Name: quantifying x-ray fluorescence data using maps
Uploaded: 2018-02-17T12:30:00.000+00:00
Duration: 14 min 58 s
Description: Watch this Scientific Journal Video about Quantifying X-Ray Fluorescence Data Using MAPS at JoVE.com

We acknowledge funding from the U.S. Department of Energy under contract DE-EE0005948. Use of the Center for Nanoscale Materials and the Advanced Photon Source, both Office of Science user facilities, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. This material is based upon work supported in part by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC-1041895. Video editing was done by VISLAB at Arizona State University. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of NSF or DOE. T.N. is supported by an IGERT-SUN fellowship funded by the National Science Foundation (Award 1144616).

<ol>
	<li>Geraki, K., Farquharson, M. J., Bradley, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=X-ray+fluorescence+and+energy+dispersive+x-ray+diffraction+for+the+quantification+of+elemental+concentrations+in+breast+tissue.">X-ray fluorescence and energy dispersive x-ray diffraction for the quantification of elemental concentrations in breast tissue.</a> Phys. Med. Biol. 49, 99-110 (2004).</li><li>Paunesku, T., Vogt, S., Maser, J., Lai, B., Woloschak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=X-ray+fluorescence+microprobe+imaging+in+biology+and+medicine.">X-ray fluorescence microprobe imaging in biology and medicine.</a> J. Cell. Biochem. 99 (6), 1489-1502 (2006).</li><li>Twining, B. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+Trace+Elements+in+Individual+Aquatic+Protist+Cells+with+a+Synchrotron+X-ray+Fluorescence+Microprobe.">Quantifying Trace Elements in Individual Aquatic Protist Cells with a Synchrotron X-ray Fluorescence Microprobe.</a> Anal. Chem. 75 (15), 3806-3816 (2003).</li><li>de Jonge, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+3D+elemental+microtomography+of+Cylotella+meneghiniana+at+400-nm+resolution.">Quantitative 3D elemental microtomography of Cylotella meneghiniana at 400-nm resolution.</a> Proc. Natl. Acad. Sci. 107 (36), 15676-15680 (2010).</li><li>Du&#265;i&#263;, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancement+in+statistical+and+image+analysis+for+in+situ+&#181;SXRF+studies+of+elemental+distribution+and+co-localization,+using+Dioscorea+balcanica.">Enhancement in statistical and image analysis for in situ &#181;SXRF studies of elemental distribution and co-localization, using Dioscorea balcanica.</a> J. Synchrotron Rad. 20, 339-346 (2013).</li><li>Kemner, K. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Elemental+and+Redox+Analysis+of+Single+Bacterial+Cells+by+X-ray+Microbeam+Analysis.">Elemental and Redox Analysis of Single Bacterial Cells by X-ray Microbeam Analysis.</a> Science. 306 (5696), 686-687 (2004).</li><li>Bertoni, M. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nanoprobe+X-ray+fluorescence+characterization+of+defects+in+large-area+solar+cells.">Nanoprobe X-ray fluorescence characterization of defects in large-area solar cells.</a> Energy Environ. Sci. 4 (10), 4252-4257 (2011).</li><li>Fenning, D. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Iron+distribution+in+silicon+after+solar+cell+processing:+Synchrotron+analysis+and+predictive+modeling.">Iron distribution in silicon after solar cell processing: Synchrotron analysis and predictive modeling.</a> Appl. Phys. Lett. 98 (162103), (2011).</li><li>Buonassisi, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+the+effect+of+metal-rich+precipitates+on+minority+carrier+diffusion+length+in+multicrystalline+silicon+using+synchrotron-based+spectrally+resolved+x-ray+beam+induced+current.">Quantifying the effect of metal-rich precipitates on minority carrier diffusion length in multicrystalline silicon using synchrotron-based spectrally resolved x-ray beam induced current.</a> Appl. Phys. Lett. 87 (044101), (2005).</li><li>Stuckelberger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Engineering+solar+cells+based+on+correlative+X-ray+microscopy.">Engineering solar cells based on correlative X-ray microscopy.</a> J. Mat. Res. 32 (10), 1825-1854 (2017).</li><li>Streeck, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grazing-incidence+x-ray+fluorescence+analysis+for+non-destructive+determination+of+In+and+Ga+depth+profiles+in+Cu(In,Ga)Se2+absorber+films.">Grazing-incidence x-ray fluorescence analysis for non-destructive determination of In and Ga depth profiles in Cu(In,Ga)Se2 absorber films.</a> Appl. Phys. Lett. 103 (113904), (2013).</li><li>Luo, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatially+Heterogeneous+Chlorine+Incorporation+in+Organic-Inorganic+Perovskite+Solar+Cells.">Spatially Heterogeneous Chlorine Incorporation in Organic-Inorganic Perovskite Solar Cells.</a> Chem. Mater. 28, 6536-6543 (2016).</li><li>Stuckelberger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Charge+Collection+in+Hybrid+Perovskite+Solar+Cells:+Relation+to+the+Nanoscale+Elemental+Distribution.">Charge Collection in Hybrid Perovskite Solar Cells: Relation to the Nanoscale Elemental Distribution.</a> IEEE J. Photovolt. 7 (2), 590-597 (2017).</li><li>Vogt, S., Maser, J., Jacobsen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+analysis+for+X-ray+fluorescence+imaging.">Data analysis for X-ray fluorescence imaging.</a> J. Phys. IV France. 104, 617-622 (2003).</li><li>West, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grain+engineering:+How+nanoscale+inhomogeneities+can+control+charge+collection+in+solar+cells.">Grain engineering: How nanoscale inhomogeneities can control charge collection in solar cells.</a> Nano Energy. 32, 488-493 (2017).</li><li>Krause, M. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Atomic+Radiative+and+Radiationless+Yields+for+K+and+L+Shells.">Atomic Radiative and Radiationless Yields for K and L Shells.</a> J. Phys. Chem. Ref. Data. 8 (2), 307-327 (1979).</li><li>Hubbell, J. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review,+Bibliography,+and+Tabulation+of+K,L,+and+Higher+Atomic+Shell+X-Ray+Fluorescence+Yields.">A Review, Bibliography, and Tabulation of K,L, and Higher Atomic Shell X-Ray Fluorescence Yields.</a> J. Phys. Chem. Ref. Data. 23 (2), 339-364 (1994).</li><li>Ravel, B., Newville, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATHENA,+ARTEMIS,+HEPHAESTUS:+data+analysis+for+X-ray+absorption+spectroscopy+using+IFEFFIT.">ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT.</a> J. Synchrotron Rad. 12, 537-541 (2005).</li><li>West, B. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=X-ray+fluorescence+at+nanoscale+resolution+for+multicomponent+layered+structures:+a+solar+cell+case+study.">X-ray fluorescence at nanoscale resolution for multicomponent layered structures: a solar cell case study.</a> J Synchrotron Rad. 24, (2017).</li><li>De Boer, D. K. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Calculation+of+X-Ray+Fluorescence+Intensities+from+Bulk+and+Multilayer+Samples.">Calculation of X-Ray Fluorescence Intensities from Bulk and Multilayer Samples.</a> X-Ray Spectrom. 19 (3), 145-154 (1990).</li><li>Lachance, G. R., Claisse, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Quantitative X-ray Fluorescence Analysis: Theory and Application. , (1995).</li><li>Sokaras, D., Karydas, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secondary+Fluorescence+Enhancement+in+Confocal+X-ray+Microscopy+Analysis.">Secondary Fluorescence Enhancement in Confocal X-ray Microscopy Analysis.</a> Anal. Chem. 81 (12), 4946-4954 (2009).</li></ol>

The authors have nothing to disclose.

The figures show the importance of fitting data using this procedure. Figures 1 (right) and 2 (bottom) show a representative result that should arise from a proper fitting. If there is an insufficient fit, the integral spectrum image will look noticeably off and the resulting quantified data will have errors in it, although these will be hard to detect in most cases. For certain sample types for which the standard is not representative of the elements in the sample, particularly in that the samples do not contain any of elements in the standard, the quantification relies solely on extrapolating information for all the elements of interest. In a situation like this, the fitting will appear to be accurate when analyzed using the integral spectrum, however, upon quantifying, the values will appear dramatically incorrect. In this situation, it becomes necessary to use a standard of known quantities that is more similar to the sample. The process of choosing and comparing standards is well demonstrated in a study by Twining et al., which made use of the NIST standard, as well as some synthesized standards, for quantifying biological samples 3. By doing so, the authors were able to verify the appropriateness of each standard and compare the areal densities produced by using each standard for the fitting. Most importantly, the paper shows the reduction of error that results from choosing an appropriate standard and oppositely, the strong impact that using the wrong standard can have on quantification.
In addition to the fitting and quantification, there are other corrections that can be done to ensure the accurate interpretation of quantified data produced by XRF. One example is described by West et al. 20, which uses attenuation calculations provided by DeBoer 21 to further correct the quantified data for thickness variations and beam attenuation within a multi-layer sample. The article uses CIGS thin film solar cells in a case study for demonstrating the importance of using such attenuation corrections prior to forming conclusions on the observed concentration variations. The correction addresses the issue that in a multilayer sample with assumed thickness variation within the layers, regions prior to correction may suggest higher relative quantities of a certain element due to increased thickness rather than increased elemental concentration. The correction also accounts for beam attenuation of the incoming and outgoing beam in multilayer samples, as well as self-attenuation of elements within the specific layer of interest. This is one example of additional analysis necessary for the accurate quantification of X-ray fluorescence microscopy data. However, the corrections applied in 20 are based on assumptions that do not hold for all types of samples, and further corrections may need to be considered depending on the sample material and structural characteristics.
It is important to note that the raw and fitted data also include counts from secondary fluorescence occurring from fluorescence photons of one element providing enough energy to produce fluorescence photons of other elements with lower binding energies 21,22. The isolation of primary fluorescence photons from others is not possible by the fitting program, resulting in over- and under-quantification of certain elements. More specifically, the elements with higher fluorescence energies provide energy to elements of lower binding energy and are therefore not counted by the detector. Meanwhile, the atoms being excited by secondary fluorescence photons may be counted more than once because they first release photons due to the incident beam and then again release photons from the other sample elements. The data, therefore, requires additional treatment if these interactions are anticipated to have a significant impact on the quantification of elements of interest. Currently, the best approach for handling secondary fluorescence is through modeling and estimating the yield, such as what is described in 23. Information on the regime over which secondary fluorescence becomes significant and additional equations for estimation are provided in 22.
This work has demonstrated the first steps necessary for the quantification of X-ray fluorescence data. Although the process still requires many improvements and issues can arise that are specific to the type of sample studied (semiconductors, plant cells, human tissues, etc.), the technique is a reliable method for extracting meaningful quantitative information from the qualitative raw data acquired from XRF measurements.

Synchrotron-based XRF has been used across multiple disciplines for many decades. For example, it has been used in biology on studies such as that done by Geraki et al., in which they quantified trace amounts of metal concentrations within cancerous and non-cancerous breast tissue 1. More generally, quantitative XRF has been applied to a wide array of biology studies concerned with metal concentrations in cells and tissues, as described by Paunesku et al.2. Similarly, marine protists were studied for trace elements 3,4 and even micro- and macronutrient distributions were observed within plant cells 5. Work by Kemner et al.6, which identified distinct differences in morphology and elemental composition in single bacteria cells, was also made possible through quantitative XRF analysis. Additionally, and specifically relevant to the example disclosed herein, materials scientists studying solar cell devices have made use of high-resolution XRF for studies on the existence of sub-micron metal impurities in silicon semiconductors 7,8, correlative work on how elemental distributions affect electrical performance in solar devices 9,10, and identifying depth-dependent gradients of CIGS thin film solar cells via grazing incidence X-ray fluorescence (GIXRF) 11.
Many of these studies make use not only of the high-resolution capabilities of synchrotron X-ray fluorescence to study spatial distribution, but also the quantification of the information for drawing numerical conclusions. In many studies it is critical to know the elemental concentrations associated with the aforementioned spatial distributions. For instance, in the work by Geraki et al., the study required quantifying the difference in concentrations of iron, copper, zinc, and potassium in cancerous and non-cancerous breast tissues, to better understand what concentrations become harmful to human tissues 1. Similarly, work by Luo et al. made use of quantified XRF to identify small amounts of chlorine incorporated in perovskite solar cells when synthesized both with and without chlorine-containing precursors 12. Therefore, for certain studies in which the concentrations of elements are needed, proper quantification is a necessary and critical step.
The process of quantifying elemental concentrations from X-ray fluorescence (XRF) measurements translates fluorescence intensity counts into mass concentrations (e.g. &#181;g/cm2). The raw spectra present the number of photons collected by the energy dispersive fluorescence detector as a function of energy. The spectra are first fit and then compared to a standard measurement to calculate the quantified data. In particular, the first step of fitting fluorescence spectra is critical even for the qualitative analysis of the elements. This is because prior to fitting, counts are binned based on their energy, which becomes a problem when two elements with similar fluorescence transitions are contained in the sample. In this situation, counts may be incorrectly binned and thus associated with the wrong element.
It is often also necessary to quantify XRF spectra in order to accurately draw conclusions on relative quantities of elements in a sample. Without proper quantification, counts of heavy elements and lighter elements will be compared directly, ignoring differences in capture cross section, absorption and fluorescence probability, attenuation of the fluorescence photons, and the distance of the element&#39;s absorption edge from the incident energy, which all affect the number of photons striking the detector. Therefore, the process of fitting the spectra for each map and comparing peak intensities to the standard, both of which are done in the following procedure, is critical for the accurate quantification of each of the elemental concentrations.
We demonstrate how to convert the raw counts of fluorescence photons to units of micrograms per square centimeter (&#181;g/cm2) by first fitting an integral spectrum, or a summed spectrum of all the individual spectra produced at each measurement spot or pixel in a 2D map. This spectrum demonstrates the relative intensities of the different elements contained in the sample. The distance the absorption edge of a certain element is from the incident beam energy influences the intensities of their fluorescence peaks. In general, the closer the two energies are, the greater the intensity produced for those elements, although this is not always the case. Figure 4 in Ref 13 shows the dependence of the absorption length of X-ray photons, which directly relates to the resulting intensity, for the majority elements in a methylammonium lead iodide perovskite solar cell. This demonstrates the fluorescence response of elements with respect to energy, and shows that it is not a continual decrease in response with increasing distance from the incident energy, but rather that it is also dependent upon the element itself.
The result of this relationship is that raw elemental concentrations may appear higher for element channels with excitation energies closer to the incident energy, even if the true quantities of those elements are lower in relation to other elements with excitation energies farther from the incident. Therefore, the energy dependence of intensity, along with other factors such as fluorescence yield variations, different absorption edges, detector sensitivity, and measurement background, etc., is why fitting the data is very important prior to drawing conclusions on the observed elemental quantities. We then apply a fitting algorithm to the integral spectrum, where the user defines the elements and parameters to fit via a text document.
The algorithm, created by Vogt et al. 14, makes use of regions of interest (ROI) filtering, in which it integrates over certain elements&#39; peak regions, and principle component analysis (PCA). First, PCA is done to identify only the elements and peaks that are very strongly apparent. This allows for the separation of noise from the true signal. Next, the principle components identified are numerically quantified, which is important for deconvoluting different element peaks with the same excitation energy, for example overlapping Au M&#945; and P K&#945;. Finally, ROI filtering may be applied to the numeric data by integrating over specified regions.
To relate counts to elemental concentrations, a well-quantified reference (often referred to as &#34;standard&#34;) is measured under the same measurement conditions, geometry and energy, as the sample under study. This standard is often from Dresden AXO or from the National Institute of Standards and Technology (NIST). They cover a variety of different elements and come with tabulated elemental distributions. The normalization of the measured counts of the sample of interest to the counts of the standard under the same measurement conditions provides the basis for the elemental quantification for the sample of interest.
More specifically, MAPS identifies the elements and their concentrations of the standard either by the fact that the standard information is known by the program (as is the case for the AXO and NIST standards) or through data entered into a separate file (in the case of a different standard being used). From this information, the program relates the measured intensities of the standard elements under the measurement settings to the anticipated concentration embedded in MAPS. It then creates a scaling factor to adjust for any offset and extrapolates this scaling factor to all the remaining elements not included in the standard. The scaling factor then includes the offset from the measurement settings and the information provided within MAPS for the linear conversion of raw counts to areal density in &#181;g/cm2.
Here, we demonstrate how to make use of the program, MAPS, developed by Dr. S. Vogt, to quantify data acquired from fluorescence-capable beamlines at Argonne National Laboratory (ANL) 14. The data used for the demonstration was acquired at sector 2-ID-D of ANL using the measurement setup shown in Figure 1 of 10. The fitting procedure may also be applied to data taken from other beamlines, however, note that certain characteristics of the ANL beamlines are embedded in the program and will need to be updated.

quantifying x-ray fluorescence data using maps

The quantification of X-ray fluorescence (XRF) microscopy maps by fitting the raw spectra to a known standard is crucial for evaluating chemical composition and elemental distribution within a material. Synchrotron-based XRF has become an integral characterization technique for a variety of research topics, particularly due to its non-destructive nature and its high sensitivity. Today, synchrotrons can acquire fluorescence data at spatial resolutions well below a micron, allowing for the evaluation of compositional variations at the nanoscale. Through proper quantification, it is then possible to obtain an in-depth, high-resolution understanding of elemental segregation, stoichiometric relationships, and clustering behavior.
This article explains how to use the MAPS fitting software developed by Argonne National Laboratory for the quantification of full 2-D XRF maps. We use as an example results from a Cu(In,Ga)Se2 solar cell, taken at the Advanced Photon Source beamline 2-ID-D at Argonne National Laboratory. We show the standard procedure for fitting raw data, demonstrate how to evaluate the quality of a fit and present the typical outputs generated by the program. In addition, we discuss in this manuscript certain software limitations and offer suggestions for how to further correct the data to be numerically accurate and representative of spatially resolved, elemental concentrations.

An example of proper fitting results can be seen in the following Figures. Firstly, in Figure 1 a direct comparison is shown between a poor fit and a good fit for the integral spectrum. The bad fit is reparable by both ensuring no elements are missing, for instance copper, which has a clear peak in Figure 1(left) but is not being included in the fit, and adjusting the branching ratios of the L and K lines to improve the accuracy. Figure 2 instead shows a comparison between the element channels before and after fitting. The first noticeable difference is the units for the values changes from &#34;raw&#34; to &#34;ug/cm^2&#34;, suggesting that the data have been quantified. Additionally, number ranges should align with those expected from the suggested calculation in section 4.2. These values should generally not go to zero. If they do, this is almost always a sign that there is an error in the fitting.
Both the iron and copper channels can be seen both before and after the fitting. Beyond observing the value changes, it is also clear that the images are better resolved and the stripes that appear in the raw data are gone upon fitting. This resolution increase comes as a result of the peak deconvolution done by the fitting for the separation of elements with overlapping peaks. It is just one of the benefits of fitting and quantifying the data, providing the ability to more accurately qualitatively and quantitatively analyze the fluorescence data. In the particular example of a CIGS solar cell, one of the properties that researchers are interested in is the distribution of the three cations, copper, indium, and gallium, throughout the device. Statistical research has been done to study the change in their concentrations within grains and grain boundaries 16. Such a study requires the improved resolution within the map so that boundaries can be more easily identified using a watershed technique. Additionally, having the capability to study the correlation and anti-correlation of the elements provides an outlook on sample homogeneity and how to improve it.
While the quantified data can now be used to relate elemental concentrations, the fitting procedure is not perfect. There will always be a degree of error introduced from the various procedure steps, including, but not limited to, the quality of the fit, the selection, matrix homogeneity, measurement, and extrapolation of the standard, and the influence of other factors such as secondary fluorescence and sample thickness variation that are not taken into consideration by MAPS. These errors can be minimized by selecting a homogenous standard with multiple common elements with the sample and improving branching ratios as much as possible, however, note that some of these, such as controlling fit quality, are systematic errors that are hard to eradicate completely. Although it is not possible to directly quantify the error incurred, purchased standards will provide an error estimation for the concentrations of the elements, which are often quite high and should be considered when trying to analyze and propagate errors.
Correcting the data further for certain issues such as thickness variation, beam attenuation, and secondary fluorescence can help reduce the error even further. Methodologies available to do such corrections are described in the discussion section.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56042/56042fig1.jpg" /> 
Figure 1. Demonstration of before (left) and after (right) the proper adjustments have been made to the fitting file to produce an accurate fitting (shown in green) of the integral spectrum (shown in white) and later, to convert raw counts to &#181;g/cm2 accurately. Two common types of error are circled in (a): the red circle identifies a missing element, in this case Cu, and the yellow circle identifies a problem with the branching ratios for the In_L line. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56042/56042fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56042/56042fig2.jpg" /> 
Figure 2. Demonstration of before (top) and after (bottom) fitting and quantification of the fluorescence channels of interest. Most notable is the change in units from &#34;raw&#34; to &#34;ug/cm^2&#34;. The quantified values for iron and copper in a 2 &#181;m thick CIGS solar cell on a 500 &#181;m thick stainless steel substrate are on the order of 1000 and 100 &#181;g/cm2.

Watch this Scientific Journal Video about Quantifying X-Ray Fluorescence Data Using MAPS at JoVE.com

Quantifying X-Ray Fluorescence Data Using MAPS

Here, we demonstrate the use of the X-ray fluorescence fitting software, MAPS, created by Argonne National Laboratory for the quantification of fluorescence microscopy data. The quantified data that results is useful for understanding the elemental distribution and stoichiometric ratios within a sample of interest.

The quantification of X-ray fluorescence (XRF) microscopy maps by fitting the raw spectra to a known standard is crucial for evaluating chemical ...

quantifying-x-ray-fluorescence-data-using-maps

Nanyang Technological University

Research

JoVE Journal

Chemistry

10.6K Views.  Arizona State University. Here, we demonstrate the use of the X-ray fluorescence fitting software, MAPS, created by Argonne National Laboratory for the quantification of fluorescence microscopy data. The quantified data that results is useful for understanding the elemental distribution and stoichiometric ratios within a sample of interest. 

Video: Quantifying X-Ray Fluorescence Data Using MAPS

Preparing Adherent Cells for X-ray Fluorescence Imaging by Chemical Fixation

X-ray fluorescence imaging allows us to non-destructively measure the spatial distribution and concentration of multiple elements simultaneously over large or small sample areas. It has been applied in many areas of science, including materials science, geoscience, studying works of cultural heritage, and in chemical biology. In the case of chemical biology, for example, visualizing the metal distributions within cells allows us to study both naturally-occurring metal ions in the cells, as well as exogenously-introduced metals such as drugs and nanoparticles. Due to the fully hydrated nature of nearly all biological samples, cryo-fixation followed by imaging under cryogenic temperature represents the ideal imaging modality currently available. However, under the circumstances that such a combination is not easily accessible or practical, aldehyde based chemical fixation remains useful and sometimes inevitable. This article describes in as much detail as possible in the preparation of adherent mammalian cells by chemical fixation for X-ray fluorescent imaging.

Here, we present a protocol on how to determine the quantity and distribution of metals in a sample using synchrotron X-ray fluorescence. We focus on adherent cells, and describe the chemical fixation method to prepare this sample. We then describe how to mount and image the sample using synchrotron X-rays.

X-ray fluorescence imaging allows us to non-destructively measure the spatial distribution and concentration of multiple elements simultaneously over ...

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Energy dispersive X-ray spectroscopy within the scanning transmission electron microscope (STEM) provides accurate elemental analysis with high spatial resolution, and is even capable of providing atomically resolved elemental maps. In this technique, a highly focused electron beam is incident upon a thin sample and the energy of emitted X-rays is measured in order to determine the atomic species of material within the beam path. This elementally sensitive spectroscopy technique can be extended to three dimensional tomographic imaging by acquiring multiple spectrum images with the sample tilted along an axis perpendicular to the electron beam direction.
Elemental distributions within single nanoparticles are often important for determining their optical, catalytic and magnetic properties. Techniques such as X-ray tomography and slice and view energy dispersive X-ray mapping in the scanning electron microscope provide elementally sensitive three dimensional imaging but are typically limited to spatial resolutions of &gt; 20 nm. Atom probe tomography provides near atomic resolution but preparing nanoparticle samples for atom probe analysis is often challenging. Thus, elementally sensitive techniques applied within the scanning transmission electron microscope are uniquely placed to study elemental distributions within nanoparticles of dimensions 10-100 nm.
Here, energy dispersive X-ray (EDX) spectroscopy within the STEM is applied to investigate the distribution of elements in single AgAu nanoparticles. The surface segregation of both Ag and Au, at different nanoparticle compositions, has been observed.

The use of energy dispersive X-ray tomography in the scanning transmission electron microscope to characterize elemental distributions within single nanoparticles in three dimensions is described.

Energy dispersive X-ray spectroscopy within the scanning transmission electron microscope (STEM) provides accurate elemental analysis with high spatial ...

Engineering

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Micro-analytical techniques based on chemical element imaging enable the localization and quantification of chemical composition at the cellular level. They offer new possibilities for the characterization of living systems and are particularly appropriate for detecting, localizing and quantifying the presence of metal oxide nanoparticles both in biological specimens and the environment. Indeed, these techniques all meet relevant requirements in terms of (i) sensitivity (from 1 up to 10 &#181;g.g-1 of dry mass), (ii) micrometer range spatial resolution, and (iii) multi-element detection. Given these characteristics, microbeam chemical element imaging can powerfully complement routine imaging techniques such as optical and fluorescence microscopy. This protocol describes how to perform a nuclear microprobe analysis on cultured cells (U2OS) exposed to titanium dioxide nanoparticles. Cells must grow on and be exposed directly in a specially designed sample holder used on the optical microscope and in the nuclear microprobe analysis stages. Plunge-freeze cryogenic fixation of the samples preserves both the cellular organization and the chemical element distribution. Simultaneous nuclear microprobe analysis (scanning transmission ion microscopy, Rutherford backscattering spectrometry and particle induced X-ray emission) performed on the sample provides information about the cellular density, the local distribution of the chemical elements, as well as the cellular content of nanoparticles. There is a growing need for such analytical tools within biology, especially in the emerging context of Nanotoxicology and Nanomedicine for which our comprehension of the interactions between nanoparticles and biological samples must be deepened. In particular, as nuclear microprobe analysis does not require nanoparticles to be labelled, nanoparticle abundances are quantifiable down to the individual cell level in a cell population, independently of their surface state.

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

We describe a procedure for the detection of chemical elements present in situ in human cells as well as their in vitro quantification. The method is well-suited to any cell type and is particularly useful for quantitative chemical analyses in single cells following in vitro metal oxide nanoparticles exposure.

Micro-analytical techniques based on chemical element imaging enable the localization and quantification of chemical composition at the cellular level. ...

Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography

Energy filtered transmission electron microscopy tomography (EFTEM tomography) can provide three-dimensional (3D) chemical maps of materials at a nanometric scale. EFTEM tomography can separate chemical elements that are very difficult to distinguish using other imaging techniques. The experimental protocol described here shows how to create 3D chemical maps to understand the chemical distribution and morphology of a material. Sample preparation steps for data segmentation are presented. This protocol permits the 3D distribution analysis of chemical elements in a nanometric sample. However, it should be noted that currently, the 3D chemical maps can only be generated for samples that are not beam sensitive, since the recording of filtered images requires long exposure times to an intense electron beam. The protocol was applied to quantify the chemical distribution of the components of two different heterogeneous catalyst supports. In the first study, the chemical distribution of aluminum and titanium in titania-alumina supports was analyzed. The samples were prepared using the swing-pH method. In the second, the chemical distribution of aluminum and silicon in silica-alumina supports that were prepared using the sol-powder and mechanical mixture methods was examined.

This paper describes a protocol to achieve 3D chemical maps combining energy filtered imaging and electron tomography. The chemical distribution of two catalyst supports formed by elements that are difficult to distinguish by other imaging techniques was studied. Each application consists of mapping overlapped chemical elements - respectively spaced-ionization edges.

Energy filtered transmission electron microscopy tomography (EFTEM tomography) can provide three-dimensional (3D) chemical maps of materials at a ...

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Energy storage has become more and more a limiting factor of today's sustainable energy applications, including electric vehicles and green electric grid based on volatile solar and wind sources. The pressing demand of developing high-performance electrochemical energy storage solutions, i.e., batteries, relies on both fundamental understanding and practical developments from both the academy and industry. The formidable challenge of developing successful battery technology stems from the different requirements for different energy-storage applications. Energy density, power, stability, safety, and cost parameters all have to be balanced in batteries to meet the requirements of different applications. Therefore, multiple battery technologies based on different materials and mechanisms need to be developed and optimized. Incisive tools that could directly probe the chemical reactions in various battery materials are becoming critical to advance the field beyond its conventional trial-and-error approach. Here, we present detailed protocols for soft X-ray absorption spectroscopy (sXAS), soft X-ray emission spectroscopy (sXES), and resonant inelastic X-ray scattering (RIXS) experiments, which are inherently elemental-sensitive probes of the transition-metal 3d and anion 2p states in battery compounds. We provide the details on the experimental techniques and demonstrations revealing the key chemical states in battery materials through these soft X-ray spectroscopy techniques.

Here, we present a protocol for typical experiments of soft X-ray absorption spectroscopy (sXAS) and resonant inelastic X-ray scattering (RIXS) with applications in battery material studies.

Energy storage has become more and more a limiting factor of today's sustainable energy applications, including electric vehicles and green electric grid ...

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

In this report, we describe a detailed procedure for acquiring and processing&#160;x-ray microfluorescence (&#956;XRF), and Laue and powder microdiffraction two-dimensional (2D) maps at beamline 12.3.2 of the Advanced Light Source (ALS), Lawrence Berkeley National Laboratory. Measurements can be performed on any sample that is less than 10 cm x 10 cm x 5 cm, with a flat exposed surface. The experimental geometry is calibrated using standard materials (elemental standards for XRF, and crystalline samples such as Si, quartz, or Al2O3 for diffraction). Samples are aligned to the focal point of the x-ray microbeam, and raster scans are performed, where each pixel of a map corresponds to one measurement, e.g., one XRF spectrum or one diffraction pattern. The data are then processed using the in-house developed software XMAS, which outputs text files, where each row corresponds to a pixel position. Representative data from moissanite and an olive snail shell are presented to demonstrate data quality, collection, and analysis strategies.

We describe a beamline setup meant to carry out rapid two-dimensional x-ray fluorescence and x-ray microdiffraction mapping of single crystal or powder samples using either Laue (polychromatic radiation) or powder (monochromatic radiation) diffraction. The resulting maps give information about strain, orientation, phase distribution, and plastic deformation.

In this report, we describe a detailed procedure for acquiring and processing&#160;x-ray microfluorescence (&#956;XRF), and Laue and powder ...

Expression of Cementitious Pore Solution and the Analysis of Its Chemical Composition and Resistivity Using X-ray Fluorescence

The goal of this method is to determine the chemical composition and electrical resistivity of cementitious pore solution expressed from a fresh paste sample. The pore solution is expressed from a fresh paste sample using a pressurized nitrogen gas system. The pore solution is then immediately transferred to a syringe to minimize evaporation and carbonation. After that, assembled testing containers are used for the X-ray fluorescence (XRF) measurement. These containers consist of two concentric plastic cylinders and a polypropylene film which seals one of the two open sides. The pore solution is added into the container immediately prior to the XRF measurement. The XRF is calibrated to detect the main ionic species in the pore solution, in particular, sodium (Na+), potassium (K+), calcium (Ca2+), and sulfide (S2-), to calculate sulfate (SO42-) using stoichiometry. The hydroxides (OH-) can be calculated from a charge balance. To calculate the electrical resistivity of the solution, the concentrations of the main ionic species and a model by Snyder et al. are used. The electrical resistivity of the pore solution can be used, along with the electrical resistivity of concrete, to determine the formation factor of concrete. XRF is a potential alternative to current methods to determine the composition of pore solution, which can provide benefits in terms of reduction in time and costs.

This protocol describes the procedure to express fresh pore solution from cementitious systems and the measurement of its ionic composition using X-ray fluorescence. The ionic composition can be used to calculate pore solution electrical resistivity, which can be used, together with concrete electrical resistivity, to determine the formation factor.

The goal of this method is to determine the chemical composition and electrical resistivity of cementitious pore solution expressed from a fresh paste ...

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of care consisting of surgical debulking and combination chemotherapy consisting of platinum and taxane compounds, almost 90% of patients recur within a few years. In these patients the development of chemoresistant disease limits the efficacy of currently available chemotherapy agents and therefore contributes to the high mortality. To discover novel therapy options that can target recurrent disease, appropriate animal models that closely mimic the clinical profile of patients with recurrent ovarian cancer are required. The challenge in monitoring intra-peritoneal (i.p.) disease limits the use of i.p. models and thus most xenografts are established subcutaneously. We have developed a sensitive optical imaging platform that allows the detection and anatomical location of i.p. tumor mass. The platform includes the use of optical reporters that extend from the visible light range to near infrared, which in combination with 2-dimensional X-ray co-registration can provide anatomical location of molecular signals. Detection is significantly improved by the use of a rotation system that drives the animal to multiple angular positions for 360 degree imaging, allowing the identification of tumors that are not visible in single orientation. This platform provides a unique model to non-invasively monitor tumor growth and evaluate the efficacy of new therapies for the prevention or treatment of recurrent ovarian cancer.

We describe a non-invasive animal imaging platform that allows the detection, quantification, and monitoring of ovarian cancer growth and recurrence. This intra-peritoneal xenograft model mimics the clinical profile of patients with ovarian cancer.

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of ...

Medicine

High Spatial Resolution Chemical Imaging of Implant-Associated Infections with X-ray Excited Luminescence Chemical Imaging Through Tissue

Microbial infections associated with implantable medical devices are a major concern in fracture fixation failure. Early diagnosis of such infection will allow successful eradication with antibiotics without an extra cost for a second surgery. Herein, we describe XELCI as a technique with high X-ray resolution, implant specificity, and chemical sensitivity to noninvasively image chemical concentrations near the surface of implanted medical devices. The devices are coated with chemically reporting surfaces. This chemically responsive surface consists of two layers coated on an implantable medical device; a pH-sensitive layer (bromothymol blue or bromocresol green incorporated hydrogel) which is coated over a red-light emitting scintillator (Gd2O2S: Eu) layer for monitoring. A focused X-ray beam irradiates a spot on the implant, and the red light generated by the scintillator (with 620 nm and 700 nm peaks) is transmitted through the sensing layer which alters the spectral ratio depending on the pH. An image is generated by scanning the X-ray beam across the implant and measuring the spectral ratio of light passing through the tissue point-by-point. We used this imaging technique for monitoring implant-associated infections previously on the bone surface of the femur with a modified implantable plate sensor. Now we are studying pH changes that occur from tibial intramedullary rod infections. Two different types of intramedullary rod designs are used in pre-pilot rabbit studies, and we learned that the XELCI technique could be used to monitor any chemical changes that occur not only on the bone surface but also inside the bone. Thus, this enables noninvasive, high spatial resolution, low background local pH imaging to study implant-associated infection biochemistry.

Here, we present a protocol for high-resolution optical detection of chemical information around implanted medical devices with X-ray excited luminescence chemical imaging (XELCI). This novel imaging technique is developed in our lab which enables studying implant-associated infection biochemistry.

Microbial infections associated with implantable medical devices are a major concern in fracture fixation failure. Early diagnosis of such infection will ...

X-ray Fluorescence (XRF)

Source: Laboratory of Dr. Lydia Finney &#8212; Argonne National Laboratory
X-ray fluorescence is an induced, emitted radiation that can be used to generate spectroscopic information. X-ray fluorescence microscopy is a non-destructive imaging technique that uses the induced fluorescence emission of metals to identify and quantify their spatial distribution.

Quantifying X-Ray Fluorescence Data Using MAPS

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Preparing Adherent Cells for X-ray Fluorescence Imaging by Chemical Fixation

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Expression of Cementitious Pore Solution and the Analysis of Its Chemical Composition and Resistivity Using X-ray Fluorescence

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

High Spatial Resolution Chemical Imaging of Implant-Associated Infections with X-ray Excited Luminescence Chemical Imaging Through Tissue

X-ray Fluorescence (XRF)