Dynamic Lithium Transport Pathway via Crack Formation in Phase-Separating Battery Particles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Nanoscale cracks within battery particles are ubiquitously induced during battery cycling. Tracking the origin of nanocrack formation and its subsequent propagation remains challenging, although it is crucial for the cycle life and kinetics of batteries. Moreover, it is even more challenging to understand how such nanocracks influence lithium (de)insertion pathways and local strain fields within battery particles. In this study, we utilized operando scanning transmission X-ray microscopy on individual LiFePO 4 (LFP) particles to visualize the relationship between lithium (de)insertion pathways and crack formation and propagation. We first demonstrate the generation mechanism of nanocracks occurs when the lithium insertion pathway at the edge of fresh LFP particles induces strong tensile stress in the middle of the particle. Then, we directly observe the nanocrack propagation mechanism, where the freshly exposed surface near the crack activates a fast lithium (de)insertion pathway, completely altering the internal stress fields near the nanocrack. Once the nanocrack transforms the dynamic lithium pathway and distribution, the delithiation process induces crack-opening tensile stress, while the lithiation process generates crack-closing compressive stress. 3D phase-field simulations support these observations, showing how dynamic lithium distribution shapes stress fields. Our findings reveal a recursive chemo-mechanical loop involving lithium (de)insertion pathways, internal stress fields, and crack development.
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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) | 0.002 | 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".