Materials Engineering toward Durable Electrocatalysts for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Abstract Proton exchange membrane fuel cells (PEMFCs) have penetrated many commercial markets, especially in the automotive market as Toyota has launched the first commercially mass‐produced fuel cell vehicle, the Mirai in 2014. Electrocatalysts play an irreplaceable role in determining the PEMFCs, performance and account for half of the total cost. Despite substantial progress in exploiting highly active platinum group metal (PGM) and PGM‐free electrocatalysts, current electrocatalysts are faced with significant durability challenges, i.e., high‐performance electrocatalysts usually suffer from rapid degradation during PEMFC operation. The lifetime of the reported electrocatalysts is far from the requirement of performing steadily over the 8000 h of operation in commercialized PEMFCs. To this end, addressing the durability issues of oxygen reduction reaction (ORR) electrocatalysts is imperative for their practical employment in PEMFCs. Herein, the state‐of‐the‐art advances in understanding the durability issues of PGM and PGM‐free catalysts for ORR under fuel cell conditions and the materials engineering strategies to tackle these issues are summarized. The insights into the durability issues, involving the degradation mechanisms and the impact of operation conditions are reviewed. Establishing strategies to mitigate catalyst degradation through rational design of stable PGM and PGM‐free catalysts is highlighted.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".