Suppressing Corrosion of Aluminum Foils via Highly Conductive Graphene-like Carbon Coating in High-Performance Lithium-Based Batteries
Bibliographic record
Abstract
Aluminum foil is the predominant cathodic current collector in lithium-based batteries due to the high electronic conductivity, stable chemical/electrochemical properties, low density, and low cost. However, with the development of next-generation lithium batteries, Al current collectors face new challenges, such as the requirement of increased chemical stability at high voltage, long-cycle-life batteries with different electrolyte systems, as well as improved electronic conductivity and adhesion for new electrode materials. In this study, we demonstrate a novel graphene-like carbon (GLC) coating on the Al foil in lithium-based batteries. Various physical and electrochemical characterizations are conducted to reveal the electronic conductivity and electrochemical stability of the GLC-Al foil in both carbonate- and ether-based electrolytes. Full-cell tests, including Li–S batteries and high-voltage Li-ion batteries, are performed to demonstrate the significantly improved cycling and rate performance of batteries with the use of the GLC-Al foil as current collectors. The cell using the GLC-Al foil can greatly reduce the potential polarization in Li–S batteries and can obtain a reversible capacity of 750 mAh g –1 over 100 cycles at 0.5C. Even with high-sulfur-loading cathodes, the Li–S battery at 1C still maintains over 500 mAh g –1 after 100 cycles. In high-voltage Li-ion batteries, the GLC-Al foil significantly improves the high-rate performance, showing an increased retained capacity by over 100 mAh g –1 after 450 cycles at 1C compared to the bare foil. It is believed that the developed GLC-Al foil brings new opportunities to enhance the battery life of lithium-based batteries.
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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) | 0.001 | 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".