Liu Guoqiang,Xiong Xiaoyan,He Yuan,Gong Yunpeng,Li Siyi,Jin Tao,Chen Yufang
Triethylamine (TEA) sensors are an effective and powerful tool for real-time monitoring of TEA, and play an important role in early warning of production processes, environmental atmospheric monitoring and human health protection. In this paper, SnO 2 /ZnO complex oxide was prepared by calcining bimetallic organic frameworks (BMOFs) to improve both the selectivity, sensitivity and accuracy of TEA-sensing materials and devices. The results show that the obtained 5Sn5Zn complex oxide prepared by mol (Sn):mol (Zn) = 1:1 has excellent selective responsiveness, repeatability and longtime reliability stability to TEA gas at 270 °C Besides, the 5Sn5Zn complex oxide exhibits a rapid response/recovery performance to TEA at 100 ppm concentration ( T res / T rec = 4 s/60 s separately), especially at the high humidity of 40–90% relative humidity (RH), and the response degree of the 5Sn5Zn complex oxide to TEA only decreases by 17%. This may be the best TEA selective response stability achieved by SnO 2 /ZnO complex oxide material under high humidity conditions.
Zengyi Tang,Yamin Liu,Mianyi Xiang,Daxiang Cui,Qichao Li
The detection of volatile organic compounds (VOCs) has numerous applications in environmental monitoring and biomedicine. Metal oxide semiconductor (MOS) gas sensors are widely used in gas detection due to their simple structure, low fabrication cost, and fast response speed. However, MOS gas sensors have broad-spectrum responsiveness to VOCs, making selectivity a challenge. The most commonly used method to obtain selectivity is the E-nose system consisting of multiple sensors. However, the size of E-noses has increased due to the number of sensors with high power consumption and complicated signal processing. In recent years, deep learning has flourished, allowing for accurate discrimination of VOCs and concentration prediction using E-nose consisting of a single sensor. This method significantly reduces power consumption and facilitates portable applications. In this work, we synthesize SnO 2 -ZnO composite oxide and fabricate a E-nose consisting of a single MEMS gas sensor. The response performance of the E-nose to VOCs is stable with a high output signal-noise ratio (SNR). We design a lightweight deep learning model based on SqueezeNet and train it using transfer learning. Linear regression is used to predict the concentration of VOCs. Our work achieves accurate identification and low-error concentration prediction of VOCs, which is valuable for subsequent portable VOC detection systems.
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