Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Abstract

The deconvolution of the molecular weight distribution (MWD) of polyolefins into Schultz–Flory most probable distributions has become the standard method to identify the number of site types on multiple‐site‐type olefin polymerization catalysts such as Ziegler–Natta, Phillips, and some supported metallocenes. This method has been used to quantify the effect of polymerization conditions and catalyst formulations on polyolefin MWD and olefin polymerization kinetics. Related methods have also been developed to deconvolute other polyolefin microstructure features, such as the chemical composition and comonomer sequence length distributions. In this paper, I explain the premises behind these deconvolution models and review the publications in this area, highlighting the advantages, disadvantages, and misuses of these methods. I also propose a revised formulation on how to model the MWD of polyolefins made with multiple‐site‐type catalysts using ratio distributions for propagation and chain transfer frequencies. The main objective of this overview article is to highlight the strengths, but also show the pitfalls, of polyolefin microstructure deconvolution methods.

Influence of the longest ethylene and isotactic propylene sequences on crystallization elution fractionation of ethylene/propylene copolymers

“…Figure 11B shows that this is not always the case, perhaps in part because the experimental CCDs may not be completely uniform—due to fluctuations in the polymerization conditions—and also because CEF profiles are related to, but are not exactly the same as, the actual copolymer CCD. [ 36 ]…”

“…Figure 11B shows that this is not always the case, perhaps in part because the experimental CCDs may not be completely uniform-due to fluctuations in the polymerization conditions-and also because CEF profiles are related to, but are not exactly the same as, the actual copolymer CCD. [36] Tables 5 and 6 list the parameters estimated for the ethylene-rich and propylene-rich polymers, respectively. Table 5 shows that the values of A c , B c , A d , and B d for the ethylene-rich samples decrease as the propylene content increases because their crystallization and dissolution temperatures also decrease, according to Equations ( 9), ( 10), (15), and ( 16).…”

Archie E. Hamielec memorial issue

“…The authors of the articles in this issue join us from many countries and institutions across the globe (Figure 1 and Table 1) to thank Archie for his enduring legacy in polymerization reaction engineering, a field of academic and industrial interest that he established practically single‐handedly. [ 1–45 ]…”

Using Probability Models to Design the Microstructure of Linear Olefin Block Copolymers

“…As a bonus, these solutions also demand much shorter computational times. 13,37,38 In this work, we developed probability models to describe distributions for the number of blocks (NBD) in the copolymer population�as well as the lengths of the hard and soft blocks�molecular weight (MWD), and chemical composition (CCD) of the OBCs. The results from the proposed closed-form solutions were validated by Monte Carlo simulations.…”