Computational Study and Design of Self-Assembling Block Polymers
2023-01
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Computational Study and Design of Self-Assembling Block Polymers
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2023-01
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Upon cooling below the order-disorder transition temperature, block polymers self-assemble into a wide variety of nanostructured morphologies. When paired with advances in synthetic chemistry that allow unprecedented control over the size and architecture of these block polymers, these self-assembly characteristics make block polymers excellent candidates for use in specialty materials with highly tunable properties. Potential applications of block polymers range from filtration membranes to photonic crystals. As it happens, however, the source of this exemplary potential is also one of the great barriers to its realization. The vast design spaces available for block polymers (through numbers and permutations of chemistries, and architectural features) make possible a potentially limitless variety of morphologies. At the same time, these design spaces combined with the subtlety of mechanisms driving morphology selection make finding systems which adopt those morphologies a daunting task.In this dissertation, we take a computational approach to address the challenge of designing block polymer specialty materials through two broad approaches. First, we directly address the challenges posed by these vast design spaces by developing an open-source software to automate the exploration of polymer parameter space. This software uses a particle swarm optimization algorithm to guide a search through polymer parameter space for positions where self-consistent field theory predicts a targeted morphology will be most stable compared to a set of competing phases. Second, we use computational studies of two classes of diblock blends seeking to understand the mechanisms that stabilize the low-symmetry Frank-Kasper phases in block polymers with the goal of improving the intuition that guides future efforts to design block polymer materials. In the first of these studies, we use an AB/B`C diblock ``alloy'' with miscible corona and immiscible core blocks to probe the effect of conformational asymmetry on the stability of Frank- Kasper Laves phases when the conformational asymmetry is confined to only particular particle positions. This study finds that conformational asymmetry can be either stabilizing or destabilizing for the Laves phases, depending on which particles are impacted. In the second of these studies, we attempt to identify the balance of core and corona bidispersity in AB/A`B` blends which can still enable formation of Frank-Kasper phases. Unfortunately, this latter study was complicated by a series of methodological flaws limiting its utility in the furtherance of block polymer design. Regardless, the flawed study serves as a lesson in proper study design, and the importance of carefully considering complicating factors.
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University of Minnesota Ph.D. dissertation. January 2023. Major: Chemical Engineering. Advisors: Kevin Dorfman, Frank Bates. 1 computer file (PDF); x, 109 pages.
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Case, Logan. (2023). Computational Study and Design of Self-Assembling Block Polymers. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/253727.
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