Theory of all-optical switching based on the Kerr nonlinearity in metallic nanohybrids
Bibliographic record
Abstract
We have developed a theory for the Kerr nonlinearity in nanohybrids made of an ensemble of metallic nanoshells and low concentration of quantum emitters. A metallic nanoshell is made of a metallic core sphere and dielectric shell. We consider that quantum emitters are four-level quantum systems. When a probe laser light falls in the metallic nanoshells, the surface plasmon polariton electric field is produced at the interface between the metal sphere and dielectric shell. This electric field along with the probe field induces dipoles in metallic nanoshells. These dipoles interact with each other via the dipole-dipole interaction. The Kerr nonlinearity has been calculated by using the quantum density matrix method in the presence of the dipole-dipole interaction (coupling). We found that in the weak-coupling limit there is an enhancement in the Kerr nonlinearity. On the other hand, in the strong-coupling limit, the peaks in the Kerr coefficient split from two peaks to four peaks when the frequency of the dipole electric field is in the resonance with the exciton frequency. The splitting in the spectrum is due to the presence of the dressed sates created in the system. We showed that heights and locations of peaks are very sensitive to the strength of the dipole-dipole interaction. Physics of the enhancement can be used fabricate Kerr nanosensors. On the other hand, physics of the splitting from two peaks (ON) to four peaks (OFF) can be used to fabricate Kerr nanoswitches.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".