Development of Small Angle Neutron Scattering Tools for Probing the Phase-Behavior and Self-Assembly of Sequence-Defined Polymers

Abstract

Sequence control in polymers offer a powerful platform for programming self-assembly and phase behavior with monomer-level precision, bridging the structural control of biological macromolecules and the functional tunability of synthetic polymers. However, understanding how primary sequence dictates structure and material properties remains a fundamental challenge due to the vast design space and the lack of high-throughput characterization tools and predictive models. This thesis addresses these challenges by combining recombinant polymer synthesis, coarse-grained simulations, and information-guided scattering techniques to interrogate how molecular-level sequence features drive mesoscale structure, phase behavior, and self-assembly in dilute and concentrated aqueous environments. To enable efficient structural screening, a high-throughput small-angle neutron scattering (SANS) workflow was developed to rapidly evaluate large libraries of sequence-defined polymers under diverse solvent and temperature conditions. By analyzing three classes of model polymer systems and simulating reduced-count datasets, it is shown that accurate parameter estimates (within 5–10% of full-count values) can be obtained using only 1–50% of the original counts, depending on the sample and parameter. Both AIC and BIC can successfully identify the correct model from a candidate set, even with limited data. This approach provides a practical framework to optimize SANS beamtime and supports efficient structural characterization of material libraries. On the experimental end, elastin like polypeptides (ELPs) were used as a model system to understand sequence-property relationships. Thermoresponsive behavior of ABA triblock ELPs was systematically investigated. Two variants differing only in midblock hydrophobicity revealed distinct phase behaviors: one formed temperature-triggered micellar aggregates with syneresis, while the other remained viscoelastic and unstructured across conditions. SANS, rheology, and depolarized light scattering collectively demonstrated how sequence-encoded hydrophobicity and block architecture govern micelle formation, water partitioning, and gel properties near the LCST. The sequence-property landscape was mapped using a series of ELPs with varied guest residues. Turbidimetry in water/ethanol/salt mixtures uncovered how hydrophobicity and charge combine define cononsolvency boundaries and critical transition temperatures, highlighting the role of solvation competition in shaping UCST- and LCST-type transitions. Finally, a highly coarse-grained dumbbell model was developed for globular protein– polymer bioconjugates. Molecular dynamics simulations demonstrated that treating the protein as a hard sphere tethered to a soft polymer tail as a soft sphere captures the essential physics of self-assembly in those systems. Simulated phase diagrams aligned with experimental trends and provided a predictive model for designing protein-polymer bioconjugates and understanding the thermodynamic driving force of self-assembly. By integrating high-throughput structural characterization, insightful coarse-grained models, and large amount of experimental data, this thesis provides quantitative design rules for encoding self-assembly and transitions across molecular, mesoscale, and macroscopic regimes— paving the way for next-generation programmable polymer materials.