Electrocatalytic CO<sub>2</sub> Reduction with Atomically Precise Au<sub>13</sub> Nanoclusters: Effect of Ligand Shell on Catalytic Performance
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Supported gold nanoclusters of the formula [Au 13 (L) 5 Cl 2 ] 3+ where L = N -heterocyclic carbene (NHC) or phosphine ligands are examined in the electrocatalytic CO 2 reduction reaction (eCO 2 RR) in a membrane electrode assembly cell configuration. Gold nanoclusters bearing bis NHC ligands are shown to exhibit improved catalytic performance compared with diphosphine-stabilized nanoclusters after activation at the optimum treatment temperatures. The thermal properties of the nanoclusters are shown to have a significant impact on their catalytic activity. Thermogravimetric analysis, UV–vis absorption spectroscopy, and X-ray photoelectron spectroscopy revealed that thermal treatment of [Au 13 (diphosphine) 5 Cl 2 ] 3+ nanoclusters results in complete loss of diphosphine ligands while [Au 13 ( bis NHC) 5 Cl 2 ] 3+ nanoclusters show stepwise and partial removal of bis NHC ligands. We propose that the partial removal of bis NHC ligands enables efficient activation of [Au 13 ( bis NHC) 5 Cl 2 ] 3+ clusters while conserving the core structure. This leads to the implication that intact clusters retaining at least some ligands in their coordination environment are more active than ligand-free clusters.
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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".