Optimization of Plasmonic Nanodipole Antenna Arrays for Sensing Applications
Why this work is in the frame
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Bibliographic record
Abstract
Nanoantennas are key optical components for several applications including biosensing. This paper presents the optimization of plasmonic nanodipole antenna arrays, operating with short-range surface plasmon polaritons, to maximize bulk sensitivity. The array is integrated on a substrate (silicon or glass) and covered by water. Using modal analysis, a full study was carried out on the dimensions of the nanodipoles at three optical wavelengths of interest, 850, 1310, and 1550 nm, and some of the results were validated using full 3D FDTD modeling. We show that nanodipoles on a glass substrate produce a greater bulk sensitivity than on a silicon substrate. The largest bulk sensitivities are produced at the longest wavelength (1550 nm) as 1000 nm/RIU on glass and 500 nm/RIU on silicon. Good performance over a wide range of nanodipole dimensions was observed, making the arrays tolerant to imperfections.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it