Abstract

Nano assemblies are two‐ or three‐dimensional (3D) collections of nanoparticles. The properties of the assemblies are determined by the number of particles, their position, shape and chemical nature as well as the bonding between them. If we are able to determine these parameters in 3D, we will be able to provide the necessary input for predicting the properties and we can guide the synthesis and development of new assemblies or superstructures. A detailed structural characterization is therefore of utmost importance. Electron tomography is a versatile and powerful tool that has been increasingly used in the field of materials science. Also for the investigation of nanoassemblies, electron tomography has been of high value as illustrated in Figure 1 [1‐3]. However, to extract quantitative information, optimization of the electron tomography experiment is required, especially for large assemblies or assemblies consisting of more than one type of nanoparticle. Here, we will show how advanced electron microscopy, in combination with novel reconstruction algorithms, enables us to determine the number of particles, their stacking, interparticle distances and outer morphology. This will be illustrated for very large assemblies consisting of spherical nanoparticles [4], assemblies that contain anisotropic particles (Figure 2) and binary assemblies (Figure 3) consisting of particles with different sizes or different compositions [5,6]. Going a step further is the investigation of self assembly and oriented attachment at the atomic scale. Using annular bright field scanning transmission electron microscopy, the possible effect of polarity can be investigated, whereas high angle annular dark field scanning transmission electron microscopy in combination with advanced computational techniques enables the investigation of the interfacial planes in two‐dimensional superlattices from nanocrystals [7]. Finally, we will discuss the use of exit wave reconstructions to investigate the effect of surface ligands on self assembly.

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