A Perspective on Modeling and Characterization of Transformations in the Blocky Nature of Olefin Block Copolymers

Abstract

Traditional characterization methods are still unable to reveal the block structure of olefin block copolymers (OBCs). Therefore, extending the predictability of our well-developed computer code (Mohammadi et al., Macromolecules, 2014, 47, 4778-4789); the blocky nature of OBCs is modeled and characterized. The expanded model could produce diversity of macromolecules varying the concentration of chain-shuttling agent, catalyst ratio, and monomer composition, as key processing variables. The OBCs were screened and distinguished in view of chain related specifics i.e., chain length and chemical composition distribution as well as block related characteristics i.e., number, length and chemical composition of hard and soft blocks. A detailed picture of blockiness was captured and visualized tracing transitions in the microstructure of copolymers, from the case corresponding to blend copolymer (without any shuttling) to OBC (with considerable shuttling) and then to random copolymers, reflecting the significance of the chosen parameters in determining the blockiness of OBCs.

Understanding the Formation of Linear Olefin Block Copolymers with Dynamic Monte Carlo Simulation

“…This is easy to understand, since the CSA also acts as a chain transfer agent, albeit being a reversible one, while increasing the value of k CS only changes how fast the shuttling reaction takes place. Increase in the average number of blocks per chain and decrease in the average length of softand hard-block populations caused by increase in CSA concentration were also reported by Zhang et al and Ahmadi et al [ 13,19 ] Our model, however, provides further detailed description on how CSA concentration affects the properties of each OBC population. Figure 15 b shows how CSA concentration affects PDI.…”

“…Microstructural distributions at the end-of-batch, such as the distribution of number of blocks per chain, block length distribution, chemical composition distribution (CCD), and ethylene sequence length distribution (ESLD), were also simulated. [ 18 ] The effects of processing parameters on the OBC microstructure at the end-of-batch, including chain shuttling agent (CSA) concentration, catalyst composition, and monomer ratio, were also studied by Ahmadi et al [ 19 ] These previous models provide useful guidelines for tailor-making OBCs, especially with respect to hard-and soft-block characteristics. However, detailed microstructures of OBC populations with different number of blocks and blocking structures (Scheme 1 ) and their contributions to the overall microstructural distributions have not yet been revealed in the open literature.…”

Characteristics of linear/branched polyethylene reactor blends synthesized by metallocene/late transitional metal hybrid catalysts

“…The introduction of long-chain branches in the main backbone using dual catalytic system with one catalyst producing the so-called macromer due to high rate of b-hydride elimination and the other being open to incorporate the bulky macromere in the growing chain belongs to this category as well [8,9]. Also, the recent discovery by Dow chemicals on production of olefinic multi-block copolymers (OBC) using a chain shuttling agent, transferring the growing chain between two catalysts with distinguishable difference in comonomer incorporation ability [10][11][12][13][14][15], can also be accounted. However, many of the dual catalytic systems simply benefit from catalysts that produce chains possessing different microstructures independently.…”

Intelligent Monte Carlo: A New Paradigm for Inverse Polymerization Engineering

“…We previously developed a KMC methodology for simulating copolymerization kinetics and microstructure of the virtually synthesized macromolecules within a relatively large simulation volume . In this work, complex OBCs produced by chain shuttling coordination copolymerization reactions have been selected to demonstrate the capabilities of the enhanced IMC approach.…”

“…We previously reported on microstructural changes in the semibatch chain shuttling polymerization of ethylene and 1‐octene using KMC simulation . The KMC simulation was able to quantify many signature distributional characteristics of ethylene/1‐octene copolymerization, such as the block composition, referred to as C 8%, the block length represented by the number‐average degree of polymerization , the relative proportion of the blocks described via the hard block content (HB%), the number of linking points between blocks (LP), ethylene sequence length (ESL), and also the longest ethylene sequence (LES).…”