Cover Feature: Efficient Near‐Infrared Electrochemiluminescence from Au<sub>18</sub> Nanoclusters (Chem. Eur. J. 60/2021)
Bibliographic record
Abstract
Near-infrared electrochemiluminescence (ECL) of Au18 nanoclusters revealed an extremely high quantum efficiency of 5.5 relative to that of Ru(bpy)32+, due to the exposed kernel facets, electrocatalytic loops and overlapped oxidation potentials of Au18 and tri-n-propylamine (TPrA) as co-reactant. ECL peak maxima at 832 and 850 nm were unambiguously identified as emissions from Au180*, Au181+* and Au182+* by means of spooling ECL and photoluminescence spectroscopy. More information can be found in the Communication by M. Hesari and Z. Ding (DOI: 10.1002/chem.202102926).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".