Regulation Strategies for Fe−N−C and Co−N−C Catalysts for the Oxygen Reduction Reaction
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) and alkaline membrane fuel cells (AEMFCs) have received great attention as energy devices of the next generation. Accelerating oxygen reduction reaction (ORR) kinetics is the key to improve PEMFC and AEMFC performance. Platinum-based catalysts are the most widely used catalysts for the ORR, but their high price and low abundance limit the commercialization of fuel cells. Non-noble metal-nitrogen-carbon (M-N-C) is considered to be the most likely material class to replace Pt-based catalysts, among which Fe-N-C and Co-N-C have been widely studied due to their excellent intrinsic ORR performance and have made great progress in the past decades. With the improvement of synthesis technology and a deeper understanding of the ORR mechanism, some reported Fe-N-C and Co-N-C catalysts have shown excellent ORR activity close to that of commercial Pt/C catalysts. Inspired by the progress, regulation strategies for Fe-N-C and Co-N-C catalysts are summarized in this Review from 5 perspectives: (1) coordinated atoms, (2) environmental heteroatoms and defects, (3) dual-metal active sites, (4) metal-based particle promoters, and (5) curved carbon layers. We also make suggestions on some challenges facing Fe-N-C and Co-N-C research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".