Stochastic Model of Randomly End-Linked Polymer Network Microregions
Stochastic Model of Randomly End-Linked Polymer Network Microregions
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DOI：
10.1021/acs.macromol.0c01346
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发表时间：
2019-08
期刊：
影响因子：
5.5
通讯作者：
Sam C. P. Norris;A. Kasko;T. Chou;M. D’Orsogna
中科院分区：
文献类型：
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作者：
Sam C. P. Norris;A. Kasko;T. Chou;M. D’Orsogna
交联聚合物网络的聚合和形成是制造、材料制造中的重要过程，并且在水合聚合物网络的情况下，是生物医学材料的合成、药物递送和组织工程的重要过程。虽然相当多的研究致力于聚合物网络的建模，以确定平均，平均场，全球性质，有很少的研究，专门研究的变化，组成跨“微区”（由大量但有限数量的聚合物网络链组成）。这里，我们对聚合物网络的随机形成进行数学建模，所述聚合物网络由经历末端连接凝胶化过程的线性同双官能网络链组成。我们引入了一个主方程，描述了可能的网络微区配置的概率作为时间和反应程度的函数的演变。我们特别关注网络形成的动力学和凝胶微区的统计变异性，特别是在中间程度的反应。我们还考虑可能的退火效应和研究如何合作的两个末端基团之间的绑定在一个单一的网络链影响网络的形成。我们的研究结果允许更详细和彻底的理解聚合物网络的动力学和网络特性的可变性。
Polymerization and formation of crosslinked polymer networks are important processes in manufacturing, materials fabrication, and in the case of hydrated polymer networks, synthesis of biomedical materials, drug delivery, and tissue engineering. While considerable research has been devoted to the modeling of polymer networks to determine averaged, mean-field, global properties, there are fewer studies that specifically examine the variance of the composition across "micro-regions" (composed of a large, but finite, number of polymer network strands) within the larger polymer network.Here, we mathematically model the stochastic formation of polymer networks comprised of linear homobifunctional network strands that undergo an end-linking gelation process. We introduce a master equation that describes the evolution of the probabilities of possible network micro-region configurations as a function of time and extent of reaction. We specifically focus on the dynamics of network formation and the statistical variability of the gel micro-regions, particularly at intermediate extents of reaction. We also consider possible annealing effects and study how cooperative binding between the two end-groups on a single network-strand affects network formation. Our results allow for a more detailed and thorough understanding of polymer network dynamics and variability of network properties.