Adiabatic mode transformation in width-graded nano-gratings enabling multiwavelength light localization.
Bibliographic record
Abstract
We delineate the four principal surface plasmon polariton coupling and interaction mechanisms in subwavelength gratings, and demonstrate their significant roles in shaping the optical response of plasmonic gratings. Within the framework of width-graded metal-insulator-metal nano-gratings, electromagnetic field confinement and wave guiding result in multiwavelength light localization provided conditions of adiabatic mode transformation are satisfied. The field is enhanced further through fine tuning of the groove-width (w), groove-depth (L) and groove-to-groove-separation (d). By juxtaposing the resonance modes of width-graded and non-graded gratings and defining the adiabaticity condition, we demonstrate the criticality of w and d in achieving adiabatic mode transformation among the grooves. We observe that the resonant wavelength of a graded grating corresponds to the properties of a single groove when the grooves are adiabatically coupled. We show that L plays an important function in defining the span of localized wavelengths. Specifically, we show that multiwavelength resonant modes with intensity enhancement exceeding three orders of magnitude are possible with w < 30 nm and 300 nm < d < 900 nm for a range of fixed values of L. This study presents a novel paradigm of deep-subwavelength adiabatically-coupled width-graded gratings-illustrating its versatility in design, hence its viability for applications ranging from surface enhanced Raman spectroscopy to multispectral imaging.
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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) | 0.000 | 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".