Selective Detection of VOCs With WO₃ Nanoplates-Based Single Chemiresistive Sensor Device Using Machine Learning Algorithms

Abstract

The paper presents the integration of single metal-oxide based chemiresistive sensor device and machine learning tools for selective discrimination of different volatile organic compounds (VOCs) for indoor air quality monitoring applications. Tungsten oxide (WO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) nanoplates has been employed as the gas sensing material which were obtained by acidification followed by low temperature hydrothermal process. Synthesized WO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> nanoplate structure was confirmed by different characterization tools explaining surface morphology and structural properties. The sensor device was fabricated by using a simple drop coating technique on top of aluminum based interdigitated electrodes. An extensive gas sensing study was carried out where adequate sensor response was observed for each target VOC. The sensing mechanism has been discussed to realize the behavior of the sensor towards the introduction of target VOCs. Collective data obtained from the sensor device were engaged with machine learning algorithms (best results shown by multilayer perceptron) to discriminate the target VOCs accurately. Furthermore, concentrations of tested VOCs were predicted in a quantitative manner using a regression model with fair accuracy.