A Mini Review on Controlling the Size of Ag Nanoclusters by Changing the Stabilizer to Ag Ratio and by Changing DNA Sequence
Bibliographic record
Abstract
Ag nanoclusters have received considerable attention in the past decade due to their distinguished photo-physical properties, which lead to very wide potential applications for biosensing and imaging. To this point, synthesis of well-defined Ag nanoclusters for practical applications is a key issue, in particular, controlling the size (or specific number of silver atoms) of Ag nanoclusters. Herein, we briefly discuss the effect of ratio of reactants, in terms of specific functional groups, on the size of Ag nanoclusters. Also, taking DNA as an example of biopolymer, we review how the DNA sequence can affect the specific number of Ag atoms in Ag nanoclusters. These conducted principles should provide significant guidance for preparation of Ag nanoclusters of precise size.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".