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“Fast pixelated detectors in scanning transmission electron microscopy. Part I: data acquisition, live processing, and storage”. Nord M, Webster RWH, Paton KA, McVitie S, McGrouther D, MacLaren I, Paterson GW, Microscopy And Microanalysis 26, Pii S1431927620001713 (2020). http://doi.org/10.1017/S1431927620001713
Abstract: The use of fast pixelated detectors and direct electron detection technology is revolutionizing many aspects of scanning transmission electron microscopy (STEM). The widespread adoption of these new technologies is impeded by the technical challenges associated with them. These include issues related to hardware control, and the acquisition, real-time processing and visualization, and storage of data from such detectors. We discuss these problems and present software solutions for them, with a view to making the benefits of new detectors in the context of STEM more accessible. Throughout, we provide examples of the application of the technologies presented, using data from a Medipix3 direct electron detector. Most of our software are available under an open source licence, permitting transparency of the implemented algorithms, and allowing the community to freely use and further improve upon them.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.8
Times cited: 4
DOI: 10.1017/S1431927620001713
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“Fast pixelated detectors in scanning transmission electron microscopy. part II : post-acquisition data processing, visualization, and structural characterization”. Paterson GW, Webster RWH, Ross A, Paton KA, Macgregor TA, McGrouther D, MacLaren I, Nord M, Microscopy And Microanalysis 26, 944 (2020). http://doi.org/10.1017/S1431927620024307
Abstract: Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post-acquisition processing and visualization of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualization of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilizing data from a 256 x 256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterization of the structural properties of materials. These include the techniques of virtual detector imaging; higher-order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate a nanoscale lattice parameter mapping with a fractional precision <= 6 x 10(-4) (0.06%).
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
Impact Factor: 2.8
Times cited: 3
DOI: 10.1017/S1431927620024307
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