Journal
IEEE SENSORS JOURNAL
Volume 22, Issue 20, Pages 19136-19143Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2022.3198014
Keywords
Sensors; Sensor arrays; Gas detectors; Gases; Zinc oxide; Sensor systems; Sensitivity; Gas sensor; machine learning; nanomaterial; sensor array; volatile organic compound (VOC)
Funding
- NSF [1844885, 1854827]
- NSF, Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) [1706620]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1844885] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1706620] Funding Source: National Science Foundation
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Modern developments in gas sensor technology have led to smaller size, increased sensitivity, and selectivity. In our research, we utilize a sensor array featuring a hybrid combination of metals and organic polymers, along with machine learning, to detect a range of VOCs and toxic gases. Through a thorough analysis of machine learning classifiers, we determined that ensemble classifiers using normalized sensor data as features yield the best classification results.
Modern developments in gas sensor technology include a decrease in size and an increase in sensitivity and selectivity. These improvements, paired with postprocessing tools, such as machine learning, are pushing gas detection toward viability for complex tasks, such as volatile organic compound (VOC) analysis in human breath. In our research, we use a sensor array fabricated in our lab featuring a hybrid combination of metals and organic polymers [palladium (Pd), zinc oxide (ZnO), polypyrrole (PPy), and polyaniline (PANI)] designed to detect a range of VOCs and toxic gases (CO, H2, CH3OH, and NO2). An exhaustive analysis of 25 machine learning classifiers using three different feature sets was completed to find the best classifier and feature set combinations for one versus rest gas classification. We determined that ensemble classifiers, using normalized sensor data as a feature set, yield the best classification results. From these results, we demonstrated that Pd, PPy, and PANI are best suited to identify H2, NO2, and CH3OH, respectively. Furthermore, PANI is best suited to identify CO, so we correctly identified four gases from three sensor materials with sensitivity values all above 85%. These promising classification results could allow us to expand our set of gases and, therefore, make this sensor array viable for real-world applications.
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