Xrf dating

The nondestructive nature of the XRF-measurement permitted a comparative study of dating methods by sequentially applying chemical dating.
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• Introduction
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• Elemental analysis using ED-XRF and 14C dating of Cuman wall paintings samples - IOPscience

This difference in information depth can be both an advantage and a hindrance when analysing small and fine-scale features. On the one hand, it is possible to identify sub-surface phases e. This contrasts with the findings of Newbury and Ritchie , who noted that, while standardless quantification procedures used in SEM-EDS work were highly precise, their accuracy was low.

During X-ray spectrometry, many variables contribute to the measured X-ray spectrum, such as elements present in the sample, the density, structure and composition of the sample matrix, absorption and enhancement of x-rays and secondary fluorescence, and the voltage, current, geometry and source of the excitation beam. As a result, converting X-ray spectra into elemental concentrations i. Influence coefficients are determined for each element of interest by analysis of well-characterized reference materials, or standards, which must be of comparable quality matrix, composition to the samples being analysed.

This is one of the simplest approaches to spectrum quantification, but the need for a large number of standards of similar matrix to the sample is a drawback Potts and Webb, The validity range of influence coefficients can be extended beyond that of available standards by using physical models for the influence coefficients. In this case, certain influence coefficients commonly those for minor and trace elements are predicted via fundamental parameter FP calculations see below , rather than measured on a suite of standards, meaning that a wider range of elements can be measured using fewer standards Potts and Webb, The range of concentrations that can be analysed with these hybrid methods is wider but a large number of standards are still required.

Introduction

Several such hybrid empirical-FP quantification schemes have been developed, mostly for special applications and from different instrument manufacturers, with targets to improve accuracy and reduce the number of required references Potts and Webb, ; Pereira and Brandao, ; Rousseau, Supplementary Information, Figures and Data have been deposited with the Principle Editor of Mineralogical Magazine and are available at http: Unfortunately, this equation cannot be inverted to allow calculation of concentrations from given X-ray intensities.

However, as computing power has improved, it has become possible to accurately estimate concentrations from X-ray spectra via forward calculation of X-ray intensities for samples with assumed concentrations. In this case the measured and calculated X-ray intensities can be compared and the assumed concentrations improved by iterating the calculation with refined concentration assumptions until convergence of predicted and measured intensities is achieved Potts and Webb, Using this method, quantified results are independent of the actual measurement conditions because these are incorporated in the calculated excitation spectrum, which is a required fundamental parameter for this method Ebel, Fundamental parameter methods give the best results when a full X-ray spectrum is calculated, rather than just the characteristic X-ray lines of interest; this uses physical theory to calculate the spectrum background, so can give improved sensitivity for trace elements whose characteristic X-ray lines might be hidden in a high background, or in tails of higher intensity peaks, and facilitates more accurate peak identification by fitting multiple X-ray lines Elam et al.

Calculation of the full spectrum also allows the influence of undetectable elements, such as O and C, to be considered by calculating major-element compositions as assumed stoichiometric compounds such as oxides and carbonates. This results in a high analytical effort for empirical-based models compared to standardless FP-based models, which is difficult to justify if the improvement in accuracy over FP-models is small.

Such algorithms rely on a database of atomic fundamental parameters for each element, the most comprehensive and up to date of which was compiled by Elam et al. However, it is still necessary to consider the influence of the X-ray focusing optics on the excitation spectrum Padilla et al. For commercially produced instruments, this is typically completed in the factory prior to delivery and is not carried out by instrument users. Sources of error for FP-based quantification include errors in the fundamental parameters themselves, incomplete consideration of all X-ray—sample interactions including incorrect assumptions regarding the concentrations of unmeasurable elements, such as oxygen, carbon and hydrogen , and incorrect description of the measurement geometry Rousseau, These errors can be minimized and the accuracy of FP-based model results further improved by using an additional type-calibration.

In this case, a single reference material of similar composition to the sample is analysed and correction factors are determined for the mass fraction of every element of interest.

We then go on to test the accuracy and precision of quantitative analysis of geological materials, by measuring international and internal silicate reference materials. To this end, large fragments of silicate glasses polished if a flat surface wasn't available and pressed pellets of powdered silicate rocks were used as reference materials. Likewise, it was not possible to compare glass vs. Scanning and sample navigation is by a motorized stage which moves the sample beneath the static X-ray beam. All data acquisition and processing was carried out using the proprietary Bruker software supplied with the instrument.

Quantitative analyses were carried out only after the X-ray tube had been switched on for at least 1. The qualitative abilities of the M4-Tornado and its associated proprietary software were assessed using a series of case studies designed to explore the capability and limitations of the instrument for characterization of geological materials and streamlining of sample preparation workflows.

Element maps and line scans were most commonly used for this purpose. Element mapping produces 2-dimensional compositional maps, by collecting an entire X-ray spectrum for each pixel in a grid; single or multiple elements can be displayed during and after map acquisition. For a given element displayed on a map, pixel intensity is proportional to the intensity of the X-ray spectrum in the selected region of interest ROI. These features allow meaningful element maps to be produced when elements with overlapping characteristic X-ray energies are present in a sample, and when artefact peaks interfere with the ROI of an element Supplementary Fig.

S1, deposited at http: Post-collection data processing can display and quantify the spectrum for the entire map, or for selected areas of the map. Line scans measure the entire X-ray spectrum emitted by a sample whilst scanning along a line between two specified points. X-ray intensity in the ROI for the element of interest is displayed as a proxy for relative element concentration.

The quantitative abilities of the M4-Tornado and its associated proprietary software and quantification algorithms were assessed by measuring and quantifying X-ray spectra on a range of international and internal reference materials. First, spectrometer drift was assessed by repeat single-spot measurements on glass standards over 2—3 days to ascertain how often spectrometer calibration should be carried out Supplementary Table S1. Based on these results, spectrometer calibration was carried out twice a day for subsequent analyses.

The reference materials used in this study were a combination of pressed powder pellets and glass samples, so all analyses were carried out using the multi-point method, which sums the spectra collected at multiple points on the sample. S2 and Table S3. This was repeated 10 times for each sample to assess precision. This proprietary FP-based algorithm automatically corrects for detector artefacts such as pile up and escape peaks. Elements present in the spectrum, but not in the sample e. Rh from the tube radiation were matched during the pattern fitting but excluded from the quantification results.

The quantification scheme initially employed here calculates abundance in weight percent wt. Cl is difficult to analyse contemporaneously with lighter elements; interference on the Cl characteristic X-ray peak from the Rh tube radiation requires quantitative Cl analyses to be carried out using an energy filter, which reduces the intensity and thus quantitative precision on low energy light element characteristic X-rays.

Such analyses are possible, but require a tedious 2 stage analysis with and without energy filters during spectrum acquisition. Five international standards and two internal references of varying type and composition have been used: Sample K5 was used to test spectrometer drift analysis of the same spot over time , but subsequent analysis revealed significant SiO 2 and K 2 O zoning related to flow banding in this sample, and so it has not been used to assess spectrum quantification. Sample characterization is an essential part of any petrological or geochemical study, providing information on the phases present in the sample, their relationship to each other, and identifying phases for further investigation.

Comprehensively characterizing a sample using traditional methods can use many techniques and thus be rather time-consuming. Traditionally, bulk characterization of a sample is carried out by visual inspection of a hand sample, followed by preparation of petrographic sections for study using a petrographic microscope and, commonly, SEM or EMPA work. This allows the phases present in the sample to be identified, and their textural relationships and internal homogeneity to be well characterized.

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While it is possible to create photomosaics of petrographic sections, such as the Open University's teaching aid The Virtual Microscope Whalley et al. Micro-XRF element distribution maps collected from a roughly cut granite, and from a polished slab of sandstone are shown in Fig. Deformed granite from Bukit Bunuh, Malaysia is shown in Figs 3 a and b. Different mineral phases, textures and their distribution through the sample can be identified with a multi-element map displaying K, Ca, Si and Fe Fig. Distinguishing quartz SiO 2 , alkali feldspar K,Na AlSi 3 O 8 and plagioclase feldspar NaAlSi 3 O 8 — CaAl 2 Si 2 O 8 , which may be difficult even in thin section if the minerals do not exhibit euhedral mineral forms or display twinning, is particularly easy using this multi-element map combination.

Note that, while the saw marks are still prominent in the photograph Fig. Cross beds are visible in hand specimen as dark bands and the sample has a low porosity. The distribution of accessory minerals in the sandstone is shown in Fig. Interestingly, these cross laminations picked out by the accessory minerals are not visually obvious in the hand specimen.

The rutile overlies the zircon, as would be expected from differential settling rates due to the density contrast between the two minerals. In the lower part of the sample the sediment is darker, reflected by a higher Fe-content in the element map. In this lower facies, rutile is much more common than zircon while the two minerals occur in roughly equal proportions in the upper facies.

Such information can help reconstruct geological histories; clearly there has been some kind of change in the fluvial system between the lower and upper facies.

Perhaps the upper facies simply reflects an increase in energy in the system, allowing denser minerals to be mobilized and redeposited. Alternatively the two facies may represent deposition from different sedimentary sources. Recent age determinations of detrital zircons from the Voltaian Formation has shown that the sandstones contain multiple age populations of zircon Kalsbeek et al.

Perhaps using X-ray maps to target sampling at higher stratigraphic resolution centimetre-to-decimetre scale may identify fine-scale fluctuations in sedimentary source location.

Large samples can be analysed with minimal preparation a flat surface is required for element mapping, but polishing is not necessary for most elements and the distribution of phases throughout a sample at the centimetre to decimetre scale can be characterized much more easily than with optical or electron microscopy. Micro-XRF mapping is clearly a useful tool for characterization at the hand-sample scale, but many geochemical applications require information on the homogeneity of individual mineral phases.

This information may traditionally be acquired by a combination of petrographical study, with SEM imaging and EMPA analysis to characterize internal variation of mineral grains. To assess this, we studied two samples: These samples are polished sections, prepared in the same way as for electron beam analysis.

The Dartmoor feldspar phenocryst has been studied previously using SEM and shows extensive evidence for in situ , fluid-mediated recrystallization and displays a range of phases and microtextures including homogenous orthoclase, pristine crypto- and microperthites, perthitic intergrowths of Ab-rich Na-rich and Or-rich K-rich feldspar and microcline veining Flude et al. Figure 4 shows a K and Ba map of the feldspar with associated line scan profiles that illustrate perthite texture, barium zoning and zones of recrystallization.

The Na-rich patches and veins in the crystal are visible as K-depleted areas but under typical mapping conditions the Na-rich patches are not displayed because the peak-to-background ratio is too small to produce sufficient contrast.

Potassium shows zones of enrichment around the edges and along the centre of the crystal. These areas of K-enrichment correspond to brown, discoloured areas which were considered by Flude et al. The crystal appears to exhibit oscillatory zoning in Ba, but not parallel to the crystal edges. This zoning reflects real variations in the intensity of the Ba X-rays rather than an artefact due to fluctuations in the spectrum background, as may happen for trace elements.

Concentric, boundary parallel Ba zoning is also displayed in the subgrain defined by mapping differences in spectral background in the energy range 7. The relative roles of magmatic and metasomatic crystallization have long been debated for feldspar phenocryst formation in granites.

Here, the lack of coherence between Ba and K distribution may reflect processes related to initial crystallization and subsequent metasomatism of the phenocrysts. The Indonesian volcanic phenocrysts exhibit oscillatory zoning under crossed polars and sometimes contain apatite inclusions Fig. Calcium zoning visible on the X-ray maps in Fig. In such cases, image contrast may be improved by increased measurement times or repeated scanning of the map to increase the net X-ray intensity for each pixel.

In the case of the oscillatory zoning, where the compositional differences between the zones are relatively small and gradational, only the largest elemental contrasts and broader-scale zoning are visible. In the case of high X-ray contrasts of small features, measurement of the dimensions of the features from X-ray maps should be carried out with caution, especially when using pixel averaging filters see Supplementary Fig.

S4 for examples of how pixel averaging filters affect the clarity of the element maps. This effect may be enhanced by image processing that averages or smoothes pixels. Micro-XRF is a potentially valuable tool for imaging wide-scale variation within mineral phases where elemental variation is strong or for trace elements.