Low-dimensional metal chalcogenides for wearable gas sensing.

Abstract

Real-time monitoring of the surrounding gas environment, including our inhaled and exhaled atmosphere, is a crucial but underdeveloped technology for personalized healthcare. Recent advancements in wearable sensing technologies and AI algorithms promise the realization of more powerful wearable gas sensing systems, such as electronic noses. However, fundamental studies are still ongoing in seeking efficient gas sensing materials, transducing mechanisms, and device structures to meet the basic requirement of wearability and low power operation. Low-dimensional metal chalcogenides have attracted significant attention in building flexible gas sensors with room-temperature operation. Their controllable synthesis and post-synthesis treatment allow precise manipulation of the gas adsorption and charge transfer process. Their high surface-to-volume ratio, abundant active surface sites, and tunable electronic properties enable high sensitivity and selectivity, and fast response/recovery even without thermal activation. This review begins with an overview of three transducing mechanisms, providing a comprehensive understanding of the gas sensing process. Aiming at achieving efficient transducers, different types of low-dimensional metal chalcogenides, especially the 0D quantum dots and 2D nanosheets families, have been discussed regarding their synthesis methods and key material design strategies. State-of-the-art low-dimensional metal chalcogenide gas sensors are analyzed based on their modifications to the gas adsorption energy, charge transfer rate, and other fundamental parameters. Moreover, potential system construction towards smart and wearable gas sensor devices has been described with the integration of diversified sensor arrays, wireless communication technologies, and AI algorithms. Finally, we propose the remaining challenges and outlook for developing low-dimensional metal chalcogenide wearable gas sensing and eventually achieving accurate gas mixture classification and odor recognition.