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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Investigation of design principles for metal-binding and conductive protein assemblies

Throughout the lifetime of this initiative, including renewals, we focused on understanding the fundamental principles of protein-protein interface design that enable predictable and modular spatial and kinetic control of multi-component protein self-assembly in 1D, 2D, and 3D, including the interface with inorganic materials, small molecules, and metal ions. We designed individual protein components that bind specific metal ions, including REEs and transport ions across lipid membranes. We created helical 1D filaments of repeating units with programmed periodicity, pitch, and multi-component environmentally responsive self-assembling protein fibers. We showed that these filaments reversibly assemble and disassemble under specific pH conditions and created end-specific caps that independently tune the balance of attachment and detachment rates at each terminus of the filament. Using similar filaments, we succeeded in binding arrays of heme and chlorophyll molecules and assembling patterned helical coatings around carbon nanotubes in efforts to create de novo conductive nanowires. By arraying REE binding sites in a large circular tandem array with a repeat protein-based cyclic oligomer, we created a molecular scaffold for superradiance and paramagnetic quantum sensing. We created a range of one-component and two-component self-assembling 2D arrays and showed that when designed to engage cell receptors, these arrays can control cell behavior from outside the cell signal to inside the cell. We designed helical repeat proteins with variable lengths displaying charged residues in a pattern matched to the cation lattice of mica. achieved a range of ordered states with an epitaxial match to the underlying crystal lattice. We further applied the learned principles of protein-induced biomineralization to design proteins with an interface lattice matching CaCO 3 and guide the formation of specific crystal forms of CaCO 3 from solution, a significant advance toward the global need to manage carbon. In all cases of mineral lattice matching and biomineralization, we followed assembly using molecularly resolved in situ AFM imaging and extracted information about assembly pathways and energetics, applying deep learning to quantify the dynamics of protein self-organization. We developed techniques for using dynamic metal-dependent interfaces on protein nanopores for discriminatively sensing dilute REEs in solution and demonstrated the use of strong metal-binding interfaces to drive nanocage disassembly for conditional nanocompartmentalization applications. This grant supported 11 people, including Asim Bera, Evans Brackenbrough, Andrew Borst, Nikita Hanikel, Timothy Huddy, Emily Joyce, Alex Young-Seug Kang, Ryan Kibler, Joshua Morris Lubner, Harley Pyles, and Shuai Zhang. The research effort culminated in the production of published papers and theses. Electronic Thesis/Dissertation are distributed by ProQuest/UMI Dissertation Publishing and made available on an open access basis through UW Libraries ResearchWorks Service.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Hawai‘i National Marine Renewable Energy Center (HINMREC)

The Hawaii National Marine Renewable Energy Center (HINMREC) was established to facilitate the commercialization of wave energy converter (WEC) devices, and to accelerate development and testing of Ocean Thermal Energy Conversion (OTEC) technologies. Housed at the Hawaii Natural Energy Institute (HNEI) of the University of Hawaii (UH), this program started with execution of the contract between the US Department of Energy (DOE) and UH in March 2009, and activities ran through the end of the project in September 2019. The DOE tasked HINMREC with facilitating and accelerating the build-out of the Navy’s Wave Energy Test Site (WETS), the nation’s first grid-connected open water wave energy conversion test facility, located off Marine Corps Base Hawaii (MCBH) in Kaneohe, on the island of Oahu. WETS was expanded from an existing test berth at 30m water depth to include test berths at 60m and 80m water depths. In recognition of the high costs associated with at-sea testing and evaluation of WEC devices, it was deemed imperative that HINMREC seek ways to reduce costs to developers by providing key research support to these early-stage technologies. WETS and HINMREC’s support allowed development of wave energy technology by providing a testing infrastructure that allows technology developers to test, demonstrate and evaluate their WEC devices, and generate data in order to advance WEC designs toward commercial readiness. HINMREC’s roles have included support in the establishment of the full site, including of wave resource characterization and site survey, followed by the essential roles of independent WEC device power performance assessment and environmental monitoring in support of deployed pre-commercial WECs. This DOE-funded work transitioned effectively to continue Navy-funded tasks for OTEC development, and activities at WETS, now under Naval Facilities Engineering Command (NAVFAC) funding. A secondary objective in the establishment of HINMREC was to assist the private sector in moving OTEC systems beyond proof-of-concept to pre-commercialization, primarily focused on system and component engineering, and local and global investigations into the potential environmental impacts of OTEC systems. HINMREC was tasked with maintaining high-resolution models of ocean thermal resources and the potential sustainable power output of OTEC systems. Ongoing tests begun previously under Navy funding at the OTEC Heat Exchangers (HXs) Test Facility at the Natural Energy Laboratory of Hawaii Authority (NELHA), in Kona on Hawaii Island, have been continued to identify cost-effective aluminum alloys for use in OTEC systems operating in the corrosive marine environment.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Phenomenological R -Matrix parameterization of direct, doorway, and compound nuclear reactions [Abstract]

Although formal expressions for scattering matrix accounting for direct, doorway, and compound nuclear (CN) resonant reactions have been derived several decades ago in both the transition ( T -)matrix formalism and the reactance ( K -)matrix formalism, the absence of corresponding expressions in phenomenological R -matrix formalism has limited the application of the latter to CN resonant reactions only. We remove this limitation by parameterizing direct, doorway, and CN resonant reactions in a phenomenological R -matrix scattering matrix, and provide a parameterization for a corresponding Reich-Moore approximation of eliminated capture channels. Direct reactions induce (previously neglected) mixing among the incoming or outgoing R -matrix channel wave functions, parameterized by real and orthonormal channel-rotation matrix, M , whereby the original scattering matrix U is transformed into M T UM . Any real and orthonormal matrix, M , can be equivalently expressed as e η , where η is a real and skew-symmetric 2 rotation-generating matrix that subsequently yields a more intuitive parameterization of eliminated direct capture reactions in Reich-Moore approximation. A phenomenological R -matrix parameterization of doorway reactions is inferred by equating the expression for reactance ( K -)matrix, given in terms of Brune’s alternative R -matrix parameterization, to a corresponding expression derived using Feshbach’s projection operator formalism. Assuming that all doorway states, just like CN states, are confined within spheres defined by R -matrix channel radii, a new R -matrix-like term induced by doorway states is gleaned, wherein each doorway state is parameterized by its energy, width, and the strength of its coupling to each CN state. Since a Reich-Moore approximation for retained-channel scattering matrix ought to approximate the effect of eliminated capture channels taking place via direct, doorway, or CN reactions, each of the three kinds of reactions contributing to the capture entails a corresponding Reich-Moore parameterization in a first-order approximation: direct contribution is parameterized by introducing finite diagonal elements of a retained-channel rotation-generating matrix, doorway contribution is parameterized by doorway capture widths, while CN contribution is parameterized by conventional Reich-Moore capture widths. We will present evidence of direct and doorway reactions observed in recent measurements of resolved resonance cross sections at the Gaerttner LINAC Center at Rensselaer Polytechnic Institute, and will outline a path for implementing this new R -matrix parameterization into the SAMMY nuclear data evaluation code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Evaluation of Polyethylene and Blank Pulsed Sphere Experiments Using Deuteron Transport Feature in COG

From the late 1960s to about 1985, LLNL undertook a series of ‘pulsed sphere’ experiments for 31 materials involving 148 different experiments using 75 different spheres. The purpose was to measure the neutron leakage spectra for different materials from 14-MeV neutrons generated by 3 H(d,n) 4 He reactions induced by an incident D+ beam from the Insulated Core Transfer (ICT) accelerator at LLNL. This deuteron beam was focused to impinge upon tritium loaded onto a titanium substrate within a low mass target assembly placed within the center of a spherical shell constructed from the material of interest. COG is a three-dimensional, continuous energy, Monte Carlo code, developed by Lawrence Livermore National Laboratory (LLNL). Recent additions to COG include alpha and deuteron particle transport in order to directly calculate (α,n) and 3 H(d,n) 4 He reactions, and resulting neutron emission spectra. Results of the direct deuteron transport calculation for the 0.8 MFP (mean free path) polyethylene sphere with PILOT B detector was previously published. The purpose of this paper is to present expanded COG results showing the neutron peak from deuterium buildup ( 2 H(d,n) 3 He) due to tritium decay in the target. Cross section data libraries used are ENDF/B-VIII.0 for neutrons and ENDF/B-VII.1 for deuterons, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Continued Validation Studies using the MOOSE Framework for Plasma Simulation with Electromagnetics

Resources and tools for the modeling and simulation of low-temperature plasma (LTP) discharges are increasingly vital to progress in the field in order to properly characterize and study complex source designs and plasma chemistries beyond the scope of traditional diagnostics. Open-source software provides powerful platforms for this work and can enable community-driven LTP R&D. Within the Multiphysics Object-Oriented Simulation Environment (MOOSE) open-source framework [1], capabilities have been demonstrated in the areas of plasma fluids (Zapdos [2]), plasma chemistry (CRANE [3]), and general electromagnetic wave theory (Electromagnetic Library for Kinetics & fluids [ELK] [4]). ELK has since been coupled to Zapdos/CRANE to enable fully coupled electromagnetic plasma simulations. This talk will detail the continued validation efforts and discuss Zapdos-ELK-CRANE code coupling with various-low temperature plasma sources. The impact of fully coupled electromagnetics on process parameters (e.g., temperature and electron/ion energy) versus an electrostatic description will also be discussed. [1] Permann et al., SoftwareX, 11 (2020) 100430. [2] Lindsay et al., J. Phys. D: Appl. Phys. 49 (2016) 235204. [3] Keniley et al., Talk, GEC Session DT3.00002 (2019). [4] Icenhour et al., Talk, GEC Session RR1.00006 (2019). Acknowledgements: INL Graduate Fellowship Program, NSF SI2 Grant 1740300, U.S. Dept. of Energy Office of Science Graduate Student Research Program

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

HST PanCET Program: A Complete Near-UV to Infrared Transmission Spectrum for the Hot Jupiter WASP-79b

We present a new optical transmission spectrum of the hot Jupiter WASP-79b. We observed three transits with the STIS instrument mounted on the Hubble Space Telescope (HST), spanning 0.3–1.0 μm. Combining these transits with previous observations, we construct a complete 0.3–5.0 μm transmission spectrum of WASP-79b. Both HST and ground-based observations show decreasing transit depths toward blue wavelengths, contrary to expectations from Rayleigh scattering or hazes. We infer atmospheric and stellar properties from the full near-UV to infrared transmission spectrum of WASP-79b using three independent retrieval codes, all of which yield consistent results. Our retrievals confirm previous detections of H{sub 2}O (at 4.0σ confidence) while providing moderate evidence of H{sup -} bound–free opacity (3.3σ) and strong evidence of stellar contamination from unocculted faculae (4.7σ). The retrieved H{sub 2}O abundance (~1%) suggests a superstellar atmospheric metallicity, though stellar or substellar abundances remain consistent with present observations (O/H = 0.3–34× stellar). All three retrieval codes obtain a precise H{sup -} abundance constraint: log(X{sub H{sup -}}) ≈ -8.0 ± 0.7. The potential presence of H{sup -} suggests that James Webb Space Telescope observations may be sensitive to ionic chemistry in the atmosphere of WASP-79b. The inferred faculae are ~500 K hotter than the stellar photosphere, covering ~15% of the stellar surface. Our analysis underscores the importance of observing UV–optical transmission spectra in order to disentangle the influence of unocculted stellar heterogeneities from planetary transmission spectra.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Midwest Nuclear-based Net-Zero Carbon Steelmaking Demonstration

Globally, the iron and steel industry accounts for around a quarter of GHG emissions from the manufacturing sector , which is about 7% of the 33 gigatonnes (Gt) of global CO 2 emissions . Thus, globally, the steel industry is a 2-3 Gt CO 2 per year emissions challenge. The steel industry is important to the U.S. economy with output equal to $0.5 trillion and domestic production capacity is critical to national defense, infrastructure, energy production, and transportation. President Biden’s executive order on climate identifies actions and policies to put the Nation on a path to achieve net-zero carbon emissions, economy-wide, by no later than 2050. To achieve this, deep carbon reductions in the power, transportation, buildings and capital-intensive industrial manufacturing segments such as steel, cement and chemicals need to be addressed. In its recent “Accelerating Decarbonization of the U.S. Energy System” report , the National Academy of Sciences (NAS) stated that, “while technology exists to decarbonize all parts of the energy system, some sectors remain at precommercial or first-of-a-kind demonstration stages and require significant improvement in cost and performance to become commercially viable.” The NAS goes on to state that these hard to decarbonize sectors include “aviation, shipping, and industrial subsectors such as steel, cement, and chemicals manufacturing .” The Administration has proposed a timeline to decarbonize energy, transportation and then industry and manufacturing. From a life-cycle approach, decarbonizing steel and other large industrial inputs are necessary to decarbonize transformations in energy and transportation. DOE’s TRI-Lab Consortium’s Integrated Energy Systems (IES) program is a collaboration amongst DOE’s three applied energy laboratories: the National Energy Technology Laboratory; National Renewable Energy Laboratory and Idaho National Laboratory. The IES approach looks at more tightly integrating low-carbon nuclear energy, renewable electricity, energy storage, load/demand balancing, and biomass (including plastics and other wastes) with one or more industrial processes that utilize heat and/or power from these clean and waste energy sources to produce a low-carbon commodity-scale product.

08 HYDROGEN↗
DOE OSTI2020

Methodology for Generating Covariance Data of Thermal Neutron Scattering Cross Sections

This paper details and implements a framework for evaluating thermal neutron scattering cross sections that provide S(α,β) data and covariance data for hydrogen in light water. This methodology involves perturbing model parameters of molecular dynamics potentials and fitting the simulation results to experimental data. The framework is general and can be applied to any material or simulation method. The fit is made using the Unified Monte Carlo method to experimentally measure double-differential scattering cross sections of light water at the Spallation Neutron Source at Oak Ridge National Laboratory. Mean values and covariance data were generated for model parameters, phonon density of states, double-differential cross sections, and total scattering cross sections. These posterior parameter values were very similar to their prior values with a maximum relative error of 0.54%. This falls within in the Unified Monte Carlo–calculated uncertainties on the order of 2.7%. Additionally, posterior double-differential cross sections agree favorably with ENDF/B-VIII.0 cross sections. The new thermal scattering law was tested by comparing it against benchmarks from the International Criticality Safety Benchmark Evaluation Project Handbook, which showed a slight improvement over the ENDF/B-VIII.0 library. Additionally, the covariance matrix of the phonon density of states was validated to confirm that the spread of k eff from the density of states used to generate the covariance matrix was similar to the spread of k eff from the density of states of the sampled covariance matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

DOE Deep Energy Retrofit Cost Survey

A survey was conducted by the Lawrence Berkeley National Laboratory on deep energy retrofit (DER) market drivers, opportunities, and challenges. The survey was part a research study sponsored by the U.S. Department of Energy to gather information on the costs of DER from home performance contractors and stakeholders. Cost data was gathered from DER projects that use a comprehensive, whole-home approach to drastically reduce energy use and improve performance. DER projects often aim at reducing energy use by 50% or more. In addition, these projects can improve home comfort and potentially benefiting occupant health. Yet, market adoption of DER has been limited. Major limiting factors include complex projects, high costs, perceived risks, extensive disruption, and unfamiliar work scopes to some contractors. In order to better understand what motivates and deters DER projects in today’s market, a survey was conducted to gather this information, and to learn about promising approaches and technologies from the industry perspective. Past surveys on homeowners and home energy performance professionals have studied the motivations and barriers of energy efficiency retrofits. Two surveys of home energy performance professionals were conducted in recent years. The Resources for the Future (RFF) Home Energy Audit and Retrofit Survey was conducted in 2011 by recruiting energy auditors and retrofit installers through members of Efficiency First and Building Performance Institute (BPI) accredited contractors. The survey asked about the business and services that respondents provide, how often homeowners follow their recommendations to retrofit their homes, and the respondents’ opinion on barriers faced by the industry. The survey found that not enough homeowners know about energy audits, but more importantly, it is the high cost of retrofits compared to low energy prices that is responsible for few energy audits and retrofits being completed. The RESNET Deep Retrofit Industry Stakeholder Survey was conducted shortly after launching of the EnergySmart Home Performance Team program. The EnergySmart Team program involves a formal agreement among allied contractors who are engaged in high performance retrofits. Using this allied team approach, teams can pool their expertise and provide each other with customer referrals. The survey asked EnergySmart Team members and outside stakeholders on questions about market and technical barriers in performing home energy retrofits. Survey respondents identified lack of consumer awareness and lack of affordable financing for consumers as the leading market barriers to home energy retrofits. In their written comments, many survey respondents also echoed that the high costs of retrofit compared to low energy prices is a market barrier. Respondents found “certain housing characteristics that prevent effective retrofit” and “energy analysis software inaccuracy or limitations” are the two leading technical barriers. Their choices for technical barriers were reflective of the energy rater/auditor role played by the majority (78%) of the survey respondents. In comparison to past surveys, this work aimed to gather inputs from a broader segment of the home performance industry to identify the opportunities and barriers faced by DERs from all perspectives. The survey asks for project costs to help breakdown the high costs of DERs. This survey is also motivated by a need to better understand the role of DERs in reducing energy use by the residential sectors and meeting climate goals. The survey is designed towards obtaining more substantive inputs from survey respondents by encouraging written comments, rather than setting the goal to reach a large number of respondents. We took this approach because DER is currently still a niche market, so it is more valuable to gather in-depth inputs from individuals who are performing this work rather than getting to the masses.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of LPBF Steels for Nuclear Applications

This report provides an update on the prioritization of existing reactor materials for advanced manufacturing. This report is a Milestone 3 deliverable in FY2023, under work package CT-23AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). The focus of FY23 for ANL included the determination of a decision criteria matrix for the prioritization of existing materials, the literature review of a select few Fe-based alloys, and working with vendors to obtain customized powders. The work package also includes fabricating test samples in a Renishaw AM400 Laser Powder Bed Fusion (LPBF) system and optimizing the process parameters. The major outcomes of this work package are: With collaborations from PNNL, ORNL, and INL, we were able to establish a decision criteria matrix containing a total of 6 categories and 31 different criteria. These will be used to downselect alloys for further evaluation; As part of that, 6 different Fe-based alloys were selected, 3 austenitic stainless steels (A709, D9, AFA) and 3 ferritic/martensitic steels (HT9, Grade 91, Grade 92). Customized powders were obtained from vendors in small quantities to fabricate initial prints to check printability of these specific alloys; A total of 72 single track experiments were performed on two alloy systems, 1 austenitic stainless steel (A709) and 1 ferritic/martensitic steel (Grade 91) in order to optimize the process parameters for the full 3d prints. The optimization led to the selection of 20 different processing conditions, 10 for each class of alloys; D9 and AFA alloys showed extensive cracking and porosity in the samples. This was due to less-than-ideal conditions present in the chamber during the deposition. A709 printed using the same process parameters showed almost fully dense samples with no noticeable porosity or any other defects. SEM and EBSD analysis revealed single phase FCC microstructure with cellular structure within the grains; HT9, Grade 91, Grade 92 alloys also showed no noticeable signs of cracking and ImageJ analysis showed porosity <0.5% in all conditions. While Grade 91 and Grade 92 showed single phase BCC microstructures, the presence of martensite laths was noted in HT9 alloy; Future work will include further characterization of these alloys to better understand the microstructural evolution during the 3d printing process.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Steam-Assisted Ammonolysis of MoO2 as a Synthetic Pathway to Oxygenated δ-MoN

A common route for the synthesis of molybdenum nitrides is through the temperature-programmed reaction of molybdenum oxides with NH3, or ammonolysis. In this work, the role of precursor phase, gas phase chemistry (impact of H2O), and temperature profile on the reaction outcome (700 °C) was examined, which resulted in varying amounts of MoO2, H2MoO5, and the nitride phases—cubic γ (nominally Mo2N) and hexagonal δ (nominally MoN). The phase fraction of the δ phase increased with precursor in the sequence MoO2 > MoO3 > H2MoO5. Steam in the reaction gas also favored the production of δ over γ, but with too much steam, MoO2 was obtained in the product. Synthesis conditions for obtaining nearly phase-pure δ were identified: MoO2 as the precursor, 2% H2O in the gas stream, and a moderate heating rate (3 °C/min). In situ X-ray diffraction provided insights into the reaction pathway. Extensive physico-chemical analysis of the δ phase, including synchrotron X-ray and neutron diffraction, electron microscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy, and prompt gamma activation analysis, revealed its stoichiometry to be MoO0.108(8)N0.892(8)H0.012(5), indicating non-trivial oxygen incorporation. The presence of N/O ordering and an impurity phase Mo5N6 were also revealed, detectable only by neutron diffraction. Notably, a computationally predicted MoON phase (doi: 10.1103/PhysRevLett.123.236402), of interest due to its potential to display a metal-insulator transition, did not appear under any reaction condition examined.

Pandey, Shobhit↗
DOE OSTI2020

GeoMelt{sup R} In-Container Vitrification (ICV){sup TM} for Fukushima Daiichi Water Treatment Secondary Wastes - 20212

The Japanese government is supporting development work implemented jointly by Veolia subsidiaries Kurion Japan, K.K., Veolia Nuclear Solutions (VNS), Inc., and Veolia Nuclear Solutions Federal Services, LLC for treating radioactive waste generated from Fukushima Daiichi Nuclear Power Station (NPS) water treatment using the GeoMelt{sup TM} In-Container Vitrification (ICV){sup TM} technology. The initial work consisted of glass formulation and engineering-scale testing which was completed in 2018, in the frame of an IRID (International Research Institute for Nuclear Decommissioning) program as part of a project subsidized by Japan's Ministry of Economy, Trade and Industry (METI). The Fukushima Daiichi NPS Mid- and Long-Term Road-map requires investigation of methods to stabilize solid wastes (and to immobilize radioisotopes in the wastes) generated as a result of emergency response and decommissioning activities. Cooling water treatment has resulted in a significant amount of solid and slurry secondary wastes (mostly adsorbents and ion-exchange materials) which will require processing at some point. GeoMelt{sup R} ICV{sup TM} is a joule-heated melter technology which uses a refractory-lined single-use container combining the melter and disposal container. There is no pouring required nor concerns with refractory corrosion which allows the process to accommodate a wide range of waste chemistries and high waste loadings. The testing described here consisted of three engineering-scale melts, each processing between 212 kg and 240 kg of waste simulants, glass formers, and non-radioactive cesium (Cs) and strontium (Sr) tracers. Continuous isokinetic stack sampling of off-gas emissions was performed for each test in order to calculate Cs and Sr retention in the glass wasteform. Single-pass retention of Cs in the final glass wasteform ranged from 91.46 to 99.30%, and single-pass retention of Sr ranged from 99.76 to 100%. Planned particulate recycle will increase these retention levels. Melt 1 processed a mixture of KUR-EH (a zeolite-based ion-exchange material), simulated Advanced Liquid Processing System (ALPS) Carbonate and Iron Slurries, and glass additives. Melt 2 processed a mixture of KUR-EH, KUR-TSG (a titanate-based adsorbent), and glass additives. Melt 3 processed a mixture of KUR-EH, simulated barium sulfate/iron ferrocyanide sludge (AREVA sludge), and glass additives. Waste loadings for these melts ranged from 70 weight percent (wt%) to 82 wt%. Vitrification produces a waste form much denser than the stored water treatment secondary waste wastes, resulting in significant volume reduction. Volume reductions for the three tests ranged from 74 to 79 vol%. Vitrification produces a chemically durable wasteform. Pacific Northwest National Laboratory (PNNL) tested three glass samples from each engineering-scale melt) by the Materials Characterization Center 1 (MCC-1) test, an international standard leach test of the chemical durability of nuclear waste glasses. PNNL also obtained one U.S. reference glass (EA Glass) and two Japan reference glasses P0798) and tested these under the same MCC-1 conditions (90 deg. C, 10 m-1, DIW, and 7, 14, 28-day) as the GeoMelt{sup R} ICV{sup TM} glasses. The GeoMelt{sup R} ICV{sup TM} glasses exhibited lower total normalized releases and 14- to 28-day normalized release rates than the three reference glasses. These results suggest that the GeoMelt{sup R} ICV{sup TM} glasses have durabilities on par with high-level waste glasses under standard test conditions. Post-melt process sampling and analysis indicated no Cs migration into the melter refractory materials and very little deposition of Cs or Sr onto the melter hood or off-gas piping internals. The results of the testing indicated good Cs retention in the glass, high volume reduction and waste loadings, and excellent chemical durability. These factors are important to minimize treatment costs and to protect workers and the environment. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Patchy nuclear chain reactions

Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficient power, since a deterministic (i.e., exactly predictable) behavior of the neutron population is required by automatic safety systems to detect unwanted power excursions. In order to characterize the reactor operating conditions at which the fluctuations vanish, an experiment was designed and took place in 2017 at the Rensselaer Polytechnic Institute Reactor Critical Facility. This experiment however revealed persisting fluctuations and striking patchy spatial patterns in neutron spatial distributions. Here we report these experimental findings, interpret them by a stochastic modeling based on branching random walks, and extend them using a “numerical twin” of the reactor core. Consequences on nuclear safety will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Tomographic galaxy clustering with the Subaru Hyper Suprime-Cam first year public data release

We analyze the clustering of galaxies in the first public data release of the HSC Subaru Strategic Program. Despite the relatively small footprints of the observed fields, the data are an excellent proxy for the deep photometric datasets that will be acquired by LSST, and are therefore an ideal test bed for the analysis methods being implemented by the LSST DESC. We select a magnitude limited sample with i<24.5 and analyze it in four redshift bins covering 0.15≲z≲1.5. We carry out a Fourier-space analysis of the two-point clustering of this sample, including all auto- and cross-correlations. We demonstrate the use of map-level deprojection methods to account for fluctuations in the galaxy number density caused by observational systematics. Through an HOD analysis, we place constraints on the characteristic halo masses of this sample, finding a good fit up to scales k max = 1Mpc –1 , including both auto- and cross-correlations. Our results show monotonically decreasing average halo masses, which can be interpreted in terms of the drop-out of red galaxies at high redshifts for a flux-limited sample. In terms of photometric redshift systematics, we show that additional care is needed in order to marginalize over uncertainties in the redshift distribution in galaxy clustering, and that these uncertainties can be constrained by including cross-correlations. We are able to make a ~3σ detection of lensing magnification in the HSC data. Our results are stable to variations in σ 8 and Ω c and we find constraints that agree well with measurements from Planck and low-redshift probes. Lastly, we use our pipeline to study the clustering of galaxies as a function of limiting flux, and provide a simple fitting function for the linear galaxy bias for magnitude limited samples as a function of limiting magnitude and redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Nuclear Data Adjustment with Whisper for Criticality Safety Applications [Slides]

Nuclear data adjustment is used by Whisper to compute residual nuclear data uncertainties within a full suite of benchmarks; in order to do so, a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications) is required. For applications that are similar to the full benchmark suite, the residual uncertainties add a small margin to the overall USL calculation. For applications that are dissimilar to the full benchmark suite, the residual uncertainties add a reasonable margin to the overall USL calculation (more conservative). This methodology helps to overcome any issues in the released covariance libraries where nuclear data uncertainties appear to be too large.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗