DNA-templated fluorescent gold nanoclusters reduced by Good’s buffer: from blue-emitting seeds to red and near infrared emitters
Bibliographic record
Abstract
DNA-templated fluorescent gold nanoclusters (AuNCs) have been recently prepared showing higher photostability than the silver counterpart. In this work, we examined the effect of pH, DNA length, DNA sequence, and reducing agent. Citrate, HEPES, and MES produce blue emitters, glucose and NaBH4 cannot produce fluorescent AuNCs, while ascorbate shows blue emission even in the absence of DNA. This is the first report of using Good’s buffer for making fluorescent AuNCs. Dimethylamine borane (DMAB) produces red emitters. Poly-C DNA produces AuNCs only at low pH and each DNA chain can only bind to a few gold atoms, regardless of the DNA length. Otherwise, large nonfluorescent gold nanoparticles (AuNPs) are formed. Each poly-A DNA might template a few independent AuNCs. The blue emitters can be further reduced to form red emitters by adding DMAB. The emission color is mainly determined by the type of reducing agent instead of DNA sequence.
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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".