Establishment of dynamic prediction model on morphology development of polypropylene/polyamide 6 blends under in-situ fibrillation flow.

In this work, the morphology development of polypropylene (PP)/polyamide 6 (PA6) blends was thoroughly investigated under in-situ fibrillation process, including shear mixing flow and uniaxial elongational flow. By establishing rheological constitutive model based on Trouton Rules, the shear and elongational rheological characteristic parameters of PP and PA6 were obtained for the establishment of the prediction model. In the shear mixing process, different screw speed was applied to evaluate the effects of the shear flow on PA6 original particle size in PP/PA6 blends. In the uniaxial stretching process, the elongational flow field characteristics were obtained through solving dynamic equation with Fourth-order Runge-Kutta method. And the evolution of PA6 droplets in obtained elongational flow field characteristics was analyzed by combining SEM results of PP/PA6 in situ fibrillar composites and theorical parameters of the deformation and breakup of the droplets (the reduced capillary number Ca*). Finally, based on force balance theory, the dynamic model of morphology development of PA6 dispersed phase in PP matrix was defined under the whole in situ fibrillation process. By comparing with experimental values, it was found that the established model was supposed to be able to predict the phase morphology development of polymer droplets well when the original particle size was smaller, and when the original particle size was larger, the coalescence between the droplets should be further considered. [Display omitted] • The polymer phase morphology is developed from spherical to fibrillar under the in-situ fibrillation technique. • The flow field characteristics of the uniaxial elongational flow is solved with four-order Runge-Kutta methods. • The dynamic prediction model describing the phase morphology evolution is established under the whole in situ fibrillation process. • At relatively smaller original particle size, the prediction model fits well with the experimental results. [ABSTRACT FROM AUTHOR]