An Overview of Commercial and Non‐commercial Anion Exchange Membranes
Bibliographic record
Abstract
Anion exchange membrane fuel cells (AEMFCs) have received increasing attention in recent years, as they allow for the application of inexpensive anion exchange membranes (AEMs) and non-precious metal catalysts, which could greatly reduce the cost of devices. However, due to limitations of key materials such as AEMs, the development of AEMFCs is slow. Therefore, in the past few years, researchers have been committed to developing high-performance AEMs. In order to better understand the research progress and development trend of AEMs, in this chapter, first, the characteristics and existing problems of the commercial membranes (such as A201 [Japan], Fumasep ® FAA3 [Germany], AEMION™ [Canada] et al.) have been summarized. Then, in view of the problems of commercial AEMs, the content and characteristics of current research on non-commercial AEMs have been clarified by comparing commercial and non-commercial AEMs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".