Three-Dimensional Split-Ring Resonators-Based Sensors for Fluid Detection
Bibliographic record
Abstract
This paper presents a sensitive microwave near-field sensor based on utilizing a three-dimensional capacitor within a planar split-ring resonator. The planar resonator is etched in the ground plane of a microstrip line, where the resonator's length is relatively smaller than the guided wavelength. The sensor was fabricated utilizing PCB technology and used to detect the presence of dielectric fluids. The numerical analysis shows that the three-dimensional capacitor significantly enhances the electric field in the sensing area (free space) leading to an increase in the sensitivity. The electric field enhancement was quantitatively investigated using numerical analysis. The analysis shows that a loaded-quality factor of 1556 can be achieved using a three-dimensional capacitor with a length of 29 mm, which is significantly higher than what can be achieved by using planar resonators working in the same range of frequency. In addition, the utilization of the high-dielectric substrate can enhance coupling to the resonator. The proposed sensor was experimentally tested on two fluids, chloroform and dichloromethane with the relative permittivity of 4.81 and 8.93, respectively. The proposed sensor shows higher sensitivity of 700% and 374.5% in detecting the presence of chloroform and dichloromethane, respectively. Furthermore, the proposed sensor was utilized to detect the changes in fluid levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".