Numerical design of high-performance WS2/metal/WS2/graphene heterostructure based surface plasmon resonance refractive index sensor
Bibliographic record
Abstract
The development of a high-performance bio-sensor utilizing the surface plasmon resonance (SPR) phenomenon has attracted great attention recently as a promising and precise sensing technology. Here, we proposed a WS2/metal/WS2/graphene heterostructure based SPR sensor with improved performances. The finite-difference time-domain (FDTD) technique was employed to model and develop the prospective sensor. The effects of integrating different layers, the analyte thickness on the performance of the sensor, and the electric field distribution were investigated systematically. We found that the optimized structure of the developed sensor exhibits the highest sensitivity of 208 deg/RIU with a detection accuracy of 1.12 and a quality factor of 223.66 RIU−1, which is superior to existing two-dimensional material based SPR sensors. The enhancement of light-material interaction caused by the coating of a monolayer WS2 on both sides of the metal improved the sensor performance significantly. These findings demonstrate a new way of performance enhancement in the composite layer sensor, aimed at further improving the identification of specific biomolecules such as glucose, blood disease, environmental monitoring, and agricultural applications.
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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".