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A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s

Robert Conka (UGent) , Yoshi Marien (UGent) , Ondrej Sedlacek (UGent) , Richard Hoogenboom (UGent) , Paul Van Steenberge (UGent) and Dagmar D'hooge (UGent)
(2022) POLYMER CHEMISTRY. 13(11). p.1559-1575
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Abstract
The synthesis of well-defined gradient, block-gradient and di-block copolymers with both asymmetric and symmetric compositions considering hydrophilic and hydrophobic monomer units is relevant for application fields, such as drug/gene delivery and (bio)compatibilization. The evaluation of the synthesis success and the resulting polymer structure remains however challenging, as ideally every chain needs to be considered, which is experimentally almost impossible. Matrix-based kinetic Monte Carlo (kMC) simulations provide a solution to this challenge, as they allow to visualize the monomer sequences of individual chains with reliable parameter tuning based on experimental data on average compositions and size exclusion chromatography. Here, such matrix-based kMC simulations are applied to visualize monomer sequences in polymers prepared by living cationic ring-opening polymerization (CROP) of 2-methyl-2-oxazoline (MeOx) and 2-phenyl-2-oxazoline (PhOx), uniquely differentiating between linear and branched chains. For the branched chains, a novel modeling protocol is presented allowing to evaluate their structural (here compositional) organization in a similar manner as linear chains by comparing arm pairs. This delivers an average compositional deviation for these branched species (SDBr) that in combination with the conventional deviation for linear chains (SDLin) and proper weighing with the mass fractions allows to obtain the overall SD. It is highlighted that di-block copolymer synthesis recipes most closely resemble the ideal target structure, benefiting from a semi-batch procedure. Such recipes allow to minimize the contribution of chain transfer to monomer and enable a more fluent transition of linear side products with bad composition in branched chains that by further growth can compensate for the compositional deviation. It is also demonstrated that reaching of the targeted structure is less trivial for a more symmetric composition and that (well-chosen) threshold SD values can be defined allowing to qualify synthesized copolymers as bad, good and excellent, at least for the guide of the eye. A sufficiently low dispersity is necessary to obtain a sufficiently high product quality, but as such is not a sufficient condition to evaluate the structural variation, highlighting the strength of the kMC framework for the identification of optimal synthesis protocols.
Keywords
RING-OPENING POLYMERIZATION, MODEL-BASED DESIGN, N-BUTYL ACRYLATE, RADICAL POLYMERIZATION, DIBLOCK COPOLY(2-OXAZOLINE)S, AMPHIPHILIC, GRADIENT, FEEDING POLICY, ICAR ATRP, POLY(2-ETHYL-2-OXAZOLINE), MICROSTRUCTURE

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MLA
Conka, Robert, et al. “A Unified Kinetic Monte Carlo Approach to Evaluate (a)Symmetric Block and Gradient Copolymers with Linear and Branched Chains Illustrated for Poly(2-Oxazoline)s.” POLYMER CHEMISTRY, vol. 13, no. 11, 2022, pp. 1559–75, doi:10.1039/d1py01391b.
APA
Conka, R., Marien, Y., Sedlacek, O., Hoogenboom, R., Van Steenberge, P., & D’hooge, D. (2022). A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s. POLYMER CHEMISTRY, 13(11), 1559–1575. https://doi.org/10.1039/d1py01391b
Chicago author-date
Conka, Robert, Yoshi Marien, Ondrej Sedlacek, Richard Hoogenboom, Paul Van Steenberge, and Dagmar D’hooge. 2022. “A Unified Kinetic Monte Carlo Approach to Evaluate (a)Symmetric Block and Gradient Copolymers with Linear and Branched Chains Illustrated for Poly(2-Oxazoline)s.” POLYMER CHEMISTRY 13 (11): 1559–75. https://doi.org/10.1039/d1py01391b.
Chicago author-date (all authors)
Conka, Robert, Yoshi Marien, Ondrej Sedlacek, Richard Hoogenboom, Paul Van Steenberge, and Dagmar D’hooge. 2022. “A Unified Kinetic Monte Carlo Approach to Evaluate (a)Symmetric Block and Gradient Copolymers with Linear and Branched Chains Illustrated for Poly(2-Oxazoline)s.” POLYMER CHEMISTRY 13 (11): 1559–1575. doi:10.1039/d1py01391b.
Vancouver
1.
Conka R, Marien Y, Sedlacek O, Hoogenboom R, Van Steenberge P, D’hooge D. A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s. POLYMER CHEMISTRY. 2022;13(11):1559–75.
IEEE
[1]
R. Conka, Y. Marien, O. Sedlacek, R. Hoogenboom, P. Van Steenberge, and D. D’hooge, “A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s,” POLYMER CHEMISTRY, vol. 13, no. 11, pp. 1559–1575, 2022.
@article{8747731,
  abstract     = {{The synthesis of well-defined gradient, block-gradient and di-block copolymers with both asymmetric and symmetric compositions considering hydrophilic and hydrophobic monomer units is relevant for application fields, such as drug/gene delivery and (bio)compatibilization. The evaluation of the synthesis success and the resulting polymer structure remains however challenging, as ideally every chain needs to be considered, which is experimentally almost impossible. Matrix-based kinetic Monte Carlo (kMC) simulations provide a solution to this challenge, as they allow to visualize the monomer sequences of individual chains with reliable parameter tuning based on experimental data on average compositions and size exclusion chromatography. Here, such matrix-based kMC simulations are applied to visualize monomer sequences in polymers prepared by living cationic ring-opening polymerization (CROP) of 2-methyl-2-oxazoline (MeOx) and 2-phenyl-2-oxazoline (PhOx), uniquely differentiating between linear and branched chains. For the branched chains, a novel modeling protocol is presented allowing to evaluate their structural (here compositional) organization in a similar manner as linear chains by comparing arm pairs. This delivers an average compositional deviation for these branched species (SDBr) that in combination with the conventional deviation for linear chains (SDLin) and proper weighing with the mass fractions allows to obtain the overall SD. It is highlighted that di-block copolymer synthesis recipes most closely resemble the ideal target structure, benefiting from a semi-batch procedure. Such recipes allow to minimize the contribution of chain transfer to monomer and enable a more fluent transition of linear side products with bad composition in branched chains that by further growth can compensate for the compositional deviation. It is also demonstrated that reaching of the targeted structure is less trivial for a more symmetric composition and that (well-chosen) threshold SD values can be defined allowing to qualify synthesized copolymers as bad, good and excellent, at least for the guide of the eye. A sufficiently low dispersity is necessary to obtain a sufficiently high product quality, but as such is not a sufficient condition to evaluate the structural variation, highlighting the strength of the kMC framework for the identification of optimal synthesis protocols.}},
  author       = {{Conka, Robert and Marien, Yoshi and Sedlacek, Ondrej and Hoogenboom, Richard and Van Steenberge, Paul and D'hooge, Dagmar}},
  issn         = {{1759-9954}},
  journal      = {{POLYMER CHEMISTRY}},
  keywords     = {{RING-OPENING POLYMERIZATION,MODEL-BASED DESIGN,N-BUTYL ACRYLATE,RADICAL POLYMERIZATION,DIBLOCK COPOLY(2-OXAZOLINE)S,AMPHIPHILIC,GRADIENT,FEEDING POLICY,ICAR ATRP,POLY(2-ETHYL-2-OXAZOLINE),MICROSTRUCTURE}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{1559--1575}},
  title        = {{A unified kinetic Monte Carlo approach to evaluate (a)symmetric block and gradient copolymers with linear and branched chains illustrated for poly(2-oxazoline)s}},
  url          = {{http://dx.doi.org/10.1039/d1py01391b}},
  volume       = {{13}},
  year         = {{2022}},
}

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