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Measuring the concentration of oxynitride NO x (including NO and NO 2 ) can provide a scientific basis for determining its potential harm to the human body. The existing metal oxide semiconductor sensor arrays are greatly limited by high operating temperature and low integration. In this article, we report on a high-integration carbon nanotubes sensor array with two different electrode separations sensitive to oxynitride mixture at low temperature. The triple-electrode sensor array is comprised of a large carbon nanotube array cathode, two extracting electrodes and two collecting electrodes and worked at non-self-sustaining discharge state, which was determined by the study on current-voltage characteristic of a double-electrode structure sensor. Through studying the relationship between gas concentration and discharge current at 1 atm, we obtained the distinct sensitivities of the binary mixture in the concentration ranges of 0–1166 ppm NO and 0–712 ppm NO 2 using the sensor array at low operating voltages and 60 °C. Collecting currents of the two sensors in the array decreased with increasing NO and NO 2 concentration in the gas mixture. The repeatable characteristics and dynamical response tests of the sensors were also conducted. The proposed sensor array has potential for the direct detection of a NO-NO 2 mixture without separating the mixed gases. [ABSTRACT FROM AUTHOR]

• A sensor array comprising four gas sensors has been utilized to detect 11 types of mixtures of NO 2 and CO, whose concentration varies from 0 to 50ppm. The range of mixed gas concentration is wide enough, and the theoretical LOD could reach dozens of ppb levels. • Features were extracted by principal component analysis (PCA), and then C-means clustering and back propagation neural network (BPNN) were employed to identify gases. Also, we have verified the optimized effect of genetic algorithm (GA) for BPNN. Through transforming response curves into gray image, CNN could be used to directly recognize various gases. • To investigate which feature of input sample is most influential to BPNN model, a random variable substitution method has been proposed. • Experiments for sensing 6 kinds of CO and NO 2 mixtures under 4 different relatively humidity (25%, 33%, 50%, 75%) have been implemented to evaluate the effect of environmental humidity on our sensor array. The results demonstrated our method could eliminate the effects of humidity. • Even though this work stressed on the identification of mixtures of NO 2 and CO, the approach presented here is generic, and could in principle, be extended to other relevant gas species. In this work, a sensor array comprised four sensors has been employed to detect 11 types of mixtures of nitrogen dioxide (NO 2) and carbon monoxide (CO), with concentration varying from 0 to 50 ppm. To reduce the effect of sensor noise and ensure high recognition accuracy, average resistance over a period of time was introduced. Then, 12 features including response value, response time and recovery time were extracted from each sample. After that, C-means clustering and back propagation neural network (BPNN) were performed to identify various gases, with classification accuracy of 94.55 % and 100 %, respectively. Genetic algorithm (GA) was also employed to further improve BPNN's performance. Moreover, a random variable substitution method has been introduced to study which feature of the input sample influence the BPNN model most. Through gray processing, dynamic curves have been transformed into gray images, from which convolutional neural network (CNN) was introduced to automatically extract high-level features, and an identification accuracy of 100 % has been realized. Finally, experiments for sensing gas mixtures of CO and NO 2 under various humidity levels have been done to test the impact of humidity on the sensor array. The results demonstrated the proposed method could eliminate the effects of humidity. [ABSTRACT FROM AUTHOR]