Quantitative Detection Of Formaldehyde And Ammonia Using A Yttrium-Doped Zno Sensor Array Combined With A Back-Propagation Neural Network Model

This paper describes the fabrication of a yttrium-doped ZnO gas sensor array using a simple hydrother-mal reaction process. The sensor array was fabricated by doping different concentrations of yttrium ions, obtaining flower-like ZnO nanosheets. Toxic gases such as formaldehyde and ammonia were detected quantitatively. The morphological structure and crystallinity of ZnO nanomaterials were investigated through SEM and XRD analysis. The optimal response temperature, sensitivity, response/recovery time, and stability of the sensor array were investigated. The sensor array and a back-propagation neural net-work (BP-NN) algorithm were used to predict the composition of gas mixtures containing formaldehyde and ammonia. The prediction error was <0.8 ppm. The gas-sensing performance for ternary mixtures was also evaluated. The BP-NN model could predict the output concentration value. Therefore, the sen-sor array (pure ZnO and Y/ZnO sensors) combined with the BP-NN model developed in this study showed promising results for the detection and composition prediction of toxic gas mixtures in the environment. (c) 2021 Elsevier B.V. All rights reserved.