Optimization of gold nanoring arrays for biosensing in the fiber-optic communication window
Bibliographic record
Abstract
To improve the limit of detection in a nanoplasmonic sensor system, the optical performance of the metal nanostructures should be optimized according to the best spectral window of the measurement instrument. We propose that the spectral window from 1460 to 1610 nm can potentially provide ultrahigh instrumental resolution for biosensing. We optimized gold nanoring arrays such that the extinction peak position is inside the proposed window, the extinction peak is sharp enough to track the peak shift with high resolution and the figure of merit (sensitivity/linewidth) of the array is optimized at the same time. The peak-sharpening effect of the array caused by coherent interaction plays a central role in the optimization. The optimized array has a lattice constant in the range [1000 nm,1060 nm], a bulk index sensitivity of around 450 nm/RIU and a figure of merit larger than 4. It is an enabling sensor element for a near-infrared sensor chip with ultrahigh resolution.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".