Computational Design of PtM (M = Au, Ir, Pd, Rh, and Ru) Binary Alloys for Enhanced Ammonia Oxidation Electrocatalysis
Bibliographic record
Abstract
The electrochemical ammonia oxidation reaction (AOR) shows considerable potential for its applications in waste removal and the production of clean energy. While Pt remains the most investigated catalyst for this reaction, its limitations have prompted research into Pt‐based bimetallic alloys. This study investigates both uniform and mixed PtM (M = Au, Ir, Pd, Rh, and Ru) alloys as catalysts for the AOR using density functional theory (DFT). A systematic selection method is used to choose suitable surfaces for testing. The findings indicate that the Oswin–Salomon mechanism is preferred across all surfaces for N 2 (g) formation. Additionally, the results demonstrate that mixed alloys exhibit superior catalytic activity compared to uniform alloys for the AOR. It is found that the atoms in the topmost layer of the alloy are the most significant factor in influencing catalytic activity. Furthermore, the linear relationship between the ‐band center and adsorption energy of key intermediate *NH 2 is confirmed in this work, highlighting the effect of the secondary metal on the electronic structure of the catalyst. The findings provide theoretical insights for the design of high‐performance Pt alloys for AOR and serve as a general guideline for modulating the reactivity of binary alloys for electrocatalysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".