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Simulating STEM Imaging of Nanoparticles in Micrometers-Thick Substrates

Published online by Cambridge University Press:  20 October 2010

H. Demers ,
N. Poirier-Demers ,
D. Drouin  and
N. de Jonge
H. Demers
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
N. Poirier-Demers
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
D. Drouin
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
N. de Jonge*
Affiliation:
Vanderbilt University School of Medicine, Department of Molecular Physiology and Biophysics, Nashville, TN 37232-0615, USA
*
Corresponding author. E-mail: [email protected]
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Abstract

Scanning transmission electron microscope (STEM) images of three-dimensional (3D) samples were simulated. The samples consisted of a micrometer(s)-thick substrate and gold nanoparticles at various vertical positions. The atomic number (Z) contrast as obtained via the annular dark-field detector was generated. The simulations were carried out using the Monte Carlo method in the CASINO software (freeware). The software was adapted to include the STEM imaging modality, including the noise characteristics of the electron source, the conical shape of the beam, and 3D scanning. Simulated STEM images of nanoparticles on a carbon substrate revealed the influence of the electron dose on the visibility of the nanoparticles. The 3D datasets obtained by simulating focal series showed the effect of beam broadening on the spatial resolution and on the signal-to-noise ratio. Monte Carlo simulations of STEM imaging of nanoparticles on a thick water layer were compared with experimental data by programming the exact sample geometry. The simulated image corresponded to the experimental image, and the signal-to-noise levels were similar. The Monte Carlo simulation strategy described here can be used to calculate STEM images of objects of an arbitrary geometry and amorphous sample composition. This information can then be used, for example, to optimize the microscope settings for imaging sessions where a low electron dose is crucial for the design of equipment, or for the analysis of the composition of a certain specimen.

Keywords

Monte Carlo simulationsscanning transmission electron microscopy (STEM)shot noiseannular dark-field (ADF) detectorthree-dimensional STEMliquid STEMnanoparticles
Type
Instrumentation and Software Developments
Information
Microscopy and Microanalysis , Volume 16 , Issue 6 , December 2010 , pp. 795 - 804
Copyright
Copyright © Microscopy Society of America 2010

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References

REFERENCES

Aoyama, K., Takagi, T., Hirase, A. & Miyazawa, A. (2008). STEM tomography for thick biological specimens. Ultramicroscopy 109, 70–80.CrossRefGoogle ScholarPubMed
Bethe, H. (1930). Theory of the passage of fast corpuscular rays through matter. Ann Phys 5(5), 325–400.CrossRefGoogle Scholar
Bethe, H.A. (1933). Quantenmechanik der Ein- und Zwei-Elektronenprobleme. In Handbuch der Physik, Geiger, J. & Scheel, K. (Eds.), pp. 273–560. Berlin: Springer.Google Scholar
Crewe, A.V., Wall, J. & Langmore, J. (1970). Visibility of single atoms. Science 168, 1338–1340.CrossRefGoogle ScholarPubMed
de Jonge, N., Peckys, D.B., Kremers, G.J. & Piston, D.W. (2009). Electron microscopy of whole cells in liquid with nanometer resolution. Proc Natl Acad Sci 106, 2159–2164.CrossRefGoogle ScholarPubMed
de Jonge, N., Poirier-Demers, N., Demers, H., Peckys, D.B. & Drouin, D. (2010a). Nanometer-resolution electron microscopy through micrometers-thick water layers. Ultramicroscopy 110(9), 1114–1119.CrossRefGoogle ScholarPubMed
de Jonge, N., Sougrat, R., Northan, B. & Pennycook, S.J. (2010b). Three-dimensional scanning transmission electron microscopy for biological specimen. Microsc Microanal 16(1), 54–63.Google Scholar
Drouin, D. & Couture, A.R. (2002). Development of a simulation tool for real world SEM applications. Microsc Microanal 8(S2), 702–703 (CD-ROM).CrossRefGoogle Scholar
Drouin, D., Couture, A.R., Joly, D., Tastet, X., Aimez, V. & Gauvin, R. (2007). CASINO V2.42—A fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users. Scanning 29(3), 92–101.CrossRefGoogle ScholarPubMed
Frank, L. (2005). Noise in secondary electron emission: The low yield case. J Electron Microsc (Tokyo) 54(4), 361–365.CrossRefGoogle ScholarPubMed
Hohmann-Marriott, M.F., Sousa, A.A., Azari, A., Glushakova, S., Zhang, G., Zimmerberg, J. & Leapman, R.D. (2009). Nanoscale 3D cellular imaging by axial scanning transmission electron tomography. Nat Methods 6, 729–732.Google Scholar
Hovington, P., Drouin, D. & Gauvin, R. (1997). CASINO: A new Monte Carlo code in C language for electron beam interaction—part I: Description of the program. Scanning 19(1), 1–14.CrossRefGoogle Scholar
Hyun, J., Ercius, P., Weyland, M. & Muller, D.A. (2007). Fundamental resolution limit in scanning transmission electron tomography from beam spreading. Microsc Microanal 13(S2), 1330–1331 (CD-ROM).Google Scholar
Hyun, J.K., Ercius, P. & Muller, D.A. (2008). Beam spreading and spatial resolution in thick organic specimens. Ultramicroscopy 109(1), 1–7.CrossRefGoogle ScholarPubMed
Jablonski, A., Salvat, F. & Powell, C.J. (2003). NIST Electron Elastic-Scattering Cross-Section Database—Version 3.1. Washington, DC: National Institute of Standards and Technology.Google Scholar
Joy, D.C. (1995). Monte Carlo Modeling for Electron Microscopy and Microanalysis. New York: Oxford University Press.CrossRefGoogle Scholar
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electrons. Scanning 11, 176–180.CrossRefGoogle Scholar
Kirkland, E.J. & Thomas, M.G. (1996). A high efficiency annular dark field detector for STEM. Ultramicroscopy 62, 79–88.Google Scholar
Kyser, D.F. (1979). Monte Carlo simulation in analytical electron microscopy. In Introduction to Analytical Electron Microscopy, Hren, J.J., Goldstein, J.I. & Joy, D.C. (Eds.), pp. 199–222. New York: Plenum Press.Google Scholar
Mueller, S.A. & Engel, A. (2006). Biological scanning transmission electron microscopy: Imaging and single molecule mass determination. Chimia 60, 749–753.Google Scholar
Nellist, P.D., Chisholm, M.F., Dellby, N., Krivanek, O.L., Murfitt, M.F., Szilagyi, Z.S., Lupini, A.R., Borisevich, A., Sides, W.H. & Pennycook, S.J. (2004). Direct sub-angstrom imaging of a crystal lattice. Science 305, 1741.Google Scholar
Newbury, D.E. & Myklebust, R.L. (1981). A Monte Carlo electron trajectory simulation for analytical electron microscopy. In Analytical Electron Microscopy, Geiss, R.H. (Ed.), pp. 91–98. San Francisco, CA: San Francisco Press.Google Scholar
Reichelt, R. & Engel, A. (1984). Monte Carlo calculations of elastic and inelastic electron scattering in biological and plastic materials. Ultramicroscopy 13(3), 279–293.CrossRefGoogle Scholar
Reimer, L. (1998). Scanning Electron Microscopy: Physics of Image Formation and Microanalysis. New York: Springer.CrossRefGoogle Scholar
Reimer, L. & Kohl, H. (2008). Transmission Electron Microscopy: Physics of Image Formation and Microanalysis. New York: Springer.Google Scholar
Rose, A. (1948). Television pickup tubes and the problem of noise. Adv Electron 1, 131–166.Google Scholar
Salvat, F., Jablonski, A. & Powell, C.J. (2005). ELSEPA—Dirac partial-wave calculation of elastic scattering of electrons and positrons by atoms, positive ions and molecules. Comput Phys Comm 165, 157–190.CrossRefGoogle Scholar
Sousa, A.A., Hohmann-Marriott, M., Aronova, M.A., Zhang, G. & Leapman, R.D. (2008). Determination of quantitative distributions of heavy-metal stain in biological specimens by annular dark-field STEM. J Struct Biol 162, 14–28.Google Scholar
Sousa, A.A., Hohmann-Marriott, M.F., Zhang, G. & Leapman, R.D. (2009). Monte Carlo electron-trajectory simulations in bright-field and dark-field STEM: Implications for tomography of thick biological sections. Ultramicroscopy 109(3), 213–221.CrossRefGoogle ScholarPubMed
van Benthem, K., Lupini, A.R., Kim, M., Baik, H.S., Doh, S.J., Lee, J.H., Oxley, M.P., Findlay, S.D., Allen, L.J. & Pennycook, S.J. (2005). Three-dimensional imaging of individual hafnium atoms inside a semiconductor device. Appl Phys Lett 87, 034104-1–034104-3.Google Scholar
Williams, D.B. & Carter, C.B. (1996). Transmission Electron Microscopy: A Textbook for Materials Science. New York: Plenum Press.Google Scholar