Exploring Microwave Reconfigurable Intelligent Surface for Wireless VOC Detection: A Comparative Study of Porous and Solid PDMS Interfaces
Bibliographic record
Abstract
Abstract Microwave gas monitoring with a low‐profile design and enhanced selectivity remains a persistent challenge. This study introduces an innovative approach employing a real‐time wireless reconfigurable intelligent surface (RIS) for comparative investigation of the interaction of two distinct polydimethylsiloxane (PDMS) interfaces, i.e., solid and porous, with acetone gas molecules. The developed PDMS‐coated microwave RIS detects varying concentrations of acetone vapor by wirelessly monitoring variations in the resonant characteristics of the resonating RIS beneath the sensitive interface. This PDMS‐coated microwave RIS is validated through exposure to incremental (15–75) parts per thousand (ppt) acetone concentrations and demonstrated a sensitivity of ≈4.4 MHz/ppt and ≈4.3 MHz/ppt of acetone for solid and porous PDMS, respectively. Integrating PDMS and microwave‐based RIS systems provides a sensitive tool for tracking the interaction of acetone gas and PDMS and demonstrates this system's capability for sensitive gas detection. This study introduces a unique development in the wireless detection of VOCs and presents a compact and passive approach with enhanced sensitivity, making it suitable for monitoring the interaction of polymers and hazardous VOCs. This technology is particularly suited for use in challenging and hard‐to‐reach environments.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".