Study of the effect of size and clay structural parameters on the yield and post-yield response of polymer/clay nanocomposites via a multiscale micromechanical modelling Original Research Article

In order to predict the nanostructure as well as the particle size dependence of the elastic–plastic stress–strain response of polymer/clay nanocomposites, a micromechanical model based upon a multiscale approach starting from the nanostructure is proposed. The multiscale micromechanical model takes into account the interphase between the polymeric matrix and the inorganic reinforcement, and the intercalated nanostructure. Considering the interphase thickness as a characteristic length scale, the nanoparticle size effect is explicitly introduced in the present model. The intercalated nanostructure is taken into account according to an equivalent stiffness method in which the clay stacks are replaced by homogeneous nanoparticles with predetermined equivalent anisotropic stiffness. The physical and mechanical properties of nylon-6/montmorillonite nanocomposites (with clay weight fractions ranging from 1 up to 7.5%) are investigated by means of differential scanning calorimetry, dynamic mechanical analysis, thermogravimetric analysis and video-controlled tensile mechanical tests. The microstructure was characterized by transmission electron microscopy. The amount of interphase was estimated from the thermal analysis. The reinforcing effect of clay is discussed with respect to the multiscale micromechanical model. A parametric study is carried out to investigate the effect of nanoparticle shape and size and nanoparticle structural parameters (i.e. number of clay layers in the nanoparticle and interlayer spacing) on the elastic–plastic stress–strain response of polymer/clay nanocomposites. Comparison of the model results with the experimental data demonstrates that the evaluation of the reinforcing effect of clay involves considering the elastic stiffness and yield stress simultaneously. It was further found that the model correctly predicts the elastic–plastic stress–strain data.