Feasibility Study of High-Voltage Ion Mobility for Gas Identification Based on Triboelectric Power Source

Abstract

We report a type of miniaturized and self-powered gas identification platform for wearable applications that works on the principle of ion mobility transients offering a high degree of selectivity for a variety of gas species. The self-powered operation of the sensor exploited the high voltage output from a systematically designed triboelectric nanogenerator (TENG). The multi-layer TENG platform provided a voltage of the order kV just by finger triggering, which was further leveraged in a special type of electrode (tip-plate) configuration making it possible to obtain plasma discharge of a wide range of gas molecules at atmospheric condition. By adding an additional collector plate at a specific distance within the device configuration, we successfully demonstrated different transient characteristics for different gas molecules which can be directly attributed to their differences in terms of ion-mobility. Our analysis clearly indicated unique and repeatable discharge characteristics at various mixture conditions and atmospheric pressure. We further employ machine learning to classify different gases based on the transient dynamics observed at the collector plate. High classification accuracy was obtained for four different gases even using a shallow network that indicated the potential of the proposed platform as a low-power, small foot-print wearable Internet of Things (IoTs) device for gas leak detection. It is envisioned that the proposed platform can enable early detection of gas species by incorporating the transient development of the multitude of time-domain finger-prints into the machine learning model.