Optimization of the Transient Feature Analysis for Graphene Chemical Vapor Sensors: A Comprehensive Study

Abstract

Graphene is very attractive for chemical vapor sensor applications due to its atomically thin structure and unique electronic properties. In this paper, a comprehensive study of transient feature analysis is carried out to optimize the discrimination capability of graphene chemical vapor sensors. Three new transient feature models, enhanced exponential fitting, logarithmic linear fitting and piecewise linear fitting, have been proposed and optimized for precise discrimination against different chemical vapors. Compared with the conventional peak-to-peak method, these three new fitting algorithms significantly improved the prediction accuracy. Among them, the enhanced exponential fitting algorithm reaches the highest prediction accuracy of 92%, about 25% better than the conventional method. Although logarithmic and piecewise models exhibit slightly lower accuracy, they are much simpler and faster in computation than the exponential models. In the application of Internet of Things involving large number of sensors and sensor networks, a fast and accurate transient feature analysis is very important. This paper described a new route in achieving high-performance sensor technologies.