Resonant optical transmission through hole‐arrays in metal films: physics and applications
Bibliographic record
Abstract
Abstract Extraordinary optical transmission through an array of holes in a metal film was reported by Ebbesen and coworkers in 1998. Since that work there has been abundant research activity aimed at understanding the physics and at the development of the many applications associated with this phenomenon, hence the topic of this review. The study of hole‐arrays in a metal is not new – theoretical contributions on a small‐hole array date back to Lord Rayleigh's description of Wood's anomaly in 1907 and there has been considerable research on metal meshes and hole‐arrays since 1962. Bethe's theory, adapted to treat hole‐arrays, is the simplest theoretical description of the transmission resonance. Following a description of this basic theory, we present the research on the additional effects from variations in real metal properties at different wavelengths, film thickness, hole‐shape and lattice configuration. The many promising applications being developed using hole‐arrays are examined, including polarization control, filtering, switching, nonlinear optics, surface plasmon resonance sensing, surface‐enhanced fluorescence, surface‐enhanced Raman scattering, absorption spectroscopy, and quantum interactions. Finally, the various approaches, developments in hole‐array fabrication, and integration of hole‐arrays into devices are described.
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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".