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NASA NTRS1985

The 13C nuclear magnetic resonance in graphite intercalation compounds

The (13)C NMR chemical shifts of graphite intercalation compounds were calculated. For acceptor types, the shifts come mainly from the paramagnetic (Ramsey) intra-atomic terms. They are related to the gross features of the two-dimensional band structures. The calculated anisotropy is about -140 ppm and is independent of the finer details such as charge transfer. For donor types, the carbon 2p pi orbitals are spin-polarized because of mixing with metal conduction electrons, thus there is an additional dipolar contribution which may be correlated with the electronic specific heat. The general agreement with experimental data is satisfactory.

Tsang, T.↗

DOE OSTI2024

Smooth trends in fermium charge radii and the impact of shell effects

The quantum-mechanical nuclear-shell structure determines the stability and limits of the existence of the heaviest nuclides with large proton numbers Z ≳ 100. Shell effects also affect the sizes and shapes of atomic nuclei, as shown by laser spectroscopy studies in lighter nuclides. However, experimental information on the charge radii and the nuclear moments of the heavy actinide elements, which link the heaviest naturally abundant nuclides with artificially produced superheavy elements, is sparse. Here we present laser spectroscopy measurements along the fermium (Z = 100) isotopic chain and an extension of data in the nobelium isotopic chain (Z = 102) across a key region. Multiple production schemes and different advanced techniques were applied to determine the isotope shifts in atomic transitions, from which changes in the nuclear mean-square charge radii were extracted. A range of nuclear models based on energy density functionals reproduce well the observed smooth evolution of the nuclear size. Both the remarkable consistency of model prediction and the similarity of predictions for different isotopes suggest a transition to a regime in which shell effects have a diminished effect on the size compared with lighter nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗

DOE OSTI2023

α -cluster microscopic study of C 12 + C 12 fusion toward the zero energy limit

The carbon burning process is a fundamental step of stellar evolution and governs the synthesis of chemical elements important for the formation of life. In this work, we utilize the microscopic hybrid α cluster (HαC) model and an analytical approach, both in the framework of the Imaginary Time Method (ITM), to study the carbon fusion reaction towards zero energy. We obtain the values of the cross sections, astrophysical factors and correlate our results to collective motion. We also include a calculation for the 2 + carbon fusion and discuss a possible experimental investigation. Our results confirm direct experimental and theoretical results close to the barrier, while suggest possible 2 + mixtures in the indirect experimental data. Furthermore, our study offers an accurate view of the burning process in the somewhat unexplored low energy region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1993

Identification of amino acid sequences in the polyomavirus capsid proteins that serve as nuclear localization signals

The molecular mechanism participating in the transport of newly synthesized proteins from the cytoplasm to the nucleus in mammalian cells is poorly understood. Recently, the nuclear localization signal sequences (NLS) of many nuclear proteins have been identified, and most have been found to be composed of a highly basic amino acid stretch. A genetic "subtractive" and a biochemical "additive" approach were used in our studies to identify the NLS's of the polyomavirus structural capsid proteins. An NLS was identified at the N-terminus (Ala1-Pro-Lys-Arg-Lys-Ser-Gly-Val-Ser-Lys-Cys11) of the major capsid protein VP1 and at the C-terminus (Glu307 -Glu-Asp-Gly-Pro-Glu-Lys-Lys-Lys-Arg-Arg-Leu318) of the VP2/VP3 minor capsid proteins.

NASA Discipline Number 93-10↗

NASA NTRS1985

C-13 nuclear magnetic resonance in graphite intercalation compounds

The C-13 NMR chemical shifts of graphite intercalation compounds have been calculated. For acceptor types, the shifts come mainly from the paramagnetic (Ramsey) intra-atomic terms. They are related to the gross features of the two-dimensional band structures. The calculated anisotropy is about - 140 ppm and is independent of the finer details such as charge transfer. For donor types, the carbon 2p pi orbitals are spin-polarized because of mixing with metal-conduction electrons, thus there is an additional dipolar contribution which may be correlated with the electronic specific heat. The general agreement with experimental data is satisfactory.

Tsang, T.↗

DOE OSTI2024

Effects of nuclear structure and quantum interference on diffractive vector meson production in ultra-peripheral nuclear collisions

Here we study diffractive vector meson production in ultra-peripheral collisions (UPCs) of heavy nuclei, utilizing a theoretical framework based on the Color Glass Condensate (CGC) formalism. We focus on Au + Au, U + U, Ru + Ru, Zr + Zr, and Pb + Pb collisions, examining the transverse momentum dependence of vector meson production cross-sections and co s(2ΔΦ)asymmetries in the decay product distributions to explore the role of nuclear geometry. The angular modulation is due to the linear polarization of the incoming photons and quantum interference effects. We extract nuclear radii and find them to be consistent with experimental data from the STAR collaboration. The amplitudes of the cos(2ΔΦ)modulation in the cross-section and the extracted radii depend on the nuclear geometry. This dependence is dominated by the geometry-dependent variation of the minimum impact parameter required for ultra-peripheral collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of 108 Ru, 110 Ru, and 116 Pd were measured to a relative mass precision $\delta$⁢$m/m$ ≈ 10 -8 via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. Further, these mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1992

PARAS program: Phased array radio astronomy from space

An orbiting radio telescope is proposed which, when operated in a Very Long Baseline Interferometry (VLBLI) scheme, would allow higher (than currently available) angular resolution and dynamic range in the maps, and the ability of observing rapidly changing astronomical sources. Using a passive phases array technology, the proposed design consists of 656 hexagonal modules forming a 150 meter diameter dish. Each observatory module is largely autonomous, having its own photovoltaic power supply and low-noise receiver and processor for phase shifting. The signals received by the modules are channeled via fiber optics to the central control computer in the central bus module. After processing and multiplexing, the data is transmitted to telemetry stations on the ground. The truss frame supporting each observatory pane is a hybrid structure consisting of a bottom graphite/epoxy tubular triangle and rigidized inflatable Kevlar tubes connecting the top observatory panel and bottom triangle. Attitude control and stationkeeping functions are performed by a system of momentum wheels in the bus and four propulsion modules located at the compass points on the periphery of the observatory dish. Each propulsion module has four monopropellant thrusters and six hydrazine arcjets, the latter supported by a nuclear reactor. The total mass of the spacecraft is 22,060 kg.

Jakubowski, Antoni K.↗

NASA NTRS2004

Improved Nuclear Reactor and Shield Mass Model for Space Applications

New technologies are being developed to explore the distant reaches of the solar system. Beyond Mars, solar energy is inadequate to power advanced scientific instruments. One technology that can meet the energy requirements is the space nuclear reactor. The nuclear reactor is used as a heat source for which a heat-to-electricity conversion system is needed. Examples of such conversion systems are the Brayton, Rankine, and Stirling cycles. Since launch cost is proportional to the amount of mass to lift, mass is always a concern in designing spacecraft. Estimations of system masses are an important part in determining the feasibility of a design. I worked under Michael Barrett in the Thermal Energy Conversion Branch of the Power & Electric Propulsion Division. An in-house Closed Cycle Engine Program (CCEP) is used for the design and performance analysis of closed-Brayton-cycle energy conversion systems for space applications. This program also calculates the system mass including the heat source. CCEP uses the subroutine RSMASS, which has been updated to RSMASS-D, to estimate the mass of the reactor. RSMASS was developed in 1986 at Sandia National Laboratories to quickly estimate the mass of multi-megawatt nuclear reactors for space applications. In response to an emphasis for lower power reactors, RSMASS-D was developed in 1997 and is based off of the SP-100 liquid metal cooled reactor. The subroutine calculates the mass of reactor components such as the safety systems, instrumentation and control, radiation shield, structure, reflector, and core. The major improvements in RSMASS-D are that it uses higher fidelity calculations, is easier to use, and automatically optimizes the systems mass. RSMASS-D is accurate within 15% of actual data while RSMASS is only accurate within 50%. My goal this summer was to learn FORTRAN 77 programming language and update the CCEP program with the RSMASS-D model.

Robb, Kevin↗

DOE OSTI2025

The Technical, Economic, Risk, and Adoption Assessment for Evaluating Work Reduction Opportunities in the Nuclear Industry

Automation and cost-saving initiatives, such as process automation with advances in artificial intelligence, are gaining traction in modernization efforts across the nuclear industry. As these innovations are increasingly adopted, it becomes crucial to evaluate their impacts comprehensively. Various technical and economic attributes, along with risk and human readiness factors, must be achieved to ensure that innovative projects enabling automation and modernization are successful. However, no systematic or integrated framework exists that allows plants to evaluate these innovative projects. To address this gap, the Technical, Economic, Risk, and Adoption (TERA) assessment offers a structured method to evaluate innovative technologies, ensuring solutions meet both operational and safety standards. The TERA framework integrates the disparate perspectives to assess modernization opportunities in nuclear operations. It combines qualitative and quantitative models to evaluate the relationship between performance and business impacts, while also enabling continuous re-evaluation during project development. This approach helps plant owners identify high-priority opportunities, optimize cost savings, and minimize risks, ensuring projects remain on track to achieve desired returns. By providing a comprehensive, systematic methodology, TERA enables informed, data-driven decisions that support successful modernization efforts, enhancing efficiency, safety, and cost savings across nuclear operations. This paper explains the TERA framework and its benefits for the nuclear industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

The structure of low-lying 1 - states in 90,94 Zr from (α,α'γ) and (p,p'γ) reactions

The low-lying dipole strength in the 90,94 Zr nuclei was investigated via (ρ,ρ'γ) at 80 MeV and (α, α', γ) at 130 MeV. The experiments, made at RCNP, used the magnetic spectrometer Grand Raiden for the scattered particles and the array CAGRA with HPGe detectors for the γ-decay. For 94 Zr these are the first data for both reactions and for 90 Zr these are the first data with (ρ,ρ'γ) and the first ones at high resolution for (α, α', γ). The comparison of the present results for the two nuclei with existing (γ, γ') data shows that both nuclear probes produce an excitation pattern different than that of the electromagnetic probes. DWBA calculations were made using form factors deduced from transition densities, based on RPA calculations, characterized by a strong neutron component at the nuclear surface. A combined analysis of the two reactions was performed for the first time to investigate the isoscalar character of the 1 - states in 90,94 Zr. The (ρ,ρ'γ) cross section was calculated using values for the isoscalar electric dipole energy-weighted sum rule (E1 ISEWSR) obtained from the (α, α', γ) data. The isoscalar strength for 90 Zr was found to exhaust 20 ± 2.5% of the EWSR in the energy range up to 12 MeV. In case of 94 Zr, a strength of 9 ± 1.1% of the EWSR was found in the range up to 8.5 MeV. Although an overall general description was obtained in the studied energy intervals, not all proton cross sections were well reproduced using the isoscalar strength from (α, α', γ). This might suggest mixing of isoscalar and isovector components and that this mixing and the degree of collectivity are not the same for all the 1 - states below the particle binding energy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Energetics of oxidation and formation of uranium mononitride

Uranium mononitride (UN) is an advanced nuclear fuel currently being considered for use in several generation IV fast and thermal neutron spectrum core designs, with additional applications to thermal and electric nuclear propulsion reactors. Here, to better understand the thermal behavior and thermodynamic stability of UN, we investigated the bulk thermal oxidation process and thermochemical reactions, including the enthalpy of oxidation and standard enthalpy of formation, by conducting thermalgravimetric analysis – differential scanning calorimetry coupled with mass spectrometry (TGA-DSC-MS), and high temperature transposed temperature drop and oxide melt drop solution calorimetry. The bulk oxidation of UN (containing a small amount of α-UN 1.5+x ) in air was found to follow a step-wise process characterized by consecutive oxidative reactions UN-UN 1.5+x -UO 2 → UO 2 -UO 3 —N k → UO 3 —N k → UO 3 → U 3 O 8 . TGA results support that the UO 2 – U 2 N 3+x passivating layer delays the onset of rapid bulk oxidation of UN in air up to 662 K. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) analyses were performed to characterize UN and its final oxidized product. The standard enthalpy of formation (ΔH° f ) of UN was determined to be –144.4 ± 5.9 kJ/mol·atom, in good agreement with previously determined values from Pt encapsulation and bomb calorimetric experiments. Lastly, a negative linear correlation between ΔH° f and the N/U molar ratio was established based on the thermochemical data obtained in this work and previously reported enthalpies of formations of β-UN 1.5-x and α-UN 1.5+x .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗