Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
Bibliographic record
Abstract
Polymer membranes play a vital role in vanadium redox flow batteries (VRFBs), acting as a separator between the two compartments, an electronic insulator for maintaining electrical neutrality of the cell, and an ionic conductor for allowing the transport of ionic charge carriers. It is a major influencer of VRFB performance, but also identified as one of the major factors limiting the large-scale implementation of VRFB technology in energy storage applications due to its cost and durability. In this work, five (5) high-priority characteristics of membranes related to VRFB performance were selected as major considerable factors for membrane screening before in-situ testing. Eight (8) state-of-the-art of commercially available ion exchange membranes (IEMs) were specifically selected, evaluated and compared by a set of ex-situ assessment approaches to determine the possibility of the membranes applied for VRFB. The results recommend perfluorosulfonic acid (PFSA) membranes and hydrocarbon anion exchange membranes (AEMs) as the candidates for further in-situ testing, while one hydrocarbon cation exchange membrane (CEM) is not recommended for VRFB application due to its relatively high VO2+ ion crossover and low mechanical stability during/after the chemical stability test. This work could provide VRFB researchers and industry a valuable reference for selecting the polymer membrane materials before VRFB in-situ testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".