Modelling the Impact of Graphene Coating of Different Thicknesses on Polyimide Substrate on the Secondary Electron Yield

Abstract

Polyimide material is widely used in the aerospace field, but its secondary electron emission yield is high. In this study, a graphene coating was used to suppress its secondary electron emission, and the secondary electron emission yield of graphene-coated materials with different thicknesses was calculated using the GEANT4 numerical simulation method. The suppression effect of different thicknesses of graphene coatings on the secondary electron emission was analyzed. The simulation results showed that the optimal graphene coating thicknesses for the lowest secondary electron yield of polyimide materials were 1 nm and 5 nm, which reduced the secondary electron emission yield by 13% in terms of simulation. The 5 nm graphene coating reduced the secondary electron emission yield by 6% compared to the polyimide material from an experimental perspective. The 5 nm coating showed better results at higher energies and was experimentally verified by preparing five layers of graphene coating, which showed good agreement between the simulation and experiment. Meanwhile, with the increase in graphene coating thickness, the surface secondary electron emission displacement range decreased, and the secondary electrons produced at the surface were of low energy. The results of this study can provide technical reference for polyimide in aerospace applications and secondary electron emission simulation.

Simulation Models in a Fluidity Test of the Al-Si Alloy

“…The simultaneous influence of four factors (operator, temperature of the melt, pouring velocity, and temperature of the mold) on the fluidity value was solved using a multiple linear regression analysis (multiple regression, program EXCEL → LINEST). The value of the coefficient of determination R 2 = 0.8549 indicates a strong relationship between the considered input factors and fluidity [25,26]. The simultaneous influence of four factors (operator, temperature of the melt, pouring velocity, and temperature of the mold) on the fluidity value was solved using a multiple linear regression analysis (multiple regression, program EXCEL → LINEST).…”

“…The simultaneous influence of four factors (operator, temperature of the melt, pouring velocity, and temperature of the mold) on the fluidity value was solved using a multiple linear regression analysis (multiple regression, program EXCEL → LINEST). The value of the coefficient of determination R 2 = 0.8549 indicates a strong relationship between the considered input factors and fluidity [25,26].…”

“…An example of the use of Equation ( 15) is the graph of the dependence of fluidity on the melt temperature and pouring velocity in a "horizontal" mold preheated to 120 °C, as seen in Figure 6. With the development of science and technology, simulation and modeling techniques are becoming more and more mature and stable [26]. The Monte Carlo method was to evaluate the stability of the model used, i.e., how will the value of fluidity, calculated with Equations ( 15) and ( 16), change when the input data are given in the form of intervals and the experiment is repeated several times.…”

“…The Monte Carlo method was used to evaluate the stability of the model used, i.e., how will the value of fluidity, calculated with Equations ( 15) and ( 16), change when the input data are given in the form of intervals and the experiment is repeated several times. This method is characterized by higher efficiency, faster time, and lower costs than other experimental measurements [26].…”