Influence of silver nanoparticles on absorption and fluorescence properties of laser dyes
Bibliographic record
Abstract
The interaction of silver nanoparticles with four laser dyes, namely, LD-425, LD-489, LD-473, and LD-423, dispersed in methanol has been studied at room temperature by absorption and fluorescence spectroscopy. The silver nanoparticles were synthesized by chemical reduction and estimated size is 11 nm according to Mie theory. The changes in absorption spectrum of these dyes with the addition of silver nanoparticles suggest possible interaction with the silver nanoparticles. The magnitudes of the association constants are estimated using the Benesi–Hildebrand model. Fluorescence quenching has been observed for all the dyes with the addition of silver nanoparticles of different concentrations. The Stern–Volmer plots of fluorescence quenching in the case of LD-425 and LD-489 are found to be linear, suggesting the fluorescence quenching is because of the dynamic quenching process. But in the case of LD-473 and LD-423, the Stern–Volmer plots are nonlinear showing positive deviation and the fluorescence quenching in these dyes is because of static quenching processes. Also, it is found that the fluorescence quenching is not solely due to diffusion.
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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 | 0.000 |
| 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".