Transition Metal Single‐Atom Catalysts for the Electrocatalytic Nitrate Reduction: Mechanism, Synthesis, Characterization, Application, and Prospects
Bibliographic record
Abstract
Abstract Excessive accumulation of nitrate in the environment will affect human health. To combat nitrate pollution, chemical, biological, and physical technologies have been developed recently. The researcher favors electrocatalytic reduction nitrate reaction (NO 3 RR) because of the low post‐treatment cost and simple treatment conditions. Single‐atom catalysts (SACs) offer great activity, exceptional selectivity, and enhanced stability in the field of NO 3 RR because of their high atomic usage and distinctive structural characteristics. Recently, efficient transition metal‐based SACs (TM‐SACs) have emerged as promising candidates for NO 3 RR. However, the real active sites of TM‐SACs applied to NO 3 RR and the key factors controlling catalytic performance in the reaction process remain ambiguous. Further understanding of the catalytic mechanism of TM‐SACs applied to NO 3 RR is of practical significance for exploring the design of stable and efficient SACs. In this review, from experimental and theoretical studies, the reaction mechanism, rate‐determining steps, and essential variables affecting activity and selectivity are examined. The performance of SACs in terms of NO 3 RR, characterization, and synthesis is then discussed. In order to promote and comprehend NO 3 RR on TM‐SACs, the design of TM‐SACs is finally highlighted, together with the current problems, their remedies, and the way forward.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".