Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Colloidal Particle Mixtures

Assembly of nanoparticle mixtures is widely used to precisely design materials with controlled optical properties. This process often requires structural characterization to link the assembled structure with the macroscopic property of interest. Many researchers utilize small angle neutron scattering (SANS) to analyze the nanoparticle assembly. Once the scattering profile, I(q), is obtained, the general practice is to use analytical models to interpret the scattering profile and produce the real space structure. However, the choice of the appropriate model for fitting the scattering profile is not always trivial, particularly for close-packed nanoparticles. To overcome this limitation, we present our recently developed evolutionary algorithm-based method to analyze the structure of binary nanoparticle assemblies in both spherical and cubic geometries from the scattering profiles obtained from SANS of nanoparticle assemblies. The method requires the I(q), particle composition, and particle size distributions as inputs and outputs the real-space structure whose scattering profile most closely matches the experimental scattering profile. Our approach resolves the local packing around the nanoparticles as well as domain sizes for varying extents of inter-particle segregation/mixing.