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预测和调整梳状聚合物涂层纳米级渗透性的通用方法

A General Approach to Predict and Tailor the Nanoscale Permeability of Comb-Shaped Polymer Coatings.

作者信息

Drossis Nicole, Gauthier Marc A, de Haan Hendrick W

机构信息

University of Ontario Institute of Technology, Faculty of Science, Oshawa, Ontario, L1H 7K4, Canada.

Institut National de la Recherche Scientifique (INRS), EMT Research Center, Varennes, Québec, J3X 1P7, Canada.

出版信息

Small Methods. 2025 Jul;9(7):e2500140. doi: 10.1002/smtd.202500140. Epub 2025 Jun 2.

Abstract

Comb-shaped polymers such as poly(oligoethylene glycol monomethyl ether) methacrylate) (pOEGMA) are used to produce molecular sieving coatings on proteins. The mechanisms and phenomena implicated in the experimentally observed sieving properties have been recently characterized by simulation. These result from an interplay between steric hindrance, microenvironmental effects, and modified protein dynamics. Steric hindrance, in particular, is expected to vary considerably as a function of the geometric parameters of the system (protein size, polymer size, grafting density, etc.). In this work, the steric and size-selective permeability characteristics of comb-polymer coatings are systematically explored across a very broad parameter space to gain a universal and predictive view of molecular sieving, for application to systems with different dimensions. All features of the data can be understood when three distinct regimes are considered: i) no-interactions, ii) weak-interactions, and iii) strong-interactions between adjacent polymer chains. Primary and secondary considerations are provided for tuning coating properties to adjust the size threshold of molecular sieving. The corresponding qualitative physical pictures and quantitative analyses give a robust framework to understand molecular sieving that will accelerate the development of future coatings.

摘要

梳状聚合物,如聚(寡聚乙二醇单甲醚)甲基丙烯酸酯(pOEGMA),用于在蛋白质上制备分子筛涂层。最近通过模拟对实验观察到的筛分特性所涉及的机制和现象进行了表征。这些是由空间位阻、微环境效应和蛋白质动力学改变之间的相互作用导致的。特别是,空间位阻预计会随着系统的几何参数(蛋白质大小、聚合物大小、接枝密度等)而有很大变化。在这项工作中,系统地探索了梳状聚合物涂层在非常广泛的参数空间中的空间和尺寸选择性渗透特性,以获得分子筛分的通用和预测性观点,用于不同尺寸的系统。当考虑三种不同的情况时,可以理解数据的所有特征:i)无相互作用,ii)弱相互作用,以及iii)相邻聚合物链之间的强相互作用。为调整涂层性能以调节分子筛分的尺寸阈值提供了主要和次要考虑因素。相应的定性物理图像和定量分析给出了一个强大的框架来理解分子筛分,这将加速未来涂层的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d01c/12285633/10bb50c01def/SMTD-9-2500140-g004.jpg

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