Crystallization rates of matched fractions of MgCl2-supported Ziegler-Natta and metallocene isotactic poly(propylene)s.: 1.: The role of chain microstructure

The microstructures of two poly(propylene)s with matched molar masses and overall defect concentrations are inferred from the crystallization behavior of their narrow molar mass fractions. One poly(propylene) was produced with a MgCl2-supported Ziegler-Natta catalyst and the other with a metallocene catalyst. The fractions obtained from the metallocene isotactic poly(propylene) display a range in molar masses but each has the same defect concentration indicating a uniform intermolecular concentration of defects in the parent metallocene isotactic poly(propylene). These fractions provide direct evidence of the single site character of the metallocene catalyst. The variations of crystallization rates with molar mass reflect different chain diffusion/transport phenomena that are governed by the remnant entanglement state of the melt during crystallization. The molar mass fractions obtained from the ZN-iPP confirm that the interchain distribution of the nonisotactic content is broad in this polymer. The stereodefects are more concentrated in the low molar mass fractions. Furthermore, the invariance of the linear growth rates among the ZN fractions and the lack of formation of any significant content of the gamma polymorph, even in the most defected fraction, is consistent with a nonrandom, blocky intramolecular distribution of defects in the ZN-iPP molecules. In contrast to the growth rates, the overall crystallization rates are a direct function of the primary nucleation density, which varies among the fractions and the unfractionated iPPs. Hence, the measured overall crystallization rates would be correlated with nucleation density and not necessarily with the microstructure of the iPP molecules. The crystallization data are also interpreted in light of results from pentad/heptad distributions predicted by two-state and three-state statistical models. Parameters from the models allow the prediction of sequence distribution curves that could be used to evaluate each of the models as to their consistency with the crystallization rate data.