Heil Christian M, Jayaraman Arthi
Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States.
Department of Materials Science and Engineering, University of Delaware, 201 DuPont Hall, Newark, Delaware 19716, United States.
ACS Mater Au. 2021 Aug 3;1(2):140-156. doi: 10.1021/acsmaterialsau.1c00015. eCollection 2021 Nov 10.
In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.
在本文中，我们描述了一种计算方法，用于分析超粒子稀溶液散射实验的结果，其中每个超粒子由纳米粒子混合物组装而成。该计算方法称为散射实验的计算逆向工程分析（CREASE），它将每个超粒子中的散射强度分布以及纳米粒子混合物的组成和尺寸分布作为输入，使用遗传算法输出有关超粒子内组装纳米粒子结构的信息（例如，实空间对相关函数、纳米粒子混合/分离程度、域的大小）。我们通过将纳米粒子二元混合物粗粒化分子模拟得到的计算机模拟散射强度分布作为输入来验证该方法，这些纳米粒子形成紧密堆积结构，并测试我们的计算方法是否能正确再现模拟中观察到的纳米粒子结构。我们使用从包含具有不同化学性质、有或没有尺寸分散性的二元纳米粒子混合物的球形或立方超粒子模拟中获得的各种计算机模拟散射强度分布来测试我们方法的优势和局限性，这些超粒子呈现出从充分混合到强烈分离的结构。所提出方法的优势包括即使在波矢范围有限时也能够分析散射强度分布、方便地提供所有成对径向分布函数，以及正确确定组装在复杂几何形状中的纳米粒子的分离/混合程度。
