Advertisement

Compositional Mapping

  • Joseph I. Goldstein
  • Dale E. Newbury
  • Joseph R. Michael
  • Nicholas W. M. Ritchie
  • John Henry J. Scott
  • David C. Joy
Chapter

Abstract

SEM images that show the spatial distribution of the elemental constituents of a specimen («elemental maps») can be created by using the characteristic X-ray intensity measured for each element with the energy dispersive X-ray spectrometer (EDS) to define the gray level (or color value) at each picture element (pixel) of the scan. Elemental maps based on X-ray intensity provide qualitative information on spatial distributions of elements. Compositional mapping, in which a full EDS spectrum is recorded at each pixel («X-ray Spectrum Imaging» or XSI) and processed with peak fitting, k-ratio standardization, and matrix corrections, provides a quantitative basis for comparing maps of different elements in the same region, or for the same element from different regions

References

  1. Bright D (2017) NIST Lispix, a software engine for image processing, available free at: ► http://www.nist.gov/lispix/doc/contents.htm
  2. Bright D, Newbury D (2004) Maximum pixel spectrum: a new tool for detecting and recovering rare, unanticipated features from spectrum image data cubes. J Microsc 216:186CrossRefGoogle Scholar
  3. Gorlen KE, Barden LK, Del Priore JS, Fiori CE, Gibson CC, Leapman RD (1984) Computerized analytical electron microscope for elemental mapping. Rev Sci Instrum 55:912Google Scholar
  4. Kotula P, Keenan MR, Joseph R, Michael JR (2003) Automated analysis of SEM X-ray spectral images: a powerful new microanalysis tool. Microsc Microanal 9:1CrossRefGoogle Scholar
  5. Newbury D, Bright D (1999) Logarithmic 3-band color encoding: Robust method for display and comparison of compositional maps in electron probe X-ray microanalysis. Microsc Microanal 5:333CrossRefGoogle Scholar
  6. Newbury D, Ritchie N (2013) Elemental mapping of microstructures by scanning electron microscopy –energy dispersive X-ray spectrometry (SEM-EDS): extraordinary advances with the Silicon Drift Detector (SDD). J Anal At Spectrom 28:973CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Joseph I. Goldstein
    • 1
  • Dale E. Newbury
    • 2
  • Joseph R. Michael
    • 3
  • Nicholas W. M. Ritchie
    • 2
  • John Henry J. Scott
    • 2
  • David C. Joy
    • 4
  1. 1.University of MassachusettsAmherstUSA
  2. 2.National Institute of Standards and TechnologyGaithersburgUSA
  3. 3.Sandia National LaboratoriesAlbuquerqueUSA
  4. 4.University of TennesseeKnoxvilleUSA

Personalised recommendations