Dual Enhancement of Gold Nanocluster Electrochemiluminescence: Electrocatalytic Excitation and Aggregation‐Induced Emission
Bibliographic record
Abstract
Abstract Ligand‐protected gold nanoclusters (AuNCs) have emerged as a new class of electrochemiluminescence (ECL) luminophores for their interesting catalytic and emission properties, although their quantum yield (ΦECL) in aqueous medium is low with a poor mechanistic understanding of the ECL process. Now it is shown that drying AuNCs on electrodes enabled both enhanced electrochemical excitation by an electrocatalytic effect, and enhanced emission by aggregation‐induced ECL (AIECL) for 6‐aza‐2‐thiothymine (ATT) protected AuNCs with triethylamine (TEA) as a coreactant. The dried ATT‐AuNCs/TEA system resulted in highly stable visual ECL with a ΦECL of 78 %, and a similar enhancement was also achieved with methionine‐capped AuNCs. The drying enabled dual‐enhancement mechanism has solved a challenging mechanistic problem for AuNC ECL probes, and can guide further rational design of ECL emitters.
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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 | 0.001 |
| 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".