Effect of Microstructure on Chemo-Mechanical Damage Evolution in Aluminum Foil Anodes for Lithium-Ion Batteries
Bibliographic record
Abstract
Aluminum-based foil anodes could enable lithium-ion batteries with high energy density comparable to silicon and lithium metal. However, mechanical pulverization and lithium trapping within aluminum tend to cause capacity fading. The complex interplay between these damage modes is not well understood, as well as the role of microstructure on reaction front evolution. Here, we investigate aluminum foils with different compositions and processing conditions to understand how microstructure influences chemo-mechanical degradation. Backscattered electron imaging of ion-milled foil cross sections is used to visualize reaction front evolution and chemo-mechanical degradation. We find that the shape of the reaction front during lithiation is strongly affected by foil composition, defect distribution, and grain size, which, in turn, affects the evolution of chemo-mechanical damage within the foils. Furthermore, a key finding is that the extent of fracture upon delithiation is inversely related to lithium trapping, with foil compositions that exhibit uniform lithiation, and therefore, less fracturing showing greater lithium trapping. Nonreactive precipitate inclusions within alloy foils are also found to induce void formation. This work shows that material design strategies designed to overcome the inverse fracture-lithium trapping relationship could be useful for improving performance. Published by the American Physical Society 2024
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".