Nodular polypropylene from reactor granule technology for the production of a stiff and ductile material.

No abstract

Understanding the complex crystallization behavior of isotactic polypropylene (iPP) in conditions comparable to those found in polymer processing, where the polymer melt experiences a combination of high shear rates and elevated pressures, is key for modeling and therefore predicting the final structure and properties of iPP products. Coupling a unique experimental setup, capable to apply wall shear rates similar to those experienced during processing and carefully control the pressure before and after flow is imposed, with in situ X-ray scattering and diffraction techniques (SAXS and WAXD) at fast acquisition rates (up to 30 Hz), a well-defined series of short-term flow experiments are carried out using 16 different combinations of wall shear rates (ranging from 110 to 440 s–1) and pressures (100–400 bar). A complete overview on the kinetics of structure development during and after flow is presented. Information about shish formation and growth of α-phase parents lamellae from the shish backbones is extracted from SAXS; the overall apparent crystallinity evolution, amounts of different phases (α, β, and γ), and morphologies developing in the shear layer (parent and daughter lamellae both in α and γ phase) are fully quantified from the analysis of WAXD data. Both flow rate and pressure were found to have a significant influence on the nucleation and the growth process of oriented and isotropic structures. Flow affects shish formation and the growth of α-parents; pressure acts on relaxation times, enhancing the effect of flow, and (mainly) on the growth rate of γ-phase. The remarkably high amount of γ-lamellae found in the oriented layer strongly indicates the nucleation of γ directly from the shish backbone. All the observations were conceptually in agreement with the flow-induced crystallization model framework developed in our group and represent a unique and valuable data set that will be used to further validate and implement our numerical modeling, filling the gap for quantitatively modeling crystallization during complicated processing operations like injection molding.