Strategies for Engineering High‐Performance PGM‐Free Catalysts toward Oxygen Reduction and Evolution Reactions
Bibliographic record
Abstract
Abstract The worldwide fossil fuel shortage and resultant environmental issues urgently require renewable and clean energy technologies. Electrocatalytic oxygen reduction/evolution reactions (ORR/OER) are the cornerstone for renewable energy conversion and storage devices, such as fuel cells, electrolyzers, unitized regenerative fuel cells, and metal‐air batteries. High‐performance electrocatalysts are required to improve the ORR and OER activity and stability, and thus the device performance. Therefore, appropriate strategies and methods are crucial for the rational design and synthesis of highly efficient ORR/OER electrocatalysts. On the other hand, the conventional platinum‐group‐metal‐based (PGM‐based) catalysts, such as Pt and Ir/Ru (oxides), have been facing great challenges, including limited resources and high cost, leading to them being less competitive in the market. Thus, a lot of effort has been devoted to developing alternative PGM‐free ORR/OER catalysts, which, however, still suffer from low activity and insufficient stability. In this review paper, the strategies for engineering high‐performance PGM‐free ORR and OER electrocatalysts are discussed by reviewing the most recent advances. At the end, perspectives on the methods to rationally design PGM‐free ORR and OER catalysts are provided.
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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 | 0.001 |
| 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".