Electrocatalytic Oxidation of Ammonia to Nitrate Occurs on NiOOH with OH/O Vacancies
Bibliographic record
Abstract
The ammonia electrooxidation reaction (AOR) has attracted significant attention for both wastewater treatment and energy storage applications. However, AOR pathways generating oxygenated products such as nitrite or nitrate remain unresolved. Attenuated total reflection-surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) and density functional theory (DFT) have been combined to determine the potential-dependent intermediates, active catalytic sites, and AOR pathways on Ni electrodes under alkaline conditions. The OH/O vacancy sites on NiOOH, revealed by XPS, are found by DFT calculations to be the active sites for the AOR, catalyzing the complete (eight-electron) oxidation of ammonia into nitrate. The formation of isolated OH/O vacancies on NiOOH is thermodynamically more favorable than that of paired vacancies in the potential range conducive to NiOOH formation and the AOR, with an increasing formation barrier at higher potentials. This synergistic ATR-SEIRAS and DFT study reveals that the selectivity of nitrite and nitrate production is potential-dependent, with nitrite and hydrazine formation initiated at moderate anodic potentials, whereas larger potentials promote conversion of nitrite into nitrate species. The release of adsorbed nitrite and nitrate, which is crucial for liberating catalytic sites for the continuous AOR, is spectroscopically and computationally shown to be facilitated at relatively low potentials. Comprehending AOR mechanisms on Ni-based catalysts as achieved in this study can pave the way for future research on catalyst design and optimization of AOR performance.
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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) | 0.000 | 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 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".