Single Sensor-Based E-Nose via Nanoheater Temperature Modulation for Ultralow Power Gas Identification

We demonstrate a single MOS-type gas sensor-based electronic nose (e-nose) capable of ultralow-power gas classification and concentration analysis. In contrast to conventional e-nose systems that rely on arrays of gas sensors, our approach utilizes a suspended onedimensional nanoheater platform for the operation of a metal oxide semiconductor-type gas sensor. This single sensor generates six distinct gas responses within a threesecond cycle through multi-amplitude duty cycling. These signals capture gas-surface interactions across different thermal states, providing rich feature diversity. Using these multiple features, gas species and concentrations can be accurately identified via deep learning, achieving approximately 95% accuracy in gas-species discrimination. In addition to the low power consumption due to the heater's nano-sized architecture, the dutycycling strategy reduces power consumption further by up to$\sim 95 {\%}$compared to continuous operation, enabling stable e-nose functionality at ultralow power levels ($\sim 360 \mu \mathrm{W}$). Furthermore, the 1D nanoheater-based sensors are fabricated via wafer-level microfabrication processes, ensuring cost-effective device manufacturing.