New Interpretation of the Performance of Nickel-Based Air Electrodes for Rechargeable Zinc–Air Batteries
Bibliographic record
Abstract
Rechargeable zinc–air batteries with high energy density, cycle life, and calendar life require corrosion-resistant support materials in the air electrode. Nickel-based air electrodes have shown promise in this regard as a substitute for conventional carbon-based air electrodes, but their performance in zinc–air batteries has not been studied in-depth. Specifically, the effect of the nickel (oxy)hydroxide passivating film on the electrode’s catalytic performance and durability requires investigation. To fill this research gap, a method involving electrochemical estimation of the nickel (oxy)hydroxide film capacity was used to link the growth of the film to performance losses experienced on the air electrode after battery cycling. The main cause of voltage loss was the nickel (oxy)hydroxide film growing overtop of and inside the catalyst-coated nickel aggregates. This resulted in significant activation and mass transfer losses, where the latter losses were caused by the film growing overtop of the catalyst and accounted for at least 65% of the total voltage degradation at 10 mA cm –2 . Potential modifications to the electrode structure which could mitigate these voltage losses are discussed, including reducing the nickel particle aggregate size, using high-aspect ratio catalysts, and physically separating the catalyst and nickel particles with nonfilm-forming conductive additives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".