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.
Sun, Yue, Gleber, Sophie-Charlotte, Jacobsen, Chris, et al., "Optimizing detector geometry for trace element mapping by X-ray fluorescence," Ultramicroscopy 152, no. C (2015), https://doi.org/10.1016/j.ultramic.2014.12.014
@article{osti_1396014,
author = {Sun, Yue and Gleber, Sophie-Charlotte and Jacobsen, Chris and Kirz, Janos and Vogt, Stefan},
title = {Optimizing detector geometry for trace element mapping by X-ray fluorescence},
annote = {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.},
doi = {10.1016/j.ultramic.2014.12.014},
url = {https://www.osti.gov/biblio/1396014},
journal = {Ultramicroscopy},
issn = {ISSN 0304-3991},
number = {C},
volume = {152},
place = {United States},
publisher = {Elsevier},
year = {2015},
month = {05}}
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