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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ultrafast Electron Microscopy for Spatial-Temporal Mapping of Charge Carriers

This LDRD supported efforts to significantly advance the scanning ultrafast electron microscope (SUEM) for spatial-temporal mapping of charge carrier dynamics in semiconductor materials and microelectronic devices. Sandia's SUEM capability in Livermore, CA, was built and demonstrated with previous LDRD funding; however, the stability and usability of the tool limited the throughput for analyzing samples. A new laser alignment strategy improved the stability of the SUEM, and the design and characterization of a new micro-channel plate (MCP)- based detector improved the signal-to-noise of the SUEM signal detection. These enhancements to the SUEM system improved throughput by over two orders of magnitude (before, a single time series of SUEM measurements would take several days to several weeks to acquire; now, the same measurements can be completed in~90 minutes in an automated fashion). The SUEM system can now be routinely used as an analytical instrument and will be a central part of several multi-year projects starting in FY22.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Quantifying the Role of APB Tubes on the Work-hardening of Ordered Phases

For sixty years, it has been theorized that antiphase boundary (APB) tubes, which are defects produced by deformation, contribute to the high strength of ordered alloys or intermetallic compounds. The concept of an APB tube was originally proposed by Vidoz and Brown [A.E. Vidoz, L.M. Brown, "On work-hardening in ordered alloys", Philosophical Magazine, 7 (1962) 1167–1175] in 1962 to explain the ~50% increase in strength observed when an alloy, such as Ni 3 Fe, is chemically ordered compared to when it is disordered. The existence of APB tubes was only definitively confirmed by Chou and Hirsch in 1981 [C.T. Chou, P.B. Hirsch Antiphase domain boundary tubes in plastically deformed ordered Fe 30.5 at.% Al alloy, Philosophical Magazine, A. 44 (1981) 1415–1419] using a transmission electron microscope (TEM). Prior to the current work, there had been no studies trying to correlate the presence of APB tubes with their effect on the mechanical properties. The main finding of this project was that APB tubes do not contribute to the strength of the strongly-ordered intermetallic compounds FeAl and Ni 3 Al. This was shown by removing the APB tubes by heating the materials and showing that the mechanical properties were unaffected. In addition, in-situ deformation experiments were undertaken in a TEM on both disordered and ordered Ni 3 Fe, in which it was shown that defects (superdislocations) in the ordered alloy underwent many more interaction events than defect (dislocations) in the disordered alloy. This difference can explain the increase in the increase in strength observed when this material is ordered. The project supported a PhD student, Rachel Osmundsen, who successfully defended her thesis in April, 2022. It also supported the principal investigator, Ian Baker, two PhD exchange students and an undergraduate researcher. The work involved collaborations with the following people and use of the following facilities: Dr. Si Chen, Advanced Photon Source at Argonne National Laboratory, for synchrotron X-ray diffraction; and Dr. Katherine Shanks and Dr. Kelly Nygren, Cornell High Energy Synchrotron Source (CHESS), for synchrotron X-ray diffraction. The work led to 12 papers published in refereed journals with two papers currently submitted to journals. There were also 16 presentation made at conferences and universities on the work, ten of which were invited.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2022

Cracking Behavior of Irradiated Ex-Plant Materials

The cracking behavior of reactor core internal materials is a key factor for the long-term safety and availability of light water reactors. Subject to high-temperature coolant, thermal and mechanical loading, and neutron irradiation, reactor core internal materials are susceptible to several degradation mechanisms during power operation. To understand the long-term effects of neutron irradiation, irradiated materials harvested from a decommissioned reactor were studied for their cracking susceptibility and fracture resistance. Small compact-tension specimens were machined from different locations on the baffle plates with irradiation doses ranging from 0.06 to 48.5 dpa. Crack growth rate and fracture toughness J-resistance (J-R) curve tests were performed on these specimens in a simulated light water reactor environment, and the fracture morphologies of the tested samples were examined with a scanning electron microscope. All samples behaved similarly under cyclic loading, and no deteriorated corrosion-fatigue behavior was observed in the test environments. Under constant load, no elevated cracking susceptibility was observed either in these samples at low stress intensity factors. However, a rapid cracking behavior featuring very high crack growth rates was observed in several high-dose samples. This rapid crack growth behavior was activated when the applied stress intensity factor, K, was sufficiently high. Once activated, the rapid crack growth remained steady even when the K was lowered to the pre-activation level and stopped only when the K was reduced drastically. Yet, the rapid crack growth could be re-activated repeatedly in these samples by raising the K above the initial activation level. This rapid cracking response may be related to the severe irradiation embrittlement experienced by these samples. The J-R curve tests performed with these samples showed a significant decline in fracture toughness with increasing neutron irradiation. Local cracking at the crack tip facilitated by the severe irradiation embrittlement of this material may be responsible for the rapid crack growth behavior demonstrated by this decommissioned material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Building better electron sources with graphene

This report is a press release and discusses how "Graphene substrates can be reused repeatedly for photocathodes that emit high brightness electron beams in electron microscopes and accelerators."

36 MATERIALS SCIENCE↗
DOE OSTI2022

Corrosion of U233-Doped Uranium Oxide using Microfluidics Methods

The aim of work this year has been to investigate the role of alpha (α)-radiation and hydrogen (H 2 ) on the corrosion of uranium oxide (UO 2 ) using a microfluidic device. The microfluidic device, termed the Particle-Attached Microfluidic Electrochemical Cell, (PAMEC), enables monitoring of the UO 2 electrochemical corrosion potential (E corr ) under different environments, including de-aerated conditions and in the presence of dissolved H 2 . The Si 3 N 4 window allows us to study morphological and chemical changes under an electron microscope. We have previously demonstrated that the PAMEC matches the results from bulk electrochemical tests with UO 2 [1]. We used the high specific activity uranium (U) isotope, 233 U, (t 1/2 = 160,000 years) incorporated into UO 2 , to generate a localized a-field. The objective of the experiments were to mimic the radiation environment that would be experience at the surface of aged spent nuclear fuel (SNF) during long-term geologic disposal under anoxic conditions. Wittman et al. [2] predicted that in the presence of a pure a-radiation field and under H 2 conditions, the concentration of the radiolytic oxidant H 2 O 2 would be suppressed or even eliminated. In a UO 2 corrosion experiment this would be exhibited through a lowering of the measured UO 2 corrosion potential compared to identical conditions in the absence of dissolved H 2 . We found that the predictions of Wittman and co-workers were supported and that the corrosion potential of the 233 U-doped UO 2 in solution lowered with presence of H 2 gas and increased in the absence of H 2 , when under anoxic conditions. The PAMEC experiments indicate that H 2 O 2 has been eliminated in a solution sparged with H 2 while exposed to an a-radiation field. The corrosion potential of the a-doped 233 U(10%)- 238 UO 2 in a solution sparged with Ar/H 2 matched the corrosion potential of 238 UO 2 in solution sparged with air. This clearly demonstrated that the H 2 O 2 had been eliminated and that the only oxidant present was O 2 in this system in complete agreement with the modeling results of Wittman et al. These results point to the need to improve the Fuel Matrix Degradation (FMD) Process Model training data set that is being used in the FMD surrogate model that is being developed for the repository program. The incorporation of realistic radiation chemistry will improve the scientific basis for the FMD Model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Building Neutron Scattering Infrastructure in Louisiana for Advanced Materials (Final Report)

The main objective of the Louisiana Consortium for Neutron Scattering (LaCNS) is to build a major neutron scattering infrastructure capable of treating both soft and hard materials. The goal is to create a sustainable effort having the quality, breadth, and depth necessary to produce competitive proposals including collaborative and center type proposals. Our scientific aim is to understand the role of coupling in emergent complex materials and its impact on the structure/property relationship and to explore how to tune the key couplings to guide the design of materials with the desired properties. This naturally includes building a base of users of the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) in Louisiana; to train highly talented graduate and post-doctoral students in synthesis, neutron scattering, and simulation and modeling techniques, thereby helping to produce the next generation of scientist who use neutron scattering techniques as a crucial part of their research. The goal of our hard matter program is to understand the interplay between spin, charge, orbital, and lattice degrees of freedom in carefully selected complex materials. We have made considerable progress on number of complex systems including the oxide Sr3(Ru1-xMnx)2O7 where short-range magnetic ordering with anisotropic spin texture is initiated at the metal-insulator transition that clearly indicates a strong spin-charge coupling. Another critical area is in quantum materials. For example, in the topological semimetal BaMnSb2, we found a 3D canted antiferromagnetic Weyl semimetal with a 2D electronic structure and a nontrivial Berry phase. In addition, in the hexagonal chiral Mn1/3NbS2 system, we found soliton/soliton and soliton/antisoliton domain walls where the application of small fields or small currents can be used to control nanoscopic magnetic domains where the control of domain walls is crucially important for information storage. In addition, chemical transformation investigations were performed on the VISION instrument at Spallation Neutron Source (SNS). The soft matter effort was focused on understanding the role of non-covalent interactions on the structure and dynamics of fluid-based soft matter. One key focus was on sequence-defined (SD) amphiphilic peptoid polymers that allow encoding of molecular interactions and thereby systematic investigations of how charge directs the solution self-assembly of amphiphilic polymers in water. Another important area was on the dynamics of lipids self-assembled into membranes for exploring the permeability and mechanical using both neutron spin echo (NSE) spectroscopy and quasielastic neutron scattering (QENS) to distinguished between viscoelasticity and permeability at the molecular scale. Overall, the LaCNS project was quite successful, generating 145 publications and 245 presentations. Our graduate and post-doctoral students were well trained resulting in positions in national laboratories (Oak Ridge National Laboratory (ORNL), Argonne National Laboratory and Los Alamos National Laboratory), major research universities, and industry. We also developed a uniaxial pressure cell along with ORNL for SNS. A critical goal of this project was to establish a foundation for competing nationally in federally funded research programs. To this end, we were quite successful in generating over twenty-two federally and non-federally funded grants including awards from NSF, DOE, and DOD, and two early career awards. Equally important, we were able to secure a key major piece of instrumentation via a large ARO grant for a state-of-the-art electron microscope.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ice Cryo-Encapsulation Balloon (Project ICEBall) Field Campaign Report

The Ice Cryo-Encapsulation Balloon (ICEBall) field campaign was designed to sample the ice crystals that compose high-altitude cirrus with a passive device. The campaign made use of a new instrument, ICEBall, which is a balloon-borne ice crystal sampling system. The ice crystal sounding system is capable of measuring ice crystal concentration, temperature, atmospheric pressure, ice crystal habit, aerosol particle morphology, and residual composition. The 3-kg instrument is carried upwards at 5 m s-1 by a high-altitude balloon. The instrument can be cut down from the balloon at any altitude up to 20 km, and the apparatus returns to the surface by parachute. Basic measurements such as temperature and pressure are recorded onboard, and high-frequency Global Positioning System (GPS) records altitude and latitude/longitude. Ice crystal concentrations are measured through the use of a high-resolution video camera mounted on the device. Ice crystals are collected through an open aperture leading to insulated collection chambers cooled with dry ice. Upon exiting the top of the cloud system, the chamber aperture is closed, and the ~1 mm3 sample cell is magnetically sealed and isolated at -78 °C, ensuring that ice particles do not sublimate or grow after collection. Once the crystals are returned to the surface, they are double-sealed and immersed at liquid nitrogen temperature in “dry-cryo shippers” before being transported back to the laboratory. Dr. Magee’s laboratory at The College of New Jersey contains a cryo-stage scanning electron microscope (SEM), which was used to interrogate the crystals and aerosol particles. The main purpose of this pilot field campaign was to provide an unprecedented level of detail on the crystal habits and ice surface complexity in mid-latitude cirrus, which may help resolve issues associated with habit identification and classification in cirrus. The ICEBall campaign was originally scheduled to run from March 28 to April 18 of 2021 at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains observatory. The COVID-19 pandemic intervened and caused us to shift the dates of the experiment to October 16-November 6 of 2021. This period is also climatologically favorable for cirrus. The approximately six-month gap between our original field campaign dates and the actual dates afforded us the opportunity to build two new ICEBall payload instruments. These instruments were tested during an August 2021 trip to The College of New Jersey. During this field testing phase, we decided to launch the ICEBall payload upstream from the ARM SGP site with the goal of landing in the vicinity of the site. Our goal was to sample the ice crystals before the cirrus were advected over the remote-sensing instruments at the SGP site. The team assembled for the field campaign consisted of the Principle Investigator (PI) and Co-Principle Investigator (Co-PI) (Drs. Harrington and Magee), The Pennsylvania State University research scientist Dr. Alfred Moyle, and two graduate students (Ms. Marley Majetic and Gwenore Pokrifka). The team operated out of a house rented in Enid, Oklahoma. We successfully sampled seven cirrus cloud systems during the three-week field campaign (October 21, 23-26, 31, and November 1). This was a much higher success rate than either of the PIs anticipated (our goal was closer to sampling three or four cases). The balloon was typically launched from oil pads or farm fields northwest of Enid and the payload was typically retrieved somewhat north of the SGP site. We never landed directly at the SGP site, and so did not need regular access to the SGP facilities. Our greatest concern going into the field campaign was the longer-term storage of crystals in the -196°C cryo dry-shipper dewars and the subsequent transport across the country. We had tested storage and transport prior to the field campaign, but we had never stored crystals for a few weeks nor had we transported the dewars over long distances. To our great relief, the storage and transport worked flawlessly and we were able to image a large number of crystals from six of the seven cases. Working with the staff at the ARM SGP office was excellent. They not only helped us find the sources we needed for helium, liquid nitrogen, and other materials, but also helped with contacts within the Federal Aviation Administration (FAA) and Vance Air Force Base. One goal of our field project was to tie the in situ measurements of ice crystal habits to the radar signatures derived from the Ka-band ARM Zenith-pointing Radar (KAZR). Unfortunately KAZR was down for the duration of our experiment. However, the Ka-band Scanning ARM Cloud Radar (KASACR) was put into vertically pointing mode during the ICEBall campaign and those data, along with Doppler lidar measurements, have proved very useful.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Harnessing Biomineralization Potential of Metal-Sequestering Bacteria for Rare Earth Elements (REE) Material Synthesis

Extremophilic methylotrophs, such as Methylotuvimicrobium alcaliphilum 20ZR are known for their natural ability to carry out REE capture and uptake. It has been predicted that proteins associated with surface layers facilitate transport and homeostasis of essential metals. Indeed, electron microscopic analysis coupled with antibody staining revealed that cell envelop of metal-grown M. alcaliphilum 20ZR contains metal-binding proteins in the base of the cup-shaped structures of S-layers. While less explored, the proteins associated with S-layers are also predicted to contribute to scavenging of other minerals essential for core metabolism. Here, we employed bottom-up proteomic study to compare the protein content of S-layer fractions of M. alcaliphilum 20ZR cultures grown in the presence and absence trace metals to identify putative components REE-scavenging machinery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning approaches to streamline and enhance the analysis of multiscale imaging data for bioaerosol and soil particles

Bioaerosol and soil particles are ubiquitous in the environment. They are multicomponent and complex in nature displaying mixed inorganic and organic components. The way components are mixed in a bioaerosol sample is referred to as its mixing state. Soil particles are also a mixture of inorganic (mineral) and organic (soil organic matter) components. Bioaerosol particles contribute to a major fraction of coarse mode atmospheric particles, especially in the tropical areas, contributing up to 80 % of the particle mass concentration. The mixing state of particles is crucial to evaluate because it impacts several important environmental processes such as warm and cold cloud formation and radiation budget. Mixing states in aerosols are accompanied by chemical reactions across solid-liquid-gas interfaces. In this study, we utilized elemental compositions and microcopy images of thousands of atmospheric particles acquired by computer-controlled scanning electron microscope equipped with an energy-dispersive x-ray spectrometer to compute the mixing state of atmospheric particles. A 2D convolutional neural network (CNN), also known as convnet, was used to model the relationship between low resolution imaging data and higher resolution spectroscopy data, with the former as training input and the latter as target output. Two types of CNNs were implemented and tested; a basic CNN and an Inception-v3 network. For binary classification, the basic CNN achieved an accuracy of 84.29 % across all atom types, and the Inception-v3-like network achieved an accuracy 85.51 %. This study demonstrates the applicability of deep learning to handle large amounts of imaging/chemical spectroscopy data efficiently and evaluate particle mixing state from a range of environmental samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Highly Efficient Vacuum Smelting of Aluminum

The goal of this SBIR project was to develop and demonstrate an innovative aluminum smelting technology that will significantly increase the range of aluminum alloys that can be manufactured from recycling of scrap aluminum. This will result in reduction of need for primary aluminum with corresponding 95% energy and CO 2 emission benefits. The proposed melting of pre-sorted aluminum alloys using UHV’s patented high throughput sorter funded by ARPA-E METALS program in an energy efficient vacuum furnace avoids the typical 5% metal loss due to oxidation of molten metal. In addition, pre-sorting scrap into different categories (cast, wrought, extrusion and aerospace) allows lower cost production of high value aluminum alloys without the need for adding alloying elements to primary aluminum. During this SBIR project, pure aluminum and several different commercial alloys were procured and melted in a laboratory furnace in vacuum, CH4 and air environments. The slag and oxidation formation were measured using weight and cross-sectional scanning electron microscope. The results otained were mixed and did not show direct correlation between furnace pressure and oxidation. We believe that the results were tainted due to small furnace size and non-uniformity of oxidation layer. A more thorough investigation is required to prove the advantages of vacuum smelting which could not be performed due to the COVID-19 lockdowns during this project. The theoretical fact remains that in the absence of oxygen in the furnace, there should be no oxide after melting, however the team could not prove it experimentally, and thus decided to dis-continue the project.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Measuring Multicomponent Adsorption of Tracer Gases on Natural Zeolites

A natural clinoptilolite sample near the Nevada National Security Site was obtained to study adsorption and retardation on gas transport. Of interest is understanding the competition for adsorption sites that may reduce tracer gas adsorption relative to single-component measurements, which may be affected by the multi-scale pore structure of clinoptilolite. Clinoptilolite has three distinct domains of pore size distributions ranging from nanometers to micrometers: micropores with 0.4–0.7 nm diameters, measured on powders by CO2 adsorption at 273 K, representing the zeolite cages; mesopores with 4–200 nm diameters, observed using liquid nitrogen adsorption at 77 K; and macropores with 300–1000 nm diameters, measured by mercury injection on rock chips (~ 100 mesh), likely representing the microfractures. These pore size distributions are consistent with X-ray computed tomography (CT) and focused ion beam scanning electron microscope (FIB-SEM) images, which are used to construct the three-dimensional (3D) pore network to be used in future gas transport modeling. To quantify tracer gas adsorption in this multi-scale pore structure and multicomponent gas species environment, natural zeolite samples initially in equilibrium in air were exposed to a mixture of tracer gases. As the tracer gases diffuse and adsorb in the sample, the remaining tracer gases outside the sample fractionate. Using a quadrupole mass spectrometer to quantify this fractionation, the degree of adsorption of tracer gases in the multicomponent gas environment and multi-scale pore structure is assessed. The major finding is that Kr reaches equilibrium much faster than Xe in the presence of ambient air, which leads to more Kr uptake than Xe over limited exposure periods. When the clinoptilolite chips were exposed to humid air, the adsorption capability decreases significantly for both Xe and Kr with relative humidity (RH) as low as 3%. Both Xe and Kr reaches equilibrium faster at higher RH. The different, unexpected, adsorption behavior for Xe and Kr is due to their kinetic diameters similar to the micropores in clinoptilolite which makes it harder for Xe to access compared to Kr.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Subsurface Aggregation of Cationic Friction Reducers: Cause and Prevention

The purpose of this work was to identify the cause of a gelatinous aggregation of friction reducers that was found post-fracturing by RWS operators. RWS sent us samples which we analyzed by scanning electron microscope (SEM). Laboratory experiments were performed to identify the cause of the aggregations and propose a method of preventing them from forming. We concluded that cationic friction reducers were crosslinking with clays, and that this could be prevented by using higher-salinity injection fluid. The results of this study help promote recycling of produced water.

geochemistry↗
DOE OSTI2025

Unveiling the Hidden Evolution of Crystal Defects and Disorder in Energy Materials

Control of point defects and disorder in functional thin films and 2D materials is critical to realizing their full potential in applications ranging from energy storage to advanced electronics. However, these phenomena are often poorly understood, difficult to characterize, and challenging to direct with precision. This presentation explores emerging multi-modal computer vision to decipher and predict order in materials across multiple length scales in the electron microscope, from the atomic to the nanoscale. By fusing data from diverse sources, these powerful models provide unprecedented insights into materials' lifecycles, enabling the control of defects and their associated properties at a fundamental level. This capability promises to transform materials design and accelerate the development of next-generation technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

NSUF RTE completion report for 23-4780: Microstructural Defect Induced Thermal Conductivity Reduction in Uranium Nitride and Thorium Nitride

Uranium nitride (UN) is known to have a higher thermal conductivity than traditional oxide fuels, which could lead to a more efficient energy transport and lower local temperature during its lifetime. But the thermal transport performance of UN in extreme environments has not been systematic studied. This study investigated the irradiation induced microstructural defects in UN and the impacts on thermal conductivity. The samples were produced by spark plasma sintering at University of Texas-San Antonio (UTSA), Los Alamos National Laboratory (LANL), and Idaho National Laboratory (INL). Thermal conductivity of UN before and after 2MeV proton irradiation were measured by using laser metrology at INL in a temperature range of 77-295K. Thermal conductivity measurements in this wide, cryogenic temperature range is critical to understand the phonon scattering mechanisms between the thermal energy carrier, phonons, and different types of irradiation-induced defects. In order to ensure the measurements were conducted in the same grains and minimize the impact of the local heterogeneities, the measurement locations were highlighted by fiducial marks using the focused ion beam (FIB) with grain orientation identified using the electron backscatter diffraction (EBSD) at the Center of Advanced Energy Studies (CAES). Irradiation experiment was conducted at the Ion Beam Laboratory at Texas A&M University (TAMU). A total of 6 samples were irradiated with different irradiation doses and temperatures. After irradiation, the microstructure was characterized by using Transmission Electron Microscope (TEM) at CAES (also in the FIB marks).

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Automated Particle Analysis of Hanford Tank Wastes 241-AN-106, 241-AN-101, and 241-AW-105

This effort is involved in developing particle size and density distribution (PSDD) for the major phases within Hanford tank waste sludge. The object of this work has been to use automated particle analysis (APA) methods on the Scanning Electron Microscope (SEM) to provide PSDDs. The PSDD can be used to quantify the proportion of gibbsite particles likely to settle quickly and enable blending of wastes that may be prone to pipeline plugging such as the uranium phase, clarkeite with other wastes. The Hanford tank waste solids were analyzed with SEM combined with x-ray Energy Dispersive Spectroscopy (EDS) using APA. This method can allow thousands of individual particles to be characterized and so may provide a more representative view of the samples and information PSDDs. We characterized several as-received sludge samples and developed PSDD with APA. Sample preparation methods were found to impact the collected results, as it was more difficult to collect representative samples of the larger particles. Much of the material was also dried and cemented together that further impacted results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Irradiated Microstructure of Zorita Materials

Reactor baffle plate materials were harvested from the decommissioned Zorita nuclear power reactor after 38 years of service and were characterized with transmission electron microscope. The samples were Type 304 stainless steels and had been irradiated to 0.05, 16.9, ad 48.6 dpa. Faulted dislocation loops, voids, and irradiation-induced precipitates were observed in this material. While dislocation loops were the dominant irradiation defects for all samples, small voids and precipitates were also observed at 16.9 and 48.6 dpa, but not at 0.05 dpa. The size of dislocation loops increased with irradiation dose and saturated below 16.9 dpa. The loop density continued to increase between 16.9 and 48.6 dpa. For voids, both the density and mean size were small, and the volume swelling was negligibly small even at 48.6 dpa. In addition, carbides or G-phase precipitates were detected at 16.9 and 48.6 dpa, and a complex dose dependence was observed. A range of irradiation hardening was estimated at each dose, and the resulting yield strengths were consistent with the previous tensile results measured from the Halden and BOR-60 specimens.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗