Investigating the optical bistability of pure spheroidal nanoinclusions in passive and active host matrices
Bibliographic record
Abstract
The study examined the effects of the depolarization factor ( L) and the real and imaginary parts of the dielectric function of the host matrix (εh) on the local field enhancement factor and optical bistability of pure spheroidal nanoinclusions with both passive and active host matrices. By solving the Laplace equation in the quasi-static limit, we derived expressions for the electric potentials of the pure spheroidal nanoinclusions. We then incorporated L and the Lorentz–Drude model into these expressions to derive the equation for the enhancement factor in the core of the spheroidal nanoinclusions. The results show that, regardless of whether L varies or remains constant, the pure spheroidal nanoinclusions exhibit only one set of enhancement factor peaks, independent of whether the host matrix is passive or active. However, for the same increase in εh, the enhancement factor intensities of the pure metal spheroidal nanoinclusions are higher when the host matrix is active compared to when it is passive. The number and intensities of the enhancement factor peaks, as well as the optical bistability of the pure spheroidal nanoinclusions, vary significantly depending on whether the core is made of a passive or active dielectric material. Furthermore, by adjusting parameters such as L and the real and imaginary parts of the host matrices, we were able to achieve tunable enhancement factors and optical bistability, which could be useful for applications in optical sensing, nonlinear optics, and quantum optics.
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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) | 0.000 | 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".