Electron Microscopy Studies of Soft Nanomaterials

Abstract

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. The even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Understanding ZIF particle chemical etching dynamics and morphology manipulation: in situ liquid phase electron microscopy and 3D electron tomography application

“…Overall, in situ liquid phase TEM is a valuable tool for investigating the growth mechanism of MOFs (including soft materials) and advancing our understanding of their nucleation, growth, self-assembly, and potential applications in various fields. 7,[13][14][15][16] Despite the progress in using in situ liquid phase TEM to investigate the growth mechanism of MOFs, there is still a lack of literature on using this technique to study the etching mechanism of MOF nanoparticles (NPs) in colloids. 11,17 The etching process can have a significant impact on the morphology and properties of MOF NPs, making it an important area of research.…”

“…Overall, in situ liquid phase TEM is a valuable tool for investigating the growth mechanism of MOFs (including soft materials) and advancing our understanding of their nucleation, growth, self-assembly, and potential applications in various fields. 7,13–16…”

Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques

“…Machine learning and artificial intelligence are becoming invaluable methods that enable faster discovery, innovation, and automation in chemical and materials sciences and engineering. − In particular, machine learning has found tremendous use in automating materials’ structural analysis, which is a vital step in establishing the design–structure–property relationship of a novel material. − For example, structural characterization of materials often depends on microscopy imaging techniques (e.g., scanning electron microscopy (SEM), transmission electron microscopy (TEM), or atomic force microscopy (AFM)) to visualize nanoscale or microscale structural features or patterns . Deep learning models used for pattern recognition and image analysis have been adopted as a viable means to automatically extract structural information from microscopy images regarding the ordered arrangements of the molecules, types of ordered assembly, and detection of objects’ (e.g., nanoparticles, assembled domains) shapes and sizes. − There are unique challenges, however, with training machine learning models that are used for everyday photographic image analysis to analyze materials’ microscopy images. For example, compared to photographic images of everyday objects that often are easily recognizable, materials’ microscopy images require detailed metadata of the material, chemistry, synthesis conditions, and imaging process conditions as labels.…”

UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning

“…The application of DL algorithms for the analysis of EM data has been thoroughly reviewed in a recent article. 14 For example, the U-Net architecture 15 with StarDist formulation for loss function 16 were trained to automate the particle size distribution analysis of electrocatalyst materials, where various shape, texture, and patterns are generated between the overlapping catalyst NPs and the support material. 17 DL models were also used for real-time segmentation of NPs in liquid phase EM movies to statistically examine the diffusion, reactivity, and assembly kinetics of cube-, prism-, and rod-shaped colloidal NPs.…”