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At least 109 records · Page 6

Laser ablation spectrometry for studies of uranium plasmas, reactor monitoring, and spent fuel safety

Nuclear security is one of the defining challenges of our time. Nuclear threats range from deliberate dispersal of radioactive material to contaminate the vital infrastructure to diversion and smuggling of special nuclear material for clandestine nuclear programs and nuclear terrorism, respectively. There is an associated need to develop and sustain the nuclear forensics capabilities, which requires the understanding of complex processes that occur in plasmas of nuclear materials. The area of nuclear safety has seen a resurgence of public interest, and there is a concomitant need to safely store used nuclear fuel and detect structural material failure in nuclear power systems, especially in innovative reactor designs envisioned for future adoption. Laser-produced plasmas are complicated extreme environments that can generate intense and rich, highly specific signatures of nuclear and radiological materials, which can then be explored in a wide range of applications. They include interdiction and rapid detection of nuclear materials, including their isotopic composition, detection over long distances, laboratory simulation of weapons effects, monitoring the condition of structural materials in dry cask storage containers, and novel instrumentation for nuclear power systems. We present a compilation of recent representative examples of the application of laser spectroscopy, and laser-induced breakdown spectroscopy in particular, to nuclear safety and security problems. A case is made that spectroscopic techniques based on laser-produced plasmas offer complementary, and sometimes unique, capabilities that motivate the continued exploration of their efficient production and understanding of the signatures they produce

(020.3260) Isotope shifts, (140.3440) Laser-induce↗

Use Machine Learning to Improve Burnup Measurement in Pebble Bed Reactors

Advanced pebble bed reactor (PBR) designs post new challenges in material control and accountancy (MC&A) because the fuel materials, distributed in many discrete pebbles, are continuously circulated through the reactor core and the refueling path compared to the bulk fuel assembly design in conventional reactors, e.g., light water reactors. In pebble bed reactors, there are hundreds of thousands of fuel pebbles in the reactor core during the normal operation, and the burnup of each pebble is measured when ejected from the core. Accurate burnup measurement is an important step in material control and spent fuel disposition. The measurement is usually based on detection of radiation signatures of fission products accumulated in the pebble fuel over irradiation in the core. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of pebbles undergoing burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Multigene engineering in plants: Technologies, applications, and future prospects

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AI-aided plant engineering↗

Bioinspiration in light harvesting and catalysis

Capturing and converting solar energy into fuels and feedstocks is a global challenge that spans numerous disciplines and fields of research. Billions of years of evolution have allowed natural organisms to hone strategies for harvesting light from the sun and storing energy in the form of carbon–carbon and carbon–hydrogen bonds. Photosynthetic antenna proteins capture solar photons and funnel photoexcitations to reaction centres with high yields, and enzymes catalyze multi-electron reactions, facilitating chemical transformations not yet efficiently implemented using artificially engineered catalysts. Researchers in renewable energy often look to nature to understand the mechanisms at work and, if possible, to explore their translation into artificial systems. Here, we review advances in bioinspiration across the fields of biological light harvesting and chemical energy conversion. We examine how multi-photon and multi-electron reactions in biology can inspire new methods in photoredox chemistry to achieve novel, selective and complex organic transformations; how carbonic-dehydrogenase-inspired design principles enable catalytic reactions such as the conversion of CO 2 into useful products such as fuels; and how concepts from photosynthetic antenna complexes and reaction centres can benefit artificial light-harvesting materials. We then consider areas in which bioinspiration could enable advances in the rational design of molecules and materials, the expansion of the synthetic capabilities of catalysts and the valorization of molecular building blocks. Here, we highlight the challenges that must be overcome to realize these advances and propose new directions that may use bioinspiration to achieve them.

36 MATERIALS SCIENCE↗

Resolving Diverse Oxygen Transport Pathways Across Sr‐Doped Lanthanum Ferrite and Metal‐Perovskite Heterostructures

Abstract Perovskite structured transition metal oxides are important technological materials for catalysis and solid oxide fuel cell applications. Their functionality often depends on oxygen diffusivity and mobility through complex oxide heterostructures, which can be significantly impacted by structural and chemical modifications, such as doping. Further, when utilized within electrochemical cells, interfacial reactions with other components (e.g., Ni‐ and Cr‐based alloy electrodes and interconnects) can influence the perovskite's reactivity and ion transport, leading to complex dependencies that are difficult to control in real‐world environments. Here, this work uses isotopic tracers and atom probe tomography to directly visualize oxygen diffusion and transport pathways across perovskite and metal‐perovskite heterostructures, that is, (Ni‐Cr coated) Sr‐doped lanthanum ferrite (La 0.5 Sr 0.5 FeO 3 ; LSFO). Annealing in 18 O 2(g) results in elemental and isotopic redistributions through oxygen exchange (OE) in the LSFO while Ni‐Cr undergoes oxidation via multiple mechanisms and transport pathways. Complementary density functional theory calculations at experimental conditions provide rationale for OE reaction mechanisms and reveal a complex interplay of different thermodynamic and kinetic drivers. These results shed light on the fundamental coupling of defects and oxygen transport in an important class of catalytic materials.

36 MATERIALS SCIENCE↗

Recent Advances in Radiation-Induced Actinide Redox Chemistry

The actinide series boasts many unique physical and chemical features worthy of both fundamental and applied study. However, the chemical influence of their inherent radiation field is often overlooked, especially as we begin to explore the late actinides in more detail than ever possible before. From the perspective of used nuclear fuel reprocessing, the absorption of ionizing radiation induces the formation of a variety of transient and steady-state excited states, radicals, ions, and molecular degradation products, many of which are highly redox active and can lead to significant changes in a reprocessing solvent system’s physical and chemical properties. For example, radiolysis of the actinides can drive steady-state redox distributions and the formation of non-traditional oxidation states which can complicate their separation and recovery from fission products. This scenario is further exacerbated when complexation is taken into account. Consequently, a molecular-level understanding of radiation effects on the actinides over multiple time, distance, and material domains is essential for supporting innovation in used nuclear fuel reprocessing technologies. Attaining this knowledge necessitates a firm grasp of actinide radiation chemistry to develop predictive, mechanistic, multiscale models to support engineering efforts. Presented here are several recent studies that highlight recent advances in actinide radiation chemistry, in particular, the effect of actinide complexation on ligand reactivity towards radiation-induced transients.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Adhesion of Titanium Coatings on Additively Manufactured Stainless Steel [Slides]

Additive manufacturing can improve fuel cell development by increasing design development speed, enabling complex designs through more flexible fabrication, and reducing wasted excess material. The goal of the project is to develop a process for strongly adhering a coating of titanium to additively manufactured stainless steel.

36 MATERIALS SCIENCE↗

Safeguards Technical Objectives for Thorium Molten Salt Reactor Fuel Cycles

China, Denmark, France, India, Indonesia, the United Kingdom, and the United States, among others, are exploring commercial thorium fuel cycles for domestic power production and/or international export. For any thorium fuel cycle facilities built and/or operated in a non-nuclear weapon State with a comprehensive safeguards agreement, those facilities and any associated nuclear material would be subject to international nuclear safeguards by the International Atomic Energy Agency (IAEA). Thorium fuel cycle facilities offered for the application of safeguards under a voluntary offer agreement by a nuclear weapon State would also be subject to safeguards, if selected by the IAEA. Compared to uranium/plutonium fuel cycle facilities, thorium fuel cycle facilities are comparatively diverse from an inspection and accounting perspective, differing greatly in material type, form, and design from uranium/plutonium fuel cycle facilities and also from each other. Of these, the most complex thorium fuel cycles – and therefore likely the most challenging to the implementation of international nuclear safeguards – are those that include thorium molten salt reactors. With thorium fuel cycles approaching near-term commercial deployment timelines, it is imperative that the international nuclear safeguards community begins to think about how to adapt existing international nuclear safeguards concepts and approaches to thorium fuel cycles, as well as mitigate any gaps that may exist. This paper focuses on the development of recommended safeguards technical objectives for efficient and effective application of international nuclear safeguards measures (e.g., containment / surveillance, non-destructive assay, destructive assay, environmental sampling) to thorium molten salt reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Exact solution of the frustrated Potts model with next-nearest-neighbor interactions in one dimension via AI bootstrapping

The one-dimensional (1D) 𝐽 1 −𝐽 2 𝑞-state Potts model is solved exactly for arbitrary 𝑞 by analytically block-diagonalizing the original 𝑞 2 ×𝑞 2 transfer matrix into a simple 2 × 2 maximally symmetric subspace, based on using OpenAI's reasoning model o3-mini-high to exactly solve the 𝑞 = 3 case. Furthermore, by matching relevant subspaces, we map the Potts model onto a simpler effective 1D 𝑞-state Potts model, where 𝐽 2 acts as the nearest-neighbor interaction and 𝐽 1 as an effective magnetic field, nontrivially generalizing a 56-year-old theorem previously limited to the simplest case (𝑞 = 2, the Ising model). Our exact results provide insights to phenomena such as atomic or electronic order stacking in layered materials and the emergence of dome-shaped phases in complex phase diagrams. In conclusion, this work is anticipated to fuel both research in 1D frustrated magnets for recently discovered finite-temperature application potentials and the fast moving topic area of AI in science.

1-dimensional spin chains↗

LEU-Mo Casting Update

The Y-12 National Security Complex (Y-12) participates in the Fuel Fabrication Pillar of the National Nuclear Security Administration’s (NNSA’s) Office of Material Minimization and Management (M3) Office of Conversion Pillar. Y-12’s primary responsibility is to establish the fabrication process for the low-enriched uranium-molybdenum (U-10Mo) feedstock.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

LEU-Mo Casting Update

The Y-12 National Security Complex (Y-12) participates in the Fuel Fabrication Pillar of the National Nuclear Security Administration’s (NNSA’s) Office of Material Minimization and Management (M3) Office of Conversion Pillar. Y-12’s primary responsibility is to establish the fabrication process for the low-enriched uranium-molybdenum (U-10Mo) feedstock.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Y-12 NATIONAL SECURITY COMPLEX LEU-MO FEEDSTOCK FABRICATION UPDATE

The Y-12 National Security Complex (Y-12) participates in the fuel fabrication pillar of lhe National Nuclear Security Administration's (NNSA's) Office of Material Minimization and Management (M3) Office of Conversion Pillar. Y-12's primary responsibility is to establish a reliable fabrication process of low-enriched uranium-molybdenum (LEU-Mo) feedstock tor United States High Performance Research Reactors (US HPRRs). This update compares US HPRR LEU-Mo feedstock fabrication ettorts over the past several casting campaigns at Y-12 which vary in process methodologies and fabrication parameters. With recent changes to the latest feedstock casting campaign, Process Design Standard-2 (PD-STD-2). Y-12 has seen a significant decrease in isotopic uranium variability of the fuel feedstock fabricated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Workshop on Addressing Rigor and Reproducibility in Thermal, Heterogeneous Catalysis

Heterogeneous catalysis has long served as the bedrock of the manufacturing of energy carriers, fuels and chemicals, and various technologies for pollution abatement. The significant complexity and variability spanning the entire breadth of catalyst material properties, synthesis methods, characterization techniques, and evaluation procedures, has focused attention on the need to establish community-accepted best practices for ensuring high-quality, benchmarked, and reproducible data. In addition, increased societal urgency to transition to clean energy and reduce greenhouse gas concentrations has incentivized interdisciplinary, convergent, and translational approaches to catalysis research in recent years. Research engineers and scientists with expertise cutting broadly across materials science, chemical synthesis, interfacial science, spectroscopy, and methods of data science and computational simulation, all bring diverse and important perspectives to catalysis research, but often with little awareness of the complexity of catalytic systems, especially in their working environment. As has already occurred in other scientific fields, there has been growing recognition and consensus in the heterogeneous catalysis research community that mechanisms are needed to improve the rigor and reproducibility (R&R) of experimental measurements, to ensure alignment of the broader research community with a common core of best practices specific to the realization of high-quality catalysis research. Similarly, the field is moving rapidly toward computationally informed and data science-driven catalyst design, but the success of implementing such predictive tools hinges on model training and validation rooted in rigorously obtained and reproducible experimental data that are benchmarked to common specifications. As such, this workshop was convened to prepare a report summarizing best practices for reporting data and performing experiments that researchers can use to benchmark, validate, and reproduce data in specific sub-fields of thermal, heterogeneous catalysis. Additionally, we discussed recommendations for future actions that may improve R&R in this field. The workshop organizers and participants include a diverse range of catalysis researchers from various employment sectors (e.g., academia, industry, national laboratory), institutional mission and resources (e.g., PhD-granting research universities, non-PhD-granting teaching universities), career stage (e.g., early, mid and late-career), technical expertise, and demographic background. This diverse group was involved in the discussion of workshop agenda items, writing this report, and discussing possible future action items for the community to consider, which helped ensure that a broad range of perspectives were captured in the description of the problems at hand and the creation of actionable solutions that may be effectively adopted by the diverse practitioners in catalysis research. Importantly, this group of workshop participants also included very early career researchers (e.g., senior PhD students, postdoctoral scholars) who will become the next generation of scientific leaders in various sectors, thus capturing emerging perspectives of newcomers to the field to shape its future while positively impacting the development of its future workforce. We envision that this effort will help advance the field of catalysis science by improving the rigor and reproducibility of experimental data collected by current researchers and future newcomers to the field, which is of broad importance to health and vitality of any scientific discipline. Therefore, best practices identified in this endeavor for thermal heterogeneous catalysis can be translated to such efforts in other areas of catalysis and other scientific fields involving the study of materials, and vice versa. We also envision this to be an ongoing effort, with future workshops that are convened to discuss issues of rigor and reproducibility on technical topics that were unable to be covered in this workshop due to its scope limitations, and as emerging methods and materials become more prevalent in the research community.

36 MATERIALS SCIENCE↗

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗