Classification of Gases With Single FET-Based Gas Sensor Through Gate Voltage Sweeping and Machine Learning

Abstract

Uncontrolled release of various harmful gases from automobiles and chemical industries demands accurate methods for gas classification and detection. In this context, this article proposes an effective method to classify and detect four gases—ammonia, formaldehyde, toluene, and acetone using a single field-effect transistor (FET)-based gas sensor. The gate voltage of the FET sensor played a pivotal role in this classification mechanism. L-ascorbic acid functionalized graphene oxide (GO) was used as the sensing material of the FET device. Initially, various features of the fabricated FET sensor (i.e., % of response, response time, and recovery time) were captured by varying the applied gate voltage. Furthermore, classification algorithms such as decision tree (DT), support vector machine (SVM), gradient boosting (GB), and random forest (RF) were trained to automatically predict the target gases. An accuracy of 73% was achieved for all three classifiers other than the SVM classifier. The use of machine learning algorithms was fruitful to accurately detect four gases at different gate voltages when any unknown one among the four was exposed to the single gate-tuned sensor. Moreover, it also saved the system’s power consumption as a single sensor was behaving like several sensors.