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 (NO) at low concentrations. First, graphitic carbon nitride (g-CN) and InO nanoparticles were composited with different mass ratios, resulting in the formation of distinct g-CN/InO 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-CN/InO 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 NO concentrations from 1 to 9 ppm. The results demonstrated the integration of high-selectivity sensing materials to NO 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 NO gas.

中文翻译：

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采用 g-C3N4/In2O3 复合材料的高性能气体传感器，用于 NO2 的低浓度预测


面对环境污染和工业排放，对空气质量检测的需求不断增长，推动了气体传感器的需求快速增长。然而，广泛使用的金属氧化物半导体（MOS）气体传感器存在气体浓度预测能力低、选择性差等缺点。在这项工作中，制作了一个由 10 个不同气体传感器组成的传感器阵列，并结合先进的深度学习算法，实现了低浓度二氧化氮 (NO) 的精确预测。首先，将石墨氮化碳（g-CN）和InO纳米颗粒以不同的质量比复合，形成独特的g-CN/InO复合材料作为传感材料。随后，利用SEM、TEM和XRD对样品的形貌、结构和元素组成进行了表征。此外，还研究了 10 种基于 g-CN/InO 复合材料的气体传感器的传感特性，包括响应、选择性和重复性。最后，提出了卷积神经网络高效通道注意门递归单元（CNN-AGRU）模型来分析当暴露于 1 至 9ppm 的各种 NO 浓度时从传感器阵列收集的信号模式。结果表明，将高选择性 NO 传感材料与先进的深度学习算法 CNN-AGRU 相结合，可以实现 NO 气体浓度预测精度约 97.04%，预测误差低至 0.20527ppm。