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Exchange of oxygen stable isotopes (δ18O values) between precipitation waters and uranium oxides is governed by thermodynamics or kinetics. It has been assumed that meteoric waters can be related to precipitation waters in uranium ore concentrates and their calcined and reduced uranium oxide products. With this assumption, the δ18O values of uranium materials could provide forensic signatures that identify the production history and geolocation of nuclear materials. To further exploit the potential of δ18O values in nuclear material analysis, this study examines the oxygen stable isotope exchange in two UOCs, magnesium diuranate (MDU) and sodium diuranate (SDU). MDU and SDU were synthesized from solutions of uranyl nitrate hexahydrate using precipitation waters with unique oxygen isotope compositions. The structures of the MDU and SDU were analyzed using powder X-ray diffraction (p-XRD) and thermal mass loss curves, while the δ18O values of waters generated during thermal decomposition were analyzed using a thermogravimetric analyzer coupled to an isotope ratio infrared spectrometer (TGA-IRIS). By p-XRD, the MDU was uniform and amorphous across all syntheses with residual crystalline material incorporated as a minor component. Combined with the TGA results, all of the MDU is likely amorphous MgU2O7·3H2O with MgO impurities present throughout. In contrast, the SDU synthesis resulted in multiple phases with many samples exhibiting crystalline phases including a combination of Na(UO2)4O2(OH)5·5H2O and Na2(UO2)6O4(OH)6·8H2O with a Na2U2O7 minor phase. A small fraction of the SDU samples were amorphous with no crystalline XRD peaks observed. Mass loss curves of the SDU samples revealed that the amorphous samples contained inclusions of similar crystalline phases compared to the crystalline materials. The uniformity of the MDU samples enabled highly reproducible measurements of δ18O values of the water vapor yielded from two dehydration events at 170 °C and 500 °C. In contrast, the multiphase composition of the SDU samples resulted in poor reproducibility in δ18O values. Neither system revealed any correlation between the δ18O values of precipitation water and the waters released during dehydration of the UOCs. [ABSTRACT FROM AUTHOR]

In the present study, surface morphological differences of mixtures of triuranium octoxide (U3O8), synthesized from uranyl peroxide (UO4) and ammonium diuranate (ADU), were investigated. The purity of each sample was verified using powder X-ray diffractometry (p-XRD), and scanning electron microscopy (SEM) images were collected to identify unique morphological features. The U3O8 from ADU and UO4 was found to be unique. Qualitatively, both particles have similar features being primarily circular in shape. Using the morphological analysis of materials (MAMA) software, particle shape and size were quantified. UO4 was found to produce U3O8 particles three times the area of those produced from ADU. With the starting morphologies quantified, U3O8 samples from ADU and UO4 were physically mixed in known quantities. SEM images were collected of the mixed samples, and the MAMA software was used to quantify particle attributes. As U3O8 particles from ADU were unique from UO4, the composition of the mixtures could be quantified using SEM imaging coupled with particle analysis. This provides a novel means of quantifying processing histories of mixtures of uranium oxides. Machine learning was also used to help further quantify characteristics in the image database through direct classification and particle segmentation using deep learning techniques based on Convolutional Neural Networks (CNN). It demonstrates that these techniques can distinguish the mixtures with high accuracy as well as showing significant differences in morphology between the mixtures. Results from this study demonstrate the power of quantitative morphological analysis for determining the processing history of nuclear materials. [ABSTRACT FROM AUTHOR]

This study aims to determine forensic signatures for processing history of UO2 based on modifications in intermediate materials within the uranyl peroxide route. Uranyl peroxide was calcined to multiple intermediate U-oxides including Am-UO3, α-UO3, and α-U3O8 during the production of UO2. The intermediate U-oxides were then reduced to α-UO2 via hydrogen reduction under identical conditions. Powder X-ray diffractometry (p-XRD) and X-ray photoelectron spectroscopy (XPS) were used to analyze powders of the intermediate U-oxides and resulting UO2 to evaluate the phase and purity of the freshly synthesized materials. All U-oxides were also analyzed via scanning electron microscopy (SEM) to determine the morphology of the freshly prepared powders. The microscopy images were subsequently analyzed using the Morphological Analysis for Materials (MAMA) version 2.1 software to quantitatively compare differences in the morphology of UO2 from each intermediate U-oxide. In addition, the microscopy images were analyzed using a machine learning model which was trained based on a VGG 16 architecture. Results show no differences in the XRD or XPS spectra of the UO2 produced from each intermediate. However, results from both the segmentation and machine learning proved that the morphology was quantifiably different. In addition, the morphology of UO2 was very similar, if not identical, to the intermediate material from which it was prepared, thus making quantitative morphological analysis a reliable forensic signature of processing history. [ABSTRACT FROM AUTHOR]

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. This review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics. [ABSTRACT FROM AUTHOR]

Many commercial processes exist for converting uranium from ore to the desired uranium compound for use in nuclear power or nuclear weapons. Accurately determining the processing history of the uranium ore concentrates (UOCs) and their calcination products, can greatly aid a nuclear forensics investigation of unknown or interdicted nuclear materials. In this study, two novel forensic signatures, based on nuclear materials synthesis, were pursued. Thermogravimetric analysis – mass spectrometry (TGA-MS) was utilized for its ability to discern UOCs based on mass changes and evolved gas species; while scanning electron microscopy (SEM), in conjunction with particle segmentation, was performed to identify microfeatures present in the calcination and reduction products (i.e. UO3, U3O8, and UO2) that are unique to the starting UOC. In total, five UOCs from common commercial processing routes including: ammonium diuranate (ADU), uranyl peroxide (UO4), sodium diuranate (SDU), uranyl hydroxide (UH), and ammonium uranyl carbonate (AUC), were synthesized from uranyl nitrate solutions. Samples of these materials were calcined in air at 400 °C and 800 °C. The 800 °C calcination product was subsequently reduced with hydrogen gas at 510 °C. The starting UOCs were investigated using TGA-MS; while SEM quantitative morphological analysis was used to identify signatures in the calcination products. Powder X-ray diffractometry (p-XRD) was used to identify the composition of each UOC and the subsequent calcination products. TGA-MS of the starting UOCs indicate temperature-dependent dehydration, volatilization, and reduction events that were unique to each material; thus making this a quantifiable signature of the initial material in the processing history. In addition, p-XRD, in conjunction with quantitative morphological analysis, was capable of discriminating calcination products of each processing history at the 99 % confidence level. Quantifying these nuclear material properties, enables nuclear forensics scientists to better identify the origin of unknown or interdicted nuclear materials. [ABSTRACT FROM AUTHOR]

Electrospray ionization-mass spectrometry (ESI-MS) was tested for its use in monitoring spent nuclear fuel (SNF) constituents including U, Pu, dibutyl phosphate (DBP), and tributyl phosphate (TBP). Both positive and negative ion modes were used to evaluate the speciation of U and Pu with TBP and DBP. Furthermore, apparent stability constants were determined for U complexed to TBP and DBP. In positive ion mode, TBP produced a strong signal with and without complexation to U or Pu, but, in negative ion mode, no TBP, U-TBP, or Pu-TBP complexes were observed. Apparent stability constants were determined for [UO2(NO3)2(TBP)2], [UO2(NO3)2(H2O)(TBP)2], and [UO2(NO3)2(TBP)3]. In contrast DBP, U-DBP, and Pu-DBP complexes were observed in both positive and negative ion modes. Apparent stability constants were determined for the species [UO2(DBP)], [UO2(DBP)3], and [UO2(DBP)4]. Analyzing mixtures of U or Pu with TBP and DBP yielded the formation of ternary complexes whose stoichiometry was directly related to the ratio of TBP to DBP. The ESI-MS protocols used in this study will further demonstrate the utility of ESI-MS and its applicability to process control monitoring in SNF reprocessing facilities. [ABSTRACT FROM AUTHOR]

Electrospray ionization-mass spectrometry (ESI-MS) shows great promise as a rapid method to identify metal-ligand complexes in solution. However, its application for quantitative determination of the distribution of species present in complicated equilibria is still in its infancy, and a direct correlation between ions observed in the gas phase and species expected in solution must be made with caution. The present work focuses on a seemingly simple system; the complexation of lanthanide cations with the acetate ligand. Using a high resolution quadrupole time-of-flight mass spectrometer, ions created by electrospray of solutions containing trivalent neodymium and acetate were identified. The gas phase distribution of species was compared to the solution phase speciation predicted using thermodynamic complexation constants. Apparent gas phase speciation diagrams were constructed as a function of solution conditions and fragmentation potential. Despite the expected variability of metal-ligand complexes as solution conditions change, the observed gas phase speciation was independent of the metal to ligand ratio but dependent on the operating conditions of the ESI-MS. [ABSTRACT FROM AUTHOR]

As the capabilities of convolutional neural networks (CNNs) for image classification tasks have advanced, interest in applying deep learning techniques for determining the natural and anthropogenic origins of uranium ore concentrates (UOCs) and other unknown nuclear materials by their surface morphology characteristics has grown. But before CNNs can join the nuclear forensics toolbox along more traditional analytical techniques – such as scanning electron microscopy (SEM), X-ray diffractometry, mass spectrometry, radiation counting, and any number of spectroscopic methods – a deeper understanding of "black box" image classification will be required. This paper explores uncertainty quantification for convolutional neural networks and their ability to generalize to out-of-distribution (OOD) image data sets. For prediction uncertainty, Monte Carlo (MC) dropout and random image crops as variational inference techniques are implemented and characterized. Convolutional neural networks and classifiers using image features from unsupervised vector-quantized variational autoencoders (VQ-VAE) are trained using SEM images of pure, unaged, unmixed uranium ore concentrates considered "unperturbed." OOD data sets are developed containing perturbations from the training data with respect to the chemical and physical properties of the UOCs or data collection parameters; predictions made on the perturbation sets identify where significant shortcomings exist in the current training data and techniques used to develop models for classifying uranium process history, and provides valuable insights into how datasets and classification models can be improved for better generalizability to out-of-distribution examples. • CNN uncertainty estimated by multiple random image crops plus Monte Carlo dropout. • Research needs to improve U morphology nuclear forensics datasets were identified. • Classification performance dropped when predicting aged or impure materials. • Models were somewhat sensitive to which SEM parameters used for data collection. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms controlling the morphology of uranium ore concentrates (UOCs) is impactful for both the nuclear forensics and nuclear fuels communities. In forensics, this understanding enables predicting the morphology of materials to help determine provenance and processing history. In fuels, this understanding aids in optimizing the morphology for higher fuel efficiency. To elucidate the influence of thermodynamics on the morphology of UOCs, uranyl peroxide, specifically metastudtite (UO 2 O 2 ·2H 2 O), was precipitated from solutions of varying complexing strength to the uranyl ion. Specifically, metastudtite was precipitated from solutions of equal ionic strength of uranyl nitrate and uranyl chloride, where the nitrate ion has greater complexing strength with the uranyl ion. Following precipitation, the metastudtite was calcined to U 3 O 8 , and the phase purity of each sample was confirmed by powder X-ray diffraction (p-XRD). The surface features were characterized using scanning electron microscopy (SEM), and the morphology was quantified using the Morphological Analysis for Materials (MAMA) software. Nanoparticles of metastudtite precipitated from uranyl nitrate, which has the higher complexing strength, were larger, more angular, and more elongated than nanoparticles produced by precipitation from uranyl chloride. Overall, a more rigid morphology was produced when replacing the nitrate with the peroxide while more rounded particles were produced when replacing a chloride with the peroxide. Even after sintering at high temperatures, differences in morphology between the two routes were still present in the U 3 O 8. [ABSTRACT FROM AUTHOR]

• Sulfuric acid leaching and commercial solvent extraction processes were used to synthesize uranium ore concentrates (UOC). • Impurities were identified via inductively couple plasma mass spectrometry. • Surface morphology features were quantified by particle segmentation techniques. • UOCs produced by different solvent extraction routes had unique impurity and morphology signatures. The effects of process history on the particle morphology of uranium ore concentrates (UOC) synthesized from natural uranium ores were investigated. A purified UOC was extracted from a uranium-rich ore by sulfuric acid leaching and purified by two commercial solvent extraction processes: the Dapex process using di-(2-ethylhexyl) phosphoric acid (DEHPA), and the Amex process using Alamine® 336. The UOC from either route was precipitated from its respective strip solution by ammonia and calcined. Powder X-ray diffraction (P-XRD) was used to confirm the crystallography of the UOC; while Rietveld refinement was performed for quantification of U oxide phase composition. Inductively coupled plasma - mass spectrometry (ICP-MS) was used to quantify impurity concentrations of the calcined material. As expected, different impurities were present due to processing via the Dapex and Amex process. To further correlate the impurities to the process history, particle morphology was quantified from scanning electron microscopy (SEM) micrographs using the MAMA segmentation software. In addition, a deep neural network was trained on the SEM images to distinguish between process histories. The results provide a real-world representation of how particle morphology will likely change based on processing at commercial facilities as opposed to high purity material experiments in a laboratory setting. [ABSTRACT FROM AUTHOR]