The use of X-ray fluorescence (XRF) scanning systems has become a common practice in many application sectors. In multistratified and heterogeneous samples, the simple analysis of an XRF spectrum as a response of the entire sample is not reliable, so different spectral analysis techniques have been proposed to detect the presence of surface stratification. One commonly studied case is that of gilding, i.e., the presence of a superimposing gold-leaf layer. The observation of changes in the net peak ratios of a single element or of several elements in an XRF spectrum is a well-developed practice, but is still not used in the case of XRF scanning (macro-X-Ray fluorescence scanning, MA-XRF), a technique that can be described as the extrapolation of XRF spot analysis to a second dimension, scanning a sample surface instead. This practice can yield information on the overlaying layer thickness, if some properties of the sample are known—or estimated—beforehand, e.g., the overlapping layer’s chemical composition and the matrix effect contribution from the bulk material (thick ratio
). This work proposes the use of an algorithm to calculate the thickness distribution of a superimposing gold layer accurately and automatically through the differential attenuation method by using MA-XRF datasets in a total noninvasive manner. This approach has the clear advantage over the traditional spot sampling of allowing the generation of a surface heightmap to better visualize and interpret the data, as well as a considerably larger sample space. Monte Carlo simulations were used to verify the influence of the medium used to adhere the gold leaves to the substrate and to generate known spectra to assess the algorithm’s accuracy.
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