Engineering topics

Schwerdt, Ian J.

Publications and source records attributed to Schwerdt, Ian J..

8 records

DOE OSTI2024

Optical vibrational spectroscopic signatures related to U 3 O 8 production processes

Uranium ore concentrates are materials found early within the nuclear fuel cycle and contain high concentrations of uranium in an easily transported form, making the concentrates a likely target for illegal diversion. These concentrates are typically converted to U 3 O 8 for further processing and, therefore, may lose specific physicochemical characteristics in determining the materials’ source and processing history. In this work, we explore the Raman spectra of eight oxide samples produced from various uranium ore concentrates and processing pathways to examine the presence of spectroscopic signatures relating to each sample's process history. Samples produced from amine extraction and dialkylphosphoric acid extraction processes show unique characteristics due to high concentrations of α-UO 3 , whereas samples calcinated from metallic diuranates do not form pure α-U 3 O 8 because of metallic ion inclusions. Pure α-U 3 O 8 oxide samples are obtained through calcination of ammonium diuranate, ammonium uranyl carbonate, and metastudtite intermediates. The Raman spectra of these oxide samples show close agreement with pristine α-U 3 O 8 spectra. However, deviations from the pristine spectra are observed in the 300–460 cm -1 spectral range. In conclusion, these deviations are unique identifying signatures that were likely created by lasting effects from the process history. Spectral center of mass calculations indicate grouping of samples based on processing history.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Morphology and particle size (MaPS) exercise: testing the applications of image analysis and morphology descriptions for nuclear forensics

Image analysis techniques have been applied and shown to be a valuable tool in nuclear forensics analysis. The interlaboratory exercise reported here has tested quantitative and qualitative approaches for characterizing nuclear materials. Particle size, surface features and morphology descriptions were compared by four laboratories on a common image set generated by Scanning Electron Microscopy and Digital Light Microscopy. Quantitative analysis of the image sets through the Morphological Analysis for MAterials software highlighted the strength of image analysis, but also that the application of the software alone can introduce significant bias in the analysis. Qualitative morphology descriptions following the process outlined by Tamasi et al. (J Radioanal Nuclear Chem 307:1611–1619, 2015) were compared with a discussion on the robustness and reproducibility of the results. Finally, future work should continue to focus on proficiency and standardization of image analysis through continued exercises within the extended nuclear forensics community.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Review of multi-faceted morphologic signatures of actinide process materials for nuclear forensic science

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. In conclusion, this review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Investigation of process history and underlying phenomena associated with the synthesis of plutonium oxides using Vector Quantizing Variational Autoencoder

Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called a Vector Quantizing Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology to attain >85% accuracy in identifying uranium oxide processing routes. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO 2 ) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO 2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO 2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE, then further reduce the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO 2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Solubility controls on plutonium and americium release in subsurface environments exposed to acidic processing wastes

To identify the role of waste composition and sediment interactions in controlling Pu and Am mobility in contaminated sediments at Hanford, a legacy nuclear site, Pu and Am concentrations in solutions equilibrated with contaminated sediments from beneath the 216-Z-9 (Z-9) Trench were compared to the solubilities of PuO 2 materials, synthesized by methods representative of the disposed wastes, in the absence of sediments. Furthermore, this work shows that the solubilities of PuO 2 materials synthesized by different methods, and with varying particle sizes, agree with PuO 2 (am,hyd), although dissolution kinetics differed between materials. According to saturation index (SI) calculations, PuO 2 (am,hyd) is likely also controlling Pu release from sediments under conditions where phosphate concentrations are low. However, both Pu-phosphate and Am-phosphate phases, identified in SI calculations and by high resolution transmission electron microscopy, play roles in controlling release in low pH, high phosphate, shallow sediments just below the Z-9 Trench. The elevated phosphate is likely due to decomposition of tributyl phosphate from waste solutions over time. Sediments from deeper in the subsurface beneath the Z-9 Trench are less acidic and contain less phosphate, with Pu solubility likely controlled by PuO 2 (am,hyd) that precipitated following neutralization of the acidic waste stream. Controls on Am concentrations in deeper sediments are more complex and potentially involve sediment adsorption and/or release from Pu (1-x) Am x O 2 following Am in-growth. The concentrations of both Pu and Am were elevated in the colloidal fraction associated with shallow sediments, but not in PuO 2 experiments, suggesting the presence of Pu/Am pseudocolloids (e.g., Pu/Am associated with mineral colloids). However, Pu and Am association with the colloidal size fraction was not observed in deeper sediments, suggesting transport of Pu and Am to these depths beneath the Z-9 Trench was not due to colloidal transport.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Quantitative Morphological Characterization of Carbide Inclusions in Uranium Metal

Uranium carbides (UCs) are prevalent inclusions in U metal that form during melting operations from interactions with crucible walls and the casting chamber atmosphere. Although UCs have been studied extensively since the beginning of U metal foundry operations, there are still unknowns regarding the effects of thermomechanical processing on their sizes and morphology. Here, we present the results of a series of controlled cooling experiments with molten uranium to elucidate the effect of cooling rate on inclusion morphology in a-U. Samples were melted using a vacuum induction melter and manually cooled at rates of 2.5, 1.7, 1.1, 0.8, 0.3 (± 1%) K/s from ~1600 K to <700 K in under 1 hour. Subsequent scanning electron microscopy (SEM) was performed on cross-sections of the samples, revealing a complex mixture of UC morphologies that are indicative of diffusion and growth influenced by the thermal processing of the U matrix. Image analysis using the Morphological Analysis of Materials (MAMA) software showed that UC sizes generally grew larger with slower cooling rates, and the two slowest cooling rates noticeably impacted the inclusion circularity and ellipse aspect ratio. These results indicate that UC morphology is sensitive to short cooling rates (<1 hour) and could therefore be controlled in the production of metallic nuclear fuels. Additionally, inclusion speciation and morphologies could potentially provide forensic clues about processing history of unknown metal samples. Understanding the driving forces involved in UC morphology evolution is beneficial for evaluating metal fuels for next generation nuclear reactors and for identifying signatures for nuclear forensics.

Athon, Matthew T.↗

DOE OSTI2021

Effects of Casting Parameters and Impurity Concentrations on As-Cast U–10Mo

This work investigates the effects of casting parameter changes on as-cast uranium-10 wt% molybdenum alloys on a 1-2kg scale. A commercial tilt-pour vacuum induction melting system was utilized to study the effects of casting atmosphere, total heating time, hold time at maximum temperature, mold temperature, and crucible type on the as-cast microstructure of U-10Mo. Ten thin plates (5.08 mm thick) and four thick plates (25.4 mm and 63.5 mm thick) were cast and analyzed for carbon, oxygen, hydrogen, and nitrogen impurities. Additionally, molybdenum content and primary dendrite length (PDL) of the microstructure was quantified. A statistical evaluation was performed to identify key carbon and oxygen relationships with operating parameters, impurities, and primary dendrite length. Additional factors investigated include: crucible material, time at maximum temperature, total heating time, and pour temperature. Zirconia crucibles reduced carbon content on average by 35%. Additionally, there was a significant statistical association between carbon and oxygen content, between oxygen content and maximum temperature hold time, between mold temperature and PDL, and between PDL, maximum temperature hold time and mold temperature.

Huber, Zachary F.↗

DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗