Nonlinear plasmonic metasurfaces: focusing on the Lorentz contribution (Conference Presentation)
Bibliographic record
Abstract
Nonlinear plasmonic metasurfaces, as a subset of metamaterials, allow for active functionality not found in natural optical materials; including switching, wavelength conversion, routing, adaptive focusing. Metasurfaces in particular are compact, cascadable and easy to fabricate with established planar technologies, and therefore deserve particular attention. Here we focus on nonlinear plasmonic metasurfaces, where the nonlinear response of the metal is considered in nanostructured plasmonic metasurfaces. Past works have demonstrated that the Lorentz contribution to nonlinear plasmonic metasurfaces is typically negligible. In this work, we discuss the physical reasons why this is true and show experimental results of designs where the Lorentz contribution is maximized, with some surprising results. Finally, the prospects of these demonstrations for future metasurface applications, including high efficiency wavelength conversion, are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".