Low‐Profile Planar Antenna Sensor Based on Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i> MXene Membrane for VOC and Humidity Monitoring
Bibliographic record
Abstract
Abstract Detection of volatile organic compounds (VOC)s is of great interest in industrial and environmental applications due to the severe impact of VOCs on human health and the environment, including climate change and ozone layer depletion. This study investigates the implementation of a Ti3C2Tx MXene as radiating and sensing element to develop a VOC and humidity sensing antenna. The performance of the sensor is validated by introducing various VOCs, including acetone, ethanol, IPA, and methanol, in an isolated custom‐made chamber while the antenna sensor is communicating with a distanced receiver antenna. Measured results confirm that the exposure of the MXene antenna sensor to VOCs causes an upshift in the operating frequency of the antenna sensor. The study successfully demonstrates the potential of the proposed Ti3C2Tx MXene antenna sensor in detecting acetone concentrations ranging from 8 to 80 parts per thousand (ppt). In addition, the sensor detects different humidity concentrations ranging between 77.8 ± 2% to 94 ± 1%, where for 94 ± 1% of humidity, a 213.1 MHz of frequency shift is observed. As a proof of concept, the developed MXene antenna sensor shows a promising potential for wireless gas and humidity sensing applications in the environmental and industrial sectors.
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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 | 0.000 |
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".