Redox-active molecules for aqueous electrolytes of energy storage devices: A review on fundamental aspects, current progress, and prospects
Bibliographic record
Abstract
The increasing demand for aqueous energy storage (AES) solutions with high energy density , enlarged voltage windows, and extended cycling stability has spurred the development of advanced electrolytes. Redox-active molecules hold the promise for formulating aqueous electrolytes with enhanced electrochemical performance . In this review, we provide a comprehensive overview of established and recently reported studies on redox electrolytes for AES devices. Delving into mechanisms at both molecular and micrometer scales, this review covers the fundamental principles governing the electrolytes, encompassing their physicochemical properties, ion solvation behavior, interfacial modulation, and transport mechanisms. We present an overview of the redox properties of various compounds from different families. While irreversible electron/mass transfer processes can facilitate the passivation of solid electrolyte interfaces, particular attention is given to the reversible redox electrolyte in enhancing the energy performance of AES systems. Redox-active molecules are categorized based on their ability to improve the cycling stability of electrodes, increase the voltage windows of electrolytes, and enhance the energy density of cells. High solubility and reversible redox behavior have been achieved via the molecular design. Trade-offs between the shuttling effect and electrolyte modification as well as controversies on molecular solubility are discussed. By examining these aspects, the review aims to stimulate advanced research in redox-active molecules for AES technologies.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.002 |
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".