Atomically Precise Metal Clusters for Efficient Catalytic Conversion of Nitrate to High‐Valued Chemicals
Bibliographic record
Abstract
Atomically precise metal clusters have demonstrated significant advantages in homogeneous catalysis, heterogeneous catalysis, electronic catalysis, and photocatalysis. As much, electrocatalytic reduction of nitrate pollution into valuable compounds is a low‐energy consumption and environmentally friendly route, which combines environmental and economic advantages by efficiently eliminating pollutant and recycling waste. This review systematically summarizes the current researches on atomically precise metal clusters in the electroreduction of nitrate to produce high‐valued chemicals such as ammonia, urea, cyclohexanone oxime, and amino acids. The emphasis on the complexed reaction pathways and key intermediates involved in the conversion of nitrate into different target products is studied. The relationship between the structure of metal clusters and catalytic properties is delved into. This review aims to provide valuable insights for the design of high‐performance metal cluster catalysts to achieve efficient conversion from nitrate to high‐value‐added chemicals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 teacher head, 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".