E-nose based on a high-integrated and low-power metal oxide gas sensor array

• Published in: Sensors and actuators. B, Chemical Vol. 380; p. 133289
• Main Authors: Li, Zhongzhou, Yu, Jun, Dong, Diandian, Yao, Guanyu, Wei, Guangfen, He, Aixiang, Wu, Hao, Zhu, Huichao, Huang, Zhengxing, Tang, Zhenan
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01.04.2023; Elsevier Science Ltd
• Subjects: Acetone; Ammonia; Artificial neural networks; Back propagation; Back propagation networks; Bridges; E-nose; Electrohydrodynamic printing; Electrohydrodynamics; Electronic noses; Error detection; Ethanol; Gas sensors; Gases; Hydrogen; Infrared lasers; Inkjet printing; Metal oxides; Microhotplate; Microparticles; Neural networks; Noise reduction; Operating temperature; Palladium; Power consumption; Power management; Qualitative identification; Quantitative estimation; Selectivity; Sensor arrays; Sensors; Toluene; Wavelet transforms; Qualitative identification; Microhotplate; Quantitative estimation; E-nose; Electrohydrodynamic printing
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2023.133289

To improve the poor selectivity of a semiconductor gas-sensor system, an array of sensors can be utilized. However, this increases the system’s size and power consumption. To overcome these limitations, we propose a high-integrated and highly selective electronic nose (E-nose) comprising two independent gas-sensing elements on a low-power microhotplate (MHP). Pd-SnO2 nanoflowers and Pd-WO3 microparticles were prepared and printed on a bridge-structured MHP 20 µm apart and over an area of 110 µm × 45 µm. This was achieved using electrohydrodynamic inkjet printing aided by in-situ infrared laser curing to form an array of sensors. A power of 17 mW was required to increase the temperature of the MHP to 300 °C, which is the optimal operating temperature of the two gas sensors. Thus, a high response and low cross-sensitivity were achieved for hydrogen, ammonia, hydrogen–ammonia, ethanol, acetone, ethanol–acetone, toluene, and formaldehyde. A wavelet transform was used to reduce the noise and dimensionality of the signals from the gas-sensor array. Qualitative identification of the eight gases with an accuracy of 99.86% was achieved using the k-nearest neighbor (kNN) model. A p neighbors back propagation neural network (pN-BPNN) model was established to remove interfering samples to quantitatively estimate the gas concentration. The quantitative identification accuracy of pN-BPNN was higher than that of the standard back propagation neural network (BPNN) model with the average absolute percentage error of hydrogen detection in the range of 15–500 ppm, decreasing from 5.44% to 2.08%. •E-nose comprising two independent gas-sensing elements on a low-power microhotplate was achieved.•Prepared Pd-SnO2 nanoflowers and Pd-WO3 microparticles achieved low cross-sensitivity to eight different gases.•Proposed p neighbors-backpropagation neural network model has high estimation accuracy.•The qualitative identification and quantitative estimation of the E-nose with high accuracy and reliability.