Optically Modulated Split Ring Resonator Sensor for Optical Density Analysis of Liquid Analytes in Microwave Regime
Bibliographic record
Abstract
Conventional optical sensors are insensitive to the variations in the dielectric properties of materials, while typical microwave sensors are insensitive to the variations in the material’s optical properties. This work presents a photoresponsive microwave split ring resonator (SRR) sensor designed for the simultaneous analysis and characterization of both the dielectric and optical properties of the liquid analytes. Through the integration of a light-sensitive element (photoresistor) into the SRR, the proposed method enables the microwave sensor to characterize the optical density of the liquid analyte while distinguishing the dielectric properties of the sample. In the designed system, optical illumination with blue light ($\lambda = 460$nm) and red light ($\lambda = 630$nm) affects the conductivity of the photoresistor integrated with the microwave SRR, depending on the optical density of the liquid analyte, thus affecting the sensor’s$S_{21}$response. The developed system demonstrates a sensitivity of −0.57 dB per log10(Colorant Volume) for red-light illumination passing through blue-colored samples and −0.17 dB per log10(Colorant Volume) for the blue-light illumination in red-colored liquids. The presented system promises potential application in industries, including chemical, biological, pharmaceutical, and food processing.
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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".