Pd4 cluster decorated SnO2 nanowire for detecting characteristic gases in oil-immersed transformers: A theoretical and experimental study

In this paper, Pd-4 cluster decorated SnO2 (Pd-4-SnO2 ) nanowire is considered as a novel gas sensor alternative for detecting the typical dissolved gases in transformer oil, including hydrogen (H-2) and acetylene (C2H2). Specifically, the most stable geometric structures of gas molecules, Pd-4-SnO2 and gases adsorbed by Pd-4-SnO2 are systematically explored via the density functional theory (DFT) method. The geometric structure formation, density of states (DOS), deformation charge density (DCD) and molecular orbital theory analysis are used to reveal the gas adsorption and sensing mechanism. Simulation calculation results show that Pd(4 & nbsp;)cluster modification can significantly enhance the gas-sensitive response of intrinsic SnO2 , Pd atoms which provide active sites for gas adsorption owing to their outstanding metallic and catalytic behaviors. It also offers excellent adsorption capacity for both gas molecules. Meanwhile, the Pd cluster decorated SnO2 sensing materials are successfully synthesized by electrospinning technology combined with magnetron sputtering process. The micromorphology structure of the synthesized samples are analyzed by using SEM and other microscopic techniques. The gas sensing investigations indicate that this composite nanomaterial could be a potential candidate for gas-sensing applications. The testing data shows high consistency to the simulation conclusion and the results in this work can be applied to produce and promote Pd cluster decorated SnO2 gas sensors, ultimately realizing online monitoring of oil-immersed transformers.

Automated summary

This study considers Pd-4 cluster decorated SnO2 nanowire as a potential candidate for gas sensors and explores its gas adsorption and sensing mechanism using density functional theory. The experimental results show that this composite nanomaterial has high gas-sensitive response and adsorption capacity, making it suitable for online monitoring of oil-immersed transformers.