High-performance gas sensor utilizing g-C3N4/In2O3 composite for low concentration prediction to NO2

Abstract

The demand for gas sensors is experiencing rapid growth, driven by the increasing need for air quality detection in the face of environmental pollution and industrial emissions. However, the widely used metal oxide semiconductor (MOS) gas sensors suffer from drawbacks such as low ability to predict gas concentration and poor selectivity. In this work, a sensor array was fabricated consisting of 10 various gas sensors and combined with an advanced deep learning algorithm to achieve precise predictions of nitrogen dioxide (NO2) at low concentrations. First, graphitic carbon nitride (g-C3N4) and In2O3 nanoparticles were composited with different mass ratios, resulting in the formation of distinct g-C3N4/In2O3 composites as sensing materials. Afterward, SEM, TEM, and XRD were employed to characterize the morphology, structure, and elemental composition of the samples. Furthermore, sensing properties including response, selectivity, and repeatability, are studied for 10 gas sensors based on g-C3N4/In2O3 composites. Finally, the convolutional neural network-efficient channel attention-gate recursive unit (CNN-AGRU) model was proposed to analyze the patterns of the signals collected from the sensor array when exposed to various NO2 concentrations from 1 to 9 ppm. The results demonstrated the integration of high-selectivity sensing materials to NO2 and an advanced deep learning algorithm CNN-AGRU enables to realize a high concentration prediction accuracy of about 97.04% and a low prediction error of 0.20527 ppm for NO2 gas.