Identification of gas mixtures using gold-decorated metal oxide based sensor arrays and neural networks

Abstract

With five sensors based on In2O3, SnO2, and TiO2 being designed, the sensor selectivity was enhanced by using composites of the metal oxides and decorating them with catalytic gold nanoparticles. An array of these sensors was used to sense three pollutant gases (CO, NO2, and NH3) with various concentrations. Further, a sensor array was used to sense two types of binary mixtures: (NO2 and CO) and (NH3 and NO2) with different concentrations. A principal component analysis was performed to evaluate the discriminative property of the sensor array. A deep neural network-based model was used to differentiate the distinct gases and their mixtures, with the input of response values. A discrimination efficiency of 100% was achieved in the cross-validated training and testing procedures with 100 iterations. For the discriminative analysis, a convolutional neural network-based model was used to circumvent processes such as feature extraction and response calculation. The CNN model could detect the presence of various gases and their mixtures with accuracies of 100% and 85% in 75 and 15 s, respectively.