Resonator-based nanoscale plasmonic sensor made of metal–graphene–insulator interfaces
Bibliographic record
Abstract
We present a nanoscale refractive index plasmonic sensor based on Fano resonance. We simulateand numerically analyze a novel double T-shaped resonator structure made of conductorinsulatorwaveguides. Our simulation results show that two Fano resonance peaks can beachieved by the interference between a broadband mode in the straight waveguide and a narrowbandmode in the T-shaped resonator. The shifts of Fano resonance peaks by changing thesample 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 parametersfor the resonator structure and analyze the transmission spectra of the sensor. By optimizingthe sensor structural parameters we achieve a maximum sensitivity of 523.5nm/RIU and figure ofmerit of 2×10⁵for the sensor made of metal-insulator waveguide. By employing graphene at thecore-cladding boundary of the waveguides we attain a high sensitivity of 662.3nm/RIU and figureof merit of 6.6×10⁵ compared to the literature. We employ samples with refractive indices rangingfrom 1.0 to 1.05 to analyze the sensor capabilities and employ blood plasma samples to analyzethe applications of our sensor structure as a biosensor. Simple fabrication, compactness and highsensitivity are the main advantages of the proposed sensor structure.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".