Nanoparticulate WN/Ni<sub>3</sub>C Coupling in Ceramic Coatings for Boosted Urea Electro‐Oxidation
Bibliographic record
Abstract
Abstract Urea electrolysis can convert urea from urea‐rich wastewater to hydrogen for environmental protection and sustainable energy production. However, the sluggish kinetics of urea oxidation reaction (UOR) requires valence‐variable sites that are generally active at high anodic overpotentials. Herein, a robust ceramic coating is constructed with coupled tungsten nitride (WN)/nickel carbide (Ni3C) nanoparticles to achieve valence‐stable catalytic sites with outstanding UOR performance. Various characterization results indicate strong interfacial electron transfer from WN to Ni3C in coupled nanoparticles, which enables reservation of Ni2+ sites without self‐oxidation during UOR, quite distinct from the kinetically slow Ni3+OOH‐catalyzed UOR pathway. Theoretical calculations show that the coupled effect in WN/Ni3C leads to enhanced electron transfer from catalytic sites to adsorbed urea, and W sites are thermodynamically favorable for UOR. This efficiently lowers the barrier of rate‐determining step (RDS: *CO‐N2 → *CO·OH), thus enabling fast UOR kinetics and a low potential of 1.336 V at 100 mA cm−2, which identifies this ceramic coating as one of the best UOR electrocatalysts. This work opens a new avenue for design of stable and active sites in ceramics coatings toward advanced electrocatalytic applications.
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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".