Miniature Gas Sensor and Sensor Array With Single- and Dual-Mode RF Dielectric Resonators
Bibliographic record
Abstract
RF resonant polymer-coated sensors are a proven effective approach to enhance the sensitivity of polymeric sensing materials. However, the level of sensitivity improvement offered by an RF resonant sensor is directly proportional to the quality factor (Q) of the resonator-the larger the Q-factor, the higher the sensitivity enhancement is. This paper first presents a miniature polymer-based dielectric resonant (DR) sensor design operated at its fundamental mode (TEH) at 24 GHz demonstrating a measured Q-factor of 3820. The sensor is coated with a (per)nigraniline-based conductive sensing material for detections of acetone and isopropanol volatile organic compounds and has a small footprint of 4.5 mm × 4.5 mm. The sensor has demonstrated a low-ppm detection capability for acetone and isopropanol gases with a response onset time less than 15 s. This paper further demonstrates a novel airborne carcinogenic agent sensor array implemented with a single dielectric resonator operating in dual mode (HEH) at 27 GHz. It is functionalized by placing two carcinogen sensing materials-polyhydroxyethylmethacrylate (PHM) and fluoroalcohol polysiloxanes (FAPS)- at the optimal geometrical locations on the DR, such that the polymers would independently affect the resonant frequencies of the two orthogonal HEH degenerate modes. By monitoring Δf0and ΔS11near f0,HEH1and f0,HEH2, both the concentrations and the response signatures of the airborne carcinogenic agents can be detected. The measurement results indicate that the HEH-mode two-sensor array carrying PHM and FAPS is successful in identifying and differentiating two carcinogenic agents- dioxane and benzene-and their respective concentrations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".