Continuously tunable middle-IR bandpass filters based on gradient metal-hole arrays for multispectral sensing and thermography
Bibliographic record
Abstract
Continuously tunable middle-infrared bandpass filters have been developed based on gradient metal-hole arrays with two distinct geometries. The rotation filter relies on an array of metal holes with gradually changing periods and hole sizes in the azimuthal direction, while the translation filter exploits a metal-hole array with a linear gradient. The filters are fabricated in a Ti film on a ZnSe substrate using electron-beam nanolithography. They are characterized experimentally using Fourier-transform infrared spectroscopy, and the observed results are compared with numerical predictions of the finite element method. The developed filters offer wide spectral tunability when operating with a focused beam. Particularly, the central wavelength of the transmission band is tunable in the λc∈(9,15)μm range, for the rotation filter, and in the λc∈(8,13)μm range for the translation one, as a linear function of the filter angular or linear displacement. The filters feature relatively broad bandwidths of Δλ≃0.2λc, while their spectral contrast and energy efficiency depend on the gradient type. The filter spectral response function shape and the extent of its spectra tunability can be further optimized by judicious design of the hole geometry and the metal-hole array gradient, respectively. The developed filters hold strong potential in the infrared multispectral sensing and imaging, thanks to their conceptual simplicity. Considering the linearity of Maxwell’s equations and availability of appropriate technologies for the fabrication of gradient arrays of sub-wavelength metal holes, the developed concept can be translated to other spectral ranges.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".