Eutectic Electrolytes Chemistry for Rechargeable Zn Batteries
Bibliographic record
Abstract
Rechargeable zinc batteries (RZBs) have proved to be promising candidates as an alternative to lithium-ion batteries due to their low cost, inherent safety, and environmentally benign features. While designing cost-effective electrolyte systems with excellent compatibility with electrode materials, high energy/power density as well as long life-span challenge their further application as grid-scale energy storage devices. Eutectic electrolytes as a novel class of electrolytes have been extensively reported and explored taking advantage of their feasible preparation and high tunability. Recently, some perspectives have summarized the development and application of eutectic electrolytes in metal-based batteries, but their infancy requires further attention and discussion. This review systematically presents the fundamentals and definitions of eutectic electrolytes. Besides, a specific classification of eutectic electrolytes and their recent progress and performance on RZB fields are introduced as well. Significantly, the impacts of various composing eutectic systems are disserted for critical RZB chemistries including attractive features at electrolyte/electrode interfaces and ions/charges transport kinetics. The remaining challenges and proposed perspectives are ultimately induced, which deliver opportunities and offer practical guidance for the novel design of advanced eutectic electrolytes for superior RZB scenarios.
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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 | 0.002 | 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) | 0.004 | 0.002 |
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".