Non-Reciprocal Whispering-Gallery-Mode Resonator for Sensitive Blood Glucose Monitoring
Bibliographic record
Abstract
In this article, a miniaturized low-cost microwave sensor of high sensitivity is proposed for noninvasive blood glucose monitoring. The traveling waves whispering gallery modes (WGM) are excited/launched on a ferrite-made ring resonator when placed nearby a microstrip line (MTL). The sensing structure is designed appropriately to support four distinctWGMmodes with the desired coupling in the frequency range of 22–32 GHz. A disk of permanent magnet is incorporated beneath the MTL substrate to generate a bias magnetic field necessary to create a nonreciprocal effect in the resonator. This nonreciprocal behavior is demonstrated in the transmission responses$S_{12}$and$S_{21}$, where theWGMresonances of similar modes are triggered at different frequencies over a narrowband. A magneto-static analysis is performed to approximate the internal biasing fields inside the FRR at multiple longitudinal layers. The complex components of the permeability tensor are computed accordingly for each layer, and then integrated into HFSS to simulate the sensor scattering responses. The EM simulations are validated when compared with practical measurements of a magnetized FRR. The latter fabricated prototype is tested in different setups for monitoring the glucose in synthetic blood of concentrations related to diabetes conditions. The proposed sensor shows high detection sensitivity atWGM600andWGM700to small changes in the dielectric properties of glucose samples as demonstrated by the frequency and phase shifts (0.07 MHz and 0.17° per 1 mg/dL, respectively) of the measured$S_{21}$and$S_{12}$with repeatability error of about ±0.2 MHz and ± 0.5°.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".