Nonlinear Propagation of Laser Light in Plasmonic Nanocomposites
Bibliographic record
Abstract
Abstract Plasmonic nanocomposites have been extensively studied for over 3 decades. According to early theoretical studies, a large enhancement of nonlinear response has been predicted. Nonetheless, the promised enhancement of coherent or Kerr‐type nonlinearities incurs major limitations related to strong absorption and saturation effects. Accordingly, diffraction‐limited interactions and long‐scale propagation in ultrafast timescales are undermined despite nanoscale‐localized electronic field enhancement. Seemingly only nanometric devices operating at low intensity regimes are benefitted from the foresaid effect. Nonetheless, numerous studies have still exploited configurable properties of the nonlinear response of plasmonic nanocomposites, such as nonlinear absorption, high‐order nonlinearities, and diffusive nonlinearities for the development of novel processes within the framework of nonlinear wave propagation described by effective medium properties. In this review, the most recent developments on the understanding of the nonlinear response of metals and plasmonic nanocomposites in various temporal regimes are presented. Furthermore, a synthesis of their experimentally determined third‐order properties obtained by various experimental techniques, along with practical considerations, is provided. Computational models used for the formulation of nonlinear wave propagation in plasmonic nanocomposites are subsequently presented, corresponding to applicable concepts. Most recent related applications are concisely summarized, indicating the directions of increasing interest in the field, and outlining shortcomings.
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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) | 0.001 | 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".