Linking TEM Analytical Spectroscopies for an Assumptionless Compositional Analysis

Research output: Contribution to journalArticlepeer-review

Abstract

The classical implementation for putting quantitative figures on maps to reveal elemental compositions in transmission electron microscopy is by analytical methods like X-ray and energy-loss spectroscopy. Typically, the technique in use often depends on whether lighter or heavier elements are present and—more practically—which calibrations are available or sample-related properties are known. A framework linking electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray (EDX) signals such that absolute volumetric concentrations can be derived without assumptions made a priori about the unknown sample, is largely missing. In order to combine both techniques and harness their respective potentials for a light and heavy element analysis, we have set up a powerful hardware configuration and implemented an experimental approach, which reduces the need for estimates on many parameters needed for quantitative work such as densities, absolute thicknesses, theoretical ionization cross-sections, etc. Calibrations on specimens with known geometry allow the measurement of inelastic mean free paths. As a consequence, mass-thicknesses obtained from the EDX ζ-factor approach can be broken up and quantities like concentrations and partial energy-differential ionization cross-sections become accessible. ζ-factors can then be used for conversion into EELS cross-sections that are hard to determine otherwise, or conversely, connecting EDXS and EELS in a quantitative manner quite effectively.
Original languageEnglish
Pages (from-to)676-686
JournalMicroscopy and Microanalysis
Volume20
Issue number3
DOIs
Publication statusPublished - 2014

Fields of Expertise

  • Advanced Materials Science

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Experimental

Fingerprint

Dive into the research topics of 'Linking TEM Analytical Spectroscopies for an Assumptionless Compositional Analysis'. Together they form a unique fingerprint.

Cite this