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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Materials and Fuels Complex FY-22 – FY-26 Five-Year Mission Strategy

The Idaho National Laboratory (INL), through its designated mission of advancing innovative nuclear energy solutions, is actively engaged in the research, development, demonstration and deployment of advanced nuclear technology. Key to the success of INL’s mission is the Materials and Fuels Complex (MFC), the only complex in the U.S. that hosts a world-class assemblage of facilities, capabilities and instruments for handling, testing, and characterizing nuclear fuel and radioactive materials. Driven by its mission/vision of “Engineering and Experiments that Drive the World’s Nuclear Energy Future,” MFC is at the center of INL’s – and indeed the Department of Energy’s – advanced nuclear technology development initiatives, providing essential capabilities such as research-scale high-assay low-enriched uranium (HALEU) fuel production, reactor demonstration facilities, post-irradiation examination, and transient irradiation testing. Furthermore, MFC provides an ideal environment for test beds that are utilized for research, development and demonstration (RD&D) activities on used fuel treatment, nuclear non-proliferation, forensics, and nuclear power sources used for space exploration missions conducted by the National Aeronautics and Space Administration (NASA).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

NA-22 IAP Review: ATONA Project [Slides]

The ATONA technology has the potential to impact measurement capabilities in the broad field nuclear forensics, and not just for uranium. We remain cautiously optimistic that we can develop ATONA based environmental measurement techniques that will be of use to some of our partners.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

First Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

Accurate, reliable nuclear data is essential for the success of Federal missions such as nonproliferation, nuclear forensics, homeland security, national defense, space exploration, clean energy generation, and scientific research. Data access is also key to innovative commercial developments such as new medicines, automated industrial controls, energy exploration, energy security, nuclear reactor design, and isotope production. The United States Nuclear Data Program (USNDP) is the domestic custodian of nuclear data. In its April 2022 meeting, the DOE/NSF Nuclear Science Advisory Committee was charged with preparing two reports on nuclear data. In this first report, we review recent accomplishments of the USNDP and discuss complementary and collaborative international efforts. Detailed descriptions of nuclear data needs for basic science, nonproliferation, national security, nuclear energy together with medical and space applications are also presented. Lastly, a set of specific cross-cutting nuclear data needs with relevance for multiple applications areas are also identified for further discussion in a follow-on report planned for release at the end of January 2023.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Preliminary Assessment of Elemental Analysis of X-Ray Fluorescence with Genetic Algorithm

X-ray fluorescence (XRF) spectroscopy is a promising approach to elemental analysis of forensic samples. Elements in the mixture can be identified from the XRF data because chemical elements are associated with unique spectra. This report consists of preliminary assessment of automated identification of unknown mixtures of elements from XRF spectra using a genetic algorithm (GA). The XRF spectra are measured at the Advanced Photon Source (APS) at the liquid-liquid interface during solvent extraction process. Possible advantages of using GA for identifying elements at the interface from XRF data are discussed, and a path for implementation of GA is outlined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A new TEVA-DGA chromatography procedure to separate Pu, Am, and Np from bulk U materials

Trace actinides present within uranium (U) material are diagnostic signatures of U processing history. For example, the emplacement of U material within the high neutron-flux environment of a nuclear reactor can cause neutron capture of U to form 241-plutonium ( 241 Pu). Plutonium-241 then decays to progeny isotopes 241-americium ( 241 Am) and 237-neptunium ( 237 Np); therefore, the presence and relative abundances of these trace actinides is indicative of the material’s production history and intended employment (Mayer et al., 2013). During a pre-detonation nuclear forensics investigation, measurement of trace actinide 241 Pu, 241 Am, and 237 Np concentrations by an analytical laboratory may be requested. However, chemically purifying these elements from bulk U is a challenging and involved procedure requiring several sequential chromatography columns across 4+ days. To expedite Pu-Am-Np separation from bulk U material, and to improve the yield recovery of these elements, this DHS Postdoc Fellowship has worked to create a new chromatography separation chemistry.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

MCNP ® Code V.6.3.0 Release Notes

The Monte Carlo N-Particle ® (MCNP ® ) code is a general-purpose, continuous-energy, generalized geometry, time-dependent, radiation transport code developed by the MCNP development team. The MCNP calculations provide predictive capabilities that can replace expensive or impossible-to perform experiments. Specific application problems include simulations of experimental diagnostics, intrinsic radiation, radiation detection and measurement, criticality safety, nuclear threat reduction and response, radiation health protection, nuclear weapons effects, and nuclear forensics. This MCNP code, version 6.3.0, follows the MCNP6.2.0 version. Since the release of MCNP6.2.0, many changes have been made to the MCNP code. These changes include new or improved features, a new build system, code enhancement and modernization, and bug fixes. The MCNP code, version 6.3.0, theory and user input information is documented in MCNP ® Code Version 6.3.0 Theory & User Manual, the build guidance for various platforms is documented in MCNP ® Code Version 6.3.0 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP ® Code Version 6.3.0 Verification & Validation Testing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Durable Module Materials (DuraMAT) Consortium (Final Technical Report)

The DuraMAT Consortium brings together DOE national lab and university research capabilities with the photovoltaic (PV) and supply-chain industries to accelerate a sustainable, just, and equitable transition to zero carbon electricity generation by 2035 through our five core objectives: development of a central data resource for PV modules, multi-scale and multi-physics modeling, disruptive acceleration science, forensic tools for fielded modules, and materials solutions for more durable, reliable, and resilient modules.

14 SOLAR ENERGY↗
DOE OSTI2023

Ion Mobility – Mass Spectrometry Rapid Imaging of Special Nuclear Materials

Robot Nose will use new fast data throughput methods and experience with laser microfocus to dramatically improve the speed and spatial resolution of an established instrument, making it a useful tool for imaging special nuclear materials (SNM). Mass spectrometry imaging instruments have traditionally been too slow and mass alone is generally an insufficient measurement for organic analyses. This situation was markedly improved by Ionwerks Inc., who pioneered the combination of ion mobility with mass spectrometry imaging. In 2D mobility-mass plots, clearly identifiable trend lines can be selected and reduced to 1D mass spectra without isobaric interferences. This is a major advantage over other imaging mass spectrometers that generally cannot distinguish isobars without custom-built lasers or high voltage accelerators. Special nuclear material (SNM) contains substantial fissile isotopes, particularly 233U, 235U, and 239Pu. A complete forensic analysis of SNM therefore requires quantitative isotopic measurement – often through some form of mass spectrometry. SNM contaminated with inclusions such as pollen grains can help identify its prior locations. The project team is presently working on an approach to allow rapid screening of 1-3 cm diameter thin samples of such material.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Fission with Exotic Nuclei (Full Technical Report)

Despite its importance for stockpile stewardship or nuclear forensics, data on fission is fragmentary: most experiments have only been performed on stable actinide nuclei and are often incomplete, and theoretical simulations often contain many parameters hard to constrain, which results in large uncertainties. Consequently, nuclear libraries have large gaps for important isotopes. The goal of this project was to prepare to take advantage of the unprecedented yields of radioactive isotopes at the upcoming DOE Facility for Rare Ion Beams and to leverage recent progress in the field of machine learning to develop a comprehensive program of fission studies that could address some of the most pressing problems in nuclear fission over the next several decades. Our project leveraged synergies between experimental nuclear physics, nuclear theory, and data science expertise at LLNL. On the experimental front, our main objective was to develop in-house expertise for inverse kinematics reactions with relativistic beams as well as to field and test new detectors to perform correlated measurements of fission properties. On the theoretical side, the objective was to develop a novel, high-fidelity and scalable approach of fissionfragment calculations based on emulating computationally expensive, quantum-mechanical calculations of nuclear properties with deep neural networks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Routine Beryllium-10 analysis from post-detonation debris and geologic materials [Slides]

Beryllium (Be) isotope measurements provide insight into weapon design, yield, etc. Sample preparation nor measurement capability currently existed at LANL. Potential capability directly benefits nuclear forensics venture. PI Goehring has nearly 20 years of Be chemistry experience, including method implementation with difficult samples.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Progress in Genetic Algorithm Fitting of X-Ray Fluorescence Data for Absorption Spectroscopy of Liquid Surfaces

X-ray fluorescence (XRF) spectroscopy of liquid surfaces is a promising approach to elemental analysis of forensic samples. Elements in the mixture can be identified from the XRF data because chemical elements are associated with unique spectra. This report describes progress in the development of automated identification of unknown mixtures of elements from XRF spectra using a genetic algorithm (GA). The XRF spectra are measured at the Advanced Photon Source (APS) at the liquid-liquid interface during solvent extraction process. The usage of the GA is demonstrated and future implementation for related problems are then discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

SEDONA and ERDE: Portable Detection of Chemical Agents by Low Magnetic Field Nuclear Magnetic Resonance [Slides]

Two spectrometer systems have been developed at LANL for hazardous and/or toxic chemical agent detection and forensic analysis: SpEctroscopic Detection Of Nerve Agents (SEDONA) is a portable bottle scanner; and Earth’s field Resonance Detection and Evaluation (ERDE) Devices are fieldable J-coupled Earth’s Field - NMR (EF-NMR) spectrometers.

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

Samoa Updater: An Application of the Levenberg-Marquardt Method to Update DELFIC Predictions Using Field Measurements

The US Department of Energy (DOE) Forensics Operations (DFO) is a member of the Ground Collections Task Force (GCTF), which is responsible for sample collection of radiological debris for attribution should a nuclear detonation ever occur in the United States. The DFO runs the Defense Land Fallout Interpretive Code (DELFIC) Fallout Planning Tool to predict the deposition of fallout from a nuclear detonation. This prediction is refined using the DELFIC Updater tool, which takes ground measurements and adjusts DELFIC inputs to minimize the difference between prediction and observation, yielding improved predictions of fallout in locations both measured and not yet measured. Samoa, a framework for uncertainty analysis and optimization, is used to improve DELFIC predictive fallout modeling. This new capability using Samoa, dubbed “Samoa Updater,” is compared with the current DELFIC Updater, a brute-force sampling approach. Samoa Updater uses the Levenberg– Marquardt (LM) method, a gradient-based nonlinear least squares approach that uses the functional shape of the input space to increase optimization speed. In simulated test cases Samoa Updater yields faster and more accurate solutions than the current Updater.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Report on branching ratios for 111 Ag

Our understanding on the distribution of fragment masses following fission, or fission yields is largely impacted by the quality of nuclear data. One of the most straightforward and reliable ways to determine the number of fissions that occurred in a chain reaction is done via detection of the characteristic γ-rays emitted during the β decay of the fission product. These γ rays are emitted in only a fraction of the decays, and this fraction (the γ-ray intensities) must be known accurately to determine the total number of fissions. Many long-lived fission products, such as 111 Ag, play an important role in science-based stockpile stewardship and nuclear forensics. The γ-ray intensities from the decay of 111 Ag are known to only 5%, leading to a 5% uncertainty in fission-chain yield. This work aims to improve the precision of these γ-ray intensities for the most intense emissions following the decay of 111 Ag, as seen in Figure 1.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Improving Fission Products at CARIBU (NA-22 Final Report)

Detailed knowledge of fission-product (FP) decay properties is needed for a variety of applications of nuclear science such as nuclear-energy production, nuclear-nonproliferation efforts, nuclear-forensics assessments, and stockpile stewardship, as well as for establishing a comprehensive understanding of the fission process, r-process nucleo-synthesis, and fundamental neutrino science. Although nearly a thousand radioactive isotopes are produced in fission, in many cases key pieces of nuclear data on only a handful of isotopes are needed to make a significant impact.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

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

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗