Serial sectioning in the SEM for three dimensional materials science

•Serial sectioning techniques for materials applications, dataset volumes, damage, and slicing resolution.•Focused ion beams, broad ion beams, ultramicrotomy, and femtosecond laser TriBeam are discussed.•Combined beam approaches are required for material challenges.•Data collection, reconstruction,...

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Bibliographic Details
Published in:Current opinion in solid state & materials science Vol. 24; no. 2; p. 100817
Main Authors: Echlin, McLean P., Burnett, Timothy L., Polonsky, Andrew T., Pollock, Tresa M., Withers, Philip J.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01-04-2020
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Summary:•Serial sectioning techniques for materials applications, dataset volumes, damage, and slicing resolution.•Focused ion beams, broad ion beams, ultramicrotomy, and femtosecond laser TriBeam are discussed.•Combined beam approaches are required for material challenges.•Data collection, reconstruction, imaging modalities, data fusion, and correlative microscopy outlooks are given. Here we explore the range of serial sectioning techniques that have evolved over the past decade, providing a comprehensive toolkit for capturing rich 3D microstructures, chemistries and crystallographic information, with sub-micron resolution at volumes that extend out to mm3 or even cm3. In each case we consider the challenges associated with their application, the volumes they can analyze, the damage to the surface they impart, and their suitability for different materials. In certain cases these warrant hybrid methods, motivating workflows that leverage multiple sectioning modes within the same instrument. Finally, we provide a perspective on their future development, including advances in data collection, segmentation, registration, data fusion, and correlative microscopy. Furthermore, the exploitation of 3D techniques for a better understanding of existing materials, and the design of new ones, is discussed through their use in multiscale modelling, digital twinning, material informatics and machine learning frameworks.
ISSN:1359-0286
DOI:10.1016/j.cossms.2020.100817