Nanostructured Pt-alloy electrocatalysts for PEM fuel cell oxygen reduction reaction
Bibliographic record
Abstract
In this critical review, we present the current technological advances in proton exchange membrane (PEM) fuel cell catalysis, with a focus on strategies for developing nanostructured Pt-alloys as electrocatalysts for the oxygen reduction reaction (ORR). The achievements are reviewed and the major challenges, including high cost, insufficient activity and low stability, are addressed and discussed. The nanostructured Pt-alloy catalysts can be grouped into different clusters: (i) Pt-alloy nanoparticles, (ii) Pt-alloy nanotextures such as Pt-skins/monolayers on top of base metals, and (iii) branched or anisotropic elongated Pt or Pt-alloy nanostructures. Although some Pt-alloy catalysts with advanced nanostructures have shown remarkable activity levels, the dissolution of metals, including Pt and alloyed base metals, in a fuel cell operating environment could cause catalyst degradation, and still remains an issue. Another concern may be low retention of the nanostructure of the active catalyst during fuel cell operation. To facilitate further efforts in new catalyst development, several research directions are also proposed in this paper (130 references).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".