Selective detection of volatile organic compounds in microfluidic gas detectors based on “like dissolves like”
Bibliographic record
Abstract
This paper studies the effect of channel coating hydrophobicity and analyte polarity on the gas detection capability of a microfluidic-based gas detector. Two detectors with two different channel surface coating combinations (resulting in different levels of hydrophobicity) are fabricated and tested against seven analytes with different polarities (methanol, ethanol, 1-propanol, 2-pentanol, acetone, pentane, and hexane). A feature extraction method is utilized to compare the discrimination capability of each of the fabricated detector. The analysis of the combined feature space presented for both detectors reveals that the Euclidean distance, which is an indicator of the device discrimination capability between different gases, between the feature vectors of the two sensors are greater for non-polar gases compared to those obtained for the polar ones. This shows that the analyte discrimination in microfluidic gas detectors is not a purely diffusion-based process, and there are analyte/channel surface interaction parameters involved in enhancing/impeding sensor selectivity. To understand these effects, the surface free energy of each fabricated channel was determined. It is shown that the difference between the solid-liquid surface tension values estimated for the two channel surfaces is higher for the non-polar analytes as compared to the polar analytes. This effect along with the low diffusion coefficients of non-polar analyte magnifies adsorption of the analytes in the diffusion-physisorption process, resulting in a greater difference in Euclidean distances between the features obtained from the two detectors responses against non-polar analytes as compared to the polar ones. This shows that the choice of the detector's channel coating material plays a key role in the selectivity of the device between different gases. As a result, non-polar channel coating surfaces are suggested for better classification of the non-polar gases, and it is shown in the cases of polar gases changing the coating surface has less effect.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".