Precise Block Copolymer Defects

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.
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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.

Award Number: 2621829
Principal Investigator: Edwin Thomas
Funds Obligated: $116,441
State: VA