Simulation and experimental study on adsorption of SF6 decomposition components and optical gas cell materials

Abstract

Fourier transform infrared spectroscopy (FTIR) is widely used in gas detection of SF6 decomposition components. A gas cell is an important element in the detection system, which can directly affect the accuracy and sensitivity of detection. During the long-term use of the gas cell, SF6 decomposition components may be adsorbed on the inner wall of the gas cell, which affects the detection results and the life of the gas cell. Therefore, the study on the adsorption between the gas cell material and the SF6 decomposition component is of great significance for improving the accuracy of detection. In this paper, based on the density functional theory, the adsorption process and adsorption degree of SF6 decomposition components (SOF2, SO2F2, CO, SO2, and H2S) with three typical main gas cell materials (Al, Cu, and Fe) and two gas cell coating materials (Teflon and Au) were simulated. It was found that the adsorption of Teflon and Au with gas was weak in the five materials and that of Teflon was the weakest. The adsorption energy of the three main gas cell materials was 2.65–6.31 times that of Teflon. The simulation results were also verified by FTIR and the GC-Materials Studio (MS) method. FTIR results showed that Teflon and Au had the weakest influence on the infrared spectral absorption peak and the effect of the two materials on SOF2 and SO2F2 gas absorption peaks is only less than 0.1. The results of gas chromatograph-mass spectrometer (GC-MS) showed that the effects of Teflon materials and Au on gas concentration were 2%–9.41% and the effects of Cu, Fe, and Al on gas concentration were 4.48%–65.43%. Therefore, Au and Teflon are suitable as coating materials for gas cells, which can reduce the adsorption of gas and improve the accuracy of infrared spectroscopy measurement. The results of this paper provide a reference for the development of application of optical gas detection technology in SF6 decomposition component detection.