Aqueous Zn‐based rechargeable batteries: Recent progress and future perspectives
Bibliographic record
Abstract
Abstract Benefiting from the advantageous features of high safety, abundant reserves, low cost, and high energy density, aqueous Zn‐based rechargeable batteries (AZBs) have received extensive attention as promising candidates for energy storage. To achieve high‐performance AZBs with high reversibility and energy density, great efforts have been devoted to overcoming their drawbacks by focusing on the modification of electrode materials and electrolytes. Based on different cathode materials and aqueous electrolytes, the development of aqueous AZBs with different redox mechanisms are discussed in this review, including insertion/extraction chemistries (e.g., Zn2+, alkali metal ion, H+, NH4+, and so forth) dissolution/deposition reactions (e.g., MnO2/Mn2+), redox couples in flow batteries (e.g., I3−/3I−, Br2/Br−, and so forth), oxygen electrochemistry (e.g., O2/OH−, O2/O22−), and carbon dioxide electrochemistry (e.g., CO2/CO, CO2/HCOOH). In particular, the basic reaction mechanisms, issues with the Zn electrode, aqueous electrolytes, and cathode materials as well as their design strategies are systematically reviewed. Finally, the remaining challenges faced by AZBs are summarized, and perspectives for further investigations are proposed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".