Precise Block Copolymer Defects

非技术性：找出特定的加工路径如何有目的地产生或最大限度地减少各种类型的缺陷，是进一步开发软物质晶体的关键。 软物质晶体，例如由嵌段共聚物分子组成的那些，通过组分分子自组装成周期性阵列而形成。 最新的进展与新类型的显微镜，现在使3D可视化的精细规模的功能组织嵌段聚合物。 虽然这些结构大多是有规律的周期性的，但会出现缺陷，目的是使用显微镜工具按顺序观察这些局部破坏，按其几何形状对其进行分类，了解它们在材料加工过程中如何形成，以及每种类型的缺陷如何影响材料性能。 周期性材料的性能在很大程度上取决于是否具有良好的有序结构，以及在需要时是否具有精确定位和对齐的缺陷。 PI旨在发现有价值的缺陷类型，然后可以对其进行处理，以增强甚至创造嵌段共聚物材料的全新技术应用。例如，在没有镜像对称的结构内部形成一种“分子镜”的缺陷可能会为操纵光波的传播创造新的机会。 由于缺陷是相对罕见的事件，因此发现和分类迄今为止大多数未知对象的搜索需要高质量的大数据集，并使用机器学习进行统计上合理和无偏见的微观结构数据分析。 定位、分析和分类在给定的一组加工条件下产生的所有各种类型的缺陷将使聚合物材料以外的领域的材料研究人员受益。 这种加工-结构关系自其成立以来一直是材料科学的目标，虽然已经了解了许多关于不同聚合物加工过程如何影响微观结构的知识，但信息水平通常限于对聚合物链、域和晶体取向、域形状和周期性的观察，但很少与类型相关，整个材料中多种类型缺陷的数量和分布。 技术：PI的团队将利用切片和双离子和电子束显微镜的进步来重建非常大体积的3D管状网络微区嵌段共聚物样品，以识别和表征缺陷，并将其与用于制备样品的加工路线及其对电荷，质量和波传输等性能的影响联系起来。由于缺陷是相对罕见的事件，搜索发现和分类迄今为止大多数未知的对象需要近失真的显微镜，以提供大的数据集。网络相缺陷固有的复杂拓扑和形态需要借助机器学习进行形态分析。在这些大数据集上的机器学习将提供统计上合理和无偏见的微观结构数据分析，用于所有各种缺陷的定位和表征，并能够找到最丰富和最独特的缺陷模式。了解各种类型的缺陷是如何在给定的加工条件下产生的，将使材料研究人员在聚合物材料之外的领域受益。 开发改进的处理协议，如膜均匀化的微粒，以避免各向异性的样品变形，甚至可能生长的嵌段共聚物的第一面真正的单晶体可以证明，聚合物可以实现接近完美的其他类别的软物质。 这种加工-结构关系从一开始就一直是材料科学的目标，虽然已经了解了许多关于不同聚合物加工过程如何影响微观结构的知识，但信息水平通常限于对聚合物链、域和晶体取向、域形状和平均周期的观察，但很少与类型相关，整个材料中许多类型的精确缺陷的数量和分布。 最终，研究将发现有价值的缺陷，这些缺陷可以被操纵，以增强和创造嵌段共聚物的新技术应用。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。

NON TECHNICAL:Finding out how a particular processing path either purposefully creates or minimizes various classes of defects is key to further progress in exploiting soft matter crystals for their intriguing material properties. Soft matter crystals such as those comprised of block copolymer molecules form via self assembly of the component molecules into periodic arrays. Recent advances made with new types of microscopy now enable 3D visualization of the fine-scale features of organized block polymers. While the structures are mostly regularly periodic, defects occur and the aim is to use microscopy tools to see these local disruptions in the order, classify them by their geometry, learn how they form during processing of the material and how each type of defect influences material properties. The performance of periodic materials depends to a great extent on having well ordered structures and when desired, precisely positioned and aligned defects. The PI seeks to discover valuable types of defects that can then be manipulated to enhance and even create brand new technological applications of block copolymer materials. For example, a defect that forms a type of "molecular mirror" inside a structure without mirror symmetry may create new opportunities for manipulating the propagation of light waves. Since defects are relatively rare events, the search to discover and classify heretofore mostly unknown objects requires high quality, large data sets and use of machine learning for statistically sound and unbiased microstructural data analysis. Locating, analyzing and classifying all the various types of defects created under a given set of processing conditions will benefit materials researchers in fields well beyond polymeric materials. Such processing – structure relations have been the goal of materials science since its inception and while much has been learned about how different polymer processing procedures influence microstructure, the level of information has generally been limited to observations on polymer chain, domain and crystal orientation, domain shapes and periodicities has but rarely been related to the type, amount and distribution of the many types of defects throughout the material. TECHNICAL:The PI's group will utilize the advances made with slice and view dual ion and electron beam microscopy for reconstruction of very large volumes of 3D tubular network microdomain block copolymer samples to identify and characterize the defects and relate them to the processing route used to prepare the sample and to their influence on properties such as charge, mass and wave transport. Since defects are relatively rare events, the search to discover and classify heretofore mostly unknown objects requires near distortion-free microscopy to provide large data sets. The inherent complex topology and morphology of defects in network phases requires the help of machine learning for morphological analysis. Machine learning on these large data sets will afford statistically sound and unbiased microstructural data analysis for location and characterization of all the various defects with the ability to find the most abundant and also distinctive patterns of defects.Understanding how various types of defects are created under a given set of processing conditions will benefit materials researchers in fields well beyond polymeric materials. Development of improved processing protocols such as membrane homogenization of microparticles to avoid anisotropic sample deformations and perhaps even growing the first faceted true single crystals of block copolymers may demonstrate that polymers can realize near-perfection as do other classes of soft matter. Such processing – structure relations have been the goal of materials science since its inception and while much has been learned about how different polymer processing procedures influence microstructure, the level of information has generally been limited to observations on polymer chain, domain and crystal orientation, domain shapes and average periodicities has but rarely been related to the type, amount and distribution of the many types of precise defects throughout the material. Ultimately the research will discover valuable defects that can be manipulated to enhance and create new technological applications of block copolymers..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.