An Improved Hydroxide Conversion Process of Anionic Exchange Membranes for Alka-line Fuel Cells
Bibliographic record
Abstract
Robust anionic exchange membranes (AEMs) are needed for alkaline fuel cells (AFCs) [1].To convert the as-produced AEMs to hydroxide form, conventionally an one step high alkalinity 1-2M alkaline solution for 24 -48hrs is used [1].However, this high alkalinity process will be shown to limit the ion exchange capacity and reduce long term viability of the AEMs.To investigate the degradation process, short-term activation and longterm stability of 3 AEMs were studied.Short-term degradation was found to be caused by the high concentration of hydroxide ions in the initial conversion process.Long-term degradation was found to be caused by the substituted hydroxide ions which caused gradual loss of conducting species from the membrane.A modification of the conventional one step high alkalinity process was developed to mitigate this membrane degradation.This modified process used multi-step low alkalinity conversion stages.Three types of commercial AEM products from Fumatech (FAS-PP-75, FAA-3-PK-130, and FAD-55) were compared with both the conventional and modified processes.The results showed that the multistep low alkalinity process improved the initial membrane performances by at least 20%.The long-term stability was substantially enhanced for FAS-PP-75 and FAA-3-PK-130.
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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) | 0.000 | 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 | 0.001 |
| 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".