预测α-环糊精在聚乙二醇轴上组装的多尺度框架。

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Soft Matter Pub Date : 2024-11-20 DOI:10.1039/d4sm01048e
Cameron D Smith, Chenfeng Ke, Wenlin Zhang
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引用次数: 0

摘要

控制聚合物轴(如聚乙二醇 (PEG) 上的α-环糊精 (αCD))上的环分布对于赋予滑环凝胶和基于聚氧乙烯的网络稳健的机械性能至关重要。之前的实验表明,聚合物末端的官能化可以调节 PEG 上 αCD 的覆盖率。为了探索这一设计规则,我们提出了一个预测 αCD 在裸 PEG 和功能化 PEG 上组装的多尺度框架。我们的方法将全原子分子动力学与二维(2D)伞状取样相结合,计算了将 αCD 穿线到末端被各种分子官能化的 PEG 上的自由能景观。结合预测的自由能图谱以及稀溶液中αCD和聚合物扩散的晶格处理方法,我们构建了一个动力学蒙特卡罗(kMC)模型,用于预测αCD沿聚合物轴的数量和链内分布。我们的模型预测了链长、浓度和穿线障碍对端功能化聚伪紫杉烷超分子结构的影响。通过简单的修改,我们的方法可以扩展到探索具有先进网络结构的聚二十六烷基材料的设计规则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multi-scale framework for predicting α-cyclodextrin assembly on polyethylene glycol axles.

Controlling the distribution of rings on polymer axles, such as α-cyclodextrin (αCD) on polyethylene glycol (PEG), is paramount in imparting robust mechanical properties to slide-ring gels and polyrotaxane-based networks. Previous experiments demonstrated that the functionalization of polymer ends could modulate the coverage of αCDs on PEG. To explore the design rule, we propose a multi-scale framework for predicting αCD assembly on bare and functionalized PEG. Our approach combines all-atom molecular dynamics with two-dimensional (2D) umbrella sampling to compute the free energy landscapes of threading αCDs onto PEG with ends functionalized by various moieties. Together with the predicted free energy landscapes and a lattice treatment for αCD and polymer diffusion in dilute solutions, we construct a kinetic Monte Carlo (kMC) model to predict the number and intra-chain distribution of αCDs along the polymer axle. Our model predicts the effects of chain length, concentration, and threading barrier on the supramolecular structure of end-functionalized polypseudorotaxane. With simple modifications, our approach can be extended to explore the design rule of polyrotaxane-based materials with advanced network architectures.

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来源期刊
Soft Matter
Soft Matter 工程技术-材料科学:综合
CiteScore
6.00
自引率
5.90%
发文量
891
审稿时长
1.9 months
期刊介绍: Where physics meets chemistry meets biology for fundamental soft matter research.
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