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Atoms — and the protons, neutrons, and electrons that compose them — are extremely small. For example, a carbon atom weighs less than 2 × 10−23 g. When describing the properties of tiny objects such as atoms, we use appropriately small units of measure, such as the atomic mass unit (amu). The amu was originally defined based on hydrogen, the lightest element, then later in terms of oxygen. Since 1961, it has been defined with regard to the most abundant isotope of carbon, atoms of which...
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Atom Probe Tomography Analysis of Exsolved Mineral Phases
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The Application of the OPTICS Algorithm to Cluster Analysis in Atom Probe Tomography Data.

Jing Wang1, Daniel K Schreiber1, Nathan Bailey2

  • 1Pacific Northwest National Laboratory,Energy and Environment Directorate,Richland,WA, 99354,USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|March 9, 2019
PubMed
Summary

A new cluster analysis method improves atom probe tomography (APT) by accurately identifying nanostructures with varying densities. This overcomes limitations of existing methods, enabling better understanding of material properties.

Keywords:
atom probe tomographycluster analysisdensity-based clusteringoxide-dispersion strengthened alloy

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Analytical Chemistry

Background:

  • Atom probe tomography (APT) is crucial for 3D nanostructure characterization.
  • Existing cluster analysis methods struggle with variable atomic densities and artifacts in APT data.
  • Accurate analysis is vital for understanding structure-property relationships and microstructural evolution.

Purpose of the Study:

  • To develop and validate a novel cluster analysis approach for APT data.
  • To address the limitations of density-based clustering in handling non-uniform atomic densities.
  • To enable more accurate characterization of nanometer-scale clusters and precipitates.

Main Methods:

  • Utilized the Ordering Points to Identify the Clustering Structures (OPTICS) algorithm.
  • Implemented an automatic cluster extraction algorithm for APT data analysis.
  • Required only one free parameter, with others estimated from physical properties.

Main Results:

  • Successfully analyzed APT datasets with varying atomic densities.
  • Demonstrated effectiveness on model and real-world datasets, including oxide-dispersion strengthened ferritic alloys.
  • Overcame limitations of previous density-based clustering methods.

Conclusions:

  • The new method provides a more robust and accurate approach to cluster analysis in APT.
  • It enhances the characterization of nanostructures with complex density variations.
  • This advancement aids in understanding material behavior and microstructural development.