Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Govind S. Thakor, Ning Zhang and Rafael M. Santos
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Govind S. Thakor: School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
Ning Zhang: School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
Rafael M. Santos: School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada

Clean Technol., 2021, vol. 3, issue 3, 1-15

Abstract: Monitoring volatile organic compounds (VOCs) places a crucial role in environmental pollutants control and indoor air quality. In this study, a metal-oxide (MOx) sensor detector (used in a commercially available monitor) was employed to delineate the composition of air containing three common VOCs (ethanol, acetone, and hexane) under various concentrations. Experiments with a single component and double components were conducted to investigate how the solvents interact with the metal oxide sensor. The experimental results revealed that the affinity between VOC and sensor was in the following order: acetone > ethanol > n-hexane. A mathematical model was developed, based on the experimental findings and data analysis, to convert the output resistance value of the sensor into concentration values, which, in turn, can be used to calculate a VOC-based air quality index. Empirical equations were established based on inferences of vapour composition versus resistance trends, and on an approach of using original and diluted air samples to generate two sets of resistance data per sample. The calibration of numerous model parameters allowed matching simulated curves to measured data. Therefore, the predictive mathematical model enabled quantifying the total concentration of sensed VOCs, in addition to estimating the VOC composition. This first attempt to obtain semiquantitative data from a single MOx sensor, despite the remaining selectivity challenges, is aimed at expanding the capability of mobile air pollutants monitoring devices.

Keywords: volatile organic compounds; air quality monitoring; metal oxide sensor; predictive mathematical model; gas composition estimation (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
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