Molecular-Scale Ligand Effects in Small Gold–Thiolate Nanoclusters
Bibliographic record
Abstract
Because of the small size and large surface area of thiolate-protected Au nanoclusters (NCs), the protecting ligands are expected to play a substantial role in modulating the structure and properties, particularly in the solution phase. However, little is known on how thiolate ligands explicitly modulate the structural properties of the NCs at atomic level, even though this information is critical for predicting the performance of Au NCs in application settings including as a catalyst interacting with small molecules and as a sensor interacting with biomolecular systems. Here, we report a combined experimental and theoretical study, using synchrotron X-ray spectroscopy and quantum mechanics/molecular mechanics simulations, that investigates how the protecting ligands impact the structure and properties of small Au 18 (SR) 14 NCs. Two representative ligand types, smaller aliphatic cyclohexanethiolate and larger hydrophilic glutathione, are selected, and their structures are followed experimentally in both solid and solution phases. It was found that cyclohexanethiolate ligands are significantly perturbed by toluene solvent molecules, resulting in structural changes that cause disorder on the surface of Au 18 (SR) 14 NCs. In particular, large surface cavities in the ligand shell are created by interactions between toluene and cyclohexanethiolate. The appearance of these small molecule-accessible sites on the NC surface demonstrates the ability of Au NCs to act as a catalyst for organic phase reactions. In contrast, glutathione ligands encapsulate the Au NC core via intermolecular interactions, minimizing structural changes caused by interactions with water molecules. The much better protection from glutathione ligands imparts a rigidified surface and ligand structure, making the NCs desirable for biomedical applications due to the high stability and also offering a structural-based explanation for the enhanced photoluminescence often reported for glutathione-protected Au NCs.
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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".