Optimization of Plasmonic Nanodipole Antenna Arrays for Sensing Applications
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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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".