Computational Reverse-Engineering Analysis of Scattering Experiments (CREASE) on Amphiphilic Block Polymer Solutions: Cylindrical and Fibrillar Assembly

MG Wessels and A Jayaraman, MACROMOLECULES, 54, 783-796 (2021).

DOI: 10.1021/acs.macromol.0c02265

In this paper, we extend a recently developed computational reverse- engineering analysis for scattering experiments (CREASE) approach to analyze cylindrical, fibrillar, and elliptical cylindrical structures assembled in amphiphilic block copolymer solutions. With CREASE, one can deduce information about assembled structures characterized via small- angle scattering without having to rely on fits using conventional analytical scattering models that may be approximate or inapplicable for the system at hand. With scattering intensity profiles and information about the polymer solution (e.g., molecular weight, composition, and sequence) provided to CREASE as an input, the output from CREASE includes the shape, morphology, dimensions, and molecular packing in the assembled polymer structures within the solution. CREASE is comprised of two steps: the first step involves a genetic algorithm (GA) to determine the shape and dimensions of the domains in the assembled structure and the second step uses molecular simulations to reconstruct chain conformations and monomer-level arrangements within the assembled structure. This paper builds on our recent work that was focused on spherical assembled structures and extends it to nonspherical assembled structures like cylinders, fibrils, and cylinders with elliptical cross sections. We validate the GA step within CREASE by taking input scattering intensity profiles from a variety of assembled shapes with known shapes and dimensions and by producing outputs that match those known shapes and target dimensions. To demonstrate its use in real situations where microscopy may hint at the potential shapes without the user knowing the dimensions with certainty, we apply CREASE with different potential assembled shapes and compare the structural dimensions of the results.

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