UC Santa Barbara

Block copolymers are important materials that self-assemble into a variety of well-ordered nanoscale morphologies, underpinning their versatility in diverse applications such as drug delivery, advanced separation membranes, thermoplastic elastomers, photonic crystals, and microelectronics. However, the vast and ever-growing design space of these intriguing materials complicates studying and predicting useful structure–property relationships a priori. Traditional methods of constructing even incomplete block copolymer phase diagrams involve time-consuming and costly iterative synthesis followed by multiple purification and isolation steps, greatly increasing the time and cost of materials discovery. An alternative to iterative synthesis is post-polymerization purification, particularly chromatography. This operationally simple, yet remarkably powerful, technique is most commonly encountered in the purification of small molecules through their selective (differential) adsorption to a column packed with a low-cost stationary phase, usually silica. Because the requisite equipment is readily available and the actual separation takes little time (on the order of 1 hour), chromatography is used extensively in small-molecule chemistry throughout industry and academia, yet is significantly less common as a preparatory separation tool in polymer science. This dissertation highlights how the combination of scalability and versatility with the integration of automation positions chromatographic separation as a tool with broad applicability in advancing polymer science, offering new avenues for exploration and discovery of well-defined materials. To illustrate the potential of this approach, the first contribution of this dissertation demonstrates through the synthesis and multigram scale separation of a family of 16 parent diblock copolymers, a library of over 300 distinct and well-defined samples was generated. The resulting materials span a wide range of compositions with exceptional resolution in volume fraction and domain spacing that allows for the impact of monomer design on polymer self-assembly to be elucidated. Leveraging this expansive and high-quality experimental dataset, we developed a novel physics-informed machine learning algorithm for the rapid analysis of small-angle X-ray scattering data to accurately identify morphologies without laborious and time-consuming manual peak indexing. The second portion of this work extends this chromatographic separation method towards ABC triblock terpolymers with increasingly complex block sequences at markedly higher molecular weights. Small-angle X-ray scattering reveals that fractionation substantially enhances long-range order compared to as-synthesized parent materials, enabling definitive identification of various nanoscale morphologies. Importantly, chromatography facilitates the detection and removal of small amounts of homopolymer and diblock copolymer impurities (<2 – 3%) generated during synthesis which are typically difficult to discard yet have been shown to significantly impact the self-assembly of block copolymers. The final portion of this dissertation presents a versatile chromatographic separation strategy leveraging an active ester-based block copolymer, enabling high-throughput exploration of broad compositional and chemical landscapes without requiring sample-specific optimization. By integrating automated chromatography with robotic synthesis coupled with orthogonal post-polymerization modification chemistry, a diverse library of 72 well-defined materials was efficiently generated from a single block copolymer synthesis. Overall, this dissertation highlights how the synergistic combination of controlled polymerization, automated chromatography, data-driven analysis, and robotic synthesis creates a powerful workflow to markedly expedite the discovery of structure–property relationships in advanced soft materials.