Artificial Neural Network Prediction and Mechanism Analysis for Migration of Environmental Contaminant Cyclic Organosiloxane Oligomer from Silicone Rubber

Migration testing of cyclic organosiloxane oligomer molecules from silicone rubber into food simulants was performed as a function of three factors: temperature, time, and food simulant type (i.e., n -hexane, n -heptane, and ethyl alcohol). A back-propagation artificial neural network (BP-ANN) was proposed to study the migration properties with an average prediction accuracy of 0.997. The analysis of the ANN indicated that high-temperature and n -hexane environments accelerate the migration of cyclic organosiloxane oligomer molecules from silicone rubber. A molecular dynamics simulation was employed to illustrate the migration mechanism at the molecular level. The results indicated that the high-temperature and n -hexane condition led to a high interaction energy, fractional free volume, and diffusion coefficient and a low difference solubility parameter of the system, which accelerate the molecules transfer process and threaten the safety of the materials. These fundamental studies provide a comprehensive understanding of the migration of cyclic organosiloxane oligomer contaminants and guidance for the failure prediction of materials.