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Author | Zhong, Z.; Goris, B.; Schoenmakers, R.; Bals, S.; Batenburg, K.J. | ||||
Title | A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM | Type | A1 Journal article | ||
Year | 2017 | Publication | Ultramicroscopy | Abbreviated Journal | Ultramicroscopy |
Volume | 174 | Issue | 174 | Pages | 35-45 |
Keywords | A1 Journal article; Electron microscopy for materials research (EMAT) | ||||
Abstract | A three-dimensional (3D) chemical characterization of nanomaterials can be obtained using tomography based on high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) or energy dispersive X-ray spectroscopy (EDS) STEM. These two complementary techniques have both advantages and disadvantages. The Z-contrast images have good image quality but lack robustness in the compositional analysis, while the elemental maps give more element-specific information, but at a low signal-to-noise ratio and a longer exposure time. Our aim is to combine these two types of complementary information in one single tomographic reconstruction process. Therefore, an imaging model is proposed combining both HAADF-STEM and EDS-STEM. Based on this model, the elemental distributions can be reconstructed using both types of information simultaneously during the reconstruction process. The performance of the new technique is evaluated using simulated data and real experimental data. The results demonstrate that combining two imaging modalities leads to tomographic reconstructions with suppressed noise and enhanced contrast. |
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Publisher | Place of Publication | Editor | |||
Language | Wos | 000403342200005 | Publication Date | 2016-12-11 | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0304-3991 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
Impact Factor | 2.843 | Times cited | 26 | Open Access | OpenAccess |
Notes | This research is supported by the Dutch Technology Foundation STW (http://www.stw.nl/), which is part of the Netherlands Organization for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs, Agriculture and Innovation under project number 13314. It is also supported by the Flemish research foundation (FWO Vlaanderen) by project funding (G038116N) and a postdoctoral research grant to B.G. Funding from the European Research Council (Starting Grant No. COLOURATOMS 335078) is acknowledged by S.B. The authors would like to thank Dr. Bernd Rieger and Dr. Richard Aveyard for useful discussions, and Prof. Dr. Luis M. Liz-Marzan for providing the investigated samples. We also acknowledge COST Action MP1207 for networking support. (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); saraecas; ECAS_Sara; | Approved | Most recent IF: 2.843 | ||
Call Number | EMAT @ emat @ c:irua:141719UA @ admin @ c:irua:141719 | Serial | 4484 | ||
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Author | Zhuge, X.; Jinnai, H.; Dunin-Borkowski, R.E.; Migunov, V.; Bals, S.; Cool, P.; Bons, A.-J.; Batenburg, K.J. | ||||
Title | Automated discrete electron tomography – Towards routine high-fidelity reconstruction of nanomaterials | Type | A1 Journal article | ||
Year | 2017 | Publication | Ultramicroscopy | Abbreviated Journal | Ultramicroscopy |
Volume | 175 | Issue | 175 | Pages | 87-96 |
Keywords | A1 Journal article; Electron microscopy for materials research (EMAT); Laboratory of adsorption and catalysis (LADCA) | ||||
Abstract | Electron tomography is an essential imaging technique for the investigation of morphology and 3D structure of nanomaterials. This method, however, suffers from well-known missing wedge artifacts due to a restricted tilt range, which limits the objectiveness, repeatability and efficiency of quantitative structural analysis. Discrete tomography represents one of the promising reconstruction techniques for materials science, potentially capable of delivering higher fidelity reconstructions by exploiting the prior knowledge of the limited number of material compositions in a specimen. However, the application of discrete tomography to practical datasets remains a difficult task due to the underlying challenging mathematical problem. In practice, it is often hard to obtain consistent reconstructions from experimental datasets. In addition, numerous parameters need to be tuned manually, which can lead to bias and non-repeatability. In this paper, we present the application of a new iterative reconstruction technique, named TVR-DART, for discrete electron tomography. The technique is capable of consistently delivering reconstructions with significantly reduced missing wedge artifacts for a variety of challenging data and imaging conditions, and can automatically estimate its key parameters. We describe the principles of the technique and apply it to datasets from three different types of samples acquired under diverse imaging modes. By further reducing the available tilt range and number of projections, we show that the proposed technique can still produce consistent reconstructions with minimized missing wedge artifacts. This new development promises to provide the electron microscopy community with an easy-to-use and robust tool for high-fidelity 3D characterization of nanomaterials. |
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Publisher | Place of Publication | Editor | |||
Language | Wos | 000403342500008 | Publication Date | 2017-01-24 | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0304-3991 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
Impact Factor | 2.843 | Times cited | 22 | Open Access | OpenAccess |
Notes | This work has been supported in part by the Stichting voor de Technische Wetenschappen (STW) through a personal grant (Veni,13610), and was in part by ExxonMobil Chemical Europe Inc. The authors further acknowledge financial support from the University of Antwerp through BOF GOA funding. S.B. acknowledges financial support from the European Research Council (ERC Starting Grant #335078-COLOURATOMS). R.D.B. is grateful for funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ ERC grant agreement number 320832. Thomas Altantzis is gratefully acknowledged for acquiring the Anatase nanosheets dataset. (ROMEO:green; preprint:; postprint:can ; pdfversion:cannot); saraecas; ECAS_Sara; | Approved | Most recent IF: 2.843 | ||
Call Number | EMAT @ emat @ c:irua:141218UA @ admin @ c:irua:141218 | Serial | 4485 | ||
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Author | Zhong, Z.; Aveyard, R.; Rieger, B.; Bals, S.; Palenstijn, W.J.; Batenburg, K.J. | ||||
Title | Automatic correction of nonlinear damping effects in HAADF-STEM tomography for nanomaterials of discrete compositions | Type | A1 Journal article | ||
Year | 2018 | Publication | Ultramicroscopy | Abbreviated Journal | Ultramicroscopy |
Volume | 184 | Issue | 184 | Pages | 57-65 |
Keywords | A1 Journal article; Electron microscopy for materials research (EMAT) | ||||
Abstract | <script type='text/javascript'>document.write(unpmarked('HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called \u0022cupping artifacts\u0022, due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography. In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data. (C) 2017 Elsevier B.V. All rights reserved.')); | ||||
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Publisher | Place of Publication | Amsterdam | Editor | ||
Language | Wos | 000417779800008 | Publication Date | 2017-10-31 | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0304-3991 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
Impact Factor | 2.843 | Times cited | 8 | Open Access | OpenAccess |
Notes | ; This research is supported by the Dutch Technology Foundation STW (http:// www.stw.nl/), which is part of the Netherlands Organization for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs, Agriculture and Innovation under project number 13314. Funding from the European Research Council (Starting grant no. COLOURATOMS 335078) is acknowledged by S. Bals. The authors would like to thank Dr. Thomas Altantzis and Dr. Bart Goris for providing the experimental data, and Prof. Dr. Luis M. Liz-Marzan for providing the investigated samples. ; ecas_sara | Approved | Most recent IF: 2.843 | ||
Call Number | UA @ lucian @ c:irua:148501UA @ admin @ c:irua:148501 | Serial | 4867 | ||
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