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AlAfeef A, Bobynko J, Cockshott WP, et al. Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing. Ultramicroscopy. 2016;170:96-106doi: 10.1016/j.ultramic.2016.08.004.
AlAfeef, A., Bobynko, J., Cockshott, W. P., Craven, A. J., Zuazo, I., Barges, P., & MacLaren, I. (2016). Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing. Ultramicroscopy, 17096-106. https://doi.org/10.1016/j.ultramic.2016.08.004
AlAfeef, Ala, et al. "Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing." Ultramicroscopy vol. 170 (2016): 96-106. doi: https://doi.org/10.1016/j.ultramic.2016.08.004
AlAfeef A, Bobynko J, Cockshott WP, Craven AJ, Zuazo I, Barges P, MacLaren I. Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing. Ultramicroscopy. 2016 Nov;170:96-106. doi: 10.1016/j.ultramic.2016.08.004. Epub 2016 Aug 09. PMID: 27566049.
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Ultramicroscopy. 2016 Nov;170:96-106. doi: 10.1016/j.ultramic.2016.08.004. Epub 2016 Aug 09.

Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing.

Ultramicroscopy

Ala AlAfeef, Joanna Bobynko, W Paul Cockshott, Alan J Craven, Ian Zuazo, Patrick Barges, Ian MacLaren

Affiliations

  1. SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK; School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK. Electronic address: [email protected].
  2. SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK.
  3. School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.
  4. ArcelorMittal Maizières Research, Maizières-lès-Metz 57283, France.
  5. SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK. Electronic address: [email protected].

PMID: 27566049 DOI: 10.1016/j.ultramic.2016.08.004

Abstract

We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionally, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS.

Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords: Compressed sensing; Deconvoluted EELS data; DualEELS tomography; Electron tomography; High manganese steel; Vanadium carbide precipitates

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