Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM)

Abstract

Here we explore the use of scanning diffraction electron microscopy (4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local structure and chemistry in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass, as a function of position with 3 nm spatial resolution. 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. This gives critical insights into the formation mechanism of these features. Unsupervised machine learning was used to extract partial PDFs from the material, modeled as quasi-binary alloy, and map them in space allowing key insights not only into the local average composition but also any chemical short-range ordering tendency in that region of the sample. 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 an analysis of the data such as positions and widths of PDF peaks or parameters refined from fits to the patterns, and have developed a flexible and rapid data 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 is explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from PDFs we call this approach scanning nano-structure electron microscopy (SNEM). We believe that it will be powerful and useful extension of current 4D-STEM methods.

Towards scanning nanostructure X-ray microscopy

“…We welcome contributions that will extend the code. For example, in principle, it would be possible to add a Rietveld refinement capability to augment the DiffPy-CMI modeling of the PDF data, extending it to accept scanning electron PDF data (Rakita et al, 2021), or other similar enhancements.…”

“…Pioneering work has been done in combining imaging with diffraction in transmission electron microscopes, known as 4D-STEM (Ophus, 2019;Willhammar et al, 2021;Johnstone et al, 2020;Gammer et al, 2018). Extending 4D-STEM to extract PDFs is also possible (Rakita et al, 2021;Mu et al, 2016Mu et al, , 2019Liu et al, 2020).…”