{"title":"通过介电光谱学模拟致密聚合物-碳黑纳米粒子复合材料中的相间界面:我们的现状和机遇是什么?","authors":"Christian Brosseau","doi":"10.1002/mats.202400009","DOIUrl":null,"url":null,"abstract":"<p>The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure-property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two-level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model-based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics-based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non-universal values of the percolation exponents. Recent work in physics-informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.</p>","PeriodicalId":18157,"journal":{"name":"Macromolecular Theory and Simulations","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mats.202400009","citationCount":"0","resultStr":"{\"title\":\"Modeling the Interface Between Phases in Dense Polymer-Carbon Black Nanoparticle Composites by Dielectric Spectroscopy: Where Are We Now and What are the Opportunities?\",\"authors\":\"Christian Brosseau\",\"doi\":\"10.1002/mats.202400009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. 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This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model-based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics-based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non-universal values of the percolation exponents. Recent work in physics-informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.</p>\",\"PeriodicalId\":18157,\"journal\":{\"name\":\"Macromolecular Theory and Simulations\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mats.202400009\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Macromolecular Theory and Simulations\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/mats.202400009\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"POLYMER SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Macromolecular Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mats.202400009","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"POLYMER SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
聚合物纳米复合材料(PNC)的宏观特性在很大程度上取决于聚合物链与填料颗粒之间的相位。解决这一问题的一个重大难题是对这些复杂材料的界面区所产生的结构-性能相关性进行定量建模。虽然介电光谱(DS)测量通常用于表征填充聚合物的有效介电常数,但将标准有效介质模型和混合方程拟合到这些数据上仍然难以解释。这是因为没有明确参考表征 PNC 界面的内部长度尺度。作为一个示例,我们提出了一个两级均质化框架,它可以根据宽带 DS 数据提取纳米长度尺度上限制的薄相间影响的有用信息。该模型带来了调整相间物的新方法,从而优化材料对电场的响应,这种情况与电磁屏蔽有关。这种方法为如何通过实验直接观察相间界面的实际特性(而不是基于模型的推断)提供了指导。除了有效介质理论中可靠的物理基础之外,这种方法的一个重要优势是,将遗传算法(GA)技术应用于这种基于物理的模型,可以考虑拟合参数的唯一性,因为 GA 方法在寻找全局最优解方面非常稳健,因此可以对渗流指数的非普遍值抱有信心。物理信息机器学习的最新研究表明,由于 PNC 的形态复杂,其有效介电性能具有很多自由度,只需考虑描述这些复合材料中各相间界面特征的几个自由度即可。
Modeling the Interface Between Phases in Dense Polymer-Carbon Black Nanoparticle Composites by Dielectric Spectroscopy: Where Are We Now and What are the Opportunities?
The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure-property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two-level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model-based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics-based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non-universal values of the percolation exponents. Recent work in physics-informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.
期刊介绍:
Macromolecular Theory and Simulations is the only high-quality polymer science journal dedicated exclusively to theory and simulations, covering all aspects from macromolecular theory to advanced computer simulation techniques.