Mechanistic Similarity in Catalytic N<sub>2</sub>Production from NH<sub>3</sub>and NO<sub>2</sub><sup>–</sup>at Pt(100) Thin Films: Toward a Universal Catalytic Pathway for Simple N-Containing Species, and Its Application to<i>in Situ</i>Removal of NH<sub>3</sub>Poisons
Bibliographic record
Abstract
Understanding of the NH 3 oxidation poisoning mechanism at Pt(100) is key to tackling a drawback of a reaction important to wastewater decontamination, electrochemical NH 3 sensors, and NH 3 fuel cells. Here we present a detailed study of poisoning adsorbates generated by NH 3 at Pt(100) thin films and identify new key species (1) by comparison to NO 2 – reduction adsorbates and (2) using literature FTIR and DEMS (differential electrochemical mass spectrometry) data. We show that NH 3 and NO 2 – generate identical intermediates at the same electrochemical potentials, suggesting that reactions as disparate as NH 3 oxidation and NO 2 – reduction follow a universal catalytic pathway for N-containing compounds at Pt(100). This represents a significant paradigm shift from 45 years of thought that suggested the two pathways were completely separate. We then use the behavior of poisoning, adsorbed intermediates to develop an in situ cleaning procedure that allows improvements in performance and lifetime for NH 3 electro-oxidation technologies. The in situ cleaning procedure, demonstrated for 1200 cleaning cycles over 2 h, required neither H 2 production nor Pt oxide formation. The latter trait allowed the employment of Pt(100) films without risk of immediate catalyst deorientation to a polycrystalline state.
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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 | 0.001 | 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".