Quantitative Characterization of Mixing in Multicomponent Nanoparticle Aggregates
The application of nano-sized multicomponent particles often requires tailored mixing characteristics. This involves mixing on primary particle level, on cluster level or on aggregate level. This mixing is often evaluated qualitatively in 2D, based on image analyses. On 2D images, however, particles are covered by other particles and the average number of particle-particle contacts is overestimated. Therefore, the transfer of the 2D data to true 3D mixing characteristics is difficult.
In this work, we present a method that allows to calculate the true 3D heterogeneous coordination number from 2D image analyses. To achieve this, single component clusters comprised of a few primary particles were combined to multicomponent aggregates using Diffusion Limited Cluster Aggregation. Adjusting the cluster size allowed to simulate differently mixed nanoparticle aggregates. These aggregates were ballistically deposited on a TEM-like grid. Analogue to experimental 2D image analysis, covered particles were neglected and 2D projections of the 3D simulated particles were generated. The comparison of the 3D simulations and their 2D projections resulted in a direct correlation function to analyze binary mixed nanoparticle aggregates. The applicability of our approach was tested with experimental data that comprised different degrees of mixing using double flame spray pyrolysis.
This correlation function allows the convenient calculation of the 3D heterogeneous coordination number from 2D image analysis without the need for extensive simulations. It can be applied on any image analysis of binary mixed nanoparticles, which makes it a universal tool in the design of multicomponent materials.