TY - JOUR
T1 - Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)
AU - Rakita, Yevgeny
AU - Hart, James L.
AU - Das, Partha Pratim
AU - Shahrezaei, Sina
AU - Foley, Daniel L.
AU - Mathaudhu, Suveen Nigel
AU - Nicolopoulos, Stavros
AU - Taheri, Mitra L.
AU - Billinge, Simon J.L.
N1 - Funding Information:
J.L.H and P.P.D contributed equally. Y.R. thank Songsheng Tau for his assistance in developing the MiniPipes code. We also acknowledge Michael L. Falk and Darius D. Alix-Williams for providing atomic structures to support diffraction simulations. Development of data analysis pipelines and protocols in the Billinge group was funded by the Next Generation Synthesis Center (GENESIS), an Energy Frontier Research Center funded by the U.S. Department of Energy , Office of Science, Basic Energy Sciences under Award Number DE-SC0019212 . Electron microscopy in the Taheri group was supported in part from the U.S. Office of Naval Research through contracts N000142012368 (a Multidisciplinary University Research Initiative (MURI) program) and N000142012788 , and in part from U.S. Department of Energy, Basic Energy Sciences, through contract DE-SC0020314. Sample creation and preparation in the Mathaudhu group was funded by the U.S. National Science Foundation under CMMI Grant 1550986 .
Publisher Copyright:
© 2022 Acta Materialia Inc.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Here we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr65Cu17.5Ni10Al7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don't require energy filters on the instrument. We spatially map different quantities of interest (QoI's), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI's. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.
AB - Here we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr65Cu17.5Ni10Al7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don't require energy filters on the instrument. We spatially map different quantities of interest (QoI's), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI's. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.
KW - 4D STEM
KW - Metallic glass
KW - Pair distribution function
UR - http://www.scopus.com/inward/record.url?scp=85141252257&partnerID=8YFLogxK
U2 - 10.1016/j.actamat.2022.118426
DO - 10.1016/j.actamat.2022.118426
M3 - Article
AN - SCOPUS:85141252257
SN - 1359-6454
VL - 242
JO - Acta Materialia
JF - Acta Materialia
M1 - 118426
ER -