Selective Detection of SO₂from Mixed SF₆Decomposition Products via Au/ZnO Nanorods with (101−0[jls-end-space/]) Plane Exposure

SF₆decomposition composition analysis (DCA) is an effective method for identifying partial discharge (PD) fault types in gas-insulated switchgear (GIS). Among various SF₆decomposition byproducts, the SO₂concentration exhibits a strong correlation with PD types, making it a key indicator for PD detection. However, detecting SO₂in an oxygen-free SF₆environment using semiconductor sensors presents challenges, such as low response values, slow recovery, and poor repeatability. To address these limitations, three ZnO micro/nanomaterials are synthesized via the hydrothermal method in this work. The ZnO nanowire sensor demonstrates optimal selectivity for 50 ppm of SO₂(S = 9.18), indicating that reduced grain size and increased exposure of the (101−0[jls-end-space/]) facet significantly enhance SO₂sensing performance. Furthermore, loading 2 at.% Au nanoparticles onto ZnO nanorods increases the response value to 27.43, while simultaneously reducing the response and recovery times to 21.86 and 28.38 s, respectively. In situ diffuse reflectance infrared Fourier transform (DRIFT) experiments reveal that SO₂molecules directly exchange electrons with Au/ZnO material in the oxygen-free SF₆background, independent of surface-adsorbed oxygen. The doping of Au nanoparticles facilitates electron exchange and accelerates the SO₂desorption processes. To assess the real-time detection capability of the proposed Au/ZnO sensor, an SF₆partial discharge simulation platform is established, and discharge experiments are conducted at 21.60 and 24.00 kV for 2 h. The SO₂concentration detected by the Au/ZnO sensor deviates by less than 3.62 ppm from gas chromatography (GC) results, demonstrating its strong potential for real-time SO₂detection in GIS equipment.