Abstract
The behavior of polymer-layered silicate nanocomposites is modeled through various factorial and mixtures design methodologies in order to optimize the composite performance and to accurately predict the properties especially for the nonpolar polymer systems. The various factors studied for the factorial design are volume fraction of inorganic clay, cation exchange capacity of the montmorillonite (MMT) substrate, and number of octadecyl chains in the ammonium modification ion exchanged on the clay surface. The constrained mixtures design includes the components as weight percent of polymer matrix, organic modification, and inorganic filler as compared to the total weight of the mixture. The mixtures design for compatibilized nanocomposites is also studied by using the components weight percent of polymer, organically modified clay, and the compatibilizer. The predicted properties from these models narrowly match the experimental results indicating the efficiency of the models to correctly represent the polymer nanocomposites system. The model equations are also used to generate response surfaces to help to achieve the required combinations of the factors or components of the system to obtain optimum composite properties. Unlike conventional models which depend on oversimplified assumptions, which are not applicable in reality, these models do not suffer from these limitations and can still predict the composite properties using a set of simple equations.
