Selective detection of individual gases and CO/H2 mixture at low concentrations in air by single semiconductor metal oxide sensors working in dynamic temperature mode

• Published in: Sensors and actuators. B, Chemical Vol. 254; pp. 502 - 513
• Main Authors: Krivetskiy, V., Efitorov, A., Arkhipenko, A., Vladimirova, S., Rumyantseva, M., Dolenko, S., Gaskov, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2018
• Subjects: Adaptive algorithms; Gas sensors; Neural networks; Semiconductor metal oxide; Signal processing; SnO2; Semiconductor metal oxide; Signal processing; SnO2; Neural networks; Gas sensors; Adaptive algorithms
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2017.07.100

•Selective gas detection with single metal oxide semiconductor gas sensor is shown.•Selectivity is achieved through adaptive signal processing model application.•Sustainability of adaptive model is demonstrated by independent data samples. Highly selective detection of various individual gases (CO, H2, CH4, C3H8, NO, NO2, H2S, SO2) at low concentrations (0.01–667ppm) in air by a single SnO2-based metal oxide sensor (MOX-sensor) is presented. The sensor operates in dynamic temperature mode combined with a number of adaptive signal processing algorithms. Artificial neural networks were proven to be more effective among the other adaptive algorithms implemented in this study. Identification of individual gases by a single sensor, averaged over all the gases and concentrations, resulted in only 13.2% false recognitions. Most of the failures were attributed to NO2 detection in 0.01–0.1ppm concentrations range. The ability of a single sensor to identify gas mixtures in a complex background was tested on the example of CO+H2 mixture in air, which simulates smoldering in the early stages of fire. The algorithm showed the ability to identify and quantify CO+H2 mixture with less than 10% error rate, even in constant presence of background gas (NO2 1.4ppm). Chemical modification of SnO2, increasing sensor response and sensitivity to individual components of the mixture, was proven to be beneficial for improvement of identification and quantification of gas mixture. Significant improvement in quantification accuracy (decrease in relative error from 7 to 2.5%) was achieved by utilizing a 3 sensor array in combination with an adaptive data processing algorithm, compared to the use of a single sensor alone. The prominent negative effect of humidity (Rh 30%, 25°C) on the performance of adaptive algorithms, sensor signal processing, system selectivity, and gas mixture identification is demonstrated.