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Analysis of Biologically-Derived Small Particles—Searching for Geometry Correction Factors Using Monte Carlo Simulation

Published online by Cambridge University Press:  10 January 2013

Grzegorz Tylko
Grzegorz Tylko*
Affiliation:
Department of Cell Biology and Imaging, Institute of Zoology, Jagiellonian University, Gronostajowa 9, 30-387, Krakow, Poland
*
*Corresponding author. E-mail: grzegorz.tylko@uj.edu.pl
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Abstract

A Monte Carlo simulation was used to determine geometry correction factors that increase accuracy of quantitative X-ray microanalysis of laterally semithick biological materials. A model composed of cellulose with homogeneously distributed biological elements and lateral dimensions between 0.5–25 μm was chosen. The specimen was exposed to 5, 10, and 15 keV electrons, the net intensities of characteristic X-rays registered for the elements, and presented as a function of the lateral dimensions of the model. This showed the double decay exponential function fitted the distribution of X-ray intensities in relation to the model size. The applicability of the function as a correction method was successfully tested for 30 specimens with varying composition and dimensions. The value of relative error decreased from ±60% to ±5% when the correction was applied. Moreover, the minimal lateral size of the material was defined, below which the correction is not required. The simulation also revealed that the difference of the weighted sum of Z2/A between the unknown and the standard could reach 25% without significant influence on the quantitative results. The correction method could be helpful for accurate assessment of elemental composition in biological or organic matrices, when their lateral dimensions are smaller than the distribution range.

Keywords

Monte Carlo simulationquantitative analysisbiological specimenslow-Z number materialsX-ray microanalysiscorrection method
Type
Software, Techniques and Equipment Development
Information
Microscopy and Microanalysis , Volume 19 , Issue 1 , February 2013 , pp. 56 - 65
Copyright
Copyright © Microscopy Society of America 2013

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References

Anderson, C.A. & Hasler, M.F. (1966). Extension of electron microprobe techniques to biochemistry by the use of long wavelength X-rays. In Proceedings of the 4th International Conference on X-ray Optics and Microanalysis, Castaing, R., Deschamps, P. & Philibert, J. (Eds.), pp. 310–327. Paris, France: Herman.Google Scholar
Armigliato, A. & Rosa, R. (2009). X-ray microanalysis combined with Monte Carlo simulation for the analysis of layered thin films: The case of carbon contamination. Microsc Microanal 15, 99–105.Google Scholar
Armstrong, J.T. (1991). Electron Probe Quantification. New York: Plenum Press.Google Scholar
Armstrong, J.T. & Buseck, P.R. (1975). Methods of quantitative analysis of individual microparticles using electron microprobe: Theoretical. Anal Chem 47, 2178–2192.Google Scholar
Casnati, E., Tartari, A. & Baraldi, C. (1989). An empirical approach to K-shell ionization cross section by electrons. J Phys B 15, 155–167.Google Scholar
Choël, M., Deboudt, K., Osán, J., Flament, P. & Van Grieken, R. (2005). Quantitative determination of low-Z number elements in single atmospheric particles on boron substrates by automated scanning electron microscopy—Energy-dispersive X-ray spectrometry. Anal Chem 77, 5686–5692.Google Scholar
Czyzewski, Z., MacCallum, D.O., Romig, A. & Joy, D.C. (1990). Calculations of Mott scattering cross sections. J Appl Phys 68, 3066–3072.Google Scholar
Demers, H. & Gauvin, R. (2004). X-ray microanalysis of a coated nonconductive specimen: Monte Carlo simulation. Microsc Microanal 10, 776–782.CrossRefGoogle ScholarPubMed
Gao, D., Kumar, G., Co, C. & Ho, C-C. (2008). Formation of capillary tube-like structures on micropatterned biomaterials. Adv Exp Med Biol 614, 199–205.Google Scholar
Gauvin, R. (2007). A universal equation for the emission range of X rays from bulk specimens. Microsc Microanal 13, 354–357.CrossRefGoogle ScholarPubMed
Gauvin, R. & Lifshin, E. (2000). Simulation of X-ray emission from rough surfaces. Mikrochim Acta 132, 201–204.Google 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.Google Scholar
Goldstein, J.I., Lyman, C.E., Newbury, D.E., Lifshin, E., Echlin, P., Sawyer, L., Joy, D.C. & Michael, J.R. (2003). Scanning Electron Microscopy and X-Ray Microanalysis. New York: Kluwer Academic/Plenum Publishers.CrossRefGoogle 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, 1–14.Google Scholar
Janik, P., Tylko, G., Ostachowicz, B. & Turnau, K. (2010). Elemental composition of Physarum compressum Alb. et Schw. sporocarps and their structures cultivated on rabbit dung and agar substrates. Microsc Res Tech 73, 1134–1142.CrossRefGoogle ScholarPubMed
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electrons. Scanning 11, 176–180.Google Scholar
Król, E., Płachno, B., Adamec, L., Stolarz, M., Dziubińska, H. & Tręcz, K. (2012). Quite a few reasons for calling carnivores “the most wonderful plants in the world.” Annals Bot 109, 47–64.Google Scholar
Laskin, A. & Cowin, J.P. (2001). Automated single-particle SEM/EDX analysis of submicrometer particles down to 0.1 μm. Anal Chem 73, 1023–1029.Google Scholar
McCarthy, J.J. (1980). Analysis of X-ray spectra by filtered least-squares fitting. Scan Elect Microsc II, 259–270.Google Scholar
Orłowska, E., Przybyłowicz, W., Orłowski, D., Turnau, K. & Mesjasz-Przybyłowicz, J. (2011). The effect of mycorrhiza on the growth and elemental composition of Ni-hyperaccumulating plant Berkheya coddii Roessler. Environ Pollut 159, 3730–3738.Google Scholar
Pamula, E., Kokoszka, J., Cholewa-Kowalska, K., Łączka, M., Kantor, L., Niedzwiedzki, L., Reilly, G.C., Filipowska, J., Madej, W., Kołodziejczyk, M., Tylko, G. & Osyczka, A.M. (2011). Degradation, bioactivity, and osteogenic potential of composites made of PLGA and two different sol-gel bioactive glasses. Ann Biomed Eng 39, 2114–2129.Google Scholar
Ritchi, N.W.M. (2009). Spectrum simulation in DTSA-II. Microsc Microanal 15, 454–468.Google Scholar
Ro, C., Osán, J., Szalóki, I., de Hoog, J., Worobiec, A. & Van Grieken, R. (2003). A Monte Carlo program for quantitative electron-induced X-ray analysis of individual particles. Anal Chem 75, 851–859.Google Scholar
Roomans, G. (1988). Quantitative X-ray microanalysis of biological specimens. J Elect Microsc Tech 9, 19–43.Google Scholar
Roomans, G. (1990). X-ray microanalysis. In Biophysical Electron Microscopy, Hawkes, P.W. & Valdrè, U. (Eds.), pp. 347–412. London: Academic Press.Google Scholar
Rosenberg, N. & Morin, C.Z.J.P. (1999). Monte Carlo simulations of coaxial backscattered electrons in SEM. Ultramicroscopy 76, 97–105.Google Scholar
Salvat, F., Fernández-Varea, J.M. & Sempau, J. (2008). PENELOPE, A code system for Monte Carlo simulation of electron and photon transport. Issy-les-Moulineaux. France: OECD/Nuclear Energy Agency. Google Scholar
Scott, K. & Ritchie, N.W.M. (2009). Analysis of 3D elemental mapping artifacts in biological specimens using Monte Carlo simulation. J Microsc 233, 331–339.CrossRefGoogle ScholarPubMed
Small, J.A. (2002). The analysis of particles at low accelerating voltages (≤10 keV) with energy—Dispersive X-ray spectroscopy (EDS). J Res Nat Inst Stand Tech 107, 555–566.Google Scholar
Storms, H.M., Janssens, K.H., Török, S.B. & Van Grieken, R.E. (1989). Evaluation of the Armstrong-Buseck correction for automated electron probe X-ray microanalysis of particles. X-ray Spectr 18, 45–52.Google Scholar
Tylko, G., Banach, Z. & Kilarski, W. (2004). PROZA and CALIBRATION CURVES for quantitative X-ray microanalysis of biological samples. Microchim Acta 144, 271–276.CrossRefGoogle Scholar
Tylko, G., Dubchak, S., Banach, Z. & Turnau, K. (2010). Monte Carlo simulation for an assessment of standard validity and quantitative X-ray microanalysis in plants. IOP Conf Series Mat Sci Eng 7, 012028. Google Scholar
Wang, M., Chen, L., Chen, S. & Ma, Y. (2012). Alleviation of cadmium-induced root growth inhibition in crop seedlings by nanoparticles. Ecotoxicol Environ Saf 79, 48–54.CrossRefGoogle ScholarPubMed
Warley, A. (1997). X-ray Microanalysis for Biologists. London: Portland Press Ltd. Google Scholar
Wróblewski, J., Müller, R.M., Wróblewski, R. & Roomans, G.M. (1983). Quantitative X-ray microanalysis of semi-thick cryosections. Histochem 77, 447–463.Google Scholar
Zapotoczny, Sz., Jurkiewicz, A., Tylko, G., Anielska, T. & Turnau, K. (2007). Accumulation of copper by Acremonium pinkertoniae, a fungus isolated from industrial wastes. Microbiol Res 162, 219–228.Google Scholar