BPN994: Atomically Dispersed Supported Metal Catalysts for Robust Chemiresistive Gas Sensors (New Project)

Abstract: 

Long-term stability of chemiresistive gas sensors is essential for their use in industrial and residential safety and air-quality monitoring systems. Incorporation of noble metals into the gas sensors has been proved to be an effective strategy to enhance their sensitivity and selectivity. However, noble metal particles are prone to poisoning, resulting in catalyst deactivation. Atomically dispersed supported metal catalysts constitute a new class of materials that contains isolated individual atoms or synergistically coupled few-atom ensembles dispersed on, and/or coordinated with the surface atoms of appropriate solid supports. Examples include noble metals such as Pd and Pt on metal oxides, graphene, graphene oxide and various other two-dimensional materials. These materials have emerged as a rapidly developing class of catalysts offering the advantage of the most efficient use of noble metals combined with unique properties considerably different from their conventional nanoparticle equivalents. These include excellent selectivity for gas adsorption, electron transport and improved resistance to poisoning and coke formation. Herein, we aim to develop atomically dispersed Pd catalysts supported on two different support materials, namely graphene oxide and tin (IV) oxide for the fabrication of robust gas sensors for in-door air quality monitoring. We propose to elucidate their gas sensing mechanisms relevant to target gases (H2, CO and CH4) through their detailed structural, chemical, electrical and optical characterization. Sensitivity, selectivity and response of the fabricated sensors towards the target gases, and recovery time, detection limit, stability, and working temperature of the sensors at different humidity levels are being investigated.

Project currently funded by: Industry Sponsor

Publication date: 
March 8, 2023
Publication type: 
BSAC Project Materials (Current)
Citation: 
PREPUBLICATION DATA - ©University of California 2023

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