Low‐Power, Multi‐Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases. Issue 3 (24th January 2023) - Henriquez, Dionisio V. Del Orbe; Kang, Mingu; Cho, Incheol; Choi, Jungrak; Park, Jaeho; Gul, Osman; Ahn, Junseong; Lee, Dae‐Sik; Park, Inkyu
Abstract: Toxic and flammable gases pose a major safety risk in industrial settings; thus, their portable sensing is desired, which requires sensors with fast response, low‐power consumption, and accurate detection. Herein, a low‐power, multi‐transduction array is presented for the accurate sensing of flammable and toxic gases. Specifically, four different sensors are integrated on a micro‐electro‐mechanical‐systems platform consisting of bridge‐type microheaters. To produce distinct fingerprints for enhanced selectivity, the four sensors operate based on two different transduction mechanisms: chemiresistive and calorimetric sensing. Local, in situ synthesis routes are used to integrate nanostructured materials (ZnO, CuO, and Pt Black) for the sensors on the microheaters. The transient responses of the four sensors are fed to a convolutional neural network for real‐time classification and regression of five different gases (H2, NO2, C2 H6 O, CO, and NH3 ). An overall classification accuracy of 97.95%, an average regression error of 14%, and a power consumption of 7 mW per device are obtained. The combination of a versatile low‐power platform, local integration of nanomaterials, different transduction mechanisms, and a real‐time machine learning strategy presented herein helps advance the constant need to simultaneously achieve fast, low‐power, and selective gas sensing of flammable and toxic gases. Abstract : A low‐power, multi‐transduction nanosensor array is demonstrated by integrating nanostructured materials on bridge‐type microheaters for accurate sensing of flammable and toxic gases. The nanosensor array operates based on chemiresistive and calorimetric mechanisms for enhanced selectivity. By applying transient responses of the nanosensor array to a convolutional neural network, it is possible to accurately identify flammable and toxic gases in real time.