Results for “degradation predictions” — Page 9

DOE OSTI2022

Gamma Radiation-Induced Degradation of Acetohydroxamic Acid (AHA) in Aqueous Nitrate and Nitric Acid Solutions Evaluated by Multiscale Modelling

Acetohydroxamic acid (AHA) has been proposed for inclusion in advanced, single-cycle, used nuclear fuel reprocessing solvent systems for the reduction and complexation of plutonium and neptunium ions. For this application, a detailed description of the degradation of AHA in dilute aqueous nitric acid is required. To this end, we present a comprehensive, multiscale computer model for the coupled radiolytic and hydrolytic degradation of AHA in aqueous sodium nitrate and nitric acid solutions. Rate coefficients for the reactions of AHA and HA with the oxidizing nitrate radical were measured for the first time using pulsed electron radiolysis and used as inputs in the kinetic model. The computer model results are validated by comparison to experimental data from steady state gamma irradiations, for which the agreement is excellent. Here, the presented model accurately predicts the yields of the major degradation products of AHA: acetic acid, hydroxylamine, nitrous oxide, and molecular hydrogen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Nonlinear gyrokinetic modelling of high confinement negative triangularity plasmas

Abstract Nonlinear gyrokinetic simulations correctly predict particle as well as ion and electron energy fluxes of high confinement plasmas with a negative triangularity cross sectional shape, showing that core transport in these plasmas is well described by standard gyrokinetic models. Experimentally inferred power balance fluxes are mostly reproduced within one standard deviation across a wide portion of the minor radius. Experimental conditions are reproduced by ion scale simulations, without the need to include density and temperature profile curvature effects. The experimental case is used as baseline to predict that the non-dimensional confinement scaling in negative triangularity plasmas increases strongly with plasma current while slightly degrading at increasing normalized pressure and decreasing collisionality. Recent experiments showed that low toroidal rotation negatively impacts confinement; consistent with the experiment, simulations predict that low rotational shear significantly affects confinement unless the plasma effective charge is maintained above a minimum level. Core confinement is predicted to significantly degrade in low aspect ratio devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

General-Purpose Heat Source Radioisotope Thermoelectric Generator Flight Unit 5 Refurbished (GPHS-RTG F5R) Power Prediction

INL has evaluated and refurbished General Purpose Heat Source (GPHS) Radioisotope Thermoelectric Generator (RTG) Flight Unit 5 (GPHS-RTG F5R or F5R) that was defueled in 2005. The generator has passed all the INL internal reviews, an end item data package has been delivered to NASA and is ready fueling for a mission. NASA requested a user’s guide for F5R with enough detail for a prospective mission proposal. This report documents the development of the power prediction model used in the user’s guide. The prediction evaluated the thermoelectric “burn-in” and the graceful degradation modes of the missions and developed a thermal inventory dependent equation that predicts the power that matches Galileo, Cassini, and PNH within a three-standard deviation of ±1%. This prediction is only valid for the 30 VDC load voltage of these missions.

30 - DIRECT ENERGY CONVERSION↗

DOE OSTI2026

General-Purpose Heat Source Radioisotope Thermoelectric Generator Flight Unit 5 Refurbished (GPHS-RTG F5R) Power Prediction

INL has evaluated and refurbished General Purpose Heat Source (GPHS) Radioisotope Thermoelectric Generator (RTG) Flight Unit 5 (GPHS-RTG F5R or F5R) that was defueled in 2005. The generator has passed all the INL internal reviews, an end item data package has been delivered to NASA and is ready fueling for a mission. NASA requested a user’s guide for F5R with enough detail for a prospective mission proposal. This report documents the development of the power prediction model used in the user’s guide. The prediction evaluated the thermoelectric “burn-in” and the graceful degradation modes of the missions and developed a thermal inventory dependent equation that predicts the power that matches Galileo, Cassini, and PNH within a three-standard deviation of ±1%. This prediction is only valid for the 30 VDC load voltage of these missions.

30 - DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Predicting PEMFC performance from a volumetric image of catalyst layer structure using pore network modeling

Not Available

Sadeghi, Mohammad Amin↗

DOE OSTI2021

Kinetic modeling of anaerobic degradation of plant-derived aromatic mixtures by Rhodopseudomonas palustris

Abstract Rhodopseudomonas palustris is a model microorganism for studying the anaerobic metabolism of aromatic compounds. While it is well documented which aromatics can serve as sole organic carbon sources, co-metabolism of other aromatics is poorly understood. This study used kinetic modeling to analyze the simultaneous degradation of aromatic compounds present in corn stover hydrolysates and model the co-metabolism of aromatics not known to support growth of R. palustris as sole organic substrates. The simulation predicted that p -coumaroyl amide and feruloyl amide were hydrolyzed to p -coumaric acid and ferulic acid, respectively, and further transformed via p -coumaroyl-CoA and feruloyl-CoA. The modeling also suggested that metabolism of p -hydroxyphenyl aromatics was slowed by substrate inhibition, whereas the transformation of guaiacyl aromatics was inhibited by their p -hydroxyphenyl counterparts. It also predicted that substrate channeling may occur during degradation of p -coumaroyl-CoA and feruloyl-CoA, resulting in no detectable accumulation of p -hydroxybenzaldehyde and vanillin, during the transformation of these CoA ligated compounds to p- hydroxybenzoic acid and vanillic acid, respectively. While the simulation correctly represented the known transformation of p -hydroxybenzoic acid via the benzoyl-CoA pathway, it also suggested co-metabolism of vanillic acid and syringic acid, which are known not to serve as photoheterotrophic growth substrate for R. palustris .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning

Abstract mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Cetnar, Daniel P.↗

DOE OSTI2022

Light Water Reactor Sustainability: Program Materials Research Pathway (FY22 Technical Program Plan)

The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) program is charged with performing R&D to develop the scientific basis for understanding and predicting the long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, the mechanistic understanding of degradation phenomena in materials must be leveraged to develop mitigation and repair strategies and new material alternatives for existing components. This research will provide data and methods to assess the performance of systems, structures, and components essential to safe and sustained reactor operations. The R&D products developed from the LWRS program will be used by stakeholders—including utilities, industry groups, and regulators—to inform operational and regulatory requirements for materials in reactor systems, structures, and components subjected to long-term operation conditions, providing key inputs to regulators and industry. Therefore, the intent of this research is to provide options to reduce the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analyses of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To provide the best options, industry experience and guidance are important because of their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS program; provide details on the individual research tasks within the MR Pathway; describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress; and describe the requirements for performing this critically important research.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Predicting encapsulant delamination in photovoltaic modules bridging photochemical reaction kinetics and fracture mechanics

Abstract Photovoltaic (PV) modules are subjected to environmental stressors (UV exposure, temperature, and humidity) that cause degradation within the encapsulant and its interfaces with adjacent glass and cell substrates. To save experimental time and to enable long‐term assessment with intensive degradation only taking place after many years, the development of predictive models is indispensable. Previous works have modeled the delamination of the ethylene vinyl acetate (EVA) encapsulant/glass and encapsulant/cell interfaces under field aging conditions with fundamental photochemical degradation reactions that lead to molecular scission and loss of interfacial adhesion, characterized by the fracture resistance, G c . However, these models were fundamentally limited in that the following aspects were not incorporated: (i) molecular crosslinking in the field, (ii) synergistic autocatalytic interactions of degradation mechanisms, (iii) connection between degraded encapsulant structure and its mechanical properties, and (iv) rigorous treatment of the plasticity contribution to G c with finite element models. Here, we present a time‐dependent multiscale model that addresses these limitations and is applicable to a wide range of encapsulants and interfaces. For the reference EVA encapsulant and its interfaces with the glass and cell, the presented model predicts an initial rise in G c in the first 3 years of field aging from crosslinking, then a subsequent sharp decline from degradation mechanisms. We used nanoindentation to measure the changes in EVA mechanical properties over exposure time to tune the model parameters. The model predictions of G c and mechanical properties match with experimental data and show an improvement compared to previous models. The model can even predict switches in failure interfaces, such as the observed EVA/cell to EVA/glass transition. We also conducted a sensitivity analysis study by varying the degradation and crosslinking kinetic parameters to demonstrate their effects on G c . Model extensions to polyolefin elastomer‐ and silicone‐encapsulants and their interfaces are also demonstrated.

Liu, Kuan↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2025

Enhancing lifetime, forecasting, and economic benefits of photovoltaic technologies undergoing UV-induced degradation with optical filtering

Ultraviolet-induced degradation (UV-ID) of various PV cell types was analyzed under optical UV filters with different cutoff wavelengths. Cell types studied included interdigitated back contact (IBC), passivated emitter and rear totally diffused (PERT), and heterojunction technology (HJT) based on crystalline Si (c-Si), and metal halide perovskite (MHP) cells. Analyzing degradation rates in two distinct regimes proved beneficial for all cell types. We used empirical linearizing functions ln(t) for c-Si technologies and 2 √t for MHP samples where t is time. These were applied to extrapolate UV-induced degradation over the lifetime of PV modules under various levels of optical UV filtering and used to predict the relative economic benefits for PV power plants. Degradation rates for all technologies were generally faster under the long pass optical filters having shorter cutoff wavelengths transmitting more UV irradiation and at elevated temperatures when testing MHP samples in the range between 60 °C and 90 °C.

14 SOLAR ENERGY↗

DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗

DOE OSTI2026

Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers

Flow maldistribution is a common challenge in heat exchanger (HX) design and particularly important for air-to-refrigerant geometries where capacity losses can approach 65%. This has a major impact on central air conditioning systems, as compact duct design motivates the use of A-type HXs which are known to be affected by airflow maldistribution. Because velocity profiles are difficult to predict, components are often oversized leading to increased material cost, system footprint, and refrigerant charge. Several studies detail airflow maldistribution for individual HXs and packages, but findings cannot always be extrapolated to new designs. In this work, a machine learning (ML) based flow profile prediction framework is developed and applied to two common package configurations: (i) A-type and (ii) U-type HXs, across a broad range of HX geometries and flow rates. Porous media CFD simulations are validated against independent data for both package types as well as comprehensive in house measurements for a finless geometry with shape optimized non-round tubes, which validates the framework for new heat transfer surfaces. The ML models are trained on the porous media CFD simulations, predicting volumetric flow rate (VFR) within 1.1% and 1.9% with maximum relative L 2 norm errors of 0.48 and 0.65, respectively, while also delivering 10 5 speed up factor compared to full porous media CFD. HX level simulations show an up to 9% reduction in heat transfer from flow maldistribution, with greater losses occurring at smaller half apex angles. This framework enables rapid and highly accurate prediction of airflow maldistribution induced capacity degradation.

42 ENGINEERING↗

DOE OSTI2023

Role of n-phenyl-β-naphthylamine in the Acid-Nitroplasticizer Thermal Aging (ANTA) Experiment

A short-term aging study, which was named as Acid-Nitroplasticizer Thermal Aging (ANTA), was conducted to explore the complex chemistry between n-phenyl-β-naphthylamine (PBNA), HNO x , and nitroplasticizer (NP). Aging of NP with PBNA and acid treatments have demonstrated that PBNA is an effective antioxidant and an anti-hydrolytic agent (e.g., inhibits HNO 3 ) and have allowed the identification of nitroso-PBNA (PBNA-NO). The direct conversion of PBNA to PBNA-NO via scavenging of HONO, known as nitrosation, is a preceding event of PBNA nitration, which provides evidence to the HONO elimination as the key mechanism in the early stage of NP aging. Through the kinetics of both nitrosation and nitration, the accuracy of a prediction on PBNA consumption in NP degradation is further improved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Efficient Reversible Operation and Stability of Novel Solid Oxide Cells

This project aims to develop and test novel Reversible Solid Oxide Cells (ReSOCs) specifically designed to yield low area specific resistance, as required to achieve high round trip efficiency in reversible operation at high current density. A key objective is to improve long-term reversible SOC durability; mechanistic degradation models will be used to predict long-term durability using input data from accelerated testing that combines electrochemical life testing with quantitative microstructural and chemical evaluation. The modeling tasks seek to design low-loss, low-cost ReSOC systems using input data from the experimental cell studies. A key challenge is to develop a thermal management strategy that maintains thermally self-sustaining operation while operating the stack at a potential lower than the thermo-neutral steam electrolysis voltage of ~1.3 V, to maintain high efficiency.

08 HYDROGEN↗

DOE OSTI2026

Broad frequency tuning of a Nb$_{3}$Sn superconducting microwave cavity for dark matter searches

We demonstrate a novel broad-frequency tuning mechanism for superconducting microwave cavities designed for dark matter searches. Using a Nb$_3$Sn-coated cigar-shaped cavity operating at approximately 9 GHz, we achieve continuous frequency tuning exceeding 1 GHz by mechanically separating the two cavity halves: a "tuning-by-opening" technique. Finite-element method simulations predict that radiative losses do not degrade the quality factor even for large openings, as a closed cavity with an intrinsic quality factor of $10^7$ maintains this value for apertures up to 9 mm, corresponding to a tuning range from 9.0 to 7.5 GHz. Experimental validation using both copper ring spacers and a continuous sliding mechanism confirms $Q_0$ values exceeding the dark matter quality factor across the entire explored frequency range, despite mechanical imperfections and film non-uniformities. This tuning approach avoids inserting elements into the resonant volume, making it particularly suitable for high-Q superconducting cavities in axion haloscope experiments and readily applicable to REBCO-based implementations capable of operating in multi-tesla magnetic fields.

Maiello, D. [Padua U.; INFN, Padua] (ORCID:0009000↗

DOE OSTI2025

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel Jana Howard1,3, Guang Yang3, Haiyan Zhao1*, Patrick Warren4, Tiankai Yao3, Steven Cavazos4 Elizabeth Sooby Wood4*, Ching-heng Shiau2 1 University of Idaho, Environmental Science, Idaho Falls, Idaho, USA 2 Boise State University, Microscopy and Characterization Suite, Idaho Falls, Idaho, USA 3 Idaho National Laboratory, Idaho Falls, Idaho, USA 4 University of Texas San Antonio, Department of Physics and Astronomy, San Antonio, Texas, USA *Corresponding author: haiyanz@uidaho.edu; elizabeth.soobywood@utsa.edu Tri-structural Isotropic (TRISO) particle fuel is the preferred choice for the newest and next-generation high-temperature nuclear reactors due to its robust construction [1]. The fuel design effectively contains fission products inside the particle at extremely high temperatures [2]. However, the effect of transition metal fission products on fuel performance and structural integrity remains largely unknown, creating uncertainties in predicting fuel performance and potential failure mechanisms [2]. For example, palladium (Pd) has a strong tendency to form intermetallic phases with uranium (U). These new phases tend to be hard and brittle which could lead to fracture of fuel kernel during irradiation [3,4]. Pd is not normally produced in high concentrations in normal fission reactions however, driving the production of Pd into the locality of a Uranium Oxycarbide (UCO) nuclear fuel provides an opportunity to closely observe the diffusion and chemical interactions. This study focuses on detailed transmission electron microscopy characterization of the intermetallic phases formed by the interaction between a UCO kernel and a Pd bar after annealing at 1100 °C for 100 hours. Figure 1 provides an overview of the UCO kernel in contact with the Pd bar. The UCO-Pd sample surface was examined using a Focused Ion Beam (FIB) Quanta 3D FEG FEI in VCD detector mode at 10kV, 83pA to reveal surface features. Several key surface features in the interaction region were observed including: (1) a small area with dendritic microstructure, (2) color contrast near the UCO kernel and Pd boundary, and (3) darker and lighter regions throughout the sample surface. To analyze diffusion behavior, FIB was used to prepare lamellae from five different areas of the UCO-Pd sample, as well as one for the as-received sample. These lamellae were examined using a Scanning Transmission Electron Microscope ThermoFisher Spectra 300 (STEM-Spectra). Energy Dispersive Spectroscopy results confirmed the diffusion between the UCO fuel and Pd bar. Figure 2 shows the identified phases of UC, UO2, and Pd dispersed throughout the interaction region. It was also found that the Pd concentration decreases as the radial distance from the UCO-Pd interface increases. The Pd concentration was 36.93% atomic fraction 11µm from the interaction region then decreased to 7.51% atomic fraction at 257 µm away from the interaction region. Figure 3 shows how concentrations of U and Pd vary across the interaction zone, highlighting the extent of diffusion. This study confirms that Pd interacts with surrounding material to form U-Pd phase and diffusion zones. These diffusion zones vary in composition depending on its radial distance from the point of contact with the Pd bar. These findings contribute to a deeper understanding of the fission product behavior in high temperature nuclear fuels, aiding in the prediction and mitigation of potential fuel degradation mechanisms. A B C Fig. 1. SEM micrographs of UCO fuel kernel in contact with solid Pd bar and the lift out locations. Figure 1A shows UCO fuel kernel in contact with solid Pd bar. Figure 1B shows a higher magnification image of the UCO fuel-Pd interaction zone. Figure 1C shows lift out sites for the lamellae. A B C Fig 2. EDS maps reveal the distribution of U, O, and Pd in location 3. Figure 2A shows the distribution of uranium. Figure 2B shows the distribution of oxygen. Figure 2C shows the distribution of palladium. A B C Fig 3. Palladium and uranium concentration across interaction zone in location 2. Figure 3A shows EDS map of lift out number 2. Figure 3B shows a zoomed in EDS map taken from the interaction zone. Figure 3C shows a line graph of uranium and palladium concentrations across the interaction zone. References: 1. B.E. Wells, N.R. Phillips, K.J. Geelhood. Pacific West Laboratory. (2021). TRISO Fuel: Properties and Failure Modes. https://www.nrc.gov/docs/ML2117/ML21175A152.pdf (Accessed January 16, 2025). 2. American Nuclear Society. TRISO Fuel Development Progresses in INL, ORNL. https://www.gen-4.org/gif/upload/docs/application/pdf/2014-03/nov13nn_fuel_reprint.pdf (Accessed January 16, 2025) 3. Clark R.A., M.A. Con

36 - MATERIALS SCIENCE↗