Correlation of photobleaching, oxidation and metal induced fluorescence quenching of DNA-templated silver nanoclusters
Bibliographic record
Abstract
Few-atom noble metal nanoclusters have attracted a lot of interest due to their potential applications in biosensor development, imaging and catalysis. DNA-templated silver nanoclusters (AgNCs) are of particular interest as different emission colors can be obtained by changing the DNA sequence. A popular analytical application is fluorescence quenching by Hg(2+), where d(10)-d(10) metallophilic interaction has often been proposed for associating Hg(2+) with nanoclusters. However, it cannot explain the lack of response to other d(10) ions such as Zn(2+) and Cd(2+). In our effort to elucidate the quenching mechanism, we studied a total of eight AgNCs prepared by different hairpin DNA sequences; they showed different sensitivity to Hg(2+), and DNA with a larger cytosine loop size produced more sensitive AgNCs. In all the cases, samples strongly quenched by Hg(2+) were also more easily photobleached. Light of shorter wavelengths bleached AgNCs more potently, and photobleached samples can be recovered by NaBH4. Strong fluorescence quenching was also observed with high redox potential metal ions such as Ag(+), Au(3+), Cu(2+) and Hg(2+), but not with low redox potential ions. Such metal induced quenching cannot be recovered by NaBH4. Electronic absorption and mass spectrometry studies offered further insights into the oxidation reaction. Our results correlate many important experimental observations and will fuel the further growth of this field.
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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 | 0.001 |
| 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".