Odor discrimination and identification by graphene-based electronic nose system

Olfaction is an evolutionary old sensory system, which provides sophisticated access to information about our surroundings. Inspired by the biological example, gas sensors in combination with efficient machine learning techniques aim to achieve similar performance and thus to digitize the sense of smell, which is termed as electronic nose (e-nose). Despite the significant progress of e-noses system, their compactness still remains challenging due to the complex layout design of sensor arrays with a multitude of receptor types or sensor materials, and the high working temperature. In this talk, we will present the odor discrimination and identification performance of graphene nanosensor-based electronic olfaction system. The developed prototype exhibits excellent odor discrimination and identification performance at room temperature, maximizing the obtained results from a single nanosensor. The underling adsorption mechanism between analyte gas molecules and functionalized graphene materials is elucidated by molecular dynamic simulations and density functional theory (DFT) calculations. This work may facilitate miniaturization of e-noses, digitization of odors, and distinction of volatile organic compounds (VOCs) in various emerging applications.