Nanocluster Transformation Induced by SbF<sub>6</sub><sup>–</sup> Anions toward Boosting Photochemical Activities
Bibliographic record
Abstract
The interactions between SbF 6 – and metal nanoclusters are of significance for customizing clusters from both structure and property aspects; however, the whole-segment monitoring of this customization remains challenging. In this work, by controlling the amount of introduced SbF 6 – anions, the step-by-step nanocluster evolutions from [Pt 1 Ag 28 (S-Adm) 18 (PPh 3 ) 4 ]Cl 2 (Pt 1 Ag 28 –Cl) to [Pt 1 Ag 28 (S-Adm) 18 (PPh 3 ) 4 ](SbF 6 ) 2 (Pt 1 Ag 28 –SbF 6 ) and then to [Pt 1 Ag 30 Cl 1 (S-Adm) 18 (PPh 3 ) 3 ](SbF 6 ) 3 (Pt 1 Ag 30 –SbF 6 ) have been mapped out with X-ray crystallography, with which atomic-level SbF 6 – counterion effects in reconstructing and rearranging nanoclusters are determined. The structure-dependent optical properties, including optical absorption, photoluminescence, and electrochemiluminescence (ECL), of these nanoclusters are then explored. Notably, the Pt 1 Ag 30 –SbF 6 nanocluster was ultrabright with a high phosphorescence quantum yield of 85% in N 2 -purged solutions, while Pt 1 Ag 28 nanoclusters were fluorescent with weaker emission intensities. Furthermore, Pt 1 Ag 30 –SbF 6 displayed superior ECL efficiency over Pt 1 Ag 28 –SbF 6, which was rationalized by its increased effectively exposed reactive facets. Both Pt 1 Ag 30 –SbF 6 and Pt 1 Ag 28 –SbF 6 demonstrated unprecedented high absolute ECL quantum efficiencies at sub-micromolar concentrations. This work is of great significance for revealing the SbF 6 – counterion effects on the control of both structures and luminescent properties.
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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".