A Comparative Study of State-of-the-Art Commercial Membranes in an Anion Exchange Membrane Water Electrolyzer
Bibliographic record
Abstract
Anion exchange membrane water electrolysis (AEM-WE) is an emerging and highly promising technology for the production of green hydrogen using renewable energy sources. This technology combines the strengths of proton exchange membrane water electrolysis (PEM-WE) and traditional alkaline water electrolysis (AEW), through the use of a solid ion-conducting membrane and cost-effective transition metal catalysts. However, research on AEM-WE generally has lagged behind that of PEM-WE, as AEMs have historically suffered poor stability in harsh alkaline conditions. Although several state-of-the-art AEMs are now commercially available, there remains a lack of benchmark materials analogous to industry standard PEM, Nafion TM . Furthermore, the absence of standardized testing conditions and protocols limits meaningful comparisons between studies and often metrics, such as hydrogen gas crossover, are unreported. To address these challenges, it is essential to establish harmonized evaluation methods and consistent diagnostic tools to determine the properties and key requirements of membranes that influence AEM-WE performance and durability. In this study, we have characterized commercially available AEMs using ex-situ and in-situ techniques to enable meaningful comparisons of membrane performance, durability and hydrogen crossover. Our findings provide valuable insights for both industry and academia, guiding the selection, design, and development of next-generation AEMs for green hydrogen production.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".