Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-21T16:02:35.472Z Has data issue: false hasContentIssue false

Secondary Fluorescence in WDS: The Role of Spectrometer Positioning

Published online by Cambridge University Press:  03 December 2018

Ben Buse*
Affiliation:
School of Earth Sciences, University of Bristol, Wills Memorial Building, Queen’s Road, Bristol BS81RJ, UK
Jon Wade
Affiliation:
Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK
Xavier Llovet
Affiliation:
Scientific and Technological Centres, Universitat de Barcelona, Lluís Solé i Sabarís, 1-3/ES-08028, Barcelona
Stuart Kearns
Affiliation:
School of Earth Sciences, University of Bristol, Wills Memorial Building, Queen’s Road, Bristol BS81RJ, UK
John J. Donovan
Affiliation:
CAMCOR, Department of Chemistry, University of Oregon, Eugene, OR, 97403, USA
*
Author for correspondence: Ben Buse, E-mail: ben.buse@bristol.ac.uk
Get access

Abstract

Secondary fluorescence (SF), typically a minor error in routine electron probe microanalysis (EPMA), may not be negligible when performing high precision trace element analyses in multiphase samples. Other factors, notably wavelength dispersive spectrometer defocusing, may introduce analytical artifacts. To explore these issues, we measured EPMA transects across two material couples chosen for their high fluorescence yield. We measured transects away from the fluorescent phase, and at various orientations with respect to the spectrometer focal line. Compared to calculations using both the Monte Carlo simulation code PENEPMA and the semi-analytical model FANAL, both codes estimate the magnitude of SF, but accurate correction requires knowledge of the position of the spectrometer with respect to the couple interface. Positioned over the fluorescent phase or otherwise results in a factor of 1.2–1.8 of apparent change in SF yield. SF and spectrometer defocusing may introduce systematic errors into trace element analyses, both may be adequately accounted for by modeling. Of the two, however, SF is the dominant error, resulting in 0.1 wt% Zn apparently present in Al at 100 μm away from the Zn boundary in an Al/Zn couple. Of this, around 200 ppm Zn can be attributed to spectrometer defocusing.

Type
Materials Science Applications
Copyright
© Microscopy Society of America 2018 

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.)

Footnotes

Cite this article: Buse B, Wade J, Llovet X, Kearns S and Donovan JJ (2008) Secondary Fluorescence in WDS: The Role of Spectrometer Positioning. Microsc Microanal. 24(6), 604–611. doi: 10.1017/S1431927618015416

References

Acosta, E, Llovet, X and Salvat, F (2002) Monte Carlo simulation of bremsstrahlung emission by electrons. Appl Phys Lett 80, 32283330.Google Scholar
Adams, GE and Bishop, FC (1986) The olivine-clinopyroxene geobarometer: Experimental results in the CaO–FeO–MgO–SiO, system. Contrib Mineral Petrol 94, 230237.Google Scholar
Dalton, JA and Lane, SJ (1996) Electron microprobe analysis of Ca in olivine close to grain boundaries: The problem of secondary X-ray fluorescence. Am Mineral 81, 194201.Google Scholar
Donovan, J, Llovet, X and Salvat, F (2012) High speed matrix and secondary fluorescence effects from fundamental parameter Monte Carlo calculations. Microsc Microanal 18, 17421743.Google Scholar
Fournelle, J, Kim, S and Perepezko, JH (2005) Monte Carlo simulation of Nb Ka secondary fluorescence in EPMA: Comparison of PENELOPE simulation with experimental results. Surf Interface Anal 37, 10121016.Google Scholar
Llovet, X and Galan, G (2003) Correction of secondary X-ray fluorescence near grain boundaries in electron microprobe analysis: Application to thermobarometry of spinel lherzolites. Am Mineral 88, 121130.Google Scholar
Llovet, X, Pinard, PT, Donovan, JJ and Salvat, F (2012) Secondary fluorescence in electron probe microanalysis of material couples. J Phys D Appl Phys 45, 225301. (12pp).Google Scholar
Llovet, X and Salvat, F (2017) PENEPMA: A Monte Carlo program for the simulation of X-ray emission in electron probe microanalysis. Microsc Microanal 23, 634646.Google Scholar
Marinenko, RB, Myklebust, RL, Bright, DS and Newbury, DE (1989) Defocus modelling correction for wavelength dispersive digital compositional mapping with the electron microprobe. J Microsc 155, 183198.Google Scholar
Myklebust, RL and Newbury, DE (1995) Monte Carlo modelling of secondary X-ray fluorescence across phase boundaries in electron probe microanalysis. Scanning 17, 235242.Google Scholar
Myklebust, RL, Newbury, DE, Marinenko, RB and Bright, DS (1986) Defocus modeling for compositional mapping with wavelength dispersive X-ray spectrometry. In Microbeam Analysis, Romig AD Jr and Chambers WF (Eds.), pp. 495497. San Francisco: San Francisco Press.Google Scholar
Newbury, DE, Marinenko, RB, Bright, DS and Myklebust, RL (1988) Computer-aided imaging: Quantitative compositional mapping with the electron probe microanalyzer. Scanning 10, 213225.Google Scholar
Swyt, CR and Fiori, CE (1986) Large-field X-ray compositional mapping with multiple dynamically focussed wavelength-dispersive spectrometers. In Microbeam Analysis, Romig AD Jr and Chambers WF (Eds.), pp. 482484. San Francisco: San Francisco Press.Google Scholar
Wade, J and Wood, BJ (2012) Metal–silicate partitioning experiments in the diamond anvil cell: A comment on potential analytical errors. Phys Earth Planet Int 192–193, 5458.Google Scholar
Wark, DA and Watson, EB (2006) The TitaniQ: A titanium-in-quartz geothermometer. Contrib Mineral Petrol 152, 743754.Google Scholar