Effect of Cerium Chloride on the Self-Corrosion and Discharge Activity of Aluminum Anode in Alkaline Aluminum-air Batteries
Bibliographic record
Abstract
One of the key impediments to aluminum (Al) as an anode in alkaline Al-air batteries is self-corrosion, which limits the battery’s efficiency due to the capacity loss and lifespan reduction. Thus, it is vital to find an efficient electrolyte additive that reduces self-corrosion in Al anodes. In this study, the effect of adding 0.5 to 1.5 wt% of cerium chloride to 4 mol l−1 KOH electrolyte on the self-corrosion of pure Al anode was investigated using electrochemical experiments. The results show that the addition of cerium chloride to the electrolyte reduces self-corrosion of the Al anode with a negligible effect on the anode activity. Cerium chloride forms cerium hydroxide (Ce (OH)3) in the alkaline electrolyte, which is adsorbed on the Al surface. Therefore, the corrosion potential increased, and self-corrosion current density decreased. As the cerium chloride concentration increased, the Al anode efficiency increased from 43.8% to 76.1%, and the capacity density increased from 1294 to 2244 mAh g−1. Furthermore, increasing the immersion time of the Al anode in the electrolyte containing cerium chloride increased the self-corrosion resistance and provided the self-healing properties for the anode.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".