Rainbows at the End of Subwavelength Discontinuities: Plasmonic Light Trapping for Sensing Applications
Bibliographic record
Abstract
Abstract This article presents recent advances in plasmonic multiwavelength rainbow light trapping, a field that has evolved over the last decade and today is an active area of research interest encompassing a manifold of potential applications which include optical biosensing, photodetection, spectroscopy, and medicine. Conventional plasmonic devices are designed and optimized to enhance optical performance at single wavelengths, and as such are not suitable for applications that require electromagnetic field localization at multiple frequencies or broad frequency ranges of interest. To overcome these limitations, the ability to slow and trap light at multiple wavelengths and at different spatial locations has attracted significant scientific attention and opened up new research endeavors. Herein, fundamental principles of plasmonic light localization are presented, recent advances using breakthrough metamaterials are discussed—as well as major achievements and diverse device configurations used in the design and fabrication of plasmonic multiwavelength light trapping platforms with an emphasis on sensing applications. A presentation of salient works in this field is encapsulated, including the earliest invention of the concept of trapped rainbow, current trends, future directions, and emergent allied themes. This review also scrutinizes key developments and technical challenges vis‐à‐vis the physics of electromagnetic spectral localization and device fabrication which together provides insights and will inspire scientists and engineers to innovate and further develop the field.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".