Inkjet-printed MOS-based MEMS sensor array combined with one-dimensional convolutional neural network algorithm for identifying indoor harmful gases.

To achieve a highly accurate and efficient analysis of mixed gases, it is crucial to develop an electronic nose system with high sensitivity of gas sensors and low data processing complexity. In this study, the metal oxide semiconductor (MOS) based micro-electromechanical system (MEMS) gas sensor array was prepared by inkjet printing sensing materials onto a micro-hotplate. The pattern recognition unit employed a one-dimensional convolutional neural network (1D-CNN) to identify 7 types of gases. While the optimal traditional machine learning algorithm achieved an 80% recognition accuracy, the 1D-CNN can achieve 99.8%. Furthermore, the impact of varying time series input lengths on the model accuracy was investigated, pinpointing an optimal sampling time of 15 s. This work showed that integrating the MEMS sensor array with the 1D-CNN algorithm might offer a promising approach for intricate gas classification and identification. [Display omitted] • Inkjet Printing MOS-based MEMS sensor array. • MEMS sensor arrays show low power consumption and miniaturization. • 1D-CNN is used to classify gas mixtures with 99.8% accuracy. • Only short time response data is needed for high accuracy classification. [ABSTRACT FROM AUTHOR]