Recent Progress in Electrically Rechargeable Zinc–Air Batteries
Bibliographic record
Abstract
Over the past decade, the surging interest for higher-energy-density, cheaper, and safer battery technology has spurred tremendous research efforts in the development of improved rechargeable zinc-air batteries. Current zinc-air batteries suffer from poor energy efficiency and cycle life, owing mainly to the poor rechargeability of zinc and air electrodes. To achieve high utilization and cyclability in the zinc anode, construction of conductive porous framework through elegant optimization strategies and adaptation of alternate active material are employed. Equally, there is a need to design new and improved bifunctional oxygen catalysts with high activity and stability to increase battery energy efficiency and lifetime. Efforts to engineer catalyst materials to increase the reactivity and/or number of bifunctional active sites are effective for improving air electrode performance. Here, recent key advances in material development for rechargeable zinc-air batteries are described. By improving fundamental understanding of materials properties relevant to the rechargeable zinc and air electrodes, zinc-air batteries will be able to make a significant impact on the future energy storage for electric vehicle application. To conclude, a brief discussion on noteworthy concepts of advanced electrode and electrolyte systems that are beyond the current state-of-the-art zinc-air battery chemistry, is presented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".