![]() ![]() ![]() However, achieving automated and robust processing of liquid-phase TEM videos is still in its infancy, due to technical challenges associated with low quality videos of spatial heterogeneity, which as we argue below are often unavoidable and generic to liquid-phase TEM. Methods for automated image processing and analysis, with high tolerance to noise and high precision, thus become instrumental to converting raw TEM images of spatial intensity profiles to chemical and physical properties, and thus to materials design rules. With the advent of fast detectors capable of capturing hundreds to thousands of frames per second, (5) the size of liquid-phase TEM videos grows ever rapidly. On the other hand, liquid-phase TEM videos are huge data files consisting of temporal stacks of spatial images. (4) Similarly, nanoparticle assembly (5) and superlattice crystallization pathways (6) have been studied by liquid-phase TEM, where kinetically stable intermediates were captured to understand phase transition. For example, by taking TEM videos of nanoparticle growth in solution, researchers have elucidated various growth mechanisms, such as nucleation, (1) oriented attachment, (2,3) and coalescence. On one hand, compared with ensemble measurements (e.g., small-angle X-ray scattering, UV–vis), liquid-phase TEM captures real-space videos at the nanometer, or sometimes atomic, resolution, which provides insights on a wide range of phenomena that are otherwise elusive. The recent proliferation of liquid-phase transmission electron microscopy (TEM) studies has offered great opportunities to fill knowledge gaps on nanoscale structural and functional dynamics by generating massive amounts of multidimensional data, which necessitate methods to extract quantitative information from them. We expect our framework to push the potency of liquid-phase TEM to its full quantitative level and to shed insights, in high-throughput and statistically significant fashion, on the nanoscale dynamics of synthetic and biological nanomaterials. Compared to the prevalent image segmentation methods, U-Net shows a superior capability to predict the position and shape boundary of nanoparticles from highly noisy and fluctuating background-a challenge common and sometimes inevitable in liquid-phase TEM videos. These systems representing properties at the nanoscale are otherwise experimentally inaccessible. A diversity of properties for differently shaped anisotropic nanoparticles are mapped, including the anisotropic interaction landscape of nanoprisms, curvature-dependent and staged etching profiles of nanorods, and an unexpected kinetic law of first-order chaining assembly of concave nanocubes. We apply this framework to three typical systems of colloidal nanoparticles, concerning their diffusion and interaction, reaction kinetics, and assembly dynamics, all resolved in real-time and real-space by liquid-phase TEM. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos’ high noisiness and spatial heterogeneity. Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. ![]()
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