Plasmon-Enhanced Resonance Energy Transfer from a Conjugated Polymer to Fluorescent Multilayer Core−Shell Nanoparticles: A Photophysical Study
Bibliographic record
Abstract
The influence of metallic silver nanoparticles on Förster resonance energy transfer (FRET) between a water-soluble cationic conjugated polymer and fluorescent multilayer Ag@SiO 2 @SiO 2 +FiTC core−shell nanoparticles was characterized using a combination of light scattering and luminescence techniques. Positioning the fluorescent polymer 7 nm away from the surface of a 45 nm silver nanoparticle with a silica spacing layer increases its quantum yield to 77%, as compared to 3% when measured as an isolated emitter. In the presence of the metallic core, the luminescence of the nanoparticle-bound acceptor fluorophore is increased at the expense of the polymer donor luminescence, and time-resolved fluorescence measurements indicate an enhancement of FRET efficiency from 4 to 50%, an increase in the Förster distance from 50 to 85 Å, and a resonant transfer rate between donors and acceptors more than 2 orders of magnitude higher than that measured in the absence of the metal core. The strong influence of plasmonic coupling in these multilayer nanocomposites offers great potential for signal amplification schemes in polymer-based and FRET-based biosensors.
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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".