Photonic Bragg waveguide platform for multichannel resonant sensing applications in the THz range
Bibliographic record
Abstract
In this paper, we study a photonic Bragg waveguide sensor for resonant sensing applications in the THz range. In order to enhance the resolution and detectivity of the sensor, we modify the relatively broad transmission spectrum of the Bragg waveguide with spectrally narrow transmission dips by creating a geometrical defect in Bragg reflector and causing anti-crossing phenomenon between the core-guided mode and defect mode. The spectral position of the resonant dip is highly sensitive to the thickness variation in the vicinity of the waveguide core. By designing and manufacturing a Bragg waveguide which includes several sections with different defect layer thicknesses, we can interrogate more than one sample simultaneously and thereby realize multichannel resonant sensing by directly tracking the independent resonant dips. Furthermore, we demonstrate the waveguide platform for online monitoring of the thickness variation of lactose powders, which is captured on the waveguide core via a centrifugal force using a home-built rotating setup. Additionally, we also demonstrate the waveguide for fingerprint detection of powder analytes, which further enriches the sensing scenario of the sensing platform. Finally, we discuss the advantages and the spectral tailoring flexibility of the THz Bragg waveguides sensors for future implementations.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".