Toward Olefin Multiblock Copolymers with Tailored Properties: A Molecular Perspective,Macromolecular Theory and Simulations

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Toward Olefin Multiblock Copolymers with Tailored Properties: A Molecular Perspective
Macromolecular Theory and Simulations ( IF 1.8 ) Pub Date : 2021-04-13 , DOI: 10.1002/mats.202100003
Yousef Mohammadi ₁ , Mohammad Reza Saeb ₂ , Alexander Penlidis ₃ , Esmaiel Jabbari ₄ , Florian J. Stadler ₅ , Philippe Zinck ₆ , Eduardo Vivaldo‐Lima ₇

Affiliation

Recent progress in macromolecular reaction engineering has enabled the synthesis of sequence-controlled polymers. The advent of olefin block copolymers (OBCs) via chain shuttling polymerization of ethylene with α-olefins has opened new horizons for the synthesis of polyolefins having a dual character of thermoplastics and elastomers. Nevertheless, the use of two catalysts with different comonomer selectivities and a chain shuttling agent, dragging and dropping live chains between active catalyst centers, made precise tailoring of OBCs microstructure containing hard and soft units a feasible challenge. This work discusses the possibility of predicting properties of OBCs from its simulated molecular patterns. The microstructural characteristics of OBCs are discussed in terms of topology-related and property-related features. An intelligent tool, which combines the benefits of kinetic Monte Carlo simulation and artificial neural network modeling, is used to explore the connection between polymerization recipe (catalyst composition, ethylene to 1-octene monomer ratio, and chain shuttling agent level) and topology-related as well as property-related microstructural features. The properties of target OBCs are reflected in the hard block percent, the number of 1-octene units in the copolymer chains, and the longest ethylene sequence length of the hard and soft segments.

中文翻译：

面向具有定制性能的烯烃多嵌段共聚物：分子视角

大分子反应工程学的最新进展使得能够合成序列控制的聚合物。经由乙烯与α-烯烃的链穿梭聚合的烯烃嵌段共聚物（OBC）的出现为合成具有热塑性和弹性体双重特征的聚烯烃开辟了新的前景。然而，使用具有不同共聚单体选择性的两种催化剂和链穿梭剂，在活性催化剂中心之间拖放活性链，使得精确地定制包含硬和软单元的OBCs微结构成为可行的挑战。这项工作讨论了从其模拟的分子模式预测OBCs性质的可能性。从拓扑相关和特性相关的角度讨论了OBC的微观结构特征。一种智能工具它结合了动力学蒙特卡洛模拟和人工神经网络建模的优点，用于探索聚合配方（催化剂组成，乙烯与1-辛烯单体的比例以及链穿梭剂含量）与拓扑相关以及性能之间的联系。相关的微结构特征。目标OBC的特性反映在硬嵌段百分比，共聚物链中1-辛烯单元的数量以及硬链段和软链段的最长乙烯序列长度上。

更新日期：2021-05-25

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