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Results for “Transmission Electron Microscopy”
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Unravelling nanometallurgy with in situ transmission electron microscopy: A case-study with copper nanowires
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Transmission electron microscopy of organic-inorganic hybrid perovskites: myths and truths
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On the use of transmission electron microscopy to quantify dislocation densities in bulk metals
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Formation and growth of cerium (III) oxalate nanocrystals by liquid-cell transmission electron microscopy
Using rare earth elements as analogs for actinide series elements enables studying the chemistry of these radioactive materials without the inherent risks. A fundamental understanding of the nucleation and growth of actinide oxalates is crucial for processing nuclear waste. Using in-situ liquid cell TEM (LC-TEM), we show particle attachment processes occurring during the nucleation and growth of cerium oxalate. The early stages of the observed non-classical growth mechanisms have important implications for particle evolution and predictions of final particle morphology. In this work, we observed particle formation via monomer-by-monomer addition or the particle aggregation pathway depending on the selected precursor concentration. At the highest precursor concentration at a high dose rate, we observed particle alignment in a branch structure is due to the interfacial instability. It was demonstrated that in-situ LC-TEM is an invaluable tool in studying the cerium oxalate formation and ultimately a path to understanding actinide chemistry.
Application of digital image correlation for in-situ deformation studies using transmission electron microscopy
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Optimizing Nonrigid Registration for Scanning Transmission Electron Microscopy Image Series
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Machine Learning of In-situ Temperature Reconstruction from Metal-nanoparticle Thermometry on Transmission Electron Microscopy
Utilizing Python programming language and third-party libraries, 2D temperature fields were reconstructed from TEM images demonstratively and a GUI was created. Local temperatures can be fast read on TEM at the nanoscale based on the nanoparticle thermometry. The Artificial Intelligence technique would speed up the in-situ heating TEM research and make real-time in-situ temperature monitoring possible at nanoscale.
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.
Towards data-driven next-generation transmission electron microscopy
Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.