Development of organic redox‐active materials in aqueous flow batteries: Current strategies and future perspectives
Bibliographic record
Abstract
Abstract Aqueous redox flow batteries, by using redox‐active molecules dissolved in nonflammable water solutions as electrolytes, are a promising technology for grid‐scale energy storage. Organic redox‐active materials offer a new opportunity for the construction of advanced flow batteries due to their advantages of potentially low cost, extensive structural diversity, tunable electrochemical properties, and high natural abundance. In this review, we present the emergence and development of organic redox‐active materials for aqueous organic redox flow batteries (AORFBs), in particular, molecular engineering concepts and strategies of organic redox‐active molecules. The typical design strategies based on organic redox species for high‐capacity, high‐stability, and high‐voltage AORFBs are outlined and discussed. Molecular engineering of organic redox‐active molecules for high aqueous solubility, high chemical/electrochemical stability, and multiple electron numbers as well as satisfactory redox potential gap between the redox pair is essential to realizing high‐performance AORFBs. Beyond molecular engineering, the redox‐targeting strategy is an effective way to obtain high‐capacity AORFBs. We further discuss and analyze the redox reaction mechanisms of organic redox species based on a series of electrochemical and spectroscopic approaches, and succinctly summarize the capacity degradation mechanisms of AORFBs. Furthermore, the current challenges, opportunities, and future directions of organic redox‐active materials for AORFBs are presented in detail.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".