Effect of Dipole–Dipole Interactions on Stimulated Raman Scattering in Plasmonic Nanohybrids
Bibliographic record
Abstract
We have developed a theory of the stimulated Raman scattering (SRS) for plasmonic nanohybrids. The nanohybrids are made of an ensemble of interacting metallic nanoshells (MNSs) and quantum dots. The surface plasmon polariton (SPP) field for the MNSs is calculated at the interface between the metallic core and the dielectric shell. An external laser field induces dipoles in MNSs, and dipoles interact with each other via dipole–dipole interactions (DDIs). It is found that the SRS depends on the third-order susceptibility of the plasmonic nanohybrid. The coupled-mode formulism based on Maxwell’s equation and the quantum mechanical density matrix method are used to obtain an analytical expression of the Raman gain coefficient (RGC) and SRS intensity. These 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. Further, we found that there is an enhancement in the RGC and SRS intensity. The enhancement is due to the SPPs and DDIs. We have also investigated the effect of geometrical parameters such as the size of the nanoshell on the SRS intensity. Finally, the present theory is applied to explain the existing SRS experiments, and good agreement was found between theory and experiments. Our findings can be used to fabricate optical nano-amplifiers and nanosensors in the regime around the Raman Stokes field frequency.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".