Single Ag Atoms Induce Ag‐N/O Bonds on WO<sub>3</sub> for Ultrastable Acid Ammonia Synthesis
Bibliographic record
Abstract
Abstract Ammonia from nitrate‐containing wastewater demands the catalysts with high activity, stability, and selectivity toward acidic electrochemical nitrate reduction owing to the corrosion effect of the catalyst and the competitive hydrogen evolution reaction (HER). Herein, we synthesized single Ag atoms induced Ag‐N/O bonds on one‐dimensional WO3 nanowires for highly efficient and stable electrochemical nitrate reduction to ammonia under acidic conditions. The resultant catalyst achieved a Faradaic efficiency (FE) of ammonia exceeding ∼90% over a potential range of −0.6 to −0.2 VRHE with a maximum FE approaching 100% at −0.4 VRHE and a maximum NH3 yield at −0.55 VRHE. Importantly, the catalyst maintained a current density at 480 mA cm−2 over 1100 h with a stable FE of ∼94%. By integrating the catalyst into a membrane electrode assembly (MEA), we succeeded in obtaining an NH3 production rate of 12.75 mmol h−1 at −4 V and maintained the stability for over 30 h with FE > 90%. Experimental and theoretical analysis have demonstrated that the formation of Ag‐N/O bonds enhanced nitrate adsorption and lowered the energy barrier for the rate‐determining step of *NO→*NHO, thereby considerably improving the efficiency of ammonia synthesis and inhibiting HER at amperometric current densities. This work provides insights into acidic nitrate reduction for ammonia electrosynthesis, which develops corrosion‐resistant electrocatalysts for energy conversion.
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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) | 0.001 | 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".