Effects of Coherent Interactions on the Sensing Characteristics of Near-Infrared Gold Nanorings
Bibliographic record
Abstract
We have investigated the surface-supported 2D periodic array of gold nanorings and found that the sensing figure of merit can be significantly improved by coherent interactions. The experiments on the periodic nanostructures fabricated on glass substrate have shown that the sensing characteristics depend on the lattice constant and the character of the substrate grating order at a grazing angle. In the evanescent grating order range, as the lattice constant increases, the plasmon peak red-shifts, line shape strongly narrows and sensitivity decreases, but the figure of merit increases. The reason for the decrease in sensitivity is found to be the decreased field confinement and enhanced substrate effects caused by the coherent interactions. In the radiative grating order range, as the lattice constant increases, the plasmon peak blue-shifts, the line shape significantly broadens and the figure of merit decreases. The experimental results were confirmed by numerical calculations using 3D finite difference time domain method. The simulation results predict that, compared to the single nanoparticle, the bulk sensing figure of merit of the periodic array can be improved by more than three times and the surface sensing figure of merit can be improved by around 2.5 times. The grating-induced modes related to the substrate grating order found only in simulations are also discussed and show strongly suppressed sensitivity and low figure of merit.
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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) | 0.000 | 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".