Multi-modal Photo-responsive Planar Microwave Resonant-based Colorimetric Analysis of Liquid-Color Compound for Biomedical Applications
Bibliographic record
Abstract
Contactless sensing and monitoring have gained increasing popularity due to their versatile applications in various industries and scientific research. This work presents a photo-responsive microwave split ring resonator (SRR) sensor for the concurrent detection and characterization of the colorimetric and dielectric properties of materials in the liquid phase. This unique implementation enabled light-insensitive microwave SRRs to utilize the optical properties of materials, resulting in enhanced sensitivity and selectivity in their performance. The sensor core was mainly based on two coupled SRRs with one photoresistor in each ring and a light source of$(\lambda=630\ \text{nm})$at 20 cm illuminating the photoresistors. The sensor's response was influenced by both the dielectric properties of the liquids contained within a cuvette and the light absorption characteristics of the sample with the SRRs operating at ~2.2 GHz, allowing for a comprehensive analysis of the liquid's properties. The sensor demonstrated an enhancement by over 112 % variation in the resonant amplitude when comparing the light-activated state to the inactivated state for the same liquid samples. This sensor has the potential to enhance the sensitivity of microwave planar sensors in biomedical applications, where variations in dielectric properties are coupled with optical changes in the media, facilitating early detection and 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 | 0.001 |
| 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".