Recent progress on mechanisms, principles, and strategies for high‐activity and high‐stability non‐PGM fuel cell catalyst design
Bibliographic record
Abstract
Abstract The commercialization of a polymer membrane H2–O2 fuel cell and its widespread use call for the development of cost‐effective oxygen reduction reaction (ORR) nonplatinum group metal (NPGM) catalysts. Nevertheless, to meet the requests for the real‐world fuel cell application and replacing platinum catalysts, it still needs to address some challenges for NPGM catalysts regarding the sluggish ORR kinetics in the cathode and their poor durability in acidic environment. In response to these issues, numerous efforts have been made to study NPGM catalysts both theoretically and experimentally, developed these into the atomically dispersed coordinated metal–nitrogen–carbon (M–N–C) form over the past decades. In this review, we present a comprehensive summary of recent advancements on NPGM catalysts with high activity and durability. Catalyst design strategies in terms of optimizing active‐site density and enhancing catalyst stability against demetalization and carbon corrosion are highlighted. It is also emphasized the importance of understanding the mechanisms and principles behind those strategies through a combination of theoretical modeling and experimental work. Especially, further understanding the mechanisms related to the active‐site structure and the formation process of the single‐atom active site under pyrolysis conditions is critical for active‐site engineering. Optimizing the active‐site distance is the basic principle for improving catalyst activity through increasing the catalyst active‐site density. Theoretical studies for the catalyst deactivation mechanism and modeling stable active‐site structures provide both mechanisms and principles to improve the NPGM catalyst durability. Finally, currently remained challenges and perspectives in the future on designing high‐performance atomically dispersed NPGM catalysts toward fuel cell application are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".