Concentration Effects of Co and Cu Dopants in β-Ni(OH)2 on Ammonia Oxidation Activity and Selectivity
Bibliographic record
Abstract
The electrochemical ammonia oxidation reaction offers a potential approach for energy generation or the remediation of aqueous nitrogenous waste. The development of affordable and stable electrocatalysts is needed for the widespread use of the AOR. Ni-based electrodes offer a cheaper alternative to other electrocatalysts while having moderate activity and long-term stability. This work uses density functional theory to investigate the effect of Co and Cu surface dopants at varying concentrations in β-Ni(OH)2 and their impact on ammonia oxidation activity and selectivity. The introduction of Co-doping to β-Ni(OH)2 reduced the *NH2 to *NH free energy, leading to lowered limiting potentials for N2 (g) formation. Cu-doping led to the reduction in energy required for the hydroxylation of *NO to *NO2H. This hydroxylation step is the typical limiting potential step for the production of NO2–(aq) and suggests Cu-doping may impact selectivity towards NO2–(aq) production. This work begins to understand the effect of surface doping β-Ni(OH)2 in differing ratios, in an attempt to improve the performance of Ni-based catalysts.
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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 | 0.001 |
| 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".