Electron microscopy in nanoparticle self-assembly research

Nanoparticle self-assembly is a vital research field with significant implications for fundamental science and a wide range of technological applications. This bottom-up approach enables the design and fabrication of mesoscopic materials with distinct electronic, magnetic, optical, mechanical, and catalytic properties. Monodisperse nanoparticles serve as fundamental building blocks for creating long-range ordered structures known as superlattices, superstructures, or supercrystals. Advances in wet chemical synthesis methods have provided access to a variety of nanoparticle shapes, sizes, and compositions, establishing a solid foundation for extensive studies in self-assembly. This review highlights the utility and advantages of various electron microscopy methods for characterizing the structures and dynamics of nanoparticle assemblies, ranging from conventional imaging and diffraction techniques to cutting-edge approaches such as electron tomography, focused ion beam scanning electron microscopy tomography, four-dimensional scanning transmission electron microscopy, and liquid cell transmission electron microscopy. These methods enable the acquisition of detailed two-dimensional and threedimensional structural information of nanoparticle superlattices in dry, frozen, and liquid states. We also highlight the development of advanced data processing algorithms and their implementation in open-source software packages to facilitate electron microscopy data analysis. By leveraging machine learning techniques, researchers can efficiently manage large and complex electron microscopy datasets and gain deeper insights into the mechanisms of nanoparticle self-assembly. We anticipate that the comprehensive electron microscopy toolkit, combined with advanced computational algorithms and machine learning, will continue to generate new knowledge and insights in nanoparticle self-assembly research.