Charge State Tuning and Photochemical Stability of Au<sub>25</sub>(SR)<sub>18</sub> Nanoclusters
Bibliographic record
Abstract
Abstract The photolysis of the gold nanoclusters Au 25 (SR) 18 z (R=C 2 H 4 Ph and C 12 H 25 , z=1−, 0 and 1+) NCs in halogenated solvents such as dichloromethane leads to changes in the charged states of the Au 25 (SR) 18 z observed using UV‐Visible and 1 H NMR spectroscopic techniques. Matrix‐assisted laser desorption/ionization mass spectrometry results proved that during charge transformation from anion to neutral and eventually cation forms, the size of nanoclusters remains intact. Electrochemistry of Au 25 (SR) 18 has enabled the monitoring of these changes by cyclic voltammetry, indicating decay in the redox peak current upon irradiation. In the absence of light and/or in a non‐halogenated solvent, e. g., tetrahydrofuran, no substantial change in the photophysical signatures of Au 25 nanoclusters was observed. These observations highlight the importance of selecting appropriate solvent(s) in the synthesis and photochemical studies of atomically precise metal nanoclusters and the possibility of using photochemistry in halogenated solvents to synthesize different charge states of atomically precise metal nanoclusters. Thus, other monodispersed molecule‐like nanoclusters, with various sizes and charges, can be achieved via this photosynthetic protocol under controlled conditions, e. g., solvent, light, and photolysis duration.
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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".