This talk focuses on the principles of 4D-STEM based electron nanodiffraction techniques for defect, strain and short-range ordering analysis using electron diffuse scattering [8, 9]. We review recent progress made in scanning electron nanodiffraction (SEND) data collection, new algorithms based on cepstral analysis, and machine learning based electron DP analysis. These progresses will be highlighted using defect detection, and short-range ordering analysis as application examples. The materials of the study are the medium entropy alloy, CrCoNi, which has exceptional low-temperature mechanical strength and ductility. We will show how SEND helps our understanding of non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering, behind the mechanical strength in CrCoNi and how these developments provide general opportunities for an atomistic-structure study in advanced alloys.
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Data-driven electron-diffraction approach reveals local short-range ordering in CrCoNi with ordering effects
The exceptional mechanical strength of medium/high-entropy alloys has been attributed to hardening in random solid solutions. Here, we evidence non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering. A data-mining approach of electron nanodiffraction enabled the study, which is assisted by neutron scattering, atom probe tomography, and diffraction simulation using first-principles theory models. Two samples, one homogenized and one heat-treated, are observed. In both samples, results reveal two types of short-range-order inside nanoclusters that minimize the Cr–Cr nearest neighbors (L12) or segregate Cr on alternating close-packed planes (L11). The L11is predominant in the homogenized sample, while the L12formation is promoted by heat-treatment, with the latter being accompanied by a dramatic change in dislocation-slip behavior. These findings uncover short-range order and the resulted chemical heterogeneities behind the mechanical strength in CrCoNi, providing general opportunities for atomistic-structure study in concentrated alloys for the design of strong and ductile materials.
more » « less- Award ID(s):
- 1809640
- NSF-PAR ID:
- 10378877
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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