Gas Sensor Array with Pattern Recognition Algorithms for Highly Sensitive and Selective Discrimination of Trimethylamine

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 In2O3 nanotubes (NTs) are investigated and a four-component sensor array for the detection of trimethylamine (TMA) is reported. All-gallium-doped/alloyed In2O3 (Ga-In2O3) sensors show improved sensitivity and selectivity to TMA at an operating temperature of 240 degrees 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.

Automated summary

This study investigates the doping effects of gallium on In2O3 nanotubes and develops a four-component sensor array for detecting TMA. The results show that Ga-In2O3 sensors with 5 mol% gallium doping exhibit the highest sensitivity and selectivity to TMA at a specific operating temperature, and they can display unique response patterns in different gas backgrounds. Additionally, neural network models and linear regression models trained with gas-sensing data can accurately classify different gases and predict the concentrations of target gases.