Pre‐oxidation of Gold Nanoclusters Results in a 66 % Anodic Electrochemiluminescence Yield and Drives Mechanistic Insights
Bibliographic record
Abstract
Gold nanoclusters (AuNCs) are attractive electrochemiluminescence (ECL) emitters because of their excellent stability, near IR emission, and biocompatibility. However, their ECL quantum yield is relatively low, and our limited fundamental understanding has hindered rational improvement of this parameter. Herein, we report drastic enhancement of the ECL of ligand-stabilized AuNCs by on-electrode pre-oxidation with triethylamine (TEA) as a co-reactant. The l-methionine-stabilized AuNCs resulted in a record high ECL yield of 66 %. This strategy was successfully extended to other AuNCs, and it is more effective for ligand shells that allow more effective electron transfer. In addition, excitation of the pre-oxidized ECL required a lower potential than conventional methods, and no additional instrument was required. This work opens avenues for solving a challenging problem of AuNC-based ECL probes and enriches fundamental understanding, greatly broadening their potential 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".