Cascade Electrocatalytic Reduction of Nitrate to Ammonia Using Bimetallic Covalent Organic Frameworks with Tandem Active Sites
Bibliographic record
Abstract
Abstract Electrochemical nitrate reduction reaction (NO 3 RR) is a promising approach to simultaneously realize pollutant removal and ammonia generation. However, this process involves the transfer of eight electrons and nine protons along with multiple by‐products, resulting in a significant challenge for achieving high ammonia yield and selectivity. Herein, we introduced bimetallic covalent organic frameworks catalysts with Cu and Co active sites to achieve a two‐step tandem reaction, avoiding excessive nitrite accumulation and enabling efficient NO 3 RR. For the initial two‐electron process, the Cu sites in the bimetallic catalyst exhibit a strong binding affinity with nitrate, promoting their conversion to nitrite. The Co sites enhance the supply and adsorption of active hydrogen and stabilize the subsequent six‐electron process, thereby improving the overall catalytic efficiency. Compared to monometallic Cu and Co catalysts, the CuCo bimetallic catalyst demonstrates superior ammonia yield and Faradaic efficiency (NH 3 yield rate = 20.8 mg·h −1 ·cm −2 , FE = 92.16% in 0.3 M nitrate). Such coordinated two‐step process advances the efficiency and applicability of NO 3 RR through optimizing a cascade catalytic reaction, thereby establishing an innovative path for the engineering of NO 3 RR 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 | 0.000 | 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 | 0.001 | 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".