Thermally activated delayed fluorescence Au‐Ag‐oxo nanoclusters: From photoluminescence to radioluminescence
Bibliographic record
Abstract
Abstract Thermally activated delayed fluorescence (TADF) materials have numerous applications in energy conversion and luminescent imaging. However, they are typically achieved as metal‐organic complexes or pure organic molecules. Herein, we report the largest Au‐Ag‐oxo nanoclusters to date, Au 18 Ag 26 (R 1 COO) 12 (R 2 C≡C) 24 ( μ 4 ‐O) 2 ( μ 3 ‐O) 2 ( Au 18 Ag 26 , where R 1 = CH 3 ‐, Ph‐, CHOPh‐ or CF 3 Ph‐; R 2 = Ph‐ or FPh‐). These nanoclusters exhibit exceptional TADF properties, including a small S 1 ‐T 1 energy gap of 55.5 meV, a high absolute photoluminescence quantum yield of 86.7%, and a microseconds TADF decay time of 1.6 μ s at ambient temperature. Meanwhile, Au 18 Ag 26 shows outstanding stability against oxygen quenching and ambient conditions. Atomic level analysis reveals the strong π⋯π and C‐H⋯π interactions from the aromatic alkynyl ligands and the enhancement of metal‐oxygen‐metal interactions by centrally coordinated O 2− . Modeling of the electronic structure shows spatially separated highest occupied molecular orbital and lowest unoccupied molecular orbital, which promote charge transfer from the ligand shell, predominantly carboxylate ligands, to O 2− ‐embedded metal core. Furthermore, TADF Au‐Ag‐oxo nanoclusters exhibit promising radioluminescence properties, which we demonstrate for X‐ray imaging. Our work paves the way for the design of TADF materials based on large metal nanoclusters for light‐emission and radioluminescence 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) | 0.000 | 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".