Novel combined waveform temperature modulation method of NiO-In2O3 based gas sensor for measuring and identifying VOC gases

The extremely widely applied metal oxide semiconductor (MOS) gas sensors suffer from underdeveloped selectivity, and dynamic measurement methods are expected to solve this issue. In this paper, the In 2 O 3 and NiO-In 2 O 3 gas sensors were prepared, and then their common gas sensing performances were measured. The response and response time of the NiO-In 2 O 3 gas sensor were more excellent, and thus it was chosen for dynamic measurement. We perform periodic temperature modulated dynamic measurement of six common VOCs. By comparing and analyzing the response curves under common heating waveforms, a combined waveform of rectangular wave and triangular wave is customized as the heating waveform. We can obtain response curves with rich features and high amplitude, which is beneficial to the qualitative and quantitative identification of VOCs. The gas types are first identified employing a support vector machine (SVM) with 100% accuracy with a stepwise identification strategy. Subsequently, the data set is processed applying principal component analysis (PCA) and the principal components with a cumulative contribution above 90% are selected for concentration prediction. Polynomial fitting is used for quantitative analysis of VOCs with a relative error of regarding 5%. The results are indicative that the combination of rectangular-triangular wave temperature modulation and stepwise identification scheme can effectively solve the issue of underdeveloped selectivity of gas sensors. • The sensing properties of In 2 O 3 and NiO-In 2 O 3 composites to VOCs were compared and NiO-In 2 O 3 was more excellent. • A combined waveform of rectangular and triangular was customized as the heating waveform. • The gas types were identified employing a support vector machine (SVM) with 100% accuracy. • Concentration prediction was performed applying a combined algorithm with a relative error of regarding 5%.