Quantifying Morphological Features of α-U - ACS Publications

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Quantifying Morphological Features of α‑U3O8 with Image Analysis for Nuclear Forensics Adam M. Olsen,† Bryony Richards,‡ Ian Schwerdt,† Sean Heffernan,† Robert Lusk,† Braxton Smith,§ Elizabeth Jurrus,§ Christy Ruggiero,∥ and Luther W. McDonald, IV*,† †

University of Utah Department of Civil and Environmental Engineering, Nuclear Engineering Program, 201 Presidents Circle, Salt Lake City, Utah 84112, United States ‡ University of Utah Energy & Geoscience Institute (EGI), 423 Wakara Way #300, Salt Lake City, Utah 84108, United States § Scientific Computing and Imaging (SCI) Institute, 72 South Central Campus Drive, Room 3750 Salt Lake City, Utah 84112, United States ∥ Los Alamos National Laboratory, Materials Chemistry, J514, Los Alamos, New Mexico 87545, United States S Supporting Information *

ABSTRACT: Morphological changes in U3O8 based on calcination temperature have been quantified enabling a morphological feature to serve as a signature of processing history in nuclear forensics. Five separate calcination temperatures were used to synthesize α-U3O8, and each sample was characterized using powder X-ray diffraction (p-XRD) and scanning electron microscopy (SEM). The p-XRD spectra were used to evaluate the purity of the synthesized U-oxide; the morphological analysis for materials (MAMA) software was utilized to quantitatively characterize the particle shape and size as indicated by the SEM images. Analysis comparing the particle attributes, such as particle area at each of the temperatures, was completed using the Kolmogorov−Smirnov two sample test (K−S test). These results illustrate a distinct statistical difference between each calcination temperature. To provide a framework for forensic analysis of an unknown sample, the sample distributions at each temperature were compared to randomly selected distributions (100, 250, 500, and 750 particles) from each synthesized temperature to determine if they were statistically different. It was found that 750 particles were required to differentiate between all of the synthesized temperatures with a confidence interval of 99.0%. Results from this study provide the first quantitative morphological study of U-oxides, and reveals the potential strength of morphological particle analysis in nuclear forensics by providing a framework for a more rapid characterization of interdicted uranium oxide samples.

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production processing parameters, such as starting material (ammonium diuranate (ADU), uranyl peroxide, and ammonium uranyl carbonate (AUC)), precipitation rates, calcination temperatures, and ramp rates.5 However, very few systematic studies have been performed to evaluate these parameters. The studies that have been performed are qualitative primarily due to the challenge of collecting enough images for statistical relevance. The primary focus of most U-oxide morphology studies has been to advance nuclear fuel research. While these studies provide fundamental data that could benefit nuclear forensics, they lack quantitative data that could provide a strong correlation for the characterization of interdicted nuclear materials. For example, as early as 1967 Cordefunke et al. investigated the sintering behavior of UO2 fuels based on preparation conditions and morphology.7 Subsequently, many studies have gathered qualitative data evaluating precipitation

raditional signatures in nuclear forensics, such as uranium parent−daughter isotope ratios, can provide critical information as to the origin and age of the source materials. Some new signatures to identify origin are based on rare earth elements such as work recently published by Balboni et al.3 Currently, the 235U/231Pa ratio can identify the age of the material but to discover process history, additional signatures are needed.4−6 All of the current uranium-based isotope ratios are stable in regards to chemical processing, and thus provide limited means by which to trace the processing history of the material.4 For this study, morphological features of U3O8 were investigated as a signature of processing history. This is the first study to reveal unique signatures in U3O8 based on calcination temperature using quantitative analysis methods. Historically, imaging methods have been used to gain qualitative insights into the origin and processing history of interdicted samples.5 Nonetheless, analyses of material textures and comparisons between images have been challenging because of subjectivity and too few images being collected. For example, chemists generally believed that the morphology of uranium oxide powders are influenced by the selected © XXXX American Chemical Society

Received: December 19, 2016 Accepted: January 26, 2017 Published: January 26, 2017 A

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produce a yellow precipitate, studtite ([(UO2)(O2)(H2O)2](H2O)2). After reacting for 30 min at room temperature (22 °C), the precipitate was collected on a 4−5.5 μm glass frit and rinsed with five aliquots of 50 mL of Millipore (18.2 MΩ) water to remove any residual nitrates. The studtite was dried at room temperature overnight, followed by drying in an oven at 100 °C for an additional 24 h to produce uranyl peroxide dihydrate (metastudtite [(UO2)(O2)(H2O)2]).22,24 Following the procedure used by Tamasi et al.22 the samples of metastudtite were calcined in Pt crucibles at 400 °C under purified air to produce A-UO3. Purity of the resulting A-UO3 was verified using powder X-ray diffraction. Synthesis of α-U3O8 was performed in triplicate from the AUO3 source material at calcination temperatures of 600, 650, 700, 750, and 800 °C. The furnace ramp-up and ramp-down rates at all calcination temperatures were 2.5 and 1.0 °C/min, respectively. The calcination soak time was 21 h, and all calcinations were done in purified air. The samples were stored at room temperature in a vacuum chamber at 24 in. Hg. Powder X-ray Diffraction (p-XRD) Analysis. Powder Xray diffraction (p-XRD) data were acquired from the A-UO3 source material and for each of the 15 separate α-U3O8 samples. Each characterization required between 70 and 90 mg of sample. A Philips Panalytical X’Pert with Cu Kα X-rays was used for all analyses. The p-XRD scans were performed with a start position of 10° 2θ and an end position of 90° 2θ. The scans were recorded for 33 min with a scan speed of 0.04 deg/s and a step-size of 0.02°. Scanning Electron Microscopy (SEM). Each of the 15 αU3O8 samples and the A-UO3 source were prepared for SEM by placing between 2 and 5 mg of the material on top of a 1 cm stainless steel SEM mount with an adhesive carbon disk.25 The SEM mounts were subsequently lightly tapped to remove loose macro-particles and to avoid the loss of sample during SEM analysis. The samples were analyzed using a FEI Quanta 600 FEG scanning electron microscope. Voltages ranging from 15 to 20 kV were used to acquire the images. The secondary electron (SE) detector was used to acquire images of the samples. Other detectors may also be used when performing SEM analysis. Nonetheless, secondary electron images were found to reveal morphological features that were most easily segmented for statistical analysis. Within each sample, at least 4 macro-particles were imaged per sample at varying magnifications to ensure reproducibility within each sample. As the magnification of the electron microscope was increased, a larger number of images were taken to capture more surface area for statistical analysis. As the macro-particle size varied with calcination temperature, the magnification of the electron microscope varied between 15 000× and 40 000× to ensure that the unique morphologies could be quantified. Five images were acquired of each of the macro-particles within 15 000× and 40 000× magnification range. Particle Segmentation. Segmentation of the acquired SEM images was done using the MAMA software developed at Los Alamos National Laboratory26 to assign a defined boundary to the particles present in an image. To establish the scale for the SEM image, the horizontal field width (HFW) from the SEM image was used. The MAMA software segments particles using a modified “tunable” watershed algorithm, which views the image as one with topographical relief and subsequently segments based on traditional watershedding techniques.27−29 The smoothest setting was used for microparticle segmentation. Segmentation enables the quantitative identification of particle

conditions, starting material, synthesis route, and calcination temperatures of UO2.8−17 Outside of nuclear fuels research, Lloyd et al. produced an extensive study on the morphological properties of U oxides in the environment.18 They investigated the combustion of scrap depleted U-metal and formation of U-oxides in the environment using scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDX) and microfocus extended X-ray absorption fine structure (μEXAFS) spectroscopy. This study revealed that most of the uraniferous particles were polycrystalline with a few spherical particles that are thought to have been formed from autothermic oxidation of melt droplets.18 However, it should be noted that analyses through this study were qualitative, with no feature quantitatively measured. More recent forensics work by Keegan et al. analyzed an uranium ore concentrate (UOC) sample of unknown origin that was obtained in a police raid in Australia.19 To identify and determine the provenance of the unknown sample Keegan et al. used several forensics methods including morphological analysis using both optical and electron microscopy. Ultimately, isotopic analysis of the unknown sample strongly suggested that the sample came from the Mary Kathleen Mine.19 The morphological analysis performed by Keegan et al. provided a qualitative analysis of the seized UOC sample compared to one from the Mary Kathleen Mine. Observed morphological differences between the samples were attributed to a difference in sample process history. In the Mary Kathleen mine study, the researchers state the fact that no quantitative data was available to allow them to discern whether the differences in surface morphology and microstructure were statistically significant. They suggested further work be pursued in developing quantitative data on particle size, aspect ratio, and other physical parameters of materials resulting from individual production processes using consistent feed materials.19 Studies by Tamasi et al. have shown morphological changes after storage in controlled temperature and humidity environments, with the U-oxide morphology dependent on the starting material.20−22 This study expands upon that work, by quantitatively determining microparticle size and circularity of U3O8 to illustrate distinct differences as a function of calcination temperatures. This is the first study to systematically probe morphology as a function of calcination temperature and to quantify the changes as a function of microparticle size and circularity. One of the goals of nuclear forensics is to be able to identify the processing history of an interdicted sample. Knowing the calcination temperature that a sample was produced at will help to isolate possible processing facilities in which the sample was made. This research will demonstrate the ability to quantitatively identify calcination temperature thus revealing useful information on a samples processing history. This process will benefit nuclear forensics by providing additional tools to the nuclear forensics community for statistical analysis of interdicted uranium oxide samples.

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EXPERIMENTAL SECTION Synthesis. Synthesis of α-U3O8 was based on previous work by Tamasi et al. and Sweet et al.22,23 A batch of amorphousUO3 (A-UO3) was prepared via the uranyl peroxide route and used as the starting material for synthesis of α-U3O8. In the uranyl peroxide route, uranyl nitrate hexahydrate (UO2(NO3)2· 6H2O, UNH) is dissolved in water, and a molar excess of 30% hydrogen peroxide H2O2 is added to the solution dropwise to B

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(reference 01-072-024634) were included to compare to the acquired data. According to literature, at temperatures exceeding 450 °C, A-UO3 will convert to the α-UO3 polymorph after which conversion to α-U3O8 begins.23,35 Figure 1D shows a development of a peak at 26.5° and 34.3°(2θ) on the 650 °C spectra which corresponds to the Miller indices of 2,0,0 and 2,0,1, respectively. These peaks are consistently present up to the 800 °C spectra. However, in the 600 °C spectra (Figure 1C) the 26.5° and 34.3°(2θ) peaks are absent and the α-UO3 reference34 shows that the 2,0,0 and 2,0,1 crystal planes are present at 46.4° and 51.6°(2θ). On the basis of the reference peaks and the development of peaks corresponding to Miller indices of 2,0,0 and 2,0,1, the transition from α-UO3 to α-U3O8 appears to be taking place in the 600 °C sample. This is supported by previously published research that shows by 650 °C, α-UO3 has completely converted to α-U3O8.31 The differences in the XRD spectra shown in Figure 1 are qualitative differences. In this study, quantitative XRD was not performed; hence it can only be qualitatively inferred that there is a difference between temperatures. Quantitative XRD analysis is quite rare due to the requirement for extensive calibration of the XRD instrument, using very carefully prepared and characterized standards, and multiple scans to develop a technique for the individual sample. As noted by Jenkins and Snyder, it often takes several days to a week to setup quantitative XRD analysis of a new sample.36 the XRD analysis coupled with an SEM analysis that allows for statistically relevant quantitative determinations. SEM morphological analysis is adding another tool to the nuclear forensic scientist’s toolkit that is complementary to existing techniques like XRD. Scanning Electron Microscopy (SEM) Image Analysis. Approximately 300 SEM images were acquired for image segmentation analysis. Collecting this large set of images ensured that a statistically relevant set of morphologies can be comparatively analyzed between each sample. For example, many of the macro-particles have microregions, which may be slightly different from the bulk of the macro-particle. With only a few images, it would not be possible to statistically distinguish between these anomalies. Figure 2 illustrates changes that were observed in the morphology of the α-U3O8 samples. These images were analyzed qualitatively based on a lexicon of descriptors developed by Tamasi et al. for the purpose of describing changes in uranium oxide materials under different synthesis environments.21,25 Using the flowsheet described by Tamasi et al., each of the samples in Figure 2 can be described as clumped/massive agglomerates. The microparticles are observed to be rounded/subrounded with subangular particles which are more prominent at 600 and 650 °C. As the calcination temperature increases to 800 °C, the number of rounded/subrounded grains increases and become more dominant. From 600 to 800 °C, there is an observable increase in the sphericity of the microparticles. At 600 °C there is a larger proportion of elongated low sphericity microparticles that appears to decrease as the calcination temperature increases. The microparticles are arranged in irregular clumps and the surfaces of the samples are somewhat rough. Bridging and grain growth, which are observed in Figure 2, is evidence of grain sintering. Sintering is the process where microparticles are bonded below the materials melting point. At 600 °C, sintering is observable, but it is more evident as the calcination temperature increases to 800 °C. These observed changes provide qualitative evidence that calcination temperature has an

attributes, such as particle area and circularity, from SEM images. Particle size and circularity data from MAMA were then processed using MATLAB for statistical analysis.

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RESULTS AND DISCUSSION Synthesis of α-U3O8. A synthesis temperature for α-U3O8 of 600 °C was chosen based on previous research by Hoekstra et al.30,31 They stated that UO3 will begin conversion to U3O8 at temperatures as low as 450−470 °C but complete conversion only occurs as the temperature reaches 650 °C. A temperature of 600 °C was chosen as the lower boundary to reflect this transition zone. The upper temperature of 800 °C was chosen based on Cordfunke and later Ackermann.32,33 These studies suggest that when U3O8 is heated above 800 °C, additional oxygen is driven off creating a substoichiometric U3O8−x. α-U3O8 p-X-ray Diffraction (XRD). Purity of the α-U3O8 samples, prepared from A-UO3 material, was confirmed by pXRD analysis. The spectra for each of the prepared α-U3O8 samples along with the A-UO3 source material are shown in Figure 1. In addition to the acquired p-XRD spectra, the peak locations for α-U3O8 (reference 01-074-21012) and α-UO3

Figure 1. Comparison of XRD Spectra of U3O8 synthesized at different temperatures. (A) Amorphous UO3 source material spectrum. (B) α-UO3 spectrum from Loopstra et al.1 (H) α-U3O8 spectrum from Ackermann et al.2 (C−G) Spectra of U3O8 synthesized at 600, 650, 700, 750, and 800 °C, respectively. At 600 °C only partial conversion of α-UO3 to α-U3O8 is observed as evident by the absence of the 26.5° and 34.3° (2θ) in the spectrum. For all other temperatures, the 26.5° and 34.3° (2θ), which corresponds to the Miller indices of the 2,0,0 and 2,0,2 crystal planes are observed indicating complete conversion to α-U3O8. C

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using the MAMA software. Figure S-11 shows increasing magnification on the bulk particles to reveal the discrete particles, which could be segmented. For example, microparticles in the images need to have clearly defined edges and not be sintered together to where the edges are not discernible. Figures S-12−S-14 show examples of microparticles, which could not be used for segmentation because of one of the reasons mentioned above. Images that met the requirements of the flowchart were then segmented using the MAMA software. Figure 3 is an example of an SEM image before and after

Figure 2. SEM images of α-U3O8 for temperature skews of 600−800 °C. As the temperature increases, there is increasing grain uniformity, surface uniformity, grain suturing, and grain rounding. In addition, there is a reduction in the intergrain porosity. Below 750 °C, the microparticles are primarily euhedral, angular grains; while at 750 °C and higher, the microparticles are primarily subrounded grains. This is supported by the quantitative analysis of circularity in which the higher temperatures are found to be more circular. Larger images at each temperature with annotations are available in Figures S-1−S-5.

impact on the morphology of the α-U3O8. Full images with additional annotation are available in Figures S-1−S-5 . Quantitative Analysis. To determine unique morphological signatures of the samples based on calcination temperature, a more quantitative approach is required. To achieve this, the images collected for analysis were processed using the MAMA particle segmentation software. An individual microparticle’s suitability for analysis was ascertained using a newly developed methodology. A flowchart, available in Figure S-6, was used to determine if a segmented microparticle could be used for analysis or excluded. The initial steps in the segmentation flowchart describe how an SEM image is chosen for segmentation. Figures S-7−S-10 show step-by-step examples of the image selection process for MAMA segmentation and how the image scale is set. After an image is selected for segmentation, discrete microparticles are segmented, based on the flowchart, for analysis. There are two major types of microparticles present in the α-U3O8 samples, discrete particles and sintered agglomerates. Both types of microparticles make up the majority of the particles available for segmentation and they are present throughout all of the samples imaged. The sintered agglomerate microparticles were not chosen for segmentation based on their irregularity and lack of clearly defined edges which makes them difficult to segment for particle analysis. The discrete microparticles were therefore chosen for segmentation because they represent a significant portion of the overall sample material and can be segmented

Figure 3. Example of an image before (A) and after (B) segmentation using MAMA. The scale bar is the same for both panels A and B. Only microparticles that passed the flowchart of requirements were used for statistical analysis.

segmentation with MAMA. From this image, many potential microparticles were not used for analysis, as they did not meet the criteria previously established in the flowchart. Additional examples of segmented SEM images are presented in Figures S15−S-17. After segmentation is complete, MAMA is used to calculate a range of morphological statistics using the open source computer vision library, OpenCV.37,38 For analysis of αU3O8, microparticle area and circularity were the morphological attributes found to be the most indicative of changes due to variations in temperature. The microparticle area attribute is calculated by a pixel count within the object boundary that is then indexed to the physical scale that is set at the beginning of segmentation.38 Circularity is calculated using the formula 4π × A /P 2 D

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for statistical relevance.41 Using this method, it was found that a minimum of 750 microparticles per calcination temperature were needed to statistically distinguish each calcination temperature based on microparticle area and circularity. For this study, over 1000 microparticles were isolated to ensure statistical relevance. To apply the K−S test, the microparticle distributions need to be converted to cumulative distribution functions Fn1(x) and Fn2(x), where n1 and n2 are the sample sizes of the respective distributions. The K−S test statistic (Dmax) is then calculated by finding the maximum absolute vertical distance between the two distribution functions.39,41

where A is the area of the microparticle and P is the calculated perimeter. The formula is normalized such that a circle will be found to have a circularity of one.38 Figure 4 illustrates the

Dmax = max x|Fn1(x) − Fn2(x)|

(2)

This can be observed in Figure 5, which is a comparison of distribution functions created from the 650 °C microparticle

Figure 5. Cumulative distribution functions created from the 650 °C microparticle area distribution with a sample size of 1083 microparticles, and at 700 °C with a sample size of 750 microparticles. The calculated maximum vertical displacement (Dmax) of 0.4335 is shown.

Figure 4. Microparticle distributions. Showing the density plots of both microparticle area (A) and circularity (B). These plots show that there is an increase in both area and in the circularity distributions as a function of temperature from 600 to 800 °C. However, in the microparticle circularity plot the 650, 700, and 750 °C distributions do not show a consistent increase in circularity as temperature increases.

distribution with a sample size of 1038 microparticles and at 700 °C with a sample size of 750 microparticles. The K−S test from Figure 5 is calculated using eq 2 and gives a Dmax of 0.4335. One way of evaluating the K−S test statistic, Dmax, is to compare it to a Dcritical value.39,42 If Dmax is greater than Dcritical, then the distributions can be considered statistically different. To determine Dcritical, the following equation is used

probability densities based on microparticle area and circularity. These plots show that there is an increase in both microparticle area and in the circularity distributions as a function of temperature from 600 to 800 °C. What cannot be determined from the density plots is if the separate distributions of microparticle area and circularity can be statistically distinguished from each other. To determine if a microparticle distribution is distinct from other microparticle distributions, a statistical analysis needs to be carried out. The Kolmogorov−Smirnov two-sample test (K−S test) is a nonparametric statistical test that was developed to test if two different distributions are statistically different or if they could be considered to be from the same distribution. The K−S test is applied by calculating a scalar statistic, the maximum vertical displacement, from the two microparticle distributions and comparing it to a critical value which is a function of the confidence level and the sample sizes of the distributions.39,40 The K−S test is discussed in detail by Young et al.39 Previously published research by Vigneau et al. developed a method based on earlier work by Kolmogorov40 to determine the minimum number of microparticles needed

Dcritical = c(α)

n1 + n2 n1n2

(3)

where c(α) is a coefficient based on the confidence level (α) and can be found in Young et al.39 The Dcritical value from Figure 5, calculated using eq 3 and a confidence interval of 99.0%, is 0.0781. Since Dmax is greater than Dcritical the two distributions are statistically different at the 99.0% confidence interval. In contrast, if only 100 microparticles of a 600 °C sample are compared to 100 microparticles at 650 °C at a 99.0% confidence interval, then the Dmax value is 0.22 and the Dcritical value is 0.23, indicating that these two microparticle distributions would not be statistically different. For this method to be of benefit to the nuclear forensics community, there is a need to determine how many microparticles are required to ensure the desired level of confidence. Hence, full microparticle distributions at one E

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Figure 7. Comparison of different microparticle circularity distributions to the full set of microparticles at 750 °C. For all sample sizes of 100, 250, 500, and 750 microparticles, only the sample at 600 °C was statistically different from the 750 °C full sample set at the 99% confidence. The 800 °C samples at 500 and 750 microparticles was also statistically different from the 750 °C full sample set. All reported error is from 3σ of the Dmax value calculated from the random sample sets.

Figure 6. Comparison of different microparticle area distributions to the full set of microparticles at 750 °C. For all sample sizes of 100, 250, 500, and 750 microparticles, the samples at 600, 650, and 800 °C were statistically different from the 750 °C full sample set at the 99% confidence. However, the 700 °C was only statistically different at the 750 microparticle sample size. All reported error is from 3σ of the Dmax value calculated from the random sample sets.

shown in Figure 7. The larger the temperature gap, the easier that it is to statistically distinguish between the microparticle distributions, but close temperatures, even with large microparticles distributions, are not statistically distinguishable at the 99% confidence interval. This data reveals that microparticle distributions based on circularity have limited conditions in which they can be of use in nuclear forensics investigations of U3O8 morphology. Conversely, the microparticle area distributions do provide (at a microparticle distribution size of 750 or greater) a statistically significant method for distinguishing microparticle distributions at a confidence interval of 99.0%.

shows an example where the full microparticle distribution at 750 °C is compared to sample distributions of 100, 250, 500, and 750 microparticles at 600, 650, 700, and 800 °C. As seen in the figure, regardless of the sample size, the microparticle distributions at 600, 650, and 800 °C are always statistically different than the full microparticle distribution at 750 °C. With fewer microparticles, the variation of the Dmax value is greater but still not within error of the Dcritical value. In contrast, it was not possible to statistically distinguish between the microparticle distributions at 700 and 750 °C unless 750 microparticles were used. When performing the same analysis on each temperature (e.g., comparing the full microparticle distribution to smaller random sets of microparticles from the other temperatures), the same trend was observed. 600, 650, and 800 °C were statistically different from each other at all populations sizes, but to statistically distinguish between 700 and 750 °C, at least 750 microparticles were needed. For plots of each of the other calcination temperatures comparing randomly selected sample sets to the full distribution, see Figures S-13−S-23. Using this same analysis for circularity, it was not possible to reliably and statistically distinguish differences between all sample distributions using the K−S test. Figure 7 shows an example where the full microparticle distribution at 750 °C is compared to sample distributions of 100, 250, 500, and 750 microparticles at 600, 650, 700, and 800 °C. From this figure, it is possible to distinguish the microparticles at 750 °C from the 600 °C microparticle distributions at all sample sizes. The 800 °C microparticle distribution is only statistically different from the 750 °C microparticle distribution when 500 or more microparticles are compared. However, 650 and 700 °C cannot be distinguished from 750 °C at a confidence interval of 99.0%. Figures S-22−S-25 contain additional plots showing the circularity distributions at each of the other calcination temperatures. These figures show a similar trend to that

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CONCLUSION Techniques are needed that reveal novel signatures in nuclear forensics. Morphology has been investigated in the past, but tools were lacking to provide the quantitative characterization needed for forensics analysis. This report provides techniques and methods for acquiring that quantitative morphology. Through the use of particle segmentation software and the K−S test, samples of α-U3O8 prepared at different calcination temperatures were statistically distinguished based on microparticle size distributions and circularity. This provides a new means of identifying processing history of U-oxides in nuclear forensic investigations.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b05020. SEM image of α-U3O8 synthesized at 600, 650, 700, 750, and 800 °C, particle segmentation flowchart, edge clarity example, large feature example, particle scale ratio, setting the scale, discrete particle representation, clearly defined edges example, sintered particle example, stacked or overlapping particles, and MAMA results, comparison of particle area distributions at 600, 650, 700, and 800 °C, F

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(19) Keegan, E.; Kristo, M. J.; Colella, M.; Robel, M.; Williams, R.; Lindvall, R.; Eppich, G.; Roberts, S.; Borg, L.; Gaffney, A.; et al. Forensic Sci. Int. 2014, 240, 111−121. (20) Tamasi, A. L.; Cash, L. J.; Mullen, W. T.; Ross, A. R.; Ruggiero, C. E.; Scott, B. L.; Wagner, G. L.; Walensky, J. R.; Zerkle, S. A.; Wilkerson, M. P. J. Radioanal. Nucl. Chem. 2016, 309 (2), 827−832. (21) Tamasi, A. L.; Cash, L. J.; Eley, C.; Porter, R. B.; Pugmire, D. L.; Ross, A. R.; Ruggiero, C. E.; Tandon, L.; Wagner, G. L.; Walensky, J. R.; et al. J. Radioanal. Nucl. Chem. 2016, 307 (3), 1611−1619. (22) Tamasi, A. L.; Boland, K. S.; Czerwinski, K.; Ellis, J. K.; Kozimor, S. A.; Martin, R. L.; Pugmire, A. L.; Reilly, D.; Scott, B. L.; Sutton, A. D.; et al. Anal. Chem. 2015, 87 (8), 4210−4217. (23) Sweet, L. E.; Henager, C. H.; Hu, S.; Johnson, T.; Meier, D.; Peper, S. M.; Schwantes, J. M. Investigation of Uranium Polymorphs; Pacific Northwest National Laboratory, 2011. (24) Sato, T. J. Appl. Chem. Biotechnol. 1976, 26 (1), 207−213. (25) Tamasi, A. L.; Cash, L. J.; Tyler Mullen, W.; Ross, A. R.; Ruggiero, C. E.; Scott, B. L.; Wagner, G. L.; Walensky, J. R.; Zerkle, S. A.; Wilkerson, M. P. J. Radioanal. Nucl. Chem. 2016, 309 (2), 827− 832. (26) Ruggiero, C.; Bloch, J. MAMA Morphological Analysis of Materials; Los Alamos National Laboroatory, 2016. (27) Ruggiero, C.; Ross, A.; Porter, R. In Segmentation and Learning in the Quantitative Analysis of Microscopy Images, SPIE/IS&T Electronic Imaging, 2015; International Society for Optics and Photonics, 2015; pp 94050L−94050L-9. (28) Porter, R.; Ruggiero, C.; Hush, D.; Harvey, N.; Kelly, P.; Scoggins, W.; Tandon, L. In Interactive Image Quantification Tools in Nuclear Material Forensics, IS&T/SPIE Electronic Imaging, 2011; International Society for Optics and Photonics, 2011; pp 787708− 787708−9. (29) Meyer, F. Signal processing 1994, 38 (1), 113−125. (30) Thein, S.; Bereolos, P. Thermal Stabilization of 233UO2, 233UO3 and 233U3O8, ORNL/TM-2000/82; Oak Ridge National Laboratory: Oak Ridge, TN, 2000. (31) Hoekstra, H. R.; Siegel, S. J. Inorg. Nucl. Chem. 1961, 18, 154− 165. (32) Cordfunke, E. The Chemistry of Uranium; Elsevier: Amsterdam, 1969. (33) Ackermann, R.; Chang, A. T. J. Chem. Thermodyn. 1973, 5 (6), 873−890. (34) Loopstra, B.; Cordfunke, E. Recueil des Travaux Chimiques des Pays-Bas 1966, 85 (2), 135−142. (35) Cordfunke, E. H. P. J. Inorg. Nucl. Chem. 1961, 23 (3), 285−286. (36) Synder, R. Introduction to X-ray Powder Diffractometry; John Wiley & Sons Publication, 1996. (37) Ruggiero, C. E.; Porter, R. MAMA User Guide v2.1; LA-UR-1625116 United States 10.2172/1291192 LANL English; Los Alamos National Laboratory (LANL): Los Alamos, NM, 2016; p 46. (38) Ruggiero, C. E.; Porter, R. B. MAMA Software Features: Attribute Summary; Los Alamos National Laboratory (LANL): Los Alamos, NM, 2016. (39) Young, I. T. J. Histochem. Cytochem. 1977, 25 (7), 935−941. (40) Kolmogoroff, A. Ann. Math. Stat. 1941, 12 (4), 461−463. (41) Vigneau, E.; Loisel, C.; Devaux, M.; Cantoni, P. Powder Technol. 2000, 107 (3), 243−250. (42) Hodges, J. Arkiv för Matematik 1958, 3 (5), 469−486.

comparison of particle circularity distributions at 600, 650, 700, and 800 °C (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 1-801-581-7768. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work is supported by the U.S. Department of Homeland Security, Domestic Nuclear Detection Office, under Grant Award Number 2015-DN-077-ARI092. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. This work made use of University of Utah Shared facilities of the Surface Analysis and Nanoscale Imaging Group sponsored by the College of Engineering, Health Sciences Center, Office of the Vice President for Research, and the Utah Science Technology and Research (USTAR) Initiative of the State of Utah.

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DOI: 10.1021/acs.analchem.6b05020 Anal. Chem. XXXX, XXX, XXX−XXX