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Elemental Analysis Using Inner-Shell Excitations: A Microanalytical Technique for Materials Characterization

  • Dennis M. Maher

Abstract

Experiments based on transmission electron microscopy play an extremely important role in materials characterization and diagnostics. The high resolution which can be achieved by modern commercial instruments is being used routinely to derive structural and crystallographic information from both the image and diffraction pattern. In materials diagnostics, these two capabilities are enhanced greatly by the ability to obtain direct elemental information at a comparable spatial resolution (i.e. ≲ 10 nm) and thereby place this elemental information in the context of the microstructure and micro-crystallography of the specimen. This combination of techniques, in part, has been the goal of analytical electron microscopy. The principle of microarea analyses is to probe a small volume of a specimen and to detect the many signals which are generated as a result of the interaction between the incident-electron beam and this volume. The desired elemental information is carried either: i) in the secondary emission of X-rays or Auger electrons which occur during the decay of the primary excitation process; or ii) in the transmitted-electron energy-loss spectrum which reflects the primary excitations (i.e. plasmons, valence-shell electrons and inner-shell electrons). Since the preliminary work of WITTRY, FERRIER and COSSLETT (1969) was reported, there has been considerable interest in the use of inner-shell excitations for direct elemental analysis and it is this aspect of electron energy-loss spectroscopy that will be detailed here. Those interested in the analysis techniques and materials applications of plasmon excitations should see a recent review by WILLIAMS and EDINGTON (1976).

Keywords

Energy Window Partial Cross Section Amorphous Carbon Film Edge Profile Edge Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1979

Authors and Affiliations

  • Dennis M. Maher
    • 1
  1. 1.Bell LaboratoriesMurray HillUSA

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