Bibliographic record
Abstract
Abstract The nonlinear optical response of materials allows optical functionality not seen in linear devices, such as switching, wavelength conversion, and adaptive optics. Unfortunately, the nonlinear optical response is weak in naturally occurring materials, making many ultrafast information processing applications impractical from an efficiency point of view. Nonlinear plasmonic metasurfaces, as a subset of metamaterials, aim to provide a more efficient and functional nonlinear optical response by tailoring the configuration of nanostructures. Metasurfaces are compact, cascadable, and easy to fabricate with established planar technologies, and therefore deserve particular attention. In this review, advances in nonlinear plasmonic metasurfaces are presented, including theoretical approaches, design methodologies, and key demonstrations of functionality. The theoretical approach first considers the linear response of the plasmonic metal and then uses this to calculate the nonlinear scattering. Design methodologies are considered including limits on gap size enhancements, tunneling and charging effects, and thermal management. Key demonstrations such as efficiency in wavelength conversion, functional wavelength conversion, and switching are also reviewed. Finally, an outlook on the future development in this field of research is offered, aiming at efficient and ultrafast optical information processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".