Synthesis and Characterization of a Monodentate N-Heterocyclic Carbene-Protected Au<sub>11</sub>-Nanocluster via Reduction with KC<sub>8</sub>
Bibliographic record
Abstract
Atomically precise gold nanoclusters are an exciting and growing class of nanomaterials. While normally protected with ligands such as thiols or phosphines, gold nanoclusters protected with N-heterocyclic carbenes (NHCs) have recently garnered attention due to potential improvements in stability and optical properties of the resulting clusters. However, as this field is in its infancy, little work has been done with reducing agents beyond sodium borohydride (NaBH 4 ), a reagent that dominated synthetic efforts in other clusters as well. Herein, we report the use of potassium intercalated graphite (KC 8 ) in the synthesis of nanoclusters, and the novel Au 11 -nanocluster protected with monodentate NHC ligands it produces, [Au 11 (NHC) 8 Br 2 ]Br. The cluster is characterized by ESI-MS, UV–vis spectroscopy, 1 H and 13 C{ 1 H} NMR. Starting from a partially resolved X-ray crystal structure showing the heavy atoms, DFT calculations enable us to propose a total structure.
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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) | 0.000 | 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".