Gas Sensor Array with Pattern Recognition Algorithms for Highly Sensitive and Selective Discrimination of Trimethylamine
Wenjie Ren, Changhui Zhao, Gaoqiang Niu, Yi Zhuang, Fei Wang
Abstract
Artificial senses like electronic nose, which ameliorates the problem of poor selectivity from single gas sensor, have elicited keen research interest to monitor hazardous gases. Herein, the doping effects of gallium on In 2 O 3 nanotubes (NTs) are investigated and a four‐component sensor array for the detection of trimethylamine (TMA) is reported. All‐gallium‐doped/alloyed In 2 O 3 (Ga‐In 2 O 3 ) sensors show improved sensitivity and selectivity to TMA at an operating temperature of 240 °C, with 5 mol% Ga‐doped/alloyed one displaying the highest response in the range of 0.5–100 ppm and the lowest detection limit of 13.83 ppb. Based on the gas‐sensing properties, a four‐component sensor array is fabricated, which shows unique response patterns in variable‐gas backgrounds. Herein, back propagation neural network (BPNN), radial basis function neural network (RBFNN), and principal component analysis‐based linear regression (PCA‐LR) are trained with the gas‐sensing data to discriminate different gases with high accuracy, as well as to predict the concentrations of target gases in different gases and gas mixtures. Furthermore, accuracies of 92.85% and 99.14% can be achieved for the classification of six gases (three single gases and three binary gas mixtures) and for the prediction of TMA concentrations in the presence of different concentrations of TMA and acetone, respectively.