Yuan, Y, MacArthur, KE, Collins, SM orcid.org/0000-0002-5151-6360 et al. (4 more authors) (2020) Extraction of 3D quantitative maps using EDS-STEM tomography and HAADF-EDS bimodal tomography. Ultramicroscopy. 113166. ISSN 0304-3991
Abstract
Electron tomography has been widely applied to three-dimensional (3D) morphology characterization and chemical analysis at the nanoscale. A HAADF-EDS bimodal tomographic (HEBT) reconstruction technique has been developed to extract high resolution element-specific information. However, the reconstructed elemental maps cannot be directly converted to quantitative compositional information. In this work, we propose a quantification approach for obtaining elemental weight fraction maps from the HEBT reconstruction technique using the physical parameters extracted from a Monte Carlo code, MC X-ray. A similar quantification approach is proposed for the EDS-STEM tomographic reconstruction. The performance of the two quantitative reconstruction methods, using the simultaneous iterative reconstruction technique, are evaluated and compared for a simulated dataset of a two-dimensional phantom sample. The effects of the reconstruction parameters including the number of iterations and the weight of the HAADF signal are discussed. Finally, the two approaches are applied to an experimental dataset to show the 3D structure and quantitative elemental maps of a particle of flux melted metal-organic framework glass.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Ultramicroscopy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | EDS-STEM tomography; HAADF-EDS bimodal tomography; electron-induced X-ray quantification; three-dimensional elemental map |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Nov 2020 15:01 |
Last Modified: | 09 Nov 2021 01:38 |
Status: | Published online |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ultramic.2020.113166 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167739 |
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