electrochemical Performance Analyzers address the requirements of all three high-precision battery analysis methods described above:

• 0.01% (1/10,000) current and voltage accuracy on all 8 channels — enabling the ±0.001 CE fluctuation required to distinguish electrolyte formulations after 16 cycles rather than 500.

• ERT7008 with integrated CV and EIS: cyclic voltammetry and electrochemical impedance spectroscopy can be inserted as steps within a long-term cycling protocol — eliminating the need to transfer cells between instruments and enabling simultaneous CE monitoring, dQ/dV analysis, and impedance tracking throughout a single cycling experiment.

• High-resolution data acquisition: the sampling density required to generate artifact-free dQ/dV and dV/dQ curves is supported natively, without requiring post-processing smoothing that can obscure genuine phase transition features.

Figure 6. IEST ECT/ERT series Electrochemical Performance Analyzer: (a) ECT series high-precision cycler; (b) ERT7008 8-channel system at 0.01% accuracy; (c) integrated CV and EIS in cycling protocol — enabling simultaneous CE measurement, dQ/dV monitoring, and impedance tracking without equipment switching between instruments.

Key technical relationships established in this article:

• Coulombic Efficiency is calculated as 𝐶⁡𝐸 =𝑄⁢𝐷⁡(𝑛)/𝑄⁢𝐶⁡(𝑛); a 0.005% CE measurement error produces 1.97% lifespan prediction error at 500 cycles for a 100 Ah cell.

• CE differences between electrolyte formulations of ≤0.003 (distinguishing good from poor electrolytes at 16 cycles) require test equipment precision of 0.01% or better — equipment at 0.05% accuracy cannot resolve this difference.

• LFP 𝑑⁢𝑄/𝑑⁢𝑉 analysis identifies three phase transition peaks whose evolution across cycles separates active material loss from active lithium loss from internal resistance increase.

• 𝐶⁢ℎ.𝐸⁢𝑛⁢𝑑.𝐶⁢𝑎⁢𝑝.(%) from the first 16 cycles predicts 110-cycle capacity retention, allowing VC additive effectiveness to be confirmed in days rather than weeks.

All three analyses require the same 0.01% precision threshold, achievable with the IEST ERT7008 8-channel Electrochemical Performance Analyzer.

6. References

[1] F.F. Yang, X.B. Song, G.Z. Dong and K.L. Tsui, A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries. Energy 171 (2019) 1173-1182.

[2] J.C. Burns, A. Kassam, N.N. Sinha, L.E. Downie, L. Solnickova, B.W. Way and J.R. Dahn, Predicting and Extending the Lifetime of Li-Ion Batteries. Journal of The Electrochemical Society 160 (2013) A1451-A1456.

[3] D.Y.H. Wang, N.N. Sinha, R. Petibon, J.C. Burns and J.R. Dahn, A systematic study of well-known electrolyte additives in LiCoO₂/graphite pouch cells. Journal of Power Sources 251 (2014) 311-318.

[4] J.C. Burns, N.N. Sinha, D.J. Coyle, G. Jain, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J.P. Gardner and J.R. Dahn, The Impact of Varying the Concentration of Vinylene Carbonate Electrolyte Additive in Wound Li-Ion Cells. Journal of The Electrochemical Society 159 (2012) A85-A90.

[5] X.B. Han,《Research on Mechanistic Model and State Estimation of Automotive Lithium-ion Batteries》, 2014.10.

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Abstract

Sodium-ion battery testing requires a multi-level characterization approach covering four hierarchical scales — particle, powder, electrode, and cell — because sodium-ion battery performance limitations originate at each level and propagate upward to cell-level failure. At the particle level, single-particle crushing strength quantifies mechanical differences in layered oxide cathode and hard carbon anode raw materials that determine resistance to fracture during slurry mixing and electrode calendering. At the powder level, powder resistivity and compaction density measurement screens incoming materials for moisture and CO₂ degradation in layered oxide cathodes, and characterizes carbonization uniformity in hard carbon anodes. At the electrode level, electronic conductivity (BER), ionic conductivity and tortuosity (EIC), and electrode flexibility (BEF) testing identify process defects in slurry preparation, coating, and calendering. At the cell level, in-situ swelling, in-situ gassing(GVM & MSG), and electrochemical characterization (EIS/CV/GITT) capture the dynamic electrochemical and mechanical responses that determine cycle life, rate capability, and safety.

1. Industry Background: Sodium-Ion Batteries at Inflection Point

Recent industry developments signal that sodium-ion batteries are transitioning from laboratory demonstrations to GWh-scale commercial deployment: CATL and Hyperstrong signed a strategic energy storage cooperation agreement for 60 GWh of sodium-ion battery capacity over three years — establishing the largest single sodium-ion battery energy storage order ever recorded globally. Sodium-ion batteries have built a growing commercial presence across stationary energy storage (residential and commercial/industrial), two-wheelers, low-speed electric vehicles, and special-purpose engineering equipment — driven by four core advantages: abundant and widely distributed raw materials (sodium), excellent low-temperature performance, strong fast-charging capability, and high intrinsic safety. China’s New Energy Storage Technology Development Roadmap 2025–2035 explicitly identifies sodium-ion batteries as a priority development direction for new energy storage.

Table 1. Application fields of sodium-ion batteries

Key technical focus areas for sodium‑ion batteries:

1. Material system optimization: For cathodes, polyanionic compounds, layered oxides, and Prussian blue analogs continue to evolve; hard carbon remains the dominant anode material.

2. Cost competitiveness enhancement: With scaled‑up production, sodium‑ion battery costs are expected to decrease further, strengthening their competitiveness against LFP batteries.

3. Complete industry chain development: Rapid progress is being made in building the full industrial chain — from raw materials to cells to complete battery systems.

Despite this commercial momentum, sodium-ion batteries face a series of persistent technical and manufacturing challenges compared to lithium-ion batteries — which have achieved >95% production yield after decades of development. Current sodium-ion battery production lines, particularly mid-to-small scale operations, often achieve only ~70% yield, driven by: poor material stability (especially moisture and CO₂ sensitivity of layered oxide cathodes); insufficient process consistency; limited cycle life; energy density disadvantage versus high-nickel lithium-ion; environmental intolerance during storage; and cell swelling and gas generation. The result is a compounding cycle: material breakthroughs are slow → production yield is low → costs remain high → market expansion is constrained.

Addressing this challenge requires not only material innovation but also systematic, quantitative testing at every level of the sodium-ion battery production chain — from raw material incoming inspection through electrode process control to cell-level qualification. This article presents a multi-level sodium-ion battery testing framework that adapts proven lithium-ion battery quality control methodology to the specific material and process characteristics of sodium-ion batteries.

Figure 1. Multi-level sodium-ion battery testing framework: four hierarchical characterization levels from particle mechanical properties through powder physical properties, electrode uniformity, and full-cell electrochemical/mechanical performance.

2. Level 1 — Particle Level: Quantifying Raw Material Mechanical Strength

2.1 Industry Pain Points: Raw Materials Strength Inconsistency & Quantification Challenges

Cathode and anode particle compressive strength is highly variable between sodium-ion battery raw material batches — particles that fracture during slurry mixing or electrode calendering create fine particle fragments that increase electrode resistance, clog separator pores, and reduce active material utilization. Layered oxide cathode particles are particularly vulnerable to mechanical fracture under calendering pressure due to the structural anisotropy of the layered crystal lattice. Hard carbon anode materials present a different challenge: diverse precursors (biomass, resin, petroleum pitch) and varying carbonization conditions produce amorphous carbon structures with wide mechanical property distributions — yet no standardized industry method existed to quantify these differences quantitatively before assembly.

2.2 Solution: Single Particle Mechanical Property Characterization (SPFT Series)

The IEST Single Particle Force Mechanical Property Tester(SPFT Series), developed in accordance with national standard GB/T 43091-2023, provides batch characterization of individual particle crushing strength — enabling incoming material screening before slurry preparation. In sodium-ion battery testing applications:

2.2.1 Layered oxide cathode characterization

Batch screening of raw material particles by compressive strength identifies high-strength candidates that resist fracture during slurry mixing and calendering, eliminating breakage-induced resistance increases before they enter the process. Figure 2 shows crushing strength results for four layered oxide cathode materials, illustrating the measurable strength differences that drive material selection.

Table 2. Single-particle compression test results for four sodium-ion cathode material variants (1# to 4#)

Figure 2. Single particle compressive strength test results for four layered oxide cathode materials (sodium-ion battery).

Test results:

• Comparison of particle crushing force distribution trends: 4# > 3# > 1# > 2#

• Pressure‑displacement curve analysis: The particle fracture phenomenon of the sample is relatively pronounced; the inflection point of the curve corresponds to the particle fracture point.

2.2.2 Hard Carbon Anode Mechanical Property Characterization

Quantifying crushing strength differences from different precursors, carbonization temperatures, and dwell times establishes the mechanical property baseline for each material variant — enabling objective process comparison and supplier qualification based on mechanical data rather than electrochemical testing alone. Table 3 and Figure 3 shows representative single-particle crushing strength measurements for hard carbon anode materials. The results indicate that the SPFT Series effectively evaluates the compressive strength of hard carbon at the single-particle level, enabling optimization of R&D processes.

Table 3. Single-particle crush test results of hard carbon anode materials

Figure 3. Single particle compressive strength characterization of hard carbon anode materials (sodium-ion battery). Variations in precursor source and carbonization conditions produce measurable crushing strength differences that correlate with electrode processing behavior.

3. Level 2 — Powder Level: Sodium Battery Powder Resistivity and Compaction Density

3.1 Industry Pain Points: Moisture Sensitivity & Powder Property Fluctuation Assessment

Layered oxide cathodes for sodium-ion batteries are among the most air-sensitive materials in secondary battery electrochemistry: exposure to moisture and CO₂ causes surface residual alkali formation, crystal structure collapse, and deterioration of electronic conductivity — within hours of air exposure in some compositions. ^[2] This moisture sensitivity makes rigorous incoming material quality control essential, yet consistent batch-level screening protocols were largely absent in early sodium-ion battery production. Hard carbon anode materials from diverse biomass precursors (wood, coconut shell, straw, glucose) exhibit significant inter-batch and inter-supplier variations in carbonization degree and microstructural disorder that directly affect electrode resistivity, compaction behavior, and ultimately cell rate performance — but quantitative screening criteria based on powder resistivity and compaction density have not been widely standardized.

3.2 Solution: PRCD Powder Characterization

The IEST Powder Resistivity and Compaction Density systems(PRCD Series) simultaneously measure powder electronic resistivity, compaction density, and compression/rebound behavior under controlled pressure — providing the quantitative incoming inspection data needed for sodium-ion battery material quality control:

3.2.1 Layered oxide cathode quality control

Figure 4 demonstrates that storage condition (fresh vs. air-exposed vs. humidity-exposed) produces measurable and quantifiable differences in both powder resistivity and compaction density for the same layered oxide material — providing a direct, rapid, non-destructive screening method for detecting moisture and CO₂ degradation before electrode coating.

Figure 4. Resistivity and compaction density curves of layered oxide cathode powder under different storage conditions. Air and humidity exposure significantly increase powder resistivity and reduce compaction density — demonstrating the sensitivity of layered oxide cathodes to moisture and CO₂ and the critical importance of incoming material quality control.

Test results:

• The NFM111 sample stored in a glovebox exhibits an O3-type crystal structure; after storage in air, however, the layered crystal structure has completely collapsed (Figure. 4a).

• The corresponding SEM images (Figure. 4b) show that after storage at RH=80%, residual alkali species form on the surface, cracks appear in the secondary particles, and some secondary particles even disintegrate.

• After air exposure, the powder resistivity of NFM111-air is approximately 2–3 orders of magnitude higher than that of NFM111. This is because layered oxides react with components such as H2O, CO2, and O2 in air upon exposure, generating surface residual alkali species including Na2CO3, NaHCO3, and NaOH (Figure. 4c).

3.2.2 Hard carbon anode incoming inspection

Figure 5 shows powder resistivity and compaction density differentiation across multiple hard carbon anode materials from different sources and carbonization processes, enabling objective batch-to-batch consistency monitoring and supplier qualification screening — directly addressing the hard carbon anode variability that degrades sodium-ion battery yield.

Figure 5. Powder resistivity and compaction density curves for multiple hard carbon anode samples. Differences in resistivity and compaction behavior reflect variations in carbonization degree, precursor source, and microstructural disorder — key incoming quality control metrics for sodium-ion battery hard carbon anodes.

4. Level 3 — Electrode Level: Uniformity Evaluation and Process Defect Detection

The electrode manufacturing stage is the highest-risk process segment for sodium-ion battery quality — defects introduced during slurry preparation, coating, drying, and calendering compound directly into cell performance variability and yield loss. Three complementary electrode-level measurements provide systematic quality control coverage:

4.1 Electronic Conductivity: BER Series Electrode Resistance Testing

Electrode uniformity evaluation for sodium-ion batteries begins with electronic conductivity mapping. The IEST BER series Electrode Resistance Tester measures electrode sheet electronic resistance at defined positions and pressure — enabling spatial identification of process-induced conductivity inhomogeneities arising from slurry mixing inconsistencies, coating weight variation, and calendering pressure non-uniformity. For layered oxide cathodes, BER Series also provides the cross-scale connection between powder-level and electrode-level resistivity: Figure 6 shows that powder resistivity changes from different storage conditions (fresh, air-exposed, humidity-exposed) translate directly and proportionally into electrode sheet resistance changes — enabling upstream defect tracing and confirming that incoming material quality screening at the powder level predicts electrode quality.

Figure 6. Electrode resistivity vs. powder resistivity comparison for layered oxide cathode under different storage conditions. The direct correspondence confirms that incoming powder quality control predicts electrode sheet performance — enabling upstream defect tracing in sodium-ion battery production.

Electrode resistance results show:

• With the addition of conductive additives, the resistivity of the electrode sheets decreases significantly compared with that of the corresponding powder samples.

• For the electrode sheet prepared from NFM111-air stored in air, its resistivity remains lower than that of the electrode sheet made from NFM111, and the change trend of electrode sheet resistivity is consistent with that of powder resistivity.

4.2 Ionic Conductivity and Electrode Tortuosity: EIC Series

Electronic conductivity is necessary but not sufficient for electrode quality characterization — ionic transport capability is equally critical and often more directly limiting for rate performance. For sodium-ion batteries, the intrinsic ionic transport advantage of Na⁺ (larger ionic radius promotes certain diffusion pathways) can only be fully realized when electrode tortuosity is minimized to support efficient electrolyte infiltration and ion transport throughout the electrode thickness.

The IEST EIC series Ion Conductivity and Electrode Tortuosity Testing System measures the MacMullin number (Nm = τ/ε) of electrode sheets via symmetric cell EIS, quantifying the degree to which pore structure complexity restricts ionic transport. This measurement directly captures the effects of disordered pore networks, insufficient electrolyte infiltration, and excessive tortuosity that limit rate capability and energy density. Figure 7 shows tortuosity test results for different hard carbon anode electrode sheets — measurable differences in MacMullin number between electrode variants reflect structural differences in pore connectivity that predict relative rate performance without requiring full cell assembly.

According to Table 4 and Figure 7:

• The two hard carbon electrode samples A and B show good measurement consistency, with COV values below 5%.

• Tortuosity: B > A.

Table 4. Electrode tortuosity characterization results: Hard Carbon Electrode A vs. Electrode B

Figure 7. Tortuosity measurement results for different hard carbon anode electrode sheets (sodium-ion battery). Higher tortuosity (higher MacMullin number) indicates more tortuous ionic transport pathways and restricted electrolyte infiltration — directly limiting rate capability and energy density of sodium-ion battery cells.

4.3 Electrode Flexibility: BEF Series

Electrode flexibility is the third critical dimension of electrode quality — particularly relevant for wound cell formats where insufficient ductility causes electrode fracture, burr formation, and internal short circuit risk during winding and hot pressing. The IEST BEF series Electrode Flexibility Tester simulates actual production winding conditions with cyclic bending tests, quantifying ductility as a function of compaction density and providing a data-driven basis for identifying the calendering pressure window that maximizes packing density without compromising electrode integrity.

Figure 8 shows flexibility test results for different hard carbon electrode variants, demonstrating measurable ductility differences between formulations that are invisible in electronic resistance or tortuosity measurements — but are decisive for production yield in wound sodium-ion battery cells.

Figure 8. Flexibility test results for hard carbon anode electrode sheets (sodium-ion battery). The BEF system simulates production winding and hot-pressing conditions, quantifying electrode ductility and identifying the compaction density window that maintains adequate flexibility to avoid cracking, burr formation, and internal short circuit risk.

As shown in Figure 8:

• Two different hard carbon electrode samples A and B were subjected to flexibility cyclic compression‑lock tests. The residual stress of A is significantly lower than that of B, indicating that the flexibility of A is superior to that of B.

• The flexibility bending test can sensitively reflect surface defects of finished electrode materials. It is commonly employed to study surface strengthening processes and surface properties, and is directly correlated with cell‑level electrochemical performance.

5. Level 4 — Cell Level: Electrochemical Performance and Safety Validation

Sodium-ion battery cells at the cell level present four inter-related failure modes that require dedicated testing approaches: swelling and bulging during charge/discharge; gas generation and voltage decay under high-temperature storage; complex electrochemical side reactions that limit cycle life; and the difficulty of predicting safety and life risks before field deployment. IEST addresses these with three complementary cell-level testing systems:

5.1 In-Situ Swelling Testing (RSS/CBS/SWE Series)

The RSS, CBS, and SWE series In-Situ Cell Swelling Systems provide real-time thickness and swelling force monitoring during charge/discharge cycling for coin cells, stacked cells, and pouch cells respectively — covering all standard sodium-ion battery cell formats. In-situ swelling data captures the dynamic mechanical response of sodium-ion cells during cycling, directly characterizing the electrode volume change and gas-induced swelling behavior that causes pack-level structural problems and safety risk.

5.2 High-Temperature Storage and Gas Generation Testing (GVM/MSG Series)

The GVM and MSG series In-Situ Gas Generation and Volume Monitoring Systems simulate multi-channel high-temperature storage environments, continuously monitoring cell volume, voltage, and gas generation over extended periods. External charge compensation capability allows the system to maintain defined SOC conditions throughout storage — matching real-world calendar aging conditions. Figure 9 shows a representative cyclic gas generation test for sodium-ion battery cells, demonstrating real-time gas volume evolution monitoring that detects electrolyte decomposition and sodium plating events before they escalate to safety-critical failure.

Figure 9. In-situ cyclic gas generation monitoring of sodium-ion battery cells. The GVM system continuously measures gas volume evolution throughout cycling, enabling early detection of electrolyte decomposition, sodium plating, and other parasitic reactions that cause cell swelling, capacity fade, and safety risk.

5.3 Electrochemical Characterization (Resistivity Characterization (ERT Series)

The IEST ERT series Electrochemical Testing System integrates CV (cyclic voltammetry), EIS (electrochemical impedance spectroscopy), GITT (galvanostatic intermittent titration technique), and charge/discharge cycling in a single platform — with voltage and current measurement accuracy of 0.01%. This precision enables detection of the minor electrochemical side reactions and sodium plating events that are characteristic of sodium-ion battery degradation, and supports calculation of Coulombic efficiency, self-discharge rate, and diffusion coefficients — providing the quantitative mechanistic data needed for cycle life optimization and life prediction.

6. Summary: Building Core Technical Strength in Sodium-Ion Battery Manufacturing

Sodium-ion batteries represent a strategically important technology direction for new energy storage, and the manufacturing and material challenges currently limiting commercial scale-up are tractable engineering problems — not fundamental technology barriers. Progress requires a systematic, quantitative approach to quality control at every level of the production chain.

• At the R&D stage: transform empirical process knowledge into quantitative material property data using particle crushing strength, powder resistivity and compaction density, electrode tortuosity, and flexibility measurements — accelerating material iteration and compressing the trial-and-error cycle for sodium-ion battery electrode development.

• At the manufacturing stage: establish multi-level quality checkpoints across the full production flow — incoming material inspection, electrode process control, and cell-level qualification — to intercept defects before they compound into finished cell failures, systematically improving production yield toward the >95% standard achieved by mature lithium-ion battery production lines.

• At the quality management stage: align quality control criteria with national standards (including GB/Z 155–2025 for sodium-ion cathode material compaction density), establish traceable measurement data chains, and build market credibility for sodium-ion battery products through documented, standardized quality evidence.

Key sodium-ion battery testing requirements at each level: (1) particle level — single-particle crushing strength screening by SPFT for layered oxide cathodes and hard carbon anodes; (2) powder level — sodium battery powder resistivity and compaction density measurement by PRCD for incoming material inspection and batch consistency monitoring; (3) electrode level — electronic conductivity (BER) for uniformity evaluation, ionic conductivity and tortuosity (EIC) for rate performance prediction, and flexibility (BEF) for yield risk assessment; (4) cell level — in-situ swelling (RSS/SWE), in-situ gassing (GVM), and electrochemical characterization (ERT with CV/EIS/GITT) for comprehensive cell performance and safety qualification. Multi-level testing at all four scales, applied systematically across R&D, manufacturing, and quality management, provides the measurement foundation to break the “low yield → high cost → slow market” cycle that currently constrains sodium-ion battery commercialization.

7. References

[1] Chen Zhiqiang. Modification research on hard carbon anode materials for sodium-ion batteries. Material Sciences, 2025, 15(01): 106–114. DOI: 10.12677/ms.2025.151013.

[2] Yang Y., Wang Z., Du C., et al. Decoupling the air sensitivity of Na-layered oxides. Science, 385, 744–752 (2024).

[3] Cui J., Rao Y., Gao J. et al. Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes. Nature Communications (2026).

8. FAQs: Sodium-Ion Battery Testing and Characterization

8.1 How is hard carbon anode mechanical property characterized for sodium-ion batteries?

Hard carbon anode mechanical property characterization for sodium-ion batteries is performed using single-particle force testing (SPFT series) in accordance with GB/T 43091-2023. Individual hard carbon particles are subjected to controlled compressive load, and the crushing force at fracture is recorded. Because hard carbon is an amorphous material with disordered turbostratic structure, different precursors (biomass, resin, petroleum pitch) and different carbonization conditions produce particles with measurably different crushing strength — even when visual morphology or BET surface area appear similar. Single-particle crushing strength data enables objective incoming material quality screening: materials with lower crushing strength are more likely to fracture during electrode slurry mixing and calendering, generating fine particles that increase electrode resistance and reduce active material utilization. Batch-level statistical characterization (typically 50–100 particles per sample) provides the mean and distribution data needed for supplier comparison and production process control.

8.2 Why is powder resistivity and compaction density testing critical for sodium-ion battery materials?

Sodium battery powder resistivity and compaction density testing is critical for two distinct reasons at the cathode and anode levels. For layered oxide cathodes: these materials are highly sensitive to moisture and CO₂, which cause surface residual alkali formation, crystal structure collapse, and significant increases in powder resistivity — degrading electrode conductivity before the cell is even assembled. PRCD testing detects this degradation rapidly and non-destructively, enabling incoming material rejection or process adjustment before defective powder is coated into electrodes. For hard carbon anodes: carbonization degree and precursor source directly affect both powder resistivity (electronic conduction through the disordered carbon network) and compaction density (how efficiently particles pack under calendering pressure). PRCD measurement of these two parameters simultaneously enables batch-to-batch consistency monitoring and supplier qualification without electrochemical cycling tests. GB/Z 155–2025 (Sodium-Ion Battery Cathode Material General Specification, issued 2025) has standardized compaction density as a required quality control parameter for sodium-ion cathode materials.

8.3 How is electrode uniformity evaluated in sodium-ion battery production?

Electrode uniformity evaluation for sodium-ion batteries covers three complementary aspects: (1) electronic conductivity uniformity — BER series electrode resistance testing maps the spatial distribution of electronic resistance across the electrode sheet, identifying local high-resistance regions from slurry mixing inconsistencies, coating weight variation, or calendering pressure non-uniformity; (2) ionic conductivity and tortuosity — EIC series symmetric cell EIS measurement determines the MacMullin number (Nm = τ/ε) for each electrode, quantifying the uniformity of pore connectivity and electrolyte access across the electrode thickness; (3) mechanical uniformity — BEF series flexibility testing identifies spatial variations in electrode ductility that predict fracture risk during winding. Multi-parameter electrode uniformity evaluation enables targeted process diagnosis: elevated BER resistance at specific positions points to coating or drying non-uniformity; elevated EIC tortuosity points to calendering-induced pore closure; reduced BEF flexibility at high compaction density defines the maximum safe calendering pressure for each formulation.

8.4 How does electrode tortuosity affect sodium-ion battery performance?

Electrode tortuosity (quantified as the MacMullin number Nm = τ/ε from symmetric cell EIS) directly limits sodium-ion battery rate capability and energy density by determining how efficiently electrolyte can penetrate the electrode pores and how quickly Na⁺ ions can transport through the electrode thickness during charge and discharge. Higher tortuosity means more tortuous ionic transport pathways, increased effective ionic resistance, higher polarization at elevated rates, and reduced practical energy density. Sodium-ion batteries have an intrinsic ionic transport advantage from the weaker Na⁺ solvation compared to Li⁺, but this advantage is only realized when electrode tortuosity is low enough to allow electrolyte to fully infiltrate the electrode pore network. EIC series tortuosity measurement on hard carbon anode electrodes in this study shows measurable MacMullin number differences between electrode variants — confirming that electrode formulation and processing conditions significantly affect tortuosity and therefore rate performance, independently of the intrinsic sodium-ion transport properties of the hard carbon active material.

8.5 How does air exposure affect layered oxide cathode performance in sodium-ion batteries?

Layered oxide cathodes for sodium-ion batteries are significantly more air-sensitive than common lithium-ion cathode materials. Exposure to moisture and CO₂ in ambient air causes multiple degradation mechanisms: surface residual alkali formation (NaOH, Na₂CO₃) that reduces surface conductivity and increases interfacial impedance; interlayer structural collapse from Na⁺/H⁺ exchange with moisture at grain boundaries; and suppression of electronic conduction pathways through the layered transition-metal oxide lattice. PRCD powder testing demonstrates that even short-duration air exposure measurably increases powder resistivity and reduces compaction density for the same layered oxide material. This air sensitivity has important manufacturing implications: layered oxide cathode powders must be stored and processed in dry room environments (<1% RH), and incoming material quality control must include resistivity and compaction density testing to confirm that received materials have not been degraded during shipping and storage before electrode coating. Battery characterization data from BER electrode resistance testing shows that powder-level resistivity changes from air exposure translate directly and proportionally into electrode sheet resistance increases — making powder-level PRCD testing a reliable upstream predictor of electrode-level quality.

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This article was originally published at: Multi-Level Sodium-Ion Battery Testing: A Systematic Characterization Framework From Particle to Battery Cell first appeared on IEST Instrument | World-leading Innovative Lithium Battery Tester Solution Provider

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