Defect Electrocatalysts and Alkaline Electrolyte Membranes in Solid‐State Zinc–Air Batteries: Recent Advances, Challenges, and Future Perspectives
Bibliographic record
Abstract
Rechargeable zinc-air batteries (ZABs) have attracted much attention due to their promising capability for offering high energy density while maintaining a long operational lifetime. One of the biggest challenges in developing all-solid-state ZABs is to design suitable bifunctional air-electrodes, which can efficiently catalyze the key oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) electrochemical processes. The other one is to develop robust electrolyte membranes with high ionic conductivity and superb water retention capability. In this review, an in-depth discussion of the challenges, mechanisms, and design strategies for the defect electrocatalyst and the electrolyte membrane in all-solid-state ZABs will be offered. In particular, the crucial defect engineering strategies to tune the ORR/OER catalysts are summarized, including direct controllable strategies: 1) atomically dispersed metal sites control, 2) vacancy defects control, and 3) lattice-strain control, and the indirect strategies: 4) crystallographic structure control and 5) metal-carbon support interaction control. Moreover, the most recent progress in designing electrolyte membranes, including polyvinyl alcohol-based membranes and gel polymer electrolyte membranes, is presented. Finally, the perspectives are proposed for rational design and fabrication of the desired air electrode and electrolyte membrane to improve the performance and prolong the lifetime of all-solid-state ZABs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".