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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterizing inhibitors of human AP endonuclease 1

AP endonuclease 1 (APE1) processes DNA lesions including apurinic/apyrimidinic sites and 3´-blocking groups, mediating base excision repair and single strand break repair. Much effort has focused on developing specific inhibitors of APE1, which could have important applications in basic research and potentially lead to clinical anticancer agents. We used structural, biophysical, and biochemical methods to characterize several reported inhibitors, including 7-nitroindole-2-carboxylic acid (CRT0044876), given its small size, reported potency, and widespread use for studying APE1. Intriguingly, NMR chemical shift perturbation (CSP) experiments show that CRT0044876 and three similar indole-2-carboxylic acids bind a pocket distal from the APE1 active site. A crystal structure confirms these findings and defines the pose for 5-nitroindole-2-carboxylic acid. However, dynamic light scattering experiments show the indole compounds form colloidal aggregates that could bind (sequester) APE1, causing nonspecific inhibition. Endonuclease assays show the compounds lack significant APE1 inhibition under conditions (detergent) that disrupt aggregation. Thus, binding of the indole-2-carboxylic acids at the remote pocket does not inhibit APE1 repair activity. Myricetin also forms aggregates and lacks APE1 inhibition under aggregate-disrupting conditions. Two other reported compounds (MLS000552981, MLS000419194) inhibit APE1 in vitro with low micromolar IC50 and do not appear to aggregate in this concentration range. However, NMR CSP experiments indicate the compounds do not bind specifically to apo- or Mg 2+ -bound APE1, pointing to a non-specific mode of inhibition, possibly DNA binding. Our results highlight methods for rigorous interrogation of putative APE1 inhibitors and should facilitate future efforts to discover compounds that specifically inhibit this important repair enzyme.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa . Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A comprehensive guide to CAN IDS data and introduction of the ROAD dataset

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID’s frames), and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets’ attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset. State-of-the-art public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, lacking fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but is missing a corresponding “raw” binary version. This issue pigeon-holes CAN IDS research into testing on limited and often inappropriate data (usually with attacks that are too easily detectable to truly test the method). The scarcity of appropriate data has stymied comparability and reproducibility of results for researchers. As our primary contribution, we present the Real ORNL Automotive Dynamometer (ROAD) CAN IDS dataset, consisting of over 3.5 hours of one vehicle’s CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real (i.e. non-simulated) fuzzing, fabrication, unique advanced attacks, and simulated masquerade attacks. To facilitate a benchmark for CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS research field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Oilwell Conversion (Well API 121913310501) to Geothermal Heat Storage Well for Flexible Electricity Storage

Geothermal growth is limited by a lack of geographically dispersed high-temperature thermal resources and high initial upfront investment in characterization and well construction. This project intended to address the challenges of energy supply intermittency and enhance grid resilience, reliability, and energy security by storing energy provided from currently available renewable resources in the subsurface to harvest it a later time during at-peak energy demand. This project intended to improve geothermal adoption, reduce initial project risk, and improve price competitiveness through utilizing existing oil and gas infrastructure such as non-productive wells, non-economic fields, dry holes, and orphaned wells. The project also intended to address the lack of geographically dispersed thermal resources and enhance grid resilience, reliability, and energy security by introducing an economical method for storing energy from currently available renewable resources in the subsurface for usage during at-peak energy demand. During this research, the project furthered the understanding of the feasibility of utilizing abandoned oil and gas wells as geothermal heat storage wells. The project team investigated the heat storage and hydrogeological characteristics of subsurface reservoirs in the Illinois Basin to evaluate their response to heat injection for determining the evolution of temperature profiles and heat losses over time using existing and available data sets. The project team then performed modeling and simulation to evaluate the heat losses of returning fluids during heat extraction. The outputs were used to select an optimal candidate reservoir and location in Southern Illinois. The team designed and performed a small-scale field test in an existing oil well to refine the model and to demonstrate the permitting and regulatory pathways necessary for the conversion of oil and gas assets to geothermal use. The field test also serves as a proof of concept and can guide the procedures for future research and implementation. Additionally, the project team, conducted initial market research and customer discovery to develop a go to market strategy for an Advanced Geothermal Energy Storage (AGES) system. The project team in this research also identified the parameters to be refined in future research, to improve the current go to market strategy economic model. To this end several subject matter experts were also identified to assist in future research with geothermal infrastructure setup, energy storage policy and law, energy storage market demand, potential siting based on demand etc. Future research will involve further sophistication of the site commercial modeling, implementing a larger-scale test, and further refinement of the thermodynamic modeling/simulation process. The output will be lifecycle costs and economics suitable for comparison to alternative approaches from a validated full-scale demonstration for venture capital investment into this technology. The project successfully demonstrated the ability to leverage existing oilfield infrastructure, permits, and land access and leasing agreements, to enable geothermal storage projects to come online faster and cheaper than a greenfield development could. This technology could allow for greater energy independence and security through long-term energy storage solutions. The longer duration allows for greater storage for renewables currently limited by hours-long storage durations of lithium-ion. The AGES system would support the growth of renewable energy farms, and provide greater opportunities for a cleaner energy infrastructure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

X-Ray Diffraction and Electron Microscopy Studies of the Size Effects on Pressure-Induced Phase Transitions in CdS Nanocrystals

In recent years, investigations of the phase transition behavior of semiconducting nanoparticles under high pressure has attracted increasing attention due to their potential applications in sensors, electronics, and optics. However, current understanding of how the size of nanoparticles influences this pressure-dependent property is somewhat lacking. In particular, phase behaviors of semiconducting CdS nanoparticles under high pressure have not been extensively reported. Therefore, in this work, CdS nanoparticles of different sizes are used as a model system to investigate particle size effects on high-pressure-induced phase transition behaviors. In particular, 7.5, 10.6, and 39.7 nm spherical CdS nanoparticles are synthesized and subjected to controlled high pressures up to 15 GPa in a diamond anvil cell. Analysis of all three nanoparticles using in-situ synchrotron wide-angle X-ray scattering (WAXS) data shows that phase transitions from wurtzite to rocksalt occur at higher pressures than for bulk material. Bulk modulus calculations not only show that the wurtzite CdS nanomaterial is more compressible than rocksalt, but also that the compressibility of CdS nanoparticles depends on their particle size. Furthermore, sintering of spherical nanoparticles into nanorods was observed for the 7.5 nm CdS nanoparticles. Our results provide new insights into the fundamental properties of nanoparticles under high pressure that will inform designs of new nanomaterial structures for emerging applications.

Meng, Lingyao↗
DOE OSTI2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leveraging the expressive power of neural networks to model unknown dynamics. However, these approaches often suffer from limited labeled training data, leading to poor generalization and suboptimal predictions. On the other hand, semi-supervised algorithms can utilize abundant unlabeled data and have demonstrated good performance in classification and regression tasks. We propose TS-NODE, the first semi-supervised approach to modeling dynamical systems with NODE. TS-NODE explores cheaply generated synthetic pseudo rollouts to broaden exploration in the state space and to tackle the challenges brought by lack of ground-truth system data under a teacher-student model. TS-NODE employs an unified optimization framework that corrects the teacher model based on the student's feedback while mitigating the potential false system dynamics present in pseudo rollouts. TS-NODE demonstrates significant performance improvements over a baseline Neural ODE model on multiple dynamical system modeling tasks.

Wang, Yu↗
DOE OSTI2020

Replacement of Cobalt in Medical Device Sterilization: Current Trends, Opportunities and Barriers to Adoption of X-ray and E-Beam Within the Medical Device Sterilization Market

Radioactive sources, specifically Co-60, are crucially important to the medical device sterilization industry; however, their benefits must be balanced against the need to ensure that the materials are secure. Accelerator-based sterilization methods, including X-ray and E-Beam, can in many cases sufficiently replace Co-60 and ethylene oxide, the dominant modalities currently in use; however, a lack of hands-on experience with accelerators, regulations, and a lack of capacity have hampered faster adoption of these technologies. The market is likely to change, however, given public pressures against ethylene oxide and concerns over the cost and long-term supply chain robustness of Co-60. This may provide an opportunity for growth for accelerator capabilities. Current research at Sandia National Laboratories and Argonne National Laboratory, on behalf of the Department of Energy’s National Nuclear Security Administration Office of Radiological Security, is focused on developing an understanding of the medical device sterilization marketplace, the different modalities that are used to sterilize goods, and the drivers that affect the actors within it. Research at Pacific Northwest National Laboratory, in conjunction with the medical device sterilization industry, has focused on expanding the industry’s understanding of the capabilities of X-ray sterilization and developing data that can inform the industry’s consideration of a transition towards accelerator-based devices.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Dragonstone Strategy – State of Cybersecurity in the Oil & Natural Gas Sector

The oil & natural gas (ONG) system touches every corner of the nation and increased communications & control capabilities have not only allowed for greater efficiency of system operation, but have also created a massive target for adversaries to launch cyber-attacks. Like any other heavy industrial process, the ONG system relies on a complex system of information technology (IT) and operational technology (OT) devices. The current state of cyber-preparedness across the ONG industry varies from organization to organization. Among the most common challenges that ONG companies face are remote locations, longlived field assets, and lacking capabilities to find and track malware on their systems. ONG companies tend to be concerned with lack of cyber-awareness from employees, risk stemming from remote access for operations & maintenance, and software vulnerabilities within third-party equipment. Various industry and government organizations are performing research and development activities to address some of these challenges, but in many cases industry stakeholders are not aware of solutions that already exist. It is clear that a need exists for a coherent, comprehensive, multi-layered strategy for assuring the security and resilience of the nation's pipeline infrastructure against cyber threats. For a variety of reasons, the general consensus from stakeholders interviewed by LLNL is that the state of cyber-security within the electric grid is currently outpacing its ONG cousin. Existing strategies for the resilience and cyber-security of the electric grid can and should be leveraged to provide immediate benefits to the ONG system. In LLNL’s view, there are two key factors currently limiting the development of necessary cyber-practices within the ONG industry: The sheer number of differing regulatory bodies and trade groups offering both standards and best-practice recommendations for ONG cyber-security makes it difficult to create a comprehensive, directed, and coherent strategy that is applicable to all players within the ONG industry. The ONG industry is unaware of potentially useful technologies that have been developed for ensuring cyber-security of other infrastructure systems, such as the electric grid. Leveraging these technologies—and the science and engineering behind them—can provide some low-hanging fruit that can greatly improve cyber-security in the ONG industry without significant investments in terms of time and money. In the months following this report, LLNL will continue to perform outreach to key oil & gas industry stakeholders in a continual effort to identify the most pressing cyber-resilience issues in the industry. This outreach will be supplemented with LLNL’s threat intelligence capabilities to begin painting a clearer picture of the overall threat landscape faced by this sector. This assessment will be threat-informed and while the strategy itself will not be classified we will leverage intelligence analysis and adversary capabilities to identify gaps in current cybersecurity practices for oil & gas pipeline systems. Recommended efforts will be compiled into a cyber-resilience roadmap for the oil & gas pipeline sector, in which LLNL will highlight priority activities to immediately improve the state of cyber-resilience in the industry.

02 PETROLEUM↗
DOE OSTI2020

Dragonstone Strategy Kickoff Report [Slides]

The oil & natural gas (ONG) system like any other heavy industrial process, relies on a complex system of information technology (IT) and operational technology (OT) devices. Cyber-preparedness varies across the ONG industry from organization to organization. Common challenges include remote locations, long-lived field assets, and insufficient capabilities to find and track malware. ONG companies tend to be concerned with lack of cyber-awareness from employees, risk stemming from remote access for operations & maintenance, and software vulnerabilities within third-party equipment. For a variety of reasons, the general consensus from stakeholders interviewed by LLNL is that the state of cybersecurity within the electric grid is currently outpacing its ONG cousin. Existing strategies for the resilience and cybersecurity of the electric grid can and should be leveraged to provide immediate benefits to the ONG system. In LLNL’s view, there are two key factors currently limiting the development of necessary cyber-practices within the ONG industry: regulatory overlap and inefficiencies, and lack of industry awareness of potentially useful technologies for solving known issues. In the months following this report, LLNL will develop a cyber-resilience roadmap for the oil & gas pipeline sector, coupling threat intelligence, industry knowledge base, and ongoing outreach

02 PETROLEUM↗
DOE OSTI2020

Eligibility Assessment of TA-52-1 and TA-52-11: The Ultra-High Temperature Reactor Experiment (UHTREX) Complex

The U.S. Department of Energy (DOE), National Nuclear Security Administration, Los Alamos Field Office (NA-LA) requests the State Historic Preservation Officer (SHPO) to concur with the eligibility determinations contained in this report for Buildings 1 and 11 in Technical Area 52 (TA-52) at Los Alamos National Laboratory (LANL or the Laboratory). Triad National Security LLC cultural resources staff have completed the evaluation of two buildings, called the UHTREX Complex, for inclusion in the National Register of Historic Places (Register). This complex includes the UHTREX Reactor Building (TA-52-1), and an associated Mechanical Assembly Building (TA-52-11). As part of LANL’s Footprint Reduction Program, both facilities of the UHTREX Complex are scheduled for characterization and demolition. In addition to evaluating their eligibility in the Register, the properties at TA-52 were assessed for potential adaptive reuse, long-term preservation, and public interpretation. Based on the findings in this assessment report, both TA-52-1 and TA-52-11 have been determined to not be eligible for inclusion in the Register. The history of the UHTREX Complex lacks association with exceptionally significant Cold War events of scientific developments. Both TA-52-1 and TA-52-11 lack the necessary internal historic integrity suitable for long-term preservation or public interpretation. And both facilities contain legacy radioactive contamination, which prohibits their reuse. In addition to its loss of integrity and context, TA-52- 11 has been determined ineligible due to its status as a support building of secondary or minor importance. In compliance with Section 106 and Section 110 of the National Historic Preservation Act of 1966, as amended, and with the Programmatic Agreement among the U.S. Department of Energy, National Nuclear Security Administration, Los Alamos Field Office, the New Mexico State Historic Preservation Office, and the Advisory Council on Historic Preservation Concerning Management of the Historic Properties at Los Alamos National Laboratory, Los Alamos, New Mexico, the SHPO is requested to concur with the eligibility determinations contained in this report for the UHTREX Complex in TA-52.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Review of "ML for Surface Complexation Model Development"

The presentation given by Jadallah Zouabe on “ML for Surface Complexation Model Development”was extremely informative, but lacked some explanation of details that made the latter half hard to follow as someone outside of the field. He began his presentation with an anecdote to spilling milk or ink on the floor and seeing it spread, cleverly connecting that to the spread of contamination in the real world. He then explained the importance of being able to model contamination, as it allows specialists to see the potential effects on different areas and environments in the coming years. Heal so made it clear that the main issue trying to be addressed here is how expensive modelling is and that their goal is to make computationally cheaper models. The introduction was overall very well formulated and attention-grabbing, but it seemed to take up too much of the presentation time, as after this point the presentation lacked details and explanations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

China’s Bid To Lead Artificial Intelligence Chip Development within the Decade

In 2017, the Chinese government announced an ambitious, broad strategy to become the global leader in artificial intelligence (AI) theories, technologies, and applications by 2030 and indicated that China’s ability to indigenously produce cutting-edge AI chips would be integral to its success. China’s goal to become a dominant producer of AI hardware within a decade is a bold undertaking because China faces domestic chip production challenges and fierce competition from US chip producers. Our review of Chinese AI chip product lines shows various levels where Chinese companies compete with global firms in the production chain. China lacks a robust indigenous AI chip production infrastructure, and the United States and its allies are tightening controls on the supply of advanced semiconductor manufacturing equipment (SME) and electronic design automation (EDA) software to China. In contrast to their Chinese counterparts, US companies operate in all areas of AI chip production and have consistently driven the development of new chip designs. AI chip production is a costly endeavor, and China’s chip producers currently lack the customer base to lead global AI chip development by the 2030 goal. An expanded customer base may help China attract the necessary expertise to bolster its AI chip production chain. This would also help Chinese producers make their investments in the requisite technology economically sustainable. Chinese researchers continue to pursue promising next-generation AI chip designs, such as neuromorphic computing-based architectures, which in the next five to ten years could position Chinese companies to make or contribute to key innovations in the field.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

AFIP6-MkII and RERTR-12 Porosity Data Collection and Analysis for Modeling and Simulation

Gathering data for the improvement of nuclear fuel modeling and simulation efforts is the primary driver for this work. Mechanistic models allow for a better understanding of the material on a micro- and macrostructural level while saving time and money over traditional experiment efforts. Historically, summarized data and correlations are the inputs for empirical material models and model validation. When improving these models for nuclear fuels with experimental results, there is a lack of reliable data readily available. Experiments - RERTR-12 and AFIP6-MkII - were conducted to understand the irradiation behavior of metallic U-10Mo monolithic fuels for use in extreme reactor environments such as research reactors like the Advanced Test Reactor (ATR) or the High Flux Isotope Reactor (HFIR). Microstructural characteristics of fission gas pores (FGP) in each experiment are collected using an automated image analysis technique developed at the University of Florida and presented here. A series of statistical tests are performed to explore the reliability of the results, as well as understand where the data is lacking and what future data collection is necessary to provide sufficient information to assist modeling efforts. The focus is on the porosity, pore size, and eccentricity of FGPs formed during irradiation in three AFIP6-MkII samples and one RERTR-12 sample. From the analysis, it is clear there are substantial impacts of fission density on the pore structure, but there also exist also underlying connections between each sample and the behavior observed in the pores. Further analyses of the pre- and post-irradiation microstructure are needed to improve the understanding of these connections. An early method for microstructural data analysis is presented within and is currently being expanded to include other microstructure data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Evaluation of Weakly Informed Priors for FLEX Data

On behalf of the U. S. Nuclear Regulatory Commission (NRC), Idaho National Laboratory (INL) has reviewed the development and use of “weakly informed prior” (WIP) probability distributions to obtain industry-wide failure probability and rate distributions for portable “FLEX” equipment, as documented in the Pressurized Water Reactor Owner’s Group (PWROG) report, FLEX Equipment Data Collection and Analysis (PWROG 18043-P) by MM Degonish. The review includes a description of the methods used by the PWROG analysts. It also includes an implementation of those methods for comparison purposes and an implementation of two constrained-noninformative (CN) distributions for further WIP comparisons. The report documents several issues that the review identified in the development of the WIP distributions. They relate to the quality and representativeness of the current portable “FLEX” component data, the choice of informed distributions for related installed components, and the lack of verification for the factors chosen to weaken the information from the installed components. Two errors occurred in the computations: the posterior distribution of the WIP was used to modify the WIP itself, resulting in a lack of independence between the WIP and the data; and an error was made in the conversion of WIP mean and variance values to beta distributions. The INL reviewers think the WIP distributions presented in the report are untenable for use in risk assessments at the present time because of these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Mechanical Behavior of Hybrids with Hyper-Connected Molecular Networks

Our research is focused on fundamental studies related to the molecular design, synthesis, characterization and modeling of molecular-reinforced hybrid glass films for superior mechanical and fracture properties. Molecular-reinforced hybrid glass films exhibit unique electro-optical properties while maintaining excellent thermal stability. They have important technological application for emerging nanoscience and energy technologies including anti-reflective and ultra-barrier layers in photovoltaics, display technologies, as well as quantum technologies. However, they are often inherently brittle in nature, do not adhere well to adjacent substrates, and exhibit poor mechanical properties. Furthermore, a robust understanding of the underlying mechanisms of deformation, fracture and fatigue has been lacking particularly given their mechanically fragile nature. The related lack of structure-property relationships between molecular structure, mechanical properties and processing represents a fundamental challenge to their integration into device technologies, compromises their thermomechanical reliability and has thwarted efforts to improve mechanical and, particularly, fracture properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Requirements and Conceptual Design of Off-gas Systems for the Reprocessing of Metallic Fuels

An assessment has been conducted to determine how key regulations regarding volatile radionuclide emissions to the atmosphere may apply to the off-gas streams associated with electrochemical reprocessing. The scope of this assessment was based upon a generic electrochemical reprocessing scheme with a throughput rate of 200 MTIHM/y applied to metallic fuel discharged from a sodium fast reactor (SFR), but the findings are able to be translated to other advanced nuclear scenarios as merited. Air dispersion modeling was performed using the EPA CAP-88 model and evaluated the uncontrolled decontamination factors (DFs) that would be required to achieve regulatory compliance with the dose-based limits set forth by EPA regulation 40 CFR 190.10(a). These DFs were compared to those required by fuel cycle–based limits set forth by EPA regulation 40 CFR 190.10(b). Two theoretical sites with disparate climatological conditions were selected for air dispersion modeling (Idaho and Tennessee). The radionuclides modeled included 3 H, 85 Kr, 129 I, and selected alpha-emitting transuranic isotopes (referred to here as 239 Pu-TRU <1y ). It was found that the fuel cycle-based limits in 40 CFR 190.10(b) are most restrictive for 85 Kr and 239 Pu-TRU <1y , with DFs of 3 and 6.1E+09, respectively. The dose-based limit as derived from 40 CFR 190.10(a) could require mitigation of tritium in some scenarios, with an estimated DF of about 3 for the reference scenarios. The fuel cycle-based limit for 129 I resulted in a DF of about 240 for the reference scenario. The need for iodine mitigation based on dose to the public depended upon the physical form of iodine as either particulate or vapor-phase species. Emission of iodine from the facility as a vapor necessitated DFs of about 2 but emission as a particulate would require DFs >6,000 to meet thyroid dose-based limits. Effects of physical form on needed iodine mitigation are significant, but the understanding of speciation of iodine both during electrochemical reprocessing and after release to the atmosphere is limited. The electrochemical processing unit operations were evaluated to identify potential release points for the volatile radionuclides and to assess the potential for retention of the radionuclides within the process (thus decreasing the need for mitigation). Mitigation strategies for 3 H, 85 Kr, 129 I, and 239 Pu-TRU <1y were identified. In all cases, there are reasonably achievable pathways to regulatory compliance, although in some cases additional R&D is merited to verify the chemical speciation of these isotopes and to develop and demonstrate potential treatment technologies for this application. Whether or not additional off-gas controls (beyond common operations such as HEPA filtration and oxygen and moisture control) are needed for any of these regulated or volatile radionuclides depends on the (a) type of facility (NRC-regulated or DOE), (b) used fuel process rate, (c) used fuel burnup and composition, (d) speciation and retention of volatile radionuclides in the process and in the cell gas cleanup system, (e) site-specific parameters such as location, meteorology, stack height, and site boundaries, and (f) levels of conservatism and safety factors used in assessing compliance to air emissions regulations. Performance of this assessment revealed several areas where information is lacking or additional research is required in order to better determine if or what kinds of off-gas control might be needed. First, and most significantly, the understanding of the chemical speciation and physical form and partitioning of iodine during electrochemical processing operations is lacking and prevents the ability to accurately assess the potential iodine mitigation requirements. Future research in this area should be multifaceted and include thermodynamic modeling of iodine speciation in different process steps, experiments to quantify the kinetics of vapor-phase and melt-phase transitions, bench-scale experiments to determine the potential chemical and physical form of iodine emissions from the electrorefining process, and verification of iodine behavior with experiments utilizing operational facilities. Similarly, an improved understanding of iodine behavior in the environment after release from the facility stack will be required to refine dose estimations, as particulate and vapor-phase emissions result in significantly different doses to the MEI.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications

This proposal is emerging from GISS ModelE3 ESM development in the area of cloud physics, so we begin with an example of research needs/gaps from that work. Here, some of our greatest development concerns arise where we lack fundamental process-level understanding, as in ice formation. Namely, it is currently unclear what is the main process that is forming the majority of ice crystals in commonly occurring convection, apparently via secondary ice production at warm temperatures. We are keenly awaiting laboratory data for candidate mechanisms, which is not yet in hand to crucially establish their efficiency. Our progress is also hampered by a lack of uncertainty characterization in currently available measurements of ice crystal number size distributions. Furthermore, the same multiplication process may be responsible for a majority of ice crystals in many extratropical mixed-phase clouds, whose variable representation in CMIP6 ESMs may be a leading cause of differences in cloud phase feedback and ECS. Yet we have been required to deliver an ESM with the cloud physics knowledge at hand. The proposed grand challenge project is AI-enabled via application of machine learning (ML) to climate model and observational data streams (focal area 3), and applications include AI-guided observing system design and model/component/parameterization selection (areas 1 and 2). The project is structurally agnostic as to whether model or observing system components use AI approaches or not, but uncertainties must be estimated and propagatable in both.

58 GEOSCIENCES↗