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Monte Carlo Simulations of Electron Energy-Loss Spectra with the Addition of Fine Structure from Density Functional Theory Calculations

Published online by Cambridge University Press:  25 February 2016

Mohammad Attarian Shandiz ,
Maxime J.-F. Guinel ,
Majid Ahmadi  and
Raynald Gauvin
Mohammad Attarian Shandiz*
Affiliation:
Department of Materials Engineering, McGill University, Montreal, H3A 0C5, Canada
Maxime J.-F. Guinel
Affiliation:
Departments of Chemistry and Physics, College of Natural Sciences, University of Puerto Rico, San Juan, PR 00936, USA
Majid Ahmadi
Affiliation:
Department of Physics, College of Natural Sciences, University of Puerto Rico, San Juan, PR 00936, USA
Raynald Gauvin
Affiliation:
Department of Materials Engineering, McGill University, Montreal, H3A 0C5, Canada
*
*Corresponding author. mohammad.attarianshandiz@mail.mcgill.ca
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Abstract

A new approach is presented to introduce the fine structure of core-loss excitations into the electron energy-loss spectra of ionization edges by Monte Carlo simulations based on an optical oscillator model. The optical oscillator strength is refined using the calculated electron energy-loss near-edge structure by density functional theory calculations. This approach can predict the effects of multiple scattering and thickness on the fine structure of ionization edges. In addition, effects of the fitting range for background removal and the integration range under the ionization edge on signal-to-noise ratio are investigated.

Keywords

electron energy-loss spectroscopyelectron energy-loss near-edge structureMonte Carlo simulationsdensity functional theory
Type
Techniques, Software, and Equipment
Information
Microscopy and Microanalysis , Volume 22 , Issue 1 , February 2016 , pp. 219 - 229
Copyright
© Microscopy Society of America 2016 

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References

Achkar, A.J., Regier, T.Z., Monkman, E.J., Shen, K.M. & Hawthorn, D.G. (2011a). Determination of total X-ray absorption coefficient using non-resonant X-ray emission. Sci Rep 1, 182.CrossRefGoogle ScholarPubMed
Achkar, A.J., Regier, T.Z., Wadati, H., Kim, Y.J., Zhang, H. & Hawthorn, D.G. (2011b). Bulk sensitive X-ray absorption spectroscopy free of self-absorption effects. Phys Rev B 83, 081106.CrossRefGoogle Scholar
Akkerman, A. & Akkerman, E. (1999). Characteristics of electron inelastic interactions in organic compounds and water over the energy range 20–10000 eV. J Appl Phys 86, 5809–5816.CrossRefGoogle Scholar
Attarian Shandiz, M., Salvat, F. & Gauvin, R. (2015). Detailed Monte Carlo simulation of electron transport and electron energy loss spectra. Scanning, doi: 10.1002/sca.21280.CrossRefGoogle Scholar
Becke, A.D. & Johnson, E.R. (2006). A simple effective potential for exchange. J Chem Phys 124, 221101.CrossRefGoogle ScholarPubMed
Blaha, P., Schwarz, K., Madsen, G., Kvasnicka, D. & Luitz, J. (2001). WIEN2k: An Augmented Plane Wave Plus Local Orbitals Program for Calculating Crystal Properties. Vienna: Technical University of Vienna.Google Scholar
Chantler, C. (1995). Theoretical form factor, attenuation, and scattering tabulation for Z=1–92 from E=1-10 eV to E=0.4-1.0 MeV. J Phys Chem Ref Data 24, 71–643.CrossRefGoogle Scholar
Chantler, C.T., Olsen, K., Dragoset, R.A., Chang, J., Kishore, A.R., Kotochigova, S.A. & Zucker, D.S. (2005). Detailed tabulation of atomic form factors, photoelectric absorption and scattering cross section, and mass attenuation coefficients for Z=1-92 from E=1-10 eV to E=0.4-1.0 MeV. NIST, Physical Measurement Laboratory. Available at http://www.nist.gov/pml/data/ffast/index.cfm.Google Scholar
Egerton, R.F. (1979). K-shell ionization cross sections for use in microanalysis. Ultramicroscopy 4, 169–179.CrossRefGoogle Scholar
Egerton, R.F. (1982). A revised expression for signal/noise ratio in EELS. Ultramicroscopy 9, 387–390.CrossRefGoogle Scholar
Egerton, R.F. (2009). Electron energy-loss spectroscopy in the TEM. Rep Prog Phys 72, 016502.CrossRefGoogle Scholar
Egerton, R.F. (2011). Electron Energy-Loss Spectroscopy in the Electron Microscope, 3rd ed.New York: Springer.CrossRefGoogle Scholar
Fano, U. & Cooper, J.W. (1968). Spectral distribution of atomic oscillator strengths. Rev Mod Phys 40, 441–507.CrossRefGoogle Scholar
Fernández-Varea, J.M., Liljequist, D., Csillag, S., Räty, R. & Salvat, F. (1996). Monte Carlo simulation of 0.1–100 keV electron and positron transport in solids using optical data and partial wave methods. Nucl Instr Meth Phys Res B 108, 35–50.CrossRefGoogle Scholar
Fernández-Varea, J.M., Mayolt, R., Liljequis, D. & Salvati, F. (1993). Inelastic scattering of electrons in solids from a generalized oscillator strength model using optical and photoelectric data. J Phys Condens Matter 5, 3593–3610.CrossRefGoogle Scholar
Fernández-Varea, J.M., Salvat, F., Dingfelder, M. & Liljequist, D. (2005). A relativistic optical-data model for inelastic scattering of electrons and positrons in condensed matter. Nucl Instr Meth Phys Res B 229, 187–218.CrossRefGoogle Scholar
Gauvin, R., Lifshin, E., Demers, H., Horny, P. & Campbell, H. (2006). Win x-ray: A new Monte Carlo program that computes x-ray spectra obtained with a scanning electron microscope. Microsc Microanal 12, 49–64.CrossRefGoogle ScholarPubMed
Hebert, C. (2007). Practical aspects of running the WIEN2k code for electron spectroscopy. Micron 38, 12–28.CrossRefGoogle ScholarPubMed
Hebert, C., Luitz, J. & Schattschneider, P. (2003). Improvement of energy loss near edge structure calculation using WIEN2k. Micron 34, 219–225.CrossRefGoogle ScholarPubMed
Hetaba, W., Blaha, P., Tran, F. & Schattschneider, P. (2012). Calculating energy loss spectra of NiO: Advantages of the modified Becke-Johnson potential. Phys Rev B 85, 205108.CrossRefGoogle Scholar
Heyd, J., Peralta, J.E., Scuseria, G.E. & Martin, R.L. (2005). Energy band gaps and lattice parameters evaluated with the Heyd-Scuseria-Ernzerhof screened hybrid functional. J Chem Phys 123, 174101.CrossRefGoogle ScholarPubMed
Jorissen, K. (2007). The ab initio calculation of relativistic electron energy loss spectra. University of Antwerp, Antwerp.Google Scholar
Jouffrey, B., Schattschneider, P. & Hebert, C. (2004). The magic angle: A solved mystery. Ultramicroscopy 102, 61–66.CrossRefGoogle Scholar
Koller, D., Tran, F. & Blaha, P. (2011). Merits and limits of the modified Becke-Johnson exchange potential. Phys Rev B 83, 195134.CrossRefGoogle Scholar
Koller, D., Tran, F. & Blaha, P. (2012). Improving the modified Becke-Johnson exchange potential. Phys Rev B 85, 155109.CrossRefGoogle Scholar
Kothleitner, G. & Hofer, F. (1998). Optimization of the signal to noise ratio in EFTEM elemental maps with regard to different eonization edge types. Micron 29, 349–357.CrossRefGoogle Scholar
Krawczyk, M., Zommer, L., Jablonski, A., Grzegory, I. & Bockowski, M. (2004). Energy dependence of electron inelastic mean free paths in bulk GaN crystals. Surf Sci 566–568, 1234–1239.CrossRefGoogle Scholar
Mauchamp, V., Jaouen, M. & Schattschneider, P. (2009). Core-hole effect in the one-particle approximation revisited from density functional theory. Phys Rev B 79, 235106.CrossRefGoogle Scholar
Menon, N.K. & Krivanek, O.L. (2002). Synthesis of electron energy loss spectra for the quantification of detection limits. Microsc Microanal 8, 203–215.CrossRefGoogle ScholarPubMed
Moreno, M.S., Jorissen, K. & Rehr, J.J. (2007). Practical aspects of electron energy-loss spectroscopy (EELS) calculations using FEFF8. Micron 38, 1–11.CrossRefGoogle ScholarPubMed
Narayan, R.D., Weiss, J.K. & Rez, P. (2014). Highly automated electron energy-loss spectroscopy elemental quantification. Microsc Microanal 20, 798–806.CrossRefGoogle ScholarPubMed
Penn, D.R. (1987). Electron mean-free-path calculations using a model dielectric function. Phys Rev B 35, 482–486.CrossRefGoogle ScholarPubMed
Perdew, J.P., Burke, K. & Ernzerhof, M. (1996). Generalized gradient approximation made simple. Phys Rev Lett 77, 3865–3868.CrossRefGoogle ScholarPubMed
Prange, M.P., Oxley, M.P., Varela, M., Pennycook, S.J. & Pantelides, S.T. (2012). Simulation of spatially resolved electron energy loss near-edge structure for scanning transmission electron microscopy. Phys Rev Lett 109, 246101.CrossRefGoogle ScholarPubMed
Riegler, K. & Kothleitner, G. (2010). EELS detection limits revisited: Ruby – A case study. Ultramicroscopy 110, 1004–1013.CrossRefGoogle Scholar
Rez, P. & Muller, D.A. (2008). The theory and interpretation of electron energy loss near-edge fine structure. Annu Rev Mater Res 38, 535–558.CrossRefGoogle Scholar
Salvat, F., Fernández-Varea, J.M., Sempau, J. & Mazurier, J. (1999). Practical aspects of Monte Carlo simulation of charged particle transport: Mixed algorithms and variance reduction techniques. Radiat Environ Biophys 38, 15–22.CrossRefGoogle ScholarPubMed
Salvat, F., Llovet, X. & Fernández-Varea, J.M. (2004). Monte Carlo Simulation of electron transport and x-ray generation. I. Electron elastic and inelastic scattering. Microchim Acta 145, 193–202.CrossRefGoogle Scholar
Schattschneider, P. (1986). Fundamentals of Inelastic Electron Scattering. Wien, New York: Springer-Verlag.CrossRefGoogle Scholar
Schattschneider, P., Hébert, C., Franco, H. & Jouffrey, B. (2005). Anisotropic relativistic cross sections for inelastic electron scattering, and the magic angle. Phys Rev B 72, 045142.CrossRefGoogle Scholar
Schwarz, K. (2003). DFT calculations of solids with LAPW and WIEN2k. J Solid State Chem 176, 319–328.CrossRefGoogle Scholar
Schwarz, K., Blaha, P. & Madsen, G.K.H. (2002). Electronic structure calculations of solids using the WIEN2k package for material sciences. Comp Phys Commun 147, 71–76.CrossRefGoogle Scholar
Serin, V., Colliex, C., Brydson, R., Matar, S. & Boucher, F. (1998). EELS investigation of the electron conduction-band states in wurtzite AlN and oxygen-doped AlN(O). Phys Rev B 58, 5106–5115.CrossRefGoogle Scholar
Tanuma, S., Powell, C.J. & Penn, D.R. (1994). Calculations of electron inelastic mean free paths. V. Data for 14 organic compounds over the 50–2000 eV range. Surf Interface Anal 21, 165–176.CrossRefGoogle Scholar
Tanuma, S., Powell, C.J. & Penn, D.R. (2005). Calculations of electron inelastic mean free paths. Surf Interface Anal 37, 1–14.CrossRefGoogle Scholar
Tran, F. & Blaha, P. (2009). Accurate band gaps of semiconductors and insulators with a semilocal exchange-correlation potential. Phys Rev Lett 102, 226401.CrossRefGoogle ScholarPubMed
Tung, C.J., Ashley, J.C. & Ritchie, R.H. (1979). Electron inelastic mean free paths and energy losses in solids II: Electron gas statistical model. Surf Sci 81, 427–439.CrossRefGoogle Scholar
Verbeeck, J. & Van Aert, S. (2004). Model based quantification of EELS spectra. Ultramicroscopy 101, 207–224.CrossRefGoogle ScholarPubMed
Verbeeck, J., Van Aert, S. & Bertoni, G. (2006). Model-based quantification of EELS spectra: Including the fine structure. Ultramicroscopy 106, 976–980.CrossRefGoogle ScholarPubMed
Zhu, J., Gao, S.P., Zhang, A.H. & Yuan, J. (2005). Theoretical electron energy-loss spectroscopy and its application in materials research. J Electron Microsc 54, 293–298.Google ScholarPubMed