Recent Advances in Non-Precious Metal Catalysts for Oxygen Reduction Reaction in Fuel Cells
Bibliographic record
Abstract
The oxygen reduction reaction (ORR) is the key reaction at the cathode of proton exchange membrane fuel cells (PEMFCs), but its slow kinetics severely limit cell efficiency and commercialization. Currently, platinum-based catalysts serve as the benchmark for ORR, yet their high cost, scarcity, and susceptibility to poisoning restrict widespread application. Therefore, developing high-performance, low-cost non-precious metal catalysts (NPMCs) has become a core focus in fuel cell research. This review summarizes recent advances in NPMCs for ORR, highlighting transition metal-nitrogen-carbon (M-N-C, M=Fe, Co, Mn, etc.) materials, heteroatom-doped carbon materials, and transition metal oxides, sulfides, and nitrides. The article discusses synthesis strategies, structural features, active site identification, and performance evaluation of these catalysts. Furthermore, challenges related to activity, stability, and scalability are analyzed, and future research directions are proposed to accelerate the practical application of NPMCs in next-generation fuel cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".