A flexible and accurate quantification algorithm for electron probe X-ray microanalysis based on thin-film element yields

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Quantitative analysis by means of electron probe X-ray microanalysis (EPXMA) of low Z materials such as silicate glasses can be hampered by the fact that ice or other contaminants build up on the Si(Li) detector beryllium window or (in the case of a windowless detector) on the Si(Li) crystal itself. These layers act as an additional absorber in front of the detector crystal, decreasing the detection efficiency at low energies (<5 keV). Since the layer thickness gradually changes with time, also the detector efficiency in the low energy region is not constant. Using the normal ZAF approach to quantification of EPXMA data is cumbersome in these conditions, because spectra from reference materials and from unknown samples must be acquired within a fairly short period of time in order to avoid the effect of the change in efficiency. To avoid this problem, an alternative approach to quantification of EPXMA data is proposed, following a philosophy often employed in quantitative analysis of X-ray fluorescence (XRF) and proton-induced X-ray emission (PIXE) data. This approach is based on the (experimental) determination of thin-film element yields, rather than starting from infinitely thick and single element calibration standards. These thin-film sensitivity coefficients can also be interpolated to allow quantification of elements for which no suitable standards are available. The change in detector efficiency can be monitored by collecting an X-ray spectrum of one multi-element glass standard. This information is used to adapt the previously determined thin-film sensitivity coefficients to the actual detector efficiency conditions valid on the day that the experiments were carried out. The main advantage of this method is that spectra collected from the standards and from the unknown samples should not be acquired within a short period of time. This new approach is evaluated for glass and metal matrices and is compared with a standard ZAF method.