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Mean Atomic Number Quantitative Assessment in Backscattered Electron Imaging

Published online by Cambridge University Press:  20 November 2012

E. Sánchez ,
M. Torres Deluigi  and
G. Castellano
E. Sánchez
Affiliation:
INQUISAL, Universidad Nacional de San Luis, San Luis, Argentina
M. Torres Deluigi
Affiliation:
Departamento de Física, INQUISAL, Universidad Nacional de San Luis, San Luis, Argentina
G. Castellano*
Affiliation:
FaMAF, Universidad Nacional de Córdoba, Córdoba, Argentina
*
*Corresponding author. E-mail: gcas@famaf.unc.edu.ar
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Abstract

A method for obtaining quantitative mean atomic number images in a scanning electron microscope for different kinds of samples has been developed. The backscattered electron signal is monotonically increasing with the mean atomic number Z, and accordingly Z can be given as a function of the image gray levels. From results obtained from Monte Carlo simulations, an exponential function is fitted to convert the backscattered registered gray levels into a Z image map. Once this fitting was performed, the reproducibility of the Z determination was checked through the acquisition of backscattered electron images from metal and mineral standards. The developed method can be applied to any unknown sample, always controlling the experimental conditions, as shown here for a thin section of a rock in which several unknown mineral phases are present; the results obtained herein are compared to quantitative assessments performed with X-ray spectra from each mineral phase.

Keywords

SEMMonte Carlo simulationbackscattered electron imagingenergy dispersive analysis
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 18 , Issue 6 , December 2012 , pp. 1355 - 1361
Copyright
Copyright © Microscopy Society of America 2012

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References

Acosta, E., Coleoni, E., Castellano, G., Riveros, J., Fernández-Varea, J. & Salvat, F. (1996). Scanning, Monte Carlo simulation of electron backscattering in solids using a general-purpose computer code. Microscopy 10, 625–638.Google Scholar
Acosta, E., Llovet, X., Coleoni, E., Riveros, J.A. & Salvat, F. (1998). Monte Carlo simulation of X-ray emission by kilovolt electron bombardment. J Appl Phys 83(11), 6038–6049.Google Scholar
Acosta, E., Llovet, X. & Salvat, F. (2002). Monte Carlo simulation of bremsstrahlung emission by electrons. Appl Phys Lett 80, 3228–3230.Google Scholar
Galván Josa, V., Bertolino, S.R., Riveros, J.A. & Castellano, G. (2009). Methodology for processing backscattered electron images. Application to Aguada archaeological paints. Micron 40, 793–799.Google Scholar
Ginibre, C., Kronz, A. & Wörner, G. (2002). High-resolution quantitative imagining of plagioclase composition using accumulated backscattered electron images: New constraints on oscillatory zoning. Contrib Mineral Petrol 142, 436–448.CrossRefGoogle Scholar
Goldstein, J., Newbury, D., Echlin, P., Joy, D., Romig, A., Lyman, C., Fiori, C. & Lifshin, E. (1992). Scanning Electron Microscopy and X-Ray Microanalysis: A Text for Biologists, Materials Scientists, and Geologists, pp. 69–147. New York: Plenum Press.Google Scholar
Harding, D.P. (2002). Mineral identification using a scanning electron microscope. Miner Metallurg Proc 19, 215–219.Google Scholar
Joy, D.C. (1991). Contrast in high-resolution scanning electron microscope images. J Microsc 161, 343–355.Google Scholar
Keune, K., Van Loon, A. & Boon, J.J. (2011). SEM backscattered-electron images of paint cross sections as information source for the presence of the lead white pigment and lead-related degradation and migration phenomena in oil paintings. Microsc Microanal 17, 1–6.Google Scholar
Kjellsen, K.O., Monsøy, A., Isachsen, K. & Detwiler, R.J. (2003). Preparation of flat-polished specimens for SEM-backscattered electron imaging and X-ray microanalysis—importance of epoxy impregnation. Cement Concrete Res 33, 611–616.Google Scholar
Llovet, X., Sorbier, L., Campos, C.S., Acosta, E. & Salvat, F. (2003). Monte Carlo simulation of X-ray spectra generated by kilo-electron-volt electrons. J Appl Phys 93, 3844–3851.Google Scholar
Peters, C.A. (2009). Accessibilities of reactive minerals in consolidated sedimentary rock: An imaging study of three sandstones. Chem Geol 265, 198–208.Google Scholar
Reed, S. (1993). Electron Probe Microanalysis, 2nd ed. Cambridge, UK: Cambridge University Press.Google Scholar
Reimer, L. (1993). Image Formation in Low-Voltage Scanning Electron Microscopy. Bellingham, WA: SPIE Optical Engineering Press.Google Scholar
Reuter, W. (1972). The ionization function and its application to the electron probe analysis of thin films. In Proceedings 6th International Congress on X-ray Optics and Microanalysis, Shinoda, G., Kohra, K. & Ichinokawa, T. (Eds.), pp. 121–130. Tokyo: University of Tokyo Press.Google Scholar
Roschger, P., Fratzl, P., Eschberger, J. & Klaushofer, K. (1998). Validation of quantitative backscattered electron imaging for the measurement of mineral density distribution in human bone biopsies. Bone 23(4), 319–326.Google Scholar
Salvat, F., Fernández-Varea, J.M. & Sempau, J. (2003). PENELOPE. A code system for Monte Carlo simulation of electron and photon transport. OECD/NEA Data Bank, Issy-les-Moulineaux, France. Google Scholar
Schalm, O., Janssens, K. & Caen, J. (2003). Characterization of the main causes of deterioration of grisaille paint layers in 19th century stained-glass windows by J.-B. Capronnier. Spectrochim Acta B 58, 589–607.Google Scholar
Sempau, J., Fernandez-Varea, J.M., Acosta, E. & Salvat, F. (2003). Experimental benchmarks of the Monte Carlo code penelope. Nucl Instrum Meth B 207(2), 107–123.Google Scholar
Steel, R.G.D. & Torrie, J.H. (1960). Principles and Procedures of Statistics, pp. 187–287. New York: McGraw-Hill.Google Scholar
Triebold, S., Kronz, A. & Wörner, G. (2006). Anorthite-calibrated backscattered electron profiles, trace elements, and growth textures in feldspars from the Teide–Pico Viejo volcanic complex (Tenerife). J Volcanol Geotherm Res 154, 117–130.Google Scholar