Abstract

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni3Al. The ability to observe a signal of atomic ordering consistent with the known L12 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al1.3CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

Highlights

  • An unambiguous understanding of structure provides the essential link between developing a predictive relationship between material processing and component performance

  • The presence of sufficient noise can smear out the information in the Fractional Cumulative Radial Distribution Function (FCRDF) resulting in a false negative, namely the failure to correctly identify the presence of atomic ordering

  • We have developed and proposed the Fractional Cumulative Radial Distribution Function (FCRDF) as a means of visualizing and identifying atomic ordering in atomic data sets

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Summary

Introduction

An unambiguous understanding of structure provides the essential link between developing a predictive relationship between material processing and component performance. Many valuable material characterization techniques provide spatially averaged information on the structure, essential for bulk properties but insufficient for uncovering the nuances that exist in the local ordering of atomic systems. Routine structural characterization techniques such as powder x-ray diffraction provide important spatially averaged structural information. As a state-of-the-art technique, APT has becoming an important tool to study novel materials, such as High Entropy Alloys (HEAs), alloys made with equal proportions of five or more elements, where the distribution of atoms at the atomic level is thought to be crucial to material properties. APT has a high spatial resolution (∼0.1–0.3 nm in depth and 0.3–0.5 nm laterally at best) with high sensitivity (∼10 ppm), and APT can be used to probe the local atomic structure, averaged-out in bulk property characterization techniques such as powder X-ray diffraction. APT has a high spatial resolution (∼0.1–0.3 nm in depth and 0.3–0.5 nm laterally at best) with high sensitivity (∼10 ppm), and APT can be used to probe the local atomic structure, averaged-out in bulk property characterization techniques such as powder X-ray diffraction. (Gault et al, 2010a) Because the understanding of multiphase microstructures, segregation at phase boundary and dislocations, local composition fluctuations and unique atomic configurations are a prerequisite for the future development of mechanical properties of HEA, analytical tools such as APT are essential in this field. (Cairney et al, 2015; Marceau et al, 2015; Diao et al, 2019; Kuo et al, 2019; Hu et al, 2020)

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