Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Mixtures of Nanoparticles

Nanoparticle assembly is a common route to generate materials with specific properties. The assembled material must undergo characterization at multiple length scales to link the structural features with the macroscopic properties. Small angle scattering (SAS) is a useful method to characterize nanoparticles’ assembled structure. The output of SAS experiments is the averaged intensity at various wave vectors, I(q) vs. q, whose interpretation often relies on fitting with analytical models. This model selection can be a limitation when appropriate analytical models do not exist. We present a computational method, CREASE, to analyze the scattering results from spherical nanoparticle mixtures assembled in various confinement geometries. We test the strengths and limitations of our method by using a variety of in silico I(q) obtained from simulations of binary nanoparticle assemblies with varying mixture composition and nanoparticle size dispersity that exhibit varying degrees of mixing/demixing within a confined spherical or thin-film geometry. We will also present extensions of this method for systems (e.g., concentrated solution of micelles) where the form factor of the particle (i.e., micelle) is unknown, and CREASE analyzes both structure and form factor in the assembled mixture.