Adiabatic Mode Transformation in Width-graded Nano-gratings: Enabling\n Multiwavelength Light Localization
Bibliographic record
Abstract
We delineate the four principal surface plasmon polariton coupling and\ninteraction mechanisms in subwavelength gratings, and demonstrate their\nsignificant roles in shaping the optical response of plasmonic gratings. Within\nthe framework of width-graded metal-insulator-metal nano-gratings, mode\nconfinement and wave guiding result in multiwavelength light localization\nprovided conditions of adiabatic mode transformation are satisfied. The field\nis enhanced further through fine tuning of the groove-width (w), groove-depth\n(L) and groove-to-groove-separation (d). By juxtaposing the resonance modes of\nwidth-graded and non-graded gratings and defining the adiabaticity condition,\nwe demonstrate the criticality of w and d in achieving adiabatic mode\ntransformation among the grooves. We observe that the resonant wavelength of a\ngraded grating corresponds to the properties of a single groove when the\ngrooves are adiabatically coupled. We show that L plays an important function\nin defining the span of localized wavelengths. We show that multiwavelength\nresonant modes with intensity enhancement exceeding 3 orders of magnitude are\npossible with w < 30nm and 300nm < d < 900nm for a range of fixed values of L.\nThis study presents a novel paradigm of deep-subwavelength\nadiabatically-coupled width-graded gratings - illustrating its versatility in\ndesign, hence its viability for applications ranging from surface enhanced\nRaman spectroscopy to multispectral imaging.\n
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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".