Bridging Field Theory and Ion Pairing in the Modeling of Polyelectrolytes and Complex Coacervation

Bridging Field Theory and Ion Pairing in the Modeling of Polyelectrolytes and Complex Coacervation
复制标题

DOI:
10.1021/acs.macromol.3c01020
复制
发表时间:
2023-07-26
期刊:
影响因子:
5.5
通讯作者:
Qin,Jian
Qin,Jian
中科院分区:
化学1区
文献类型:
--
作者:
Sing,Charles E.;Qin,Jian

文献摘要

相似文献

复凝聚是由带相反电荷的大分子物质之间的静电吸引力驱动的相分离现象。聚电解质之间的凝聚的兴趣最近激增已被这些系统的实验表征的基本进展和认识到它们的相关性的生物系统,如生物分子缩合物以及工业相关的消费品。同时,有几个理论能够预测复杂的凝聚,用于解释这些实验观察。虽然凝聚的基本物理已经有了普遍的概念共识,但这些理论方法迄今为止仍然是不同的。聚合物场论、液态理论、离子配对理论和标度理论都提供了有用的见解,但是每个候选理论的假设如何相互关联仍然在很大程度上未被探索。在本文中,我们试图展示如何从一个单一的出发点,使用集群扩展作为讨论的基础上,这两个类的模型都包括在场论和离子配对理论的相互作用。这使我们能够比较和对比这些方法,评估每个模型应该相关的条件,并提出改进或参数化现有模型的方法。
Complex coacervation is a phase separation phenomenon driven by the electrostatic attraction between oppositely charged macromolecular species. A recent surge of interest in coacervation between polyelectrolytes has been driven by both fundamental advances in experimental characterization of these systems and recognition of their relevance for both biological systems such as biomolecular condensates as well as industrially relevant consumer products. Concomitantly, there have been several theories capable of predicting complex coacervation that are used to explain these experimental observations. While there has been a general conceptual consensus on the underlying physics of coacervation, these theoretical approaches have so far remained distinct. Polymer field theory, liquid state theory, ion pairing theories, and scaling theories all provide useful insights, but how the assumptions of each candidate theory are interrelated remains largely unexplored. In this paper, we attempt to show how two such classes of models can be derived from a single starting point using cluster expansions as the basis for discussing which interactions are included in both field theory and ion pairing theory. This allows us to compare and contrast these approaches, evaluate conditions where each model should be relevant, and suggest ways in which existing models can be improved or parameterized.