Interactions between Ultrastable Na <sub>4</sub> Ag <sub>44</sub> (SR) <sub>30</sub> Nanoclusters and Coordinating Solvents: Uncovering the Atomic-Scale Mechanism
Bibliographic record
Abstract
Recently, silver nanoclusters have garnered considerable attention after the high-yield synthesis and crystallization of a thiolate-protected silver nanocluster, Na 4 Ag 44 (SR) 30 (SR, protecting thiolate ligand). One intriguing feature of Na 4 Ag 44 (SR) 30 is its outstanding stability and resistance to chemical reactions, in striking difference from other silver nanostructures whose susceptibility to oxidation (tarnishing) has been commonly observed and thus limits their applications in nanotechnology. Herein, we report the mechanism on the ultrahigh stability of Na 4 Ag 44 (SR) 30 by uncovering how coordinating solvents interact with the Na 4 Ag 44 (SR) 30 nanocluster at the atomic scale. Through synchrotron X-ray experiments and theoretical calculations, it was found that strongly coordinating aprotic solvents interact with surface Ag atoms, particularly between ligand bundles, which compresses the Ag core and relaxes surface metal–ligand interactions. Furthermore, water was used as a cosolvent to demonstrate that semiaqueous conditions play an important role in protecting exposed surface regions and can further influence the local structure of the silver nanocluster itself. Notably, under semiaqueous conditions, aprotic coordinating solvent molecules preferentially remain on the metal surface while water molecules interact with ligands, and ligand bundling persisted across the varied solvation conditions. This work offers an atomic level mechanism on the ultrahigh stability of the Na 4 Ag 44 (SR) 30 nanoclusters from the nanocluster-coordinating solvent interaction perspective, and implies that nanocluster-solvent interactions should be carefully considered moving forward for silver nanoclusters, as they can influence the electronic/chemical properties of the nanocluster as well as the surface accessibility of small molecules for potential catalytic and biomedical applications.
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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".