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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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The instrumentation of atomic emission spectrometry (AES) involves various components, including atomization devices that convert samples into gas-phase atoms and ions. There are two main types of atomization devices: continuous and discrete atomizers.  Continuous atomizers, like plasmas and flames, introduce samples in a constant stream, while discrete atomizers inject individual samples using syringes or autosamplers. The most common discrete atomizer is the electrothermal atomizer.
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Simple automation of SEM-EDS spectral maps analysis with Python and the edxia framework.

Fabien Georget1,2, William Wilson1,3, Karen L Scrivener1

  • 1Laboratory of Construction Materials, EPFL, Lausanne, Switzerland.

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|March 15, 2022
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Summary

The edxia framework enhances cementitious material microstructure analysis using SEM-EDS hypermaps. Custom extensions enable automated batch processing and advanced data workflows for future research.

Keywords:
SEM-EDXautomationcement pasteedxiaimage analysis

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Area of Science:

  • Materials Science
  • Geochemistry
  • Data Science

Background:

  • The edxia framework was previously introduced for analyzing cementitious material microstructure via SEM-EDS hypermaps.
  • Manual analysis with edxia is effective but lacks efficiency for large datasets and automated processing.

Purpose of the Study:

  • To demonstrate customizable extensions of the edxia framework.
  • To introduce automated data analysis capabilities for microstructure characterization.
  • To facilitate the development of custom analytical workflows.

Main Methods:

  • Extension of the existing edxia framework.
  • Implementation of automatic clustering algorithms.
  • Utilization of Python scientific libraries for flexible workflow development.

Main Results:

  • Demonstrated the adaptability of the edxia framework for customized analyses.
  • Successfully implemented an example of automatic clustering for microstructure segmentation.
  • Established a foundation for developing more sophisticated, automated analytical pipelines.

Conclusions:

  • The edxia framework can be extended to support automated and customized microstructure analysis of cementitious materials.
  • Integration with Python libraries allows for flexible and scalable data processing.
  • Future work can build upon these extensions for advanced research questions.