回顾了估算橡胶和玻璃聚合物膜溶解度参数的实验技术和理论模型

IF 4.9 Q1 ENGINEERING, CHEMICAL
Matthew T. Webb , Lucas C. Condes , William J. Box , Harold G. Ly , Sepideh Razavi , Michele Galizia
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引用次数: 0

摘要

聚合物和小分子的希尔德布兰德溶解度参数(δ)的估计和关联是膜材料科学中的一种常见做法,通过实验和数值方法实现。在本文中,我们重新审视、更新和比较了这两种途径,以提高δ测定的准确性。提供了聚合物溶解度参数实验测定的最佳实践,并证明了动态光散射(DLS)作为传统耗时和材料消耗技术(如乌氏粘度计和溶胀测量)的替代方法的可行性。玻璃状和橡胶状聚合物,包括高自由体积分数(FFV)微孔聚合物,如PIM-1和聚(1-三甲基甲硅烷基-1-丙炔)(PTMSP),是本研究中包含的与膜科学具有重大相关性的样品之一。根据Smith等人最近报道的技术,为了通过基团贡献法提高聚合物溶解度参数数值估计的准确性,我们提供了更新的基团贡献参数及其不确定性。这些更新的基团贡献参数导致在40种聚合物的测试集上预测溶解度参数的平均绝对相对误差为9.0%,这与之前报道的平均10%的误差持平。我们还使用机器学习技术表明,用额外的参数或非线性关系来扩充群体贡献模型并不能提高其准确性。将更新的基团贡献技术和动态光散射测量的结果与四种测试聚合物的实验粘度法进行了比较,并比较了三种技术之间的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revisiting experimental techniques and theoretical models for estimating the solubility parameter of rubbery and glassy polymer membranes

Estimation and correlation of the Hildebrand solubility parameter (δ) of polymers and small molecules is a common practice in membrane material science and is accomplished by experimental and numerical routes. In this paper, we revisit, update, and compare both routes to enhance the accuracy in the determination of δ. Best practices for the experimental determination of polymer solubility parameters are provided, and the viability of Dynamic Light Scattering (DLS) was demonstrated as an alternative to conventional time- and material-consuming techniques, such as Ubbelohde viscometry and swelling measurements. Glassy and rubbery polymers, including high fractional free volume (FFV) microporous polymers such as PIM-1 and poly(1-trimethylsilyl-1-propyne) (PTMSP), are among the samples included in this study with great relevance to membrane science. In an attempt to enhance the accuracy of numerical estimate of polymer solubility parameters via the group contribution method, we provide updated group contribution parameters, along with their uncertainty, according to the technique recently reported by Smith et al. These updated group contribution parameters result in a mean absolute relative error of 9.0% in predicting the solubility parameter on a test set of 40 polymers, which is on par with the average 10% error reported previously. We also show, using machine learning techniques, that augmenting the group contribution model with extra parameters or non-linear relationships does not improve its accuracy. Results of the updated group contribution technique and dynamic light scattering measurements were compared to experimental viscometry on four test polymers, and the difference between the three techniques is compared.

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