Deciphering the Interplay Between Wetting and Chemo‐Mechanical Fracture in Lithium‐Ion Battery Cathode Materials
Bibliographic record
Abstract
Abstract Crack growth in lithium‐ion battery electrodes is typically detrimental and undesirable. However, recent experiments suggest that stabilized fracture of cathode active materials in liquid electrolytes can increase electrochemically active surfaces, shorten diffusion pathway, enhance (de)lithiation, and improve overall capacity. To decipher the fundamental couplings between electrolyte wetting and fracture evolution and evaluate their influences on macroscopic battery performance, an integrated experiment‐simulation study is conducted on α‐V 2 O 5 single crystals and polycrystalline NCM as model cathode materials. Single crystals of α‐V 2 O 5 offers clearer fundamental insights than polycrystalline counterparts with grain‐boundary complexities. Fracture patterns and lithiation heterogeneities on the samples are mapped using X‐ray spectromicroscopy techniques after chemical (de)lithiation cycles, exhibiting excellent agreement with simulations by the developed multiphysics model. Results reveal a mutually reinforcing interplay between wetting and fracture: i) electrolyte infiltration at fracture surfaces enhances (de)lithiation and compositional heterogeneity; ii) wetting influences fracture dynamics, including fracture modes, propagation distance, and directionality. The validated modelling framework is further applied to simulations on polycrystalline NCM particles under constant‐current (dis)charging, highlighting the critical role of wetting in promoting fracture and improving overall capacity. This work bridges fundamental understanding of wetting–fracture coupling with practical implications for battery performance optimization via controlled fracture engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".