Benchmarking the Degradation Behavior of Aluminum Foil Anodes for Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Aluminum is an attractive candidate for replacing graphite anodes in lithium‐ion batteries because of its high specific capacity and the potential for direct use as foil. However, achieving reversible reaction of aluminum is challenging due to volume changes, SEI formation, and sluggish ion transport. Although prior work has investigated electrochemical transformation behavior of aluminum, the effects of key variables, including areal capacity per cycle and alloy composition, are not well understood. Here, we carry out comprehensive electrochemical testing to benchmark the performance of two different aluminum foils (99.999 % Al and Al 8111). We find that for constant thickness, both foil compositions exhibit a power‐law dependence of cycle life on the lithiated areal capacity per cycle, revealing that degradation is significantly more rapid at higher areal capacities. This behavior is interpreted as an “electrochemical fatigue” mechanism, in analogy to mechanical fatigue. Additionally, the alloy composition was found to strongly affect the Coulombic efficiency (CE), with high‐purity foils exhibiting higher initial CE but reduced long‐term stability. Finally, operando optical microscopy revealed different spatiotemporal reaction mechanisms amongst the different materials. This improved understanding of aluminum foil anodes paves the way for efforts to engineer aluminum‐based foils with enhanced stability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".