Stimulated Rayleigh Scattering in Plasmonic Nanofibers Doped with Metallic Nanoparticles and Quantum Dots
Bibliographic record
Abstract
A theory of stimulated Rayleigh scattering (SRS) has been developed for metallic nanohybrids made of an ensemble of metallic nanoparticles and quantum dots (QDs). The intensity of the output stimulated Rayleigh scattered light was found using the coupled-mode formalism based on Maxwell’s equations. By means of the density matrix method, it is found that the output light depends on third-order susceptibility. Analytical expressions of the intensity of the electrostrictive stimulated Rayleigh scattering and the thermal stimulated Rayleigh scattering are calculated in the presence of the surface plasmon polaritons (SPPs) and the dipole–dipole interactions (DDIs). We compared our theory with two experimental data sets for nanohybrids of this type. The first is a nanohybrid made of an ensemble of Ag nanoparticles and rhodamine 6G dye, and the second is for a nanohybrid composed of Ag nanoparticles and pyrromethene-597 dye. We found good agreement between theory and experiments. We also predicted an enhancement in the SRS intensity. The enhancement is due to the two extra scattering mechanisms of the SPP and DDI polaritons with the QDs. We also found that the SRS intensity spectrum has two peaks (i.e., maximum and minimum) at low values of the SPP and DDI couplings. However, when we increase the strength of the SPP and DDI couplings, the SRS intensity spectrum has only one peak (i.e., maximum). Finally, we can say that this type of work has never been reported in the literature. The findings of this article can be very useful. For example, the analytical expressions can be useful for experimental scientists and engineers, who can use them to compare their experiments and make new types of plasmonic devices. The enhancement of the SRS intensity can be used to fabricate SRS nanosensors. Similarly, our finding about the SRS intensity having two peaks instead of one peak due to the SPP and DDI couplings can be used to fabricate SRS nanoswitches, where the two peaks can be thought of as the ON position and the one peak can be considered as the OFF position.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".