Microbead-assisted high resolution microwave planar ring resonator for organic-vapor sensing
Bibliographic record
Abstract
A microbead-assisted planar microwave resonator for organic vapor sensing applications is presented. The core of this sensor is a planar microstrip split-ring resonator, integrated with an active feedback loop to enhance the initial quality factor from 200 to ∼1 M at an operational resonance frequency of 1.42 GHz. Two different types of microbeads, beaded activated carbon (BAC) and polymer based (V503) beads, are investigated in non-contact mode for use as gas adsorbents in the gas sensing device. 2-Butoxyethanol (BE) is used in various concentrations as the target gas, and the transmitted power (S21) of the two port resonator is measured. The two main microwave parameters of resonance frequency and quality factor are extracted from S21 since these parameters are less susceptible to environmental and instrumental noise than the amplitude. Measured results demonstrate a minimum resonance frequency shift of 10 kHz for a 35 ppm concentration of BE exposure to carbon beads and 160 kHz for the polymer based adsorbent at the same concentration. The quality factor of the resonator also changed for different concentrations, but a distinguishable variation is observed for the BAC adsorbents. The high quality factor of the sensor provides the opportunity of real time monitoring of the adsorbent behaviors in remote sensing mode with very high resolution.
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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 | 0.001 | 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".