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Record W2910897208 · doi:10.1038/s41598-018-36615-6

Selective detection of volatile organic compounds in microfluidic gas detectors based on “like dissolves like”

2019· article· en· W2910897208 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsOkanagan University CollegeKelowna General HospitalUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAnalyteDetectorPolarAnalytical Chemistry (journal)MicrofluidicsAdsorptionChemistryDiffusionSurface tensionMaterials scienceChromatographyNanotechnologyOpticsOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.191
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it