Design of an agile optical switcher based on a 1D silver plasmonic nanograting filled with a nonlinear Kerr material
Bibliographic record
Abstract
Abstract In this paper, we investigate the nonlinear optical response characteristics of a metallic nanograting with nonlinear Kerr media within its slits using the finite element method. The proposed nanograting system is illuminated by a nanosecond Gaussian pulse laser under normal incidence and the electric filed pointing across the slits. The results show a perfect linear absorption at resonance wavelength thanks to the coupling of the surface plasmon resonance (SPR) mode and photon cavity mode. We simulate the transient nonlinear absorption variation of the system when the pulse laser is set up at either resonance or off-resonance wavelengths. The results indicate that the unit linear absorption drastically decreases by increasing the laser fluence around the center of the pulse. Interestingly, one can also enhance the weak linear off-resonance absorption to the value of unit by increasing the pulse laser fluence. The higher the laser fluence, the higher the maximum absorption contrast between linear and nonlinear regimes occurs owing to the nonlinear Kerr effect. Indeed, when the laser fluence reaches a critical value, it can excite the Kerr nonlinearity, which changes the coupling strength of SPR mode and the photon cavity mode leading to the absorption adjustment in the nanograting. These properties indicate the possibility of utilizing the proposed nanograting in dual functional absorber and nonabsorber systems, which make it an appropriate candidate for agile optical switching devices.
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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 | 0.001 | 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".