A discussion of PIXAN and PIXANPC: The AAEC PIXE analysis computer packages

Clayton, E.; Duerden, P.; Cohen, David D.

doi:10.1016/0168-583x(87)90295-3

Cited by 65 publications

(16 citation statements)

References 4 publications

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“…For each slice, the contribution to the yield can readily be calculated and summed to obtain the total X‐ray yield. This procedure has been implemented by several PIXE analysis codes . However, they are generally written to use only one of the data sets.…”

Section: Resultsmentioning

confidence: 99%

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Influence of different mass absorption coefficient datasets on PIXE yields

Siegele

Cohen

2013

X-Ray Spectrometry

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Tabulated mass absorption coefficient data from the XCOM, Chantler and ion beam analysis (IBA) Handbook are compared, and differences are shown against X‐ray energy and target atomic number. Over the X‐ray energy range 1–40 keV systematic differences of several tens of percent are noted between these datasets, particularly for the earlier IBA Handbook dataset. The influence of these different mass absorption coefficients on the X‐ray yields for thick target proton‐induced X‐ray emission (PIXE) are investigated and compared as a function of X‐ray energy and target atomic number. For contemporary experimental PIXE users trying to work quantitatively over a broad range of elements and X‐ray energies, differences between PIXE results obtained using these three separate mass attenuation coefficient datasets can be larger than the typical accuracy limits of ±3% to ±5%. There are systematic differences in the mass attenuation coefficients of 5–10% between the XCOM and Chantler, while the differences for the IBA Handbook dataset can be larger (up to 40% and greater) at high X‐ray energies. At this time, we recommend the dataset of Chantler as it is more recent, and the synchrotron experimental results seem to favour it over the older XCOM and IBA Handbook data. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

“…This procedure has been implemented by several PIXE analysis codes. [12][13][14] However, they are generally written to use only one of the data sets. Hence, a programme was written to calculate the yields using Eqn (2) for the three different datasets.…”

Section: Resultsmentioning

confidence: 99%

Influence of different mass absorption coefficient datasets on PIXE yields

Siegele

Cohen

2013

X-Ray Spectrometry

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“…When dealing with real spectra from the experiment, one can use non-iterative background approximation methods like the three-window method [43, p. 397], interpolation, and polynomial fitting [44]. One may also use iterative methods like simple multiple-point smoothing [45], or more sophisticated ones like the Statistics-sensitive Non-linear Iterative Peak-clipping (SNIP) algorithm [46]. All of these methods can be problematic if inappropriate parameters (such as sampling width, or number of iterations) are chosen, or when the background is irregularly shaped.…”

Section: X-ray Fluorescence and Signal Estimatesmentioning

confidence: 99%

Optimizing detector geometry for trace element mapping by X-ray fluorescence

et al. 2015

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Trace metals play critical roles in a variety of systems, ranging from cells to photovoltaics. X-Ray Fluorescence (XRF) microscopy using X-ray excitation provides one of the highest sensitivities available for imaging the distribution of trace metals at sub-100 nm resolution. With the growing availability and increasing performance of synchrotron light source based instruments and X-ray nanofocusing optics, and with improvements in energy-dispersive XRF detectors, what are the factors that limit trace element detectability? To address this question, we describe an analytical model for the total signal incident on XRF detectors with various geometries, including the spectral response of energy dispersive detectors. This model agrees well with experimentally recorded X-ray fluorescence spectra, and involves much shorter calculation times than with Monte Carlo simulations. With such a model, one can estimate the signal when a trace element is illuminated with an X-ray beam, and when just the surrounding non-fluorescent material is illuminated. From this signal difference, a contrast parameter can be calculated and this can in turn be used to calculate the signal-to-noise ratio (S/N) for detecting a certain elemental concentration. We apply this model to the detection of trace amounts of zinc in biological materials, and to the detection of small quantities of arsenic in semiconductors. We conclude that increased detector collection solid angle is (nearly) always advantageous even when considering the scattered signal. However, given the choice between a smaller detector at 90° to the beam versus a larger detector at 180° (in a backscatter-like geometry), the 90° detector is better for trace element detection in thick samples, while the larger detector in 180° geometry is better suited to trace element detection in thin samples.

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“…[15] Reconstruction By means of an inverse Radon transform or equivalent procedure, the sinograms (generally of dimension n × m where n represents the number of translation steps and m the number of rotation steps executed during the tomographic data acquisition process) can be converted into n × n images g(x, y) called tomograms. The latter represent the distribution of the recorded (XRF or XRD) signal in the horizontal tomographic plane:…”

Section: Data Reductionmentioning

confidence: 99%

Micro X‐ray diffraction and fluorescence tomography for the study of multilayered automotive paints

Nolf

Janssens

2009

Surface & Interface Analysis

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Combined microscopic X-ray fluorescence/microscopic X-ray diffraction (µ-XRF/µ-XRD) tomography is a recently developed method that allows the visualization of the distribution of chemical elements and the associated crystalline phases inside complex, heterogeneous materials of extended thickness (millimeter range) in a nondestructive fashion. In this paper, the accuracy and resolution with which the individual layers in a multilayer stack of automotive paints can be distinguished is evaluated, and some of their properties measured. A paint layer system of eight layers was investigated, in which eight different crystalline substances were identified, each layer consisting of an organic, synthetic resin doped with finely milled inorganic compounds that serve as pigments or to strengthen the layer. In the XRD tomograms, all paint layers could be straightforwardly distinguished and their average thickness calculated. In case the filtered back projection method was used for tomogram reconstruction, a spatial resolution comparable to the microbeam size was obtained indicating no significant reconstruction blurring. When a more robust reconstruction method, such as the maximum-likelihood expectation maximization method, was employed, tomograms showing fewer artifacts were obtained, but with a spatial resolution that was two times worse. In the corresponding XRF tomograms, significant self-absorption distorted the element-specific tomograms corresponding to the low-energy ( Keywords: X-ray diffraction tomography; X-ray powder diffraction; automotive paint; pigment identification IntroductionFor the three-dimensional characterization of meso-to millimetersized objects in terms of their chemical and structural properties, several X-ray-based imaging techniques have been developed. The major advantage of these methods is that the three-dimensional structure and other properties of such materials can be determined in a noninvasive and nondestructive manner, i.e. without physically sectioning the materials under investigation. Many situations exist in which it is undesirable or not allowed to destroy material during an analysis: for example, forensic materials must be retained as judicial evidence; precious materials should retain their value and not show traces of sampling; unique cultural/paleontological artifacts should remain available for other investigations, etc.Many of these methods involve tomographic scanning of the sample materials via irradiation with penetrative types of radiation. In this paper, we devote our attention to the possibilities offered by combined X-ray fluorescence/X-ray diffraction microbeam (µ-XRF/µ-XRD) tomography for nondestructive characterization of paint multilayer systems. Such materials are characterized by a relatively large number of 5-50 µm thick layers of X-raytransparent materials (alkyd or acryl resins), doped with a variety of fine-grained inorganic components and therefore represent, on one hand, materials with a well-defined two-dimensional structure while, on the other, contain a ...

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A discussion of PIXAN and PIXANPC: The AAEC PIXE analysis computer packages

Cited by 65 publications

References 4 publications

Influence of different mass absorption coefficient datasets on PIXE yields

Influence of different mass absorption coefficient datasets on PIXE yields

Optimizing detector geometry for trace element mapping by X-ray fluorescence

Micro X‐ray diffraction and fluorescence tomography for the study of multilayered automotive paints

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