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The X-ray spectra of pure elements, excited using MeV energy beam of protons from the nuclear microprobe, have known spectra signatures. This makes X-ray spectra for more complex mixtures amenable to decomposition into contributions from the component elements. By devising this procedure as a matrix operation that transforms directly from spectrum vector to elemental concentration vector, the decomposition can be performed very efficiently enabling the real-time projection of the component element signals. In the case of a raster-scanned beam, with data that contain position information for each X-ray event, this approach enables the real-time projection of component element spatial distribution images. This paper describes the matrix transform approach called dynamic analysis (DA), which enables on-line real-time imaging of major and trace elements using proton-induced X-ray emission (PIXE). The method also provides off-line iterative yield corrections to these images to compensate for changing sample composition across an image area. The resulting images are quantitative in two respects: (1) they resolve the pure element components and strongly reject interferences from other elements and (2) they can be directly interrogated for sample composition at each pixel, over areas, or along lines across the image area, with accuracy comparable to microanalytical point analysis methods. The paper describes the DA method, presents tests, and discusses its application to quantitative major and trace element imaging in geology. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 219–230, 2000 [ABSTRACT FROM AUTHOR]

Synchrotron X-ray fluorescence (SXRF) and X-ray absorption spectroscopy (XAS) have become standard tools to measure element concentration, distribution at micrometer- to nanometer-scale, and speciation (e.g., nature of host phase; oxidation state) in inhomogeneous geomaterials. The new Maia X-ray detector system provides a quantum leap for the method in terms of data acquisition rate. It is now possible to rapidly collect fully quantitative maps of the distribution of major and trace elements at micrometer spatial resolution over areas as large as 1 x 5 cm². Fast data acquisition rates also open the way to X-ray absorption near-edge structure (XANES) imaging, in which spectroscopic information is available at each pixel in the map. These capabilities are critical for studying inhomogeneous Earth materials. Using a 96-element prototype Maia detector, we imaged thin sections of an oxidized pisolitic regolith (2 x 4.5 mm² at 2.5 x 2.5 µm² pixel size) and a metamorphosed, sedimentary exhalative Mn-Fe ore (3.3 x 4 mm² at 1.25 x 5 µm²). In both cases, As K-edge XANES imaging reveals localized occurrence of reduced As in parts of these oxidized samples, which would have been difficult to recognize using traditional approaches. [ABSTRACT FROM AUTHOR]

Environmental samples are extremely diverse but share a tendency for heterogeneity and complexity. This heterogeneity poses methodological challenges when investigating biogeochemical processes. In recent years, the development of analytical tools capable of probing element distribution and speciation at the microscale have allowed this challenge to be addressed. Of these available tools, laterally resolved synchrotron techniques such as X-ray fluorescence mapping are key methods for the in situ investigation of micronutrients and inorganic contaminants in environmental samples. This article demonstrates how recent advances in X-ray fluorescence detector technology are bringing new possibilities to environmental research. Fast detectors are helping to circumvent major issues such as X-ray beam damage of hydrated samples, as dwell times during scanning are reduced. They are also helping to reduce temporal beamtime requirements, making particularly time-consuming techniques such as micro X-ray fluorescence (μXRF) tomography increasingly feasible. This article focuses on μXRF mapping of nutrients and metalloids in environmental samples, and suggests that the current divide between mapping and speciation techniques will be increasingly blurred by the development of combined approaches. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]