A review on non-precious metal electrocatalysts for PEM fuel cells
Bibliographic record
Abstract
With the approaching commercialization of PEM fuel cell technology, developing active, inexpensive non-precious metal ORR catalyst materials to replace currently used Pt-based catalysts is a necessary and essential requirement in order to reduce the overall system cost. This review paper highlights the progress made over the past 40 years with a detailed discussion of recent works in the area of non-precious metal electrocatalysts for oxygen reduction reaction, a necessary reaction at the PEM fuel cell cathode. Several important kinds of unsupported or carbon supported non-precious metal electrocatalysts for ORR are reviewed, including non-pyrolyzed and pyrolyzed transition metal nitrogen-containing complexes, conductive polymer-based catalysts, transition metal chalcogenides, metal oxides/carbides/nitrides/oxynitrides/carbonitrides, and enzymatic compounds. Among these candidates, pyrolyzed transition metal nitrogen-containing complexes supported on carbon materials (M–Nx/C) are considered the most promising ORR catalysts because they have demonstrated some ORR activity and stability close to that of commercially available Pt/C catalysts. Although great progress has been achieved in this area of research and development, there are still some challenges in both their ORR activity and stability. Regarding the ORR activity, the actual volumetric activity of the most active non-precious metal catalyst is still well below the DOE 2015 target. Regarding the ORR stability, stability tests are generally run at low current densities or low power levels, and the lifetime is far shorter than targets set by DOE. Therefore, improving both the ORR activity and stability are the major short and long term focuses of non-precious metal catalyst research and development. Based on the results achieved in this area, several future research directions are also proposed and discussed in this paper.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 0.003 |
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".