Dual-channel narrowband polarization absorber with high field enhancement and refractive index sensitivity based on a nanorod array
Bibliographic record
Abstract
In this paper, we present a dual-channel narrowband polarization absorber based on a metal-dielectric-metal structure, which consists of a top metallic nanorod array, a metal substrate, and an ultrathin middle dielectric spacer. The proposed structure can achieve high absorptance above 96% in a wide angular range of incidence around ±20° at two remarkable absorption peaks for transverse magnetic polarization under normal incidence. Most significantly, the extremely highly confined enhancement of electromagnetic fields between Au film and nanorods has been observed by employing numerical simulation based on a finite element method, which is up to 110 times compared with the incident electric field. The underlying physics mechanism of a strong gap plasmon resonance is analyzed, and it is primarily attributed to simultaneous excitation of multiple localized electric dipole and magnetic dipole resonance modes in this film-coupled nanorods system. Additionally, we also investigate the dependence of dual resonance peaks on structural parameters as well as their sensitivities to the refractive index of media surrounding nanorods. The wavelength modulation and intensity modulation are also shown simultaneously. This structure is near-perfect absorbing, plasmonic refractive index sensing, and surface-enhanced Raman spectroscopy, all rolled into one. It will have great significance and potential in developing new miniaturized multifunctional photonics devices and their high integrations.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".