High‐Performance Aluminum Ion Battery Using Cost‐Effective AlCl<sub>3</sub>‐Trimethylamine Hydrochloride Ionic Liquid Electrolyte
Bibliographic record
Abstract
Abstract The critical need for cost‐effective and sustainable large‐scale battery technologies for harvesting renewable energy has led to a new research wave on novel batteries made of low‐cost, high‐abundance, high‐performance, and safe components. Among the emerging candidates for post‐lithium‐ion batteries, aluminum‐based batteries are particularly promising due to the high theoretical capacities, low cost, and high abundance of raw materials. Most advanced nonaqueous rechargeable Al batteries rely on costly dialkylimidazolium chloride‐based chloroaluminate ionic liquids and this added cost inevitably diminishes various benefits of utilizing Al as the anode material. Here, a high‐performance Al battery made of Al anode, graphene nanoplatelets (GNPs) cathode, and a cost‐effective AlCl3‐trimethylamine hydrochloride (AlCl3‐TMAHCl) ionic liquid electrolyte is reported. The battery delivers a high specific capacity of 134 mAh g−1 at 2000 mA g−1 while maintaining Coulombic efficiency (CE) above 98% over 3000 cycles. Moreover, it delivers a specific capacity of 83 mAh g−1 with a CE of 97% under ultrafast charging at 4000 mA g−1 (1 min) and slow discharging at 100 mA g−1 (50 min) conditions. Considering the low cost and high performance, AlCl3‐TMAHCl electrolyte opens up a new avenue for the development of next‐generation Al 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) | 0.000 | 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 | 0.001 |
| 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".