Accelerated Catalysis of Atomically Precise Thiolate-Protected Gold Nanoclusters by Supramolecular Ligand Engineering
Bibliographic record
Abstract
We herein report the reaction acceleration effect of a supramolecular reaction field constructed with dendritic peptide thiolate ligands on the Au 25 nanocluster toward the catalytic cyclization of alkynoic acids. A remarkable reaction acceleration was achieved by peptide dendron thiolate ligands, which was 40 times greater than that achieved by simple alkyl thiolate ligands. Association experiments with 1 H NMR spectroscopy revealed that the unprecedented intermolecular hydrogen bonds between peptide dendron ligands and ammonium salts of alkynoic acids play a critical role in the reaction acceleration effect. The high stability of the nanocluster catalyst bearing the supramolecular reaction field was also represented by the turnover number over 820,000 in this catalytic reaction. Mechanistic investigations revealed the involvement of pi-coordinated alkynes and subsequent vinyl–Au intermediates. DFT calculations support the possible reaction pathway, including both the anionic and neutral forms of the nanocluster as the catalyst. Our findings demonstrate the usefulness of the supramolecular ligand approach for metallic nanocluster catalysis, enabling enhanced catalytic efficiency and selectivity toward various organic transformations.
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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".