Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-30T14:35:52.223Z Has data issue: false hasContentIssue false

Automated Image Acquisition for Low-Dose STEM at Atomic Resolution

Published online by Cambridge University Press:  23 May 2017

Andreas Mittelberger
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Christian Kramberger
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Christoph Hofer
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Clemens Mangler
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
Jannik C. Meyer*
Affiliation:
Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
*
*Corresponding author. jannik.meyer@univie.ac.at
Get access

Abstract

Beam damage is a major limitation in electron microscopy that becomes increasingly severe at higher resolution. One possible route to circumvent radiation damage, which forms the basis for single-particle electron microscopy and related techniques, is to distribute the dose over many identical copies of an object. For the acquisition of low-dose data, ideally no dose should be applied to the region of interest before the acquisition of data. We present an automated approach that can collect large amounts of data efficiently by acquiring images in a user-defined area-of-interest with atomic resolution. We demonstrate that the stage mechanics of the Nion UltraSTEM, combined with an intelligent algorithm to move the sample, allow the automated acquisition of atomically resolved images from micron-sized areas of a graphene substrate. Moving the sample stage automatically in a regular pattern over the area-of-interest enables the collection of data from pristine sample regions without exposing them to the electron beam before recording an image. Therefore, it is possible to obtain data with minimal dose (no prior exposure during focusing), which is only limited by the minimum signal needed for data processing. This enables us to minimize beam-induced damage in the sample and to acquire large data sets within a reasonable amount of time.

Type
Instrumentation and Software
Copyright
© Microscopy Society of America 2017 

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.)

References

Banhart, F., Kotakoski, J. & Krasheninnikov, A.V. (2011). Structural defects in graphene. ACS Nano 5(1), 2641.Google Scholar
Bartesaghi, A., Merk, A., Banerjee, S., Matthies, D., Wu, X., Milne, J.L.S. & Subramaniam, S. (2015). 2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor. Science 348(6239), 11471151.Google Scholar
Cheng, Y. (2015). Single-particle Cryo-EM at crystallographic resolution. Cell 161(3), 450457.Google Scholar
Cheng, Y., Grigorieff, N., Penczek, P. & Walz, T. (2015). A primer to single-particle cryo-electron microscopy. Cell 161(3), 438449.Google Scholar
Dierksen, K., Typke, D., Hegerl, R., Koster, A. & Baumeister, W. (1992). Towards automatic electron tomography. Ultramicroscopy 40(1), 7187.Google Scholar
Egerton, R.F. (2012). Mechanisms of radiation damage in beam-sensitive specimens, for TEM accelerating voltages between 10 and 300 kV. Microsc Res Tech 75(11), 15501556.Google Scholar
Frank, J., Goldfarb, W., Eisenberg, D. & Baker, T.S. (1978). Reconstruction of glutamine synthetase using computer averaging. Ultramicroscopy 3(C), 283290.Google Scholar
Fultz, B. & Howe, J.M. (2008). Transmission electron microscopy and diffractometry of materials . Berlin, Heidelberg, New York: Springer-Verlag.Google Scholar
Koster, A., Chen, H., Sedat, J. & Agard, D. (1992). Automated microscopy for electron tomography. Ultramicroscopy 46(1–4), 207227.Google Scholar
Kotakoski, J., Krasheninnikov, A.V., Kaiser, U. & Meyer, J.C. (2011). From point defects in graphene to two-dimensional amorphous carbon. Phys Rev Lett 106(10), 105505.Google Scholar
Kotakoski, J., Mangler, C. & Meyer, J.C. (2014). Imaging atomic-level random walk of a point defect in graphene. Nat Commun 5, 3991.Google Scholar
Kramberger, C. & Meyer, J.C. (2016). Progress in structure recovery from low dose exposures: Mixed molecular adsorption, exploitation of symmetry and reconstruction from the minimum signal level. Ultramicroscopy 170, 6068.Google Scholar
Mastronarde, D.N. (2005). Automated electron microscope tomography using robust prediction of specimen movements. J Struct Biol 152(1), 3651.Google Scholar
Meyer, J.C., Eder, F., Kurasch, S., Skakalova, V., Kotakoski, J., Park, H.J., Roth, S., Chuvilin, A., Eyhusen, S., Benner, G., Krasheninnikov, A.V. & Kaiser, U. (2012). Accurate measurement of electron beam induced displacement cross sections for single-layer graphene. Phys Rev Lett 108(19), 196102.Google Scholar
Meyer, J.C., Kisielowski, C., Erni, R., Rossell, M.D., Crommie, M.F. & Zettl, A. (2008). Direct Imaging of Lattice Atoms and Topological Defects in Graphene Membranes. Nano Letters 8(11), 35823586.Google Scholar
Meyer, J.C., Kotakoski, J. & Mangler, C. (2014). Atomic structure from large-area, low-dose exposures of materials: A new route to circumvent radiation damage. Ultramicroscopy 145, 1321.CrossRefGoogle ScholarPubMed
Robertson, A.W., Allen, C.S., Wu, Y.a., He, K., Olivier, J., Neethling, J., Kirkland, A.I. & Warner, J.H. (2012). Spatial control of defect creation in graphene at the nanoscale. Nat Commun 3, 1144.Google Scholar
Shi, J., Williams, D.R. & Stewart, P.L. (2008). A Script-Assisted Microscopy (SAM) package to improve data acquisition rates on FEI Tecnai electron microscopes equipped with Gatan CCD cameras. J Struct Biol 164(1), 166169.Google Scholar
Suloway, C., Pulokas, J., Fellmann, D., Cheng, A., Guerra, F., Quispe, J., Stagg, S., Potter, C.S. & Carragher, B. (2005). Automated molecular microscopy: The new Leginon system. J Struct Biol 151(1), 4160.Google Scholar
Zhang, P., Beatty, A., Milne, J.L. & Subramaniam, S. (2001). Automated data collection with a Tecnai 12 electron microscope: Applications for molecular imaging by cryomicroscopy. J Struct Biol 135(3), 251261.Google Scholar
Zhang, P., Borgnia, M.J., Mooney, P., Shi, D., Pan, M., O’Herron, P., Mao, A., Brogan, D., Milne, J.L. & Subramaniam, S. (2003). Automated image acquisition and processing using a new generation of 4Kx4K CCD cameras for cryo electron microscopic studies of macromolecular assemblies. J Struct Biol 143(2), 135144.Google Scholar
Zhang, X., Jin, L., Fang, Q., Hui, W.H. & Zhou, Z.H. (2010). 3.3 Å cryo-EM structure of a nonenveloped virus reveals a priming mechanism for cell entry. Cell 141(3), 472482.Google Scholar