Epitaxially Grown Pt<sub>x</sub>Ir<sub>100-X</sub> Alloys with Enhanced Properties for Ammonia Electro-Oxidation
Bibliographic record
Abstract
Ammonia is a highly toxic gas, a major environmental pollutant, and a potential fuel for fuel cells. Thus the electro-oxidation of NH3 has important applications in NH3 safety sensors, NH3 decontamination in wastewater, and power production. However, on most electrode surfaces, the electro-oxidation of NH3 is plagued with low surface reactivity and surface contamination by reaction intermediates. The vast majority of studies on the NH3 oxidation reaction have been performed on Pt electrocatalysts, and Pt remains the electrocatalyst which shows the highest current density at the lowest overpotential for catalyzing the oxidation of NH3 to N2. On Pt, the oxidation of ammonia occurs predominantly on (100) terraces. However, to the best of our knowledge, the effect of strain in the outmost metal layers and hybridization of the Pt d-states with Ir atoms (ligand effect) on the electro-oxidation of NH3 have never been studied. Accordingly, we have investigated the epitaxial growth PtxIr100-x alloys model electrocatalysts that mimic single crystalline systems. This was achieved by deposition on MgO(100) via pulsed laser deposition. It will be shown that Pt and Ir form a kinetically stable alloy and that the PtxIr100-x alloys grew with the desired PtIr(100)[010]//[010](100)MgO configuration over the whole composition range. Also, the XPS data show that a surface alloy is formed over the whole composition range. Electrochemical measurements were performed in 0.5 M H2SO4 to highlight the preferential (100) surface orientation. Cyclic-voltametry and chrono-amperometry experiments in alkaline media were then carried out in presence of ammonia. They demonstrate the importance of the (100) surface structure for the electro-oxidation of ammonia compared to randomly oriented surface (polycrystalline sample). The presence of iridium decreases the surface poisoning at PtIr (100) alloys. Finally, it will be shown that surface poisoning can be further reduced using a simple cyclic-voltammetry procedure.
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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".