Gerrit W.M. Peters, Enrico Troisi, Giovanna Grosso
Eindhoven University of Technology, Department of Mechanical Engineering, Polymer Technology Group, PO Box 513, 5600 MB Eindhoven, The Netherlands
Understanding the complex crystallization behavior of isotactic polypropylene (iPP) in conditions comparable to, e.g. injection molding, 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 the ones 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 on using 16 different combinations of wall shear rates (110 to 440 1/s) 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 parent lamellae from the shish back-bones is extracted from SAXS; the overall apparent crystallinity evolution, amounts of different phases (α, β and γ), and morphologies developing in the shear layer (parents and daughters 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 both the nucleation and the growth process of oriented and isotropic structures. Flow affects both shish formation and the growth of α-parents, pressure acts both on relaxation times, enhancing the effect of flow, and (mainly) on the growth rate of γ-phase . The high number of γ-lamellae found in the oriented layer strongly suggested 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 represents a unique and valuable dataset for model validation.
 E.M. Troisi, H.J.M. Caelers, G.W.M. Peters, Macromolecules (2017). (link)
 P.C. Roozemond, T.B. van Erp, G.W.M. Peters, Polymer, 89, 69-80 (2016). (link)
 T.B. van Erp, P.C. Roozemond, G.W.M. Peters, Macromolecular Theory and Simulations, 22(5), 307-318 (2013) (link)