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Assessing phase discrimination via the segmentation of an elemental energy dispersive X-ray spectroscopy map: a case study of Bi 2 Te 3 and Bi 2 Te 2 S.

J B ByrnesA A GazderSima Aminorroaya Yamini
Published in: RSC advances (2018)
The present case study critically assesses the efficacy of a previously proposed segmentation methodology as a means to discriminate phases via post-processing the image of an elemental map. In the Bi 2 Te 2.5 S 0.5 multiphase compound, the reference spectra of the Bi 2 Te 3 and Bi 2 Te 2 S phases are distinct enough to effectively distinguish two phases during map acquisition. Since the counts of the sulphur-K peak in the X-ray emission data are significantly higher for Bi 2 Te 2 S compared to Bi 2 Te 3 , the segmentation methodology exploits this variation and enables successful phase discrimination via post-processing the image of the elemental map.
Keyphrases
  • deep learning
  • convolutional neural network
  • high resolution
  • high density
  • machine learning
  • magnetic resonance
  • electronic health record
  • ionic liquid
  • mass spectrometry
  • artificial intelligence
  • data analysis