Tunable ultrasensitive terahertz sensing based on surface plasmon polariton of doped monolayer graphene
Bibliographic record
Abstract
We reported the surface plasmon resonance (SPR) on doped monolayer graphene (MLG) for terahertz (THz) sensing of refractive index of testing samples using a prism‐coupling attenuated total reflection configuration. The theoretical detection range and sensitivity of the THz plasmonic sensor were investigated. The sensor performance in terms of variation of detection accuracy with different refractive index was explored. It was demonstrated that the Fermi level energy of MLG and also the gap distance between the prism base and the MLG had great effects on the performance of THz sensing. The gap distance not only caused the coupling resonance frequency shift in the reflection spectrum, but also affected the full width at half maxima (FWHM) of the SPR curve. The incident angle of THz radiation was also the key impact factor of the detection accuracy of the sensor. The effective SPR frequency could be tuned by controlling the incident angle or the Fermi level energy. The results revealed the maximum sensitivity of the THz plasmonic sensor up to 6.65 THz RIU −1 with a figure of merit (FOM) of 1187 RIU −1 for Fermi level energy ranges from 0.4 to 1.2 eV, making the sensor potential for ultra‐sensitive SPR sensing in the terahertz regime.
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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 | 0.000 | 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".