Multibeam Electron Diffraction

Hong, Xuhao; Savitzky, Benjamin H.; DaCosta, Luis Rangel; Mueller, Alexander; Minor, Andrew M.; Bustillo, Karen C.; Ophus, Colin

doi:10.1017/s1431927620024770

Cited by 13 publications

(12 citation statements)

References 55 publications

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“…For the analysis of nanoparticle catalysts such as those surveyed in this work, this paves the way toward statistical analysis of catalyst grain size distributions and orientations through the screening of thousands of particles before and after use, providing crucial insight into the factors affecting catalyst efficiency and lifetime. Future work can also make use of multibeam electron diffraction, using an aperture to form multiple probes that increase the angular range surveyed per scan point and thus enable robust indexing due to the richer diffraction patterns obtained (Hong et al, 2020), as well as automated indexing based on template matching using image libraries of simulated diffraction patterns for all orientations of each crystal structure (Rauch & Véron, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

et al. 2021

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Section: Discussionmentioning

confidence: 99%

“…Moreover, as we show here, 4D-STEM without precessing the beam presents a powerful technique for grain orientation mapping when both high throughput and high spatial resolution are required. 4D-STEM using an aperture to form multiple beams is now also poised to enhance the technique even further (Hong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

et al. 2021

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“…We believe that our implementation of ACOM is efficient and accurate enough to be incorporated into automated online TEM software (Spurgeon et al, 2021). In the future, we will improve our ACOM method using machine learning methods (Munshi et al, 2021), and we will extend our ACOM methods to include multibeam electron diffraction experiments (Hong et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

et al. 2022

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Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

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“…For the analysis of nanoparticle catalysts such as those surveyed in this work, this paves the way towards statistical analysis of catalyst grain size distributions and orientations through the screening of thousands of particles before and after use, providing crucial insight into the factors affecting catalyst efficiency and lifetime. Future work can also make use of multibeam electron diffraction, using an aperture to form multiple probes that increase the angular range surveyed per scan point and thus enable robust indexing due to the richer diffraction patterns obtained (Hong et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Allen,

Pekin,

Persaud

et al. 2021

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High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct electron detection. An electron probe size down to 0.5 nm in diameter is implemented and the sample investigated is a gold-palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: Principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

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Multibeam Electron Diffraction

Abstract: Abstract

Cited by 13 publications

References 55 publications

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

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