Serial sectioning in the SEM for three dimensional materials science. Issue 2 (April 2020)

• Record Type: Journal Article
• Authors: Echlin, McLean P.; Burnett, Timothy L.; Polonsky, Andrew T.; Pollock, Tresa M.; Withers, Philip J.
• Is Part Of: Current opinion in solid state & materials science. Volume 24:Issue 2(2020)
• Publication Date: 2020-04
• Subjects: Serial sectioning -- Materials science -- Life sciences -- Biology -- Scanning electron microscopy; Materials science -- Periodicals; Solid state physics -- Periodicals; 620.11
• DOI: 10.1016/j.cossms.2020.100817
• Languages: English
• ISSNs: 1359-0286

Abstract

Highlights: 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. Abstract: 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 mm 3 or even cm 3 . 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.