Unspoken aspects of chain shuttling reactions: Patterning the molecular landscape of olefin multi-block copolymers

Abstract

Molecular landscape of olefin block copolymers (OBCs) was patterned by hybridizing capabilities of Kinetic Monte Carlo (KMC) and Artificial Neural Network (ANN) stochastic modeling approaches to explore complexities with chain shuttling copolymerization of ethylene with α-olefins. Theoretical data on chain microstructure were obtained by an in-house KMC simulator. The interdependence between microstructure and operating conditions was uncovered by ANN modeling. The average number of linkage points per OBC chain is monitored as a direct criterion reflecting the multi-block nature of OBCs. We also quantified hard and soft block length and ethylene sequence length of both blocks in terms of catalyst composition, ethylene to 1-octene ratio, and chain shuttling agent level, giving useful insights to be applied to developing tailored OBCs. The proposed hybrid stochastic modeling approach successfully predicts the conditions for producing OBCs with predesigned structure; i.e., block length, block number, and ethylene sequence length in hard and soft segments of OBC. As a unique feature of this work, we suggest operation condition for developing and identifying new families of OBCs with microstructures that were previously unexplored.