Golden Single‐Atom Alloys Selectively Boosting Oxygen Reduction and Methanol Oxidation
Bibliographic record
Abstract
Abstract Engineering electrocatalysts at a single‐atomic site can enable unprecedented atomic utilization and catalytic activity, yet it remains challenging in multimetallic active centers to simultaneously achieve high catalytic selectivity and stability. Herein, the atomic design and control of golden single‐atom alloys (PdAu 1 and PtAu 1 SAAs) based on fully ordered PdBi and PtBi matrixes is presented, serving as highly selective, active, and stable cathode and anode electrocatalysts, respectively, to trigger direct methanol fuel cell (DMFC). The octahedral PdAu 1 SAA exhibits ultrahigh mass‐activity of 5.37 A mg Pd + Au −1 without noticeable decay for 12 0000 cycles toward oxygen reduction. While PdAu 1 SAA is inactive for methanol oxidation, PtAu 1 SAA exhibits an ultrahigh mass‐activity of 28.59 A mg Pt + Au −1 . The selective electrocatalysts drive a practical DMFC with a high‐power density of 155.0 mW cm −2 . Density functional theory calculations reveal the desired regulation of selectivity via reducing the energy barrier for potential‐determining steps (PDS) of * OH to H 2 O and * HCOO to CO 2 . This work provides a general strategy to engineer multimetallic alloys at the atomic level, advancing the development of high‐performance electrocatalysts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".