Understanding the Mechanism of Urea Oxidation from First-Principles Calculations
Bibliographic record
Abstract
Developing electrocatalysts for urea oxidation reaction (UOR) works toward sustainably treating urea-enriched water. Without a clear understanding of how UOR products form, advancing catalyst performance is currently hindered. This work examines the thermodynamics of UOR pathways to produce N2, NO2–, and NO3– on a (0001) β-Ni(OH)2 surface using density functional theory with the computational hydrogen electrode model. Our calculations show support for two major experimental observations: (1) N2 favours an intramolecular mechanism, and (2) NO2–/NO3– are formed in a 1:1 ratio with OCN–. In addition, we found that selectivity between N2 and NO2–/NO3– on our model surface appears to be controlled by two key factors, the atom that binds the surface intermediates to the surface and how they are deprotonated. These UOR pathways were also examined with a Cu dopant, revealing that an experimentally observed increased N2 selectivity may originate from increasing the limiting potential required to form NO2–. This work builds towards developing a more complete atomic understanding of UOR at the surface of NiOxHy electrocatalysts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".