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 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) | 0.001 | 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 | 0.001 | 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".