Homogeneously Mixed Cu–Co Bimetallic Catalyst Derived from Hydroxy Double Salt for Industrial-Level High-Rate Nitrate-to-Ammonia Electrosynthesis
Bibliographic record
Abstract
Electrocatalytic nitrate reduction reaction (NO 3 RR) presents an innovative approach for sustainable NH 3 production. However, selective NH 3 production is hindered by the multiple intermediates involved in the NO 3 RR process and the competitive hydrogen evolution reaction. Hence, the development of highly efficient NO 3 RR catalysts is paramount. Herein, we report highly efficient bimetallic catalysts derived from hydroxy double salt (HDS). Under NO 3 RR conditions, Cu 1 Co 1 -HDS undergoes in situ reconstruction, forming nanocomposites of homogeneously distributed metallic Cu 0 and Co(OH) 2 . Reconstruction-induced Cu 0 rapidly converts NO 3 – to NO 2 –, which is further hydrogenated to NH 3 by Co(OH) 2 . Homogeneously mixed Cu and Co species maximize this synergistic effect, achieving outstanding NO 3 RR performance including the highest NH 3 yield rate (4.625 mmol h –1 cm –2 ) reported for powder-type NO 3 RR catalysts. Integration of Cu 1 Co 1 -HDS with a commercial Si solar cell attained 4.53% solar-to-ammonia efficiency from industrial wastewater-level concentrations of NO 3 – (2000 ppm), demonstrating practical application potential for solar-driven NH 3 production. This study provides a strategy for enhancing the NH 3 yield rate by optimizing the compositions and distributions of Cu and Co.
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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".