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espm: A Python library for the simulation of STEM-EDXS datasets.

Adrien Teurtrie1, Nathanaël Perraudin2, Thomas Holvoet2

  • 1Electron Spectrometry and Microscopy Laboratory, Institute of Physics (IPHYS), École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland; Unité Matériaux et Transformations, UMR-CNRS 8207, Université de Lille, Cité scientifique, Bâtiment C6, 59655, Villeneuve d'Ascq, France.

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Summary
This summary is machine-generated.

We developed two Python packages, espm and emtables, to simulate electron microscopy data. These tools aid in analyzing scanning transmission electron microscopy energy-dispersive X-ray spectroscopy (STEM-EDX) data and designing experiments.

Keywords:
Cross-section tableEnergy-dispersive X-ray spectroscopyOpen-source Python softwareScanning transmission electron microscopySimulationSpectrum image

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Area of Science:

  • Materials Science
  • Computational Science
  • Chemistry

Background:

  • Scanning Transmission Electron Microscopy Energy-Dispersive X-ray Spectroscopy (STEM-EDX) generates complex datacubes requiring advanced analysis techniques.
  • Developing accurate simulations is crucial for validating analytical methods and understanding experimental limitations.

Purpose of the Study:

  • To introduce two open-source Python packages, espm and emtables, for simulating STEM-EDX datacubes.
  • To provide a framework for testing decomposition algorithms on STEM-EDX data with a known ground truth.
  • To assist in experimental design for electron microscopy.

Main Methods:

  • The espm package simulates STEM-EDX datacubes using user-defined chemical compositions and spatial abundance maps.
  • X-ray emission cross-sections are calculated using the emtables package, which is based on state-of-the-art computations.
  • The simulation framework was validated by comparing simulated and experimental datasets of a complex geological sample using non-negative matrix factorization.

Main Results:

  • The developed Python packages enable the simulation of realistic STEM-EDX datacubes.
  • The simulation framework allows for the rigorous testing and validation of data analysis algorithms.
  • The study demonstrated the utility of the packages in analyzing a complex geological sample.

Conclusions:

  • The espm and emtables packages offer valuable tools for researchers in electron microscopy and materials science.
  • These open-source packages can accelerate the development and application of advanced analytical techniques for STEM-EDX data.
  • The software facilitates improved experimental design and data interpretation in nanoscale chemical analysis.