All-Optical Tunable Plasmonic Biosensor Made of Graphene and Metamaterial
Bibliographic record
Abstract
Abstract We demonstrate a novel, label-free and real-time tunable infrared biosensor by employing surface-plasmon polaritons in asymmetric Mach-Zehnder interferometer. The waveguides cladding in the Mach-Zehnder interferometer is made of lossy media with positive and negative electromagnetic susceptibilities, including metamaterial, metal and graphene. The core consists of dielectric media. We introduce two configurations for our biosensor structure. First configuration is an open-path structure and the second one consists of a sample housing made of a silicon layer around the structure. We also present a tunable biosensor by applying a gate voltage to the graphene in the structure. We employ three different cancerous cells, including cervical, breast and basal, as samples to examine the capabilities of the biosensor. Our biosensor structure is highly sensitive, compared to the existing biosensors in the literature, with the sensitivity for basal cancer cell of 1034THz/RIU. The proposed biosensor structure is compact and easy to fabricate with applications in biomedical sensing and environmental control to detect water pollutants.
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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 | 0.001 | 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".