Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-07-04T12:50:48.078Z Has data issue: false hasContentIssue false
  • English
  • Français

Three-Dimensional Characterization of Iron Oxide (α-Fe2O3) Nanoparticles: Application of a Compressed Sensing Inspired Reconstruction Algorithm to Electron Tomography

Published online by Cambridge University Press:  05 December 2012

Niven Monsegue ,
Xin Jin ,
Takuya Echigo ,
Ge Wang  and
Mitsuhiro Murayama
Niven Monsegue*
Affiliation:
Institute for Critical Technology and Applied Science, Virginia Tech, Blacksburg, VA 24061, USA
Xin Jin
Affiliation:
School of Biomedical Engineering & Sciences, Virginia Tech, Blacksburg, VA 24061, USA Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Takuya Echigo
Affiliation:
Center for NanoBioEarth, Department of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA Japan International Research Center for Agricultural Sciences, Ohwashi 1-1, Tsukuba 305-8686, Ibaraki, Japan
Ge Wang
Affiliation:
School of Biomedical Engineering & Sciences, Virginia Tech, Blacksburg, VA 24061, USA
Mitsuhiro Murayama
Affiliation:
Institute for Critical Technology and Applied Science, Virginia Tech, Blacksburg, VA 24061, USA Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA 24061, USA
*
*Corresponding author. E-mail: nmonsegu@vt.edu
Get access

Abstract

In this article, we demonstrate the application of a new compressed sensing three-dimensional reconstruction algorithm for electron tomography that increases the accuracy of morphological characterization of nanostructured materials such as nanocrystalline iron oxide particles. A powerful feature of the algorithm is an anisotropic total variation norm for the L1 minimization during algebraic reconstruction that effectively reduces the elongation artifacts caused by limited angle sampling during electron tomography. The algorithm provides faithful morphologies that have not been feasible with existing techniques.

Keywords

computed tomographySTEMcompressed sensing3D reconstructionquantification
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 18 , Issue 6 , December 2012 , pp. 1362 - 1367
Copyright
Copyright © Microscopy Society of America 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

Present address: Functional Geomaterials Group, Environmental Remediation Materials Unit, National Institute for Materials Science, Namiki 1-1, Tsukuba, Ibaraki 305-0044, Japan

References

Binev, P., Dahmen, W., DeVore, R., Lamby, P., Savu, D. & Sharpley, R. (2011). Compressed sensing and electron microscopy. Technical Report. Aachen Institute for Advanced Study in Computational Engineering Science.Google Scholar
Candes, E.J., Romberg, J. & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE T Inf Theory 52(2), 489–509.Google Scholar
Donoho, D.L. (2006). Compressed sensing. IEEE T Inf Theory 52(4), 1289–1306.Google Scholar
Echigo, T., Monsegue, N., Aruguete, D.M., Murayama, M. & Hochella, M.F. Jr. (forthcoming). Nanopores in hematite (α-Fe2-O3) nanocrystals observed by electron tomography. Amer Miner 98(1). Available at http://dx.doi.org/10.2138/am.2013.4120.CrossRefGoogle Scholar
Fischer, W.R. & Schwertmann, U. (1975). Formation of hematite from amorphous iron(III) hydroxide. Clay Clay Miner 23(1), 33–37.Google Scholar
Goris, B., Van den Broek, W., Batenburg, K.J., Heidari Mezerji, H. & Bals, S. (2012). Electron tomography based on a total variation minimization reconstruction technique. Ultramicroscopy 113, 120–130.Google Scholar
Kawase, N., Kato, M., Nishioka, H. & Jinnai, H. (2007). Transmission electron microtomography without the “missing wedge” for quantitative structural analysis. Ultramicroscopy 107(1), 8–15.Google Scholar
Kelly, D.F., Lake, R.J., Walz, T. & Artavanis-Tsakonas, S. (2007). Conformational variability of the intracellular domain of Drosophila Notch and its interaction with Suppressor of Hairless. Proc Natl Acad Sci USA 104(23), 9591–9596.CrossRefGoogle ScholarPubMed
Lustig, M., Donoho, D. & Pauly, J.M. (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6), 1182–1195.Google Scholar
Midgley, P.A. & Weyland, M. (2003). 3D electron microscopy in the physical sciences: The development of Z-contrast and EFTEM tomography. Ultramicroscopy 96(3-4), 413–431.Google Scholar
Otsu, N. (1979). Threshold selection method from gray-level histograms. IEEE T Syst Man Cybern 9(1), 62–66.Google Scholar
Rodriguez, R.D., Demaille, D., Lacaze, E., Jupille, J., Chaneac, C. & Jolivet, J.P. (2007). Rhombohedral shape of hematite nanocrystals synthesized via thermolysis of an additive-free ferric chloride solution. J Phys Chem C 111, 16866–16870.Google Scholar
Saghi, Z., Holland, D.J., Leary, R., Falqui, A., Bertoni, G., Sederman, A.J., Gladden, L.F. & Midgley, P.A. (2011). Three-dimensional morphology of iron oxide nanoparticles with reactive concave surfaces. A compressed sensing-electron tomography (CS-ET) approach. Nano Lett 11(11), 4666–4673.Google Scholar
Schwertmann, U. & Murad, E. (1983). Effect of pH on the formation of goethite and hematite from ferrihydrite. Clay Clay Miner 31(4), 277–284.Google Scholar
Wang, G. & Yu, H.Y. (2010). Can interior tomography outperform lambda tomography? Proc Natl Acad Sci USA 107(22), E92–E93.Google ScholarPubMed
Weyland, M. (2002). Electron tomography of catalysts. Top Catal 21(4), 175–183.Google Scholar
Xin, J., Liang, L., Zhiqiang, C., Li, Z. & Yuxiang, X. (2010). Anisotropic total variation for limited-angle CT reconstruction. In 2010 IEEE Nuclear Science Symposium Conference Record (NSS/MIC), pp. 2232–2238.Google Scholar

Niven Monsegue Supplementary Material

Video

Download Niven Monsegue Supplementary Material(Video)
Video 8.4 MB