Unspoken aspects of chain shuttling ...
Document type :
Article dans une revue scientifique: Article original
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Title :
Unspoken aspects of chain shuttling reactions: Patterning the molecular landscape of olefin multi-block copolymers
Author(s) :
Saeb, Mohammad Reza [Auteur]
Institute for Color Science and Technology
Mohammadi, Yousef [Auteur]
Kermaniyan, Tayebeh Sadat [Auteur]
Amirkabir University of Technology [AUT]
Zinck, Philippe [Auteur]
Unité de Catalyse et Chimie du Solide - UMR 8181 [UCCS]
Stadler, Florian J. [Auteur]
Shenzhen University [Shenzhen]
Institute for Color Science and Technology
Mohammadi, Yousef [Auteur]
Kermaniyan, Tayebeh Sadat [Auteur]
Amirkabir University of Technology [AUT]
Zinck, Philippe [Auteur]
Unité de Catalyse et Chimie du Solide - UMR 8181 [UCCS]
Stadler, Florian J. [Auteur]
Shenzhen University [Shenzhen]
Journal title :
Polymer
Volume number :
116
Pages :
55-75
Publisher :
Elsevier
Publication date :
2017-05-05
ISSN :
0032-3861
English keyword(s) :
Olefin multi-block copolymer
Artificial neural network
Chain shuttling reaction
Tailored copolymer
Molecular pattern
Artificial neural network
Chain shuttling reaction
Tailored copolymer
Molecular pattern
HAL domain(s) :
Chimie/Catalyse
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
ENSCL
CNRS
Centrale Lille
Univ. Artois
Université de Lille
CNRS
Centrale Lille
Univ. Artois
Université de Lille
Collections :
Research team(s) :
Catalyse et synthèse éco-compatible (CASECO)
Submission date :
2019-09-25T14:05:38Z
2021-03-29T14:57:56Z
2021-06-02T10:05:30Z
2021-09-07T09:00:43Z
2021-03-29T14:57:56Z
2021-06-02T10:05:30Z
2021-09-07T09:00:43Z
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