Redox Flow Battery Membranes: Improving Battery Performance by Leveraging Structure–Property Relationships
Bibliographic record
Abstract
Membranes are a critical component of redox flow batteries (RFBs), and their major purpose is to keep the redox-active species in the two half cells separate and allow the passage of charge-balancing ions. Despite significant performance enhancements in RFB membranes, further developments are still needed that holistically consider conductivity, selectivity, stability, sustainability, and cost. In this Focus Review, structure–property relationships that have led to advances in membranes for various RFB types (vanadium, zinc, iron, etc. ) are analyzed. First, two strategies to increase conductivity are highlighted: tuning membrane microstructure and controlling electrolyte uptake. Next, selectivity improvements through size and/or Donnan exclusion are reviewed. With respect to stability, methods to enhance the mechanical robustness of membranes and factors that affect chemical stability are discussed. Additionally, avenues to reduce battery cost and increase sustainability are explored. Future directions are suggested, which include how more in-depth theoretical studies, microstructure optimization, and enhanced characterization will push the field of RFB membranes forward.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".