Exposure of Active Metal Sites on Cu <sub>14</sub> Nanoclusters for Highly Selective Electrocatalytic Nitrate Reduction
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Atomically precise metal nanoclusters (NCs) have been extensively used as catalysts for various reactions owing to the ultimate controllability of their parameters, such as number of constituent atoms, crystal structure, and alloying characteristics. However, for many metal NCs, their surfaces are entirely covered by ligands, preventing the exposure of surface metal atoms that serve as active sites and thus hindering their catalytic functionality. Herein, we report that the exposure of metal sites in Cu 14 NC can be achieved by a facile modification of the thiolate ligands. Consequently, we found that Cu 14 NC with an exposed Cu site exhibits significantly higher ammonia selectivity and production rate in electrochemical nitrate reduction. These findings underscore the importance of atomically precise control for metal NCs, not only of their overall geometric structures but also of their reactive sites, for achieving highly selective and active catalysts, contributing to the future design of diverse metal NC catalysts.
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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".