New Alternating Current Noise Analytics Enables High Discrimination in Gas Sensing

Feature analysis has been increasingly considered as an important way to enhance the discrimination performance of gas sensors. In this work, a new analytical method based on alternating current noise spectrum is developed to discriminate chemically and structurally similar gases with remarkable performance. Compared with the conventional analytics based on the maximum, integral, and time of response, the noise spectrum of electrical response introduces a new informative feature to discriminate chemical gases. In experiment, three chemically and structurally similar gases, mesitylene, toluene, and o-xylene, are tested on ZnO thin film gas sensors. The result indicated that the noise analytics assisted by the support vector machine algorithm discriminated these similar gases with 94.2% in precision, about 20% higher than those alternating current noise analytics is very promising for application in sensorsobtained by conventional methods. Such a new for high discrimination precision.