NHC-Stabilized Au<sub>10</sub> Nanoclusters and Their Conversion to Au<sub>25</sub> Nanoclusters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Herein, we describe the synthesis of a toroidal Au 10 cluster stabilized by N -heterocyclic carbene and halide ligands via reduction of the corresponding NHC–Au–X complexes (X = Cl, Br, I). The significant effect of the halide ligands on the formation, stability, and further conversions of these clusters is presented. While solutions of the chloride derivatives of Au 10 show no change even upon heating, the bromide derivative readily undergoes conversion to form a biicosahedral Au 25 cluster at room temperature. For the iodide derivative, the formation of a significant amount of Au 25 was observed even upon the reduction of NHC–Au–I. The isolated bromide derivative of the Au 25 cluster displays a relatively high ( ca . 15%) photoluminescence quantum yield, attributed to the high rigidity of the cluster, which is enforced by multiple CH−π interactions within the molecular structure. Density functional theory computations are used to characterize the electronic structure and optical absorption of the Au 10 cluster. 13 C-Labeling is employed to assist with characterization of the products and to observe their conversions by NMR spectroscopy.
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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) | 0.002 | 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".