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.

非技术摘要从运动鞋到信用卡，由合成聚合物组成的材料在当今社会中无处不在。在聚合物中添加填充材料可以导致新型特性，从而实现专门的应用，例如非常硬涂层，防火织物或现代汽车轮胎。填充材料由旨在在聚合物内均匀分布的小纳米尺寸颗粒组成。但是，像水和油一样，已知纳米颗粒和聚合物具有固有的趋势，可以彼此分离并形成不希望的纳米颗粒聚集，而不是均匀的纳米复合材料。有趣的是，有实验证据表明，创建纳米复合材料的过程仍然可以导致具有所需特性的均匀分散体的形成。拟议的工作的目的是定量了解加工方式背后的科学以及聚合物 - 纳米颗粒混合物的随之而来的机械性能。为此，该项目将结合数据丰富的方法，理论计算和计算机模拟。第一步，机器学习算法将在可用的大型聚合物 - 纳米粒子复合材料，其制备方法和所得分散体状态下进行培训。这些工具在实施时将能够识别出有趣现象的参数空间的关键区域，例如，材料从混杂良好到聚集的纳米颗粒。然后，这项工作将集中在这些区域上，并结合计算机模拟和理论计算来描述控制纳米颗粒分散态的物理机制以及纳米复合材料的随之而来的机械性能。这种集成的数据科学理论 - 仿真 - 实验数据工作流将能够确定最佳纳米颗粒分散态对一组所需的机械性能以及如何操纵处理以实现这些状态的方法。该项目将培训学生对下一代材料的集成设计和建模，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，开源的研究数据和机器学习工具的开源存储库，以在科学社区中进行广泛的传播。Technical摘要现在已经很好地添加了与商品材料一起添加Nanoparticles（NPS）的构图，以实现hyprodity Polymersers的构图。遇到的最显着的并发症经常阻止这些特性改善被实现，是无机NP是亲水性的，而有机聚合物是疏水。因此，这些物理混合物具有很强的分离倾向。与这些期望相反，大量实验表明，创建这些纳米复合材料的过程，例如，通过溶剂铸造，可以利用各种非平衡现象来产生急剧不同但具有时间稳定的NP分散剂状态。一个规范的例子是聚合物溶液中NP表面的竞争性吸附。这导致形成长寿命的结合聚合物层，该层在空间上稳定了散布良好的NP。 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从一系列不同的溶剂和NP分散液中铸造的实验铸造，这些溶剂去除后产生。训练后，这些ML方法将能够识别出有趣现象的参数空间的关键区域，例如，材料从混杂良好到凝聚的NP。然后，这项工作将集中在这些区域上，并结合计算机模拟和理论来描述控制溶剂铸造过程的物理学。 （2）随后列举了不同NP分散态在线性和非线性机械性能上的作用。这种集成的数据科学理论 - 实验数据工作流将使几个关键科学问题的答案：（1）聚合物-NP-Common-Common溶剂溶剂相互作用的空间是什么？先前的工作表明，临界参数是有效的溶剂介导的聚合物-NP相互作用能。该描述是否准确，并且可以通过已知的指标（例如溶解度参数和测得的NP表面电势）得出有效的NP-NP相互作用？ （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？该结合层的结构如何取决于溶剂质量，如何产生良好的分散体？ （3）超越了从一个溶剂铸造的，添加第二个不溶剂的添加如何允许带有散布良好的NP的NP聚合物复合材料的沉淀？这个过程是否真的仅利用熵因素来影响NP分散？ （4）在什么条件下，动力学问题（例如溶液粘度）成为NP分散剂的重要决定因素？ （5）NP色散状态如何影响线性和非线性方案中的机械性能？可以找到和理解机械性能的最佳NP分散状态（以及相关的溶剂铸造条件）吗？该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。