Self-adjusting anode catalyst layer for smart water management in anion exchange membrane fuel cells
Bibliographic record
Abstract
Water management in the anode catalyst layer (ACL) during fuel cell operation is of importance to the performance of anion exchange membrane fuel cells (AEMFCs). Until now, only a few methods, such as controlling external conditions and adjusting other components like the gas diffusion layer (GDL), attempt to indirectly regulate water content within the ACL. Here, we report a self-regulating ACL having stratified, gradient pore sizes from 8 nm (membrane side) to 150 nm (GDL side). Self-regulating moisture behavior was observed for the gradient ACL, constituted individually of Pt/C layer and PtRu/C layer, which was distinctly different than that from catalyst layers (CLs) composed of homogeneous catalysts (Pt/C CL, PtRu/C CL, and mixed CL). The results of this study are anticipated to give an insight into the design of CLs with smart structures for improving AEMFC performance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".