Reconfigurable Modular Platform for Prolonged Sensing of Toxic Gases in Particle Polluted Environments
Bibliographic record
Abstract
The prolonged sensing of toxic gases in polluted particles and harsh environments is a challenging task that is also in high demand. In this work, the proof of principle of a sensitive, low-cost, and low-maintenance reconfigurable platform for filter-free and continuous ammonia (NH3) sensing in polluted environments is simulated. The platform can be modified for the detection of various toxic gases and includes three main modules: a microfluidic system for in-line continuous dust filtering; a toxic gas adsorption module; and a low-frequency microwave split-ring resonator (SRR). An inertia-based spiral microfluidic system has been designed and optimized through simulation for the in-line filtration of small particles from the intake air. Zeolite Y is selected as the adsorbent in the adsorption module. The adsorption module is a non-metallic thin tube that is filled with zeolite Y powder and precisely fixed at the drilled through-hole into the 3D microwave system. For the sensing module, a low-frequency three-dimensional (3D) split-ring resonator is proposed and optimally designed. A microwave resonator continuously monitors the permittivity of zeolite Y and can detect small permittivity alterations upon the presence of ammonia in the intake air. The microwave resonator is optimized at a frequency range of 2.5–3 GHz toward the detection of ammonia under different ammonia concentrations from 400 to 2800 ppm. The microwave simulation results show a clear contrast of around 4 MHz that shifts at 2.7 GHz for 400 ppm ammonia concentration. The results show the proof of principle of the proposed microfluidic-microwave platform for toxic gas detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".