Computational Design of Pt-M (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 sources. While platinum remains the most investigated catalyst for this reaction, its limitations have prompted research into platinum-based bimetallic alloys. This study investigates both uniform and mixed Pt-M (M = Au, Ir, Pd, Rh, and Ru) alloys as catalysts for the AOR using density functional theory (DFT).We use a systematic surface selection method defined in a previous study to choose suitable surfaces for testing which involves evaluating pairwise interactions, considering configurational entropy, and validation with DFT. Our findings indicate that the Oswin-Salomon mechanism is preferred across all surfaces for N2(g) formation. Additionally, our results demonstrate that mixed alloys exhibit superior catalytic activity compared to uniform alloys for the AOR, which supports the conclusion that the atoms comprising the surface layer of the alloy are the most significant factor in influencing catalytic activity. Furthermore, the linear relationship between the d-band centre and adsorption energy of key intermediate *NH2 was confirmed in this work, highlighting the influence of the secondary metal on the electronic structure of the catalyst. Our findings pro- vide 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".