Resonator-based nanoscale plasmonic sensor made of metal–graphene–insulator interfaces
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Bibliographic record
Abstract
We present a nanoscale refractive index plasmonic sensor based on Fano resonance. We simulate and numerically analyze a novel double T-shaped resonator structure made of conductor–insulator waveguides. Our simulation results show that two Fano resonance peaks can be achieved by the interference between a broadband mode in the straight waveguide and a narrowband mode in the T-shaped resonator. The shifts of Fano resonance peaks by changing the sample refractive index in the resonator structure facilitates the design of a refractive index sensor. To attain a sensor with high sensitivity and figure of merit we employ different geometrical parameters for the resonator structure and analyze the transmission spectra of the sensor. By optimizing the sensor structural parameters we achieve a maximum sensitivity of 523.5 nm/RIU and figure of merit of 2 × 1 0 5 for the sensor made of metal–insulator waveguide. By employing graphene at the core–cladding boundary of the waveguides we attain a high sensitivity of 662.3 nm/RIU and figure of merit of 6 . 6 × 1 0 5 compared to the literature. We employ samples with refractive indices ranging from 1.0 to 1.05 to analyze the sensor capabilities and employ blood plasma samples to analyze the applications of our sensor structure as a biosensor. Simple fabrication, compactness and high sensitivity are the main advantages of the proposed sensor structure. • A compact refractive index (RI) plasmonic sensor based on Fano resonance. • Two T-shaped resonators in sensor structure made of metal–graphene–insulator interfaces. • T-shaped architectures may promote single mode optical guiding, which eliminates crosstalk. • T-shaped structures is easy to fabricate and highly sensitive. • Achieved maximum sensitivity of 662.3 nm/RIU and figure of merit of 6.6 × 105.
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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 | 0.000 |
| 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 it