Data-Enabled Theoretical Understanding of the Structure and Properties of Solvent-cast Polymer Nanocomposites

基于数据的理论理解溶剂浇铸聚合物纳米复合材料的结构和性能

• 批准号: 2126660
• 负责人: Sanat Kumar
• 金额: $ 39万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126660&HistoricalAwards=false
• 关键词: Data; Enabled; Theoretical; Understanding; Structure

NONTECHNICAL SUMMARYFrom sport shoes to credit cards, materials consisting of synthetic polymers are ubiquitous in present-day society. Adding a filler material to the polymer can lead to novel properties that enable specialized applications such as extraordinarily hard coatings, fire-resistant fabrics, or modern car tires. Filler materials consist of small nano-sized particles that are intended to distribute homogeneously within the polymer. However, like water and oil, the nanoparticles and polymers are known to have an intrinsic tendency to separate from each other and form undesired nanoparticle agglomerations instead of a homogeneous nanocomposite. Interestingly, there is experimental evidence that the process of creating the nanocomposite can nevertheless lead to the formation of homogeneous dispersions, with desired properties. The goal of the proposed work is to quantitatively understand the science behind how processing determines the dispersion states and the ensuing mechanical properties of polymer-nanoparticle mixtures. To this end, the project will combine data-rich methods, theoretical calculations, and computer simulations. In a first step, a machine learning algorithm will be trained on the available, large database of polymer-nanoparticle composites, their preparation methods, and the resulting dispersion states. These tools, when implemented, will be able to identify critical regions of parameter space where interesting phenomena occur, e.g., where the material goes from well-mixed to agglomerated nanoparticles. The work will then focus on these regions and use a combination of computer simulations and theoretical calculations to delineate the physical mechanisms that control the nanoparticle dispersion states and the ensuing mechanical properties of the nanocomposite material. This integrated data science-theory-simulation-experimental data workflow will enable the determination of what the optimal nanoparticle dispersion states are for a set of desired mechanical properties and how processing can be manipulated to achieve these states. The project will train students in integrated design and modeling of next-generation materials, develop online learning modules related to nanocomposites and their applications, and implement a shared, open source repository of research data and machine learning tools for wide dissemination in the scientific community.TECHNICAL SUMMARYIt is now well-accepted that adding nanoparticles (NPs) to commodity polymers can lead to hybrid materials with substantially improved properties. The most significant complication encountered, which frequently prevents these property improvements from being realized, is that inorganic NPs are hydrophilic while organic polymers are hydrophobic. These physical mixtures thus have a strong propensity to phase separate. In contrast to these expectations, a large body of experiments has shown that the process of creating these nanocomposites, e.g., by solvent casting, can leverage a variety of non-equilibrium phenomena to yield dramatically different but temporally stable NP dispersion states. A canonical example is the competitive sorption of the polymer in solution to the NP surface; this leads to the formation of a long-lived bound polymer layer which sterically stabilizes well-dispersed NPs. The goal of this proposed work is to quantitatively understand the poorly enunciated science underpinning solution-based processing protocols so as to obtain NP dispersion states with optimized mechanical properties at will.The proposed work will synergistically combine data rich methods and theory/computer simulations on two inter-related tasks: (1) An ML algorithm will be trained on the available, large database of polymer/NP composites that have been experimentally cast from a range of different solvents and the NP dispersion states that result after solvent removal. After training, these ML methods will be able to identify critical regions of parameter space where interesting phenomena occur, e.g., where the material goes from well-mixed to agglomerated NPs. The work will then focus on these regions and use a combination of computer simulations and theory to delineate the physics that control the solvent casting process. (2) The role of different NP dispersion states on linear and non-linear mechanical properties will then be quantitatively enumerated. This integrated data science-theory-simulation-experimental data workflow will enable answers to several key scientific questions: (1) What is the space of polymer-NP-common solvent interactions that yield different NP dispersions? Previous work has suggested that the critical parameter is the effective solvent mediated polymer-NP interaction energy. Is this description accurate, and can effective NP-NP interactions be derived through known metrics such as solubility parameters and measured NP surface potentials? (2) What is the structure of the bound layer formed when polymer/NP interactions are more favorable than solvent/NP interactions? How does the structure of this bound layer depend on solvent quality and how does it yield good dispersion? (3) Going beyond casting from one solvent, how does the addition of a second, non-solvent allows for the precipitation of a NP-polymer composite with well-dispersed NPs? Does this process really only utilize entropic factors in effecting NP dispersion? (4) Under what conditions do kinetic issues, such as solution viscosity, become important determinants of NP dispersion? (5) How does NP dispersion state affect mechanical properties in the linear and non-linear regimes? Can the optimal NP dispersion states (and the associated solvent casting conditions) for mechanical properties be located and understood?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从运动鞋到信用卡，由合成聚合物组成的材料在当今社会中无处不在。向聚合物中添加填充材料可以产生新的特性，从而实现特殊应用，例如非常坚硬的涂层，耐火织物或现代汽车轮胎。填料材料由旨在均匀分布在聚合物内的小纳米尺寸颗粒组成。然而，像水和油一样，已知纳米颗粒和聚合物具有彼此分离并形成不期望的纳米颗粒附聚物而不是均匀的纳米复合材料的内在趋势。有趣的是，有实验证据表明，产生纳米复合材料的过程仍然可以导致形成具有所需性能的均匀分散体。拟议工作的目标是定量地了解加工过程如何决定聚合物-纳米颗粒混合物的分散状态和随后的机械性能背后的科学。为此，该项目将结合联合收割机数据丰富的方法，理论计算和计算机模拟。在第一步中，机器学习算法将在聚合物-纳米颗粒复合材料的可用大型数据库、其制备方法以及由此产生的分散状态上进行训练。这些工具在实施后将能够识别参数空间中发生有趣现象的关键区域，例如，其中材料从充分混合到聚集的纳米颗粒。然后，这项工作将集中在这些区域，并使用计算机模拟和理论计算相结合，以描绘控制纳米颗粒分散状态和随后的纳米复合材料的机械性能的物理机制。这种集成的数据科学-理论-模拟-实验数据工作流程将能够确定一组所需机械性能的最佳纳米颗粒分散状态，以及如何操作处理以实现这些状态。该项目将培训学生进行下一代材料的综合设计和建模，开发与纳米复合材料及其应用相关的在线学习模块，并实施一个共享的，研究数据和机器学习工具的开源存储库，可在科学界广泛传播。技术概述现在人们普遍认为，可以产生具有显著改进的性能的杂化材料。遇到的最重要的复杂性，这经常阻止这些性能的改善被实现，是无机纳米粒子是亲水性的，而有机聚合物是疏水性的。因此，这些物理混合物具有强烈的相分离倾向。与这些预期相反，大量实验表明，制造这些纳米复合材料的过程，例如，通过溶剂浇铸，可以利用各种非平衡现象产生显著不同但暂时稳定的NP分散状态。一个典型的例子是溶液中的聚合物对NP表面的竞争性吸附;这导致形成长寿命的结合聚合物层，其在空间上稳定分散良好的NP。这项工作的目标是定量地了解基于溶液的处理方案的基础科学，以便随意获得具有优化机械性能的NP分散状态。这项工作将协同地将联合收割机数据丰富的方法和理论/计算机模拟结合在两个相互关联的任务上：（1）ML算法将在聚合物/NP复合材料的可用的大型数据库上进行训练，所述聚合物/NP复合材料已经从一系列不同的溶剂和溶剂去除后产生的NP分散状态实验性地浇铸。在训练之后，这些ML方法将能够识别参数空间中发生有趣现象的关键区域，例如，其中材料从充分混合到附聚的NP。这项工作将集中在这些地区，并使用计算机模拟和理论相结合，以描绘控制溶剂浇铸过程的物理。(2)不同的NP分散状态的线性和非线性力学性能的作用，然后将被定量枚举。这种集成的数据科学-理论-模拟-实验数据工作流程将能够回答几个关键的科学问题：（1）产生不同NP分散体的聚合物-NP-常见溶剂相互作用的空间是什么？以前的工作表明，关键参数是有效的溶剂介导的聚合物-NP相互作用能。这种描述是否准确，有效的NP-NP相互作用是否可以通过已知的指标（如溶解度参数和测量的NP表面电位）推导出来？(2)当聚合物/NP相互作用比溶剂/NP相互作用更有利时，形成的结合层的结构是什么？这种结合层的结构如何取决于溶剂的质量，以及它如何产生良好的分散？(3)除了从一种溶剂浇铸之外，添加第二种非溶剂如何允许具有良好分散的NP的NP-聚合物复合物沉淀？这个过程真的只利用熵因素来影响NP分散吗？(4)在什么条件下动力学问题，如溶液粘度，成为NP分散的重要决定因素？(5)NP分散状态如何影响线性和非线性区域的力学性能？能找到并理解用于机械性能的最佳NP分散状态（以及相关的溶剂浇铸条件）吗？该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。