Noninvasive Glucose Sensing in Aqueous Solutions Using an Active Split-Ring Resonator
Bibliographic record
Abstract
It is shown here that microwave sensors can be used to monitor glucose in serum concentration with minimum detectable as well as resolution of 1 mMol ·L-1( ≈ 18 mg ·dL-1). The ultrasensitive detection technique relies on a split ring resonator, operating at the frequency of 1.156 GHz, as the core of the sensor where its loss is compensated to enhance the quality factor from ~ 190 (passive mode) to ~ 3850 (active mode) to enable high resolution (modified frequency detection error from ±12 kHz down to ±2.5 kHz) frequency-shift sensing. Initially, glucose concentrations of 100-1000 mMol ·L-1(1800-18000 mg ·dL-1) in water were detected within 250 kHz of dynamic range (between two spectrum ends). Selectivity of the sensor to glucose is verified with respect to common interstitial fluid ingredients with biological levels. Finally, to enhance the resolution of the proposed sensor, its loss-compensation is further improved leading to increased accuracy of measuring glucose samples in a 0.9 % NaCl solution containing 10 % horse serum that closely resembles blood plasma and interstitial fluid. This allows exploration of lower concentrations in the physiological range 1-30 mMol ·L-1(18-540 mg ·dL-1) with improved frequency detection error down to ±0.75 kHz for two cases of with/without serum solutions with dynamic range of 30 kHz/38 kHz. The highly accurate glucose monitoring technique could be utilized for developing noninvasive glucose sensors for biomedical applications in real-time glucose monitoring.
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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) | 0.001 | 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".