Design of an Efficient Fabry-Perot Biosensor Using High-Contrast Slanted Grating Couplers on a Dual-Core Single-Mode Optical Fiber Tip
Bibliographic record
Abstract
A novel and efficient Fabry-Perot biosensor on the tip of a dual-core single-mode optical fiber is proposed. The incident light emerges from one of the cores and is coupled to the second core via high-contrast silicon nitride slanted grating couplers (SGCs), separated by a silicon nitride waveguide segment disposed along the fiber tip. The challenges in designing high-contrast SGCs of very short length were overcome using an efficient analytical design approach augmented by simulations in 2D, resulting in grating designs that produce core-to-core coupling efficiencies of up to 29%. The waveguide segment separating the SGCs acts as a Fabry-Perot sensing cavity producing narrow Fabry-Perot fringes in the transmittance spectrum of the structure. Monitoring these fringes leads to bulk sensitivity and figure of merit of 50 - 100 nm/RIU and 16.67 - 33.34 (RIU $^{-1}$ ), for sensing a large range of biomaterials. The structure operates in transmission, thus eliminating the need to separate the reflected light from the incident light at the input of the fiber, which simplifies the interrogation system. Properly packaged, the fiber tip can be dipped or inserted directly into the sensing medium, which removes the need for microfluidics. The scheme enables robust, flexible, compact, and remotely-interrogated biosensors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".