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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

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2023

NREL 25-cm2 High-Pressure Low-Temperature Electrolysis Cell Hardware (Open Source)

This data resource describes an open-source cell hardware that enables low temperature electrolysis (LTE) testing at elevated pressures. Existing commercial options have several downsides when it comes to R&D testing. They are often not designed for repeated reassembly, may not be able to accommodate porous transport layers with different thicknesses, and do not give state-of-the-art performance. Therefore, this hardware was developed specifically with LTE R&D in mind and its design is being made available to the global LTE community. This work was planned and funded by the U.S. Department of Energy's H2NEW consortium (https://h2new.energy.gov/). The hardware design package (.zip file) details the drawings, auxiliary materials, and procedures required to fabricate, assemble, and operate the National Renewable Energy Laboratory's (NREL's) high-pressure low-temperature electrolysis cells. While the hardware itself—end plates, current collectors, flow fields, bolts and washers, tube fittings—is always the same, the assembly and operating procedures may change depending on the active materials being tested, especially for the membrane. Material-specific assembly and operating procedures will be posted to the H2NEW website as they are developed and validated. Disclaimer: The documents and drawings included in this download package describe a design for a low temperature electrolysis hardware that is intended to comply with leak testing according to ASME B31.1. Safe operation at ambient and elevated pressures is the sole responsibility of the end user, which should be evaluated on a case-by-case basis for each individual cell. Factors affecting the sealing capability may depend on, for example, machining quality, cell assembly components, operating conditions, and operating history. Operation at pressure should only be performed on qualified test stands by qualified operators. NREL/ALLIANCE FOR SUSTAINABLE ENERGY, LLC/DOE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, AND MAKES NO WARRANTY AS TO THE ACCURACY, COMPLETENESS, OR USEFULNESS OF ANY INFORMATION PROVIDED HEREIN. USE OF THIS PACKAGE IS AT THE USER’S OWN RISK.

08 HYDROGEN↗
DOE OSTI2025

gaia: An R package to estimate crop yield responses to temperature and precipitation

gaia is an open-source R package designed to estimate crop yield shocks in response to annual weather variations and CO 2 concentrations at the country scale for 17 major crops. This innovative tool streamlines the workflow from raw climate data processing to projections of annual shocks to crop yields at the country level, using the response surfaces from an empirical econometric model developed and documented in Waldhoff et al. (2020), which leverages historical weather, CO 2 , and crop yield data for robust empirical fitting for 17 crops. gaia uses these response surfaces with monthly temperature and precipitation projections (e.g., from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O’Neill et al., 2016) climate data bias-adjusted and statistically downscaled by the ISIMIP3BASD approach (Lange, 2019) in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014)) to project yield shocks that can be applied to agricultural productivity changes at the country level for use in multisectoral economic models. The historical and future projections use gridded, country-and-crop specific monthly growing season precipitation and temperature data, aggregated to the national level, and weighted by cropland area derived from the global Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset (Portmann et al., 2010). These annual, country, and crop-specific yield shocks can be aggregated to different definitions of regions, crop commodities, and time periods, as needed by specific multisectoral economic models. gaia serves as a lightweight, powerful tool that can aid exploration of crop yield responses under a broad range of future climate projections, enhancing human-Earth system analysis capabilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Two early eudicot fossil flowers from the Kamikitaba assemblage (Coniacian, Late Cretaceous) in northeastern Japan

Two new fossil taxa referable to the basal eudicot grade are described from the Kamikitaba locality (ca. 89 MYBP, early Coniacian: Late Cretaceous) in the Ashizawa Formation (Asamigawa Member) of Futaba Group in northeastern Japan. These charcoalified mesofossils exhibit well-preserved three-dimensional structure and were analyzed using synchrotron-radiation X-ray microtomography (SRXTM) at the Advanced Photon Source (APS) to document the composition and internal structure. Cathiaria japonica sp. nov. is represented by infructescence segments that consist of an axis bearing three to four fruits. The capsular fruits are sessile and dehiscent and consist of a gynoecium subtended by a bract. No perianth parts are present. The gynoecium is monocarpellate containing two pendulous seeds. The carpel is ascidiate in the lower half and conduplicate in the upper part, style is deflected abaxially with a dorsiventral suture and a large, obliquely decurrent stigma. Pollen grains are tricolpate with a reticulate exine. The morphological features of Cathiaria are consistent with an assignment to the Buxaceae s. l. (including Didymelaceae). Archaeostella verticillata gen. et sp. nov. is represented by flowers that are small, actinomorphic, pedicellate, bisexual, semi-inferior, and multicarpellate. The floral receptacle is cup shaped with a perigynous perianth consisting of several tepals inserted around the rim. The androecium comprises ca. 120 stamens with clear differentiation into anther and filament. The anthers are basifixed and tetrasporangiate. The gynoecium consists of a whorl of ten conduplicate, laterally connate but distally distinct carpels with a conspicuous dorsal bulge, including a central cavity. The styles are short, becoming recurved with a ventrally decurrent stigma. The fruit type is a follicle. Seeds are ca. 10 per carpel, marginal, pendulous from the broad, oblique summit of the locule. Seeds are small, spindle-shaped, with a chalazal extension, Pollen grains are tricolpate with a reticulate exine pattern, suggesting a relationship to eudicots. The morphological features of Archaeostella indicate a possible relationship to Trochodendraceae in the basal grade of eudicots, although it is uncertain if the anther thecae open by longitudinal slits as in extant Trochodendron. The fossil currently provides the earliest record of the family and documents the presence of Trochodendraceae in eastern Eurasia during the middle part of the Late Cretaceous. Furthermore, the recovery of Cathiaria japonica and Archaeostella verticillata from the Kamikitaba locality suggests that the basal eudicot families Trochodendraceae and Buxaceae s. l. (including Didymelaceae) were differentiated and distributed in eastern Eurasia area during the Late Cretaceous.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A distinct subpopulation of membrane vesicles in Pseudomonas putida is enriched in enzymes for lignin catabolism

Bacterial membrane vesicles (MVs) mediate diverse microbial processes and are emerging as powerful biomedical tools, but MV population heterogeneity remains an open question. Here, we separate, enumerate, and characterize two MV populations from the soil bacterium Pseudomonas putida during growth with or without lignin-derived carbon, a major carbon source from plant cells in the rhizosphere. Small MVs (MV-S, diameter ~100 nm) were produced from all cultures, whereas large MVs (MV-L, diameter ~300 nm) were observed during the late stationary phase of lignin cultivations. MV-S contained selectively packaged proteins with diverse physiological functions, whereas the MV-L proteome was smaller and largely enriched in outer membrane proteins. Interestingly, enzymes known to mediate the catabolism of lignin-derived aromatic compounds were enriched in MV-S. Overall, this study highlights the need for careful consideration of MV populations in microbial systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Coexisting kagome and heavy fermion flat bands in YbCr 6 Ge 6

Flat bands, electronic states with nearly dispersionless energy-momentum structure, provide fertile ground for unconventional quantum phases. Recent observations of flat bands at the Fermi level in kagome metals open the possibility of unifying topology and correlation-driven heavy-fermion physics. Here we show that topology and heavy-fermion correlations coexist in the layered kagome metal YbCr 6 Ge 6 . At high temperatures, an intrinsic kagome flat band—arising from frustrated hopping on the kagome lattice—dominates the Fermi level. Upon cooling, localized Yb 4f-states hybridize with the topological kagome flat bands, transforming this state into momentum-independent Kondo resonance states across the entire Brillouin zone. Topological analysis of the hybridization gaps reveals filling-tunable weak and strong topological Kondo-insulating regimes, and identifies a topological Dirac–Kondo semimetal. Taken together, these results identify YbCr 6 Ge 6 as a prototype of a topological heavy-fermion system and a platform where geometric frustration, strong correlations, and topology converge, with broad implications for correlated quantum matter.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Connecting the Radiative Influences of Aerosol upon the Mass Flux Profiles of Shallow Cumuli across the Southeast Atlantic Ocean Basin and its Boundaries (Final Report)

The Atlantic Ocean covers approximately 25% of Earth’s surface and the atmosphere above it is home to a complex array of clouds and aerosols that have important influences on regional and global weather and climate. These influences must be accurately depicted in short range, medium range, seasonal, and climate forecast models. Conditions over the tropical Atlantic are particularly complex due to continental scale plumes of dust from the Sahara Desert and smoke from agricultural burning in Africa that drift across the Atlantic Ocean basin toward the Americas. These plumes are often found meandering in the lower atmosphere above shallow tropical clouds that form above the ocean surface, presumably mingling with these clouds on occasion due to convective mixing processes. Elevated dust and smoke particles absorb incoming sunlight and substantially warm the marine atmosphere in the layer in which they are present. This warming may alter the thermal stability of the marine atmosphere and may throttle or enhance the development of clouds, change their internal structure and the rate at which they precipitate. Alternatively, it may isolate the lower atmosphere from drier layers above enabling water vapor to accumulate near the ocean surface potentially leading to the development of deeper convection. Our study was organized around the principal concept of determining the impact of African smoke and dust plumes upon cloud development at ASI and understanding how a popular shallow convection parameterization used in models responds to the presence of this aerosol. We analyzed observations collected during the US Department of Energy (DOE) Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign using the Atmospheric Radiation Measurement Program’s Mobile Facility #1 (AMF-1), which was deployed to Ascension Island (ASI) in Southeastern Atlantic for a one-year period. Ascension Island is immediately downwind from an African source of these plumes, but far enough removed to enable the lower atmosphere to have reacted to their presence. A main goal of our study was to compute radiative heating rate profiles over Ascension Island and along the trajectory from the biomass burning regions along coastal Africa to Ascension Island. To compute the radiative heating rate due to aerosols and clouds, we employed observed profiles of temperature, humidity, and clouds from LASIC alongside aerosol optical properties from the Modern-era Retrospective analysis for Research and Applications Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM). Radiative heating was also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We were successful in this effort and the resulting publication is already being cited despite its relatively short lifetime in the literature. The second part of our study involved the process-level representation of the clouds observed over the Southeastern Atlantic in models. Our initial task, upon which the balance of this portion of the study depended, was to evaluate the representativeness of the clouds observed by AMF-1. What orographic influence did Ascension Island have on the measured cloud structure? We set out to answer this question by trying to separate observations that were clearly indicative of orographic forcing from those that were representative of open ocean. The siting of AMF-1, which was 340-m above sea-level on the slope of a steep escarpment, proved demonstrably problematic for cloud process measurements. Despite considerable effort using multiple approaches including artificial intelligence (AI), we were unable to successfully compensate for the orographic forcing present at the AMF-1 site on ASI and, hence, produce process-level summaries of the convective mass flux representative of open ocean over the Southeastern Atlantic. Even though the project has officially ended, we are making a final attempt using two new types of clustering algorithm (i.e., AI) on the recommendation of a former student, who is an expert in this area. Since we have only recently begun to test these new AI schemes, the recommendations from the second part of our project outlined below are based on our experience at the time of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Near-Term Electricity Requirement and Emission Implications for Sustainable Aviation Fuel Production with CO 2 -to-Fuels Technologies

Aviation contributed approximately 10% of the U.S. transportation sector's greenhouse gas (GHG) emissions and about 3% of the nation's total GHG production before the coronavirus disease (COVID-19) pandemic (EPA 2022). Unlike the ground transportation sector, which can be decarbonized by using batteries and hydrogen fuel cell powertrain technologies, the technical and economic challenges of aviation electrification open the opportunity for CO 2 utilization (CO2U) using clean power sources. The U.S. government set a goal to produce 3 billion gallons per year of sustainable aviation fuels (SAF) by 2030 and scale up the production to 35 billion gallons per year by 2050 (Bioenergy Technologies Office 2022). In this report, we examine three potential locations, in California, Iowa, and Louisiana, for SAF production using two production pathways that are expected to be available by 2030. We analyze the electricity cost to satisfy 10% of the SAF production potential for the select locations. Specifically, we consider (1) retail electricity cost from the default local utility in each location; (2) physical power purchase agreement (PPA) for renewable power and battery storage hybrid systems with preset prices; (3) financial PPA from a dedicated renewable plant; and (4) estimated real-time pricing (RTP) from the wholesale power market with utility delivery adders. Retail rates are available for the three potential locations; however, the developer should negotiate with the local utility in Iowa for new tariff riders, because the load of the proposed SAF plant (1.4 GW) is significantly higher than the current industrial tariff structure requirement (200 kW). The developers are not able to claim federal credits (i.e., the Inflation Reduction Act of 2022) when sourcing electricity from local utilities. Developers could consider prioritizing physical and financial PPAs as purchase options. While physical PPAs can be imported out of state, this structure imposes availability issues as developers must locate in the same electricity grid regions. Financial PPAs do not have the same location restrictions and provide the same cost savings as physical PPAs, but financial PPAs impose financial risks in the event of system curtailment and grid interruption. RTPs are available for customers in California with flexible load and hourly load management capability; however, utilities in our studied regions in Louisiana and Iowa only offer time-of-use and curtailment programs to incentivize lower energy usage in peak hours. These utilities do not currently offer RTP programs, and the developer may need to have further negotiations with the local utilities to access RTP programs. The results show that purchased electricity prices may range from 2.6 cents/kWh to 7.1 cents/kWh for the three studied plants.

02 PETROLEUM↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗
DOE OSTI2023

Chromium Tolerant, Highly Active and Stable Electrocatalytic Internal Surface Coating for Cathode of Commercial SOFCs (Final Report)

This project is aimed to develop a chromium (Cr) tolerant, highly active, and stable coating layer on the internal surfaces of the porous composite cathode from commercially available SOFCs. Such coating layer was developed using the additive manufacturing process of Atomic Layer Deposition (ALD) and has been applied on the cathode consisting of either an electronic conductor of LaxSr 1-x MnyO 3-δ (LSM) or mixed ionic and electronic conducting La x Sr 1-x Co y Fe 1-y O 3-δ (LSCF). PI's work has demonstrated that the internal surface of cathode from the commercial cells, can be further tailored using ALD coating to dramatically enhance the cell performance. For instance, ALD layer consisting heterostructured nano composite of nano-Pt and nano-(Mn 0.8 Co 0.2 ) 3 O 4 oxide on the internal surface of porous LSM/YSZ cathode from SOFCs, has resulted in the large reduction of the cell polarizations resistance by up to 53%, and enormous increase of power density over 370%. For the cells with LSCF/Sm 2 O 3 doped CeO 2 (SDC) cathode, the conformal layer of nano-composite consisting of superjacent CoOx and subjacent minimum amount of Pt nano-grains has resulted in the power density enhancement by 126% for the large scale industry tubular cells at 750°C, and both the performance enhancement and nanostructure of the ALD layer are stable over ~ 2000 h continuous operation performed at industry test station. In the meanwhile, those ALD coating layer developed by PI's work is also inherently Cr-tolerant, and could act as physical barrier for preventing Cr diffusion into the cathode backbone, so as to mitigate the Cr poisoning effect on the cathode. In this project, the impact of Cr on the performance of those ALD coated commercial cells has been evaluated. Based on evolution of the cell performance, the ALD coating layer chemistry and ALD coating layer thickness has been optimized to maximize the overall Cr tolerance, cell power density and cell longevity. Different ALD coating has been applied onto the internal surface of LSM/YSZ and LSCF/SDC backbone respectively. The architecture/scaffold structures on the internal surface of different cathode, designed by this project, was catalogued and analyzed using High Resolution Transmission Electron Microscopy (HRTEM), and cell power/durability performance are assured via comprehensive electrochemical performance testing in the industry operation relevant conditions. The impact of the electrochemical operation current density, the water humidity, the cell operation temperature, and cell operation duration on the Cr tolerance of ALD coated cells has been systematically investigated. There is completely different nanostructure degradation mechanisms between LSM and LSCF cells induced by Cr contamination. For the LSCF/SDC baseline cell, With the Cr source, there is no apparent Sr surface segregation phase even for the baseline cell operated for 3000 h at 750 °C. With the Cr source, there is significant amorphous (SrCr)Ox phase accumulated in the original pore region. For the commercial baseline cells, Cr contaminants on the LSM electrode severely impacted the entire cell's electrochemical performance and nanostructure degradation. Those degradations include (1). Peak power density loss of 64 % after 109 h of operation. The dramatic increase in Rp (2). They are cracking at LSM/SSZ interface, LSM grains. SSZ remains intact but with (CrMn)Ox. By contrast, ALD coating (MnCo)Ox/Pt dramatically improves the Cr resistance, as follows (1). ALD-coated cell with a power density is 280-380 % of the baseline cell, depending on the ALD layer thickness. (2). For a cell with a 20 nm thick ALD layer, there is a large performance enhancement (> 200 % power density) induced by ALD coating of Cr-tolerant Mn 0.8 Co 0.2 Ox. (3). For a cell with a 20 nm thick ALD layer, after 168 h at 750 °C power density of the ALD-coated cell is ~ 600% of that baseline cell upon operation with Cr contamination for 109 h. The ALD coating on the internal surface of cathode developed by this project integrated multi-functions. Those multi-functions include (1). Dramatically improving the cell power density for the commercial cells; (2). Dramatically improving contamination resistance of the cathode, for being an excellent protection coating layer sealing off Cr contamination. (3). Dramatically increasing the cell longevity by potentially preventing the microstructure evolution and grain coarsening of the cathode. Overall, this project will provide a simple solution to simultaneously enhance power density and increase the reliability, robustness, and endurance of commercial SOFCs, over the entire operating temperature range of 650-800 °C. For the inherently functional SOFC, the ALD coating of LSM based cathode mitigate the Cr-contamination. Power density of ALD-coated cell is ~ 600% of that baseline cell upon operation with Cr contamination. In addition to SOFCs, the novel on-demand design approach and creation of multifunctional heterogeneous architecture on the electrode surface presented in this work opens further research for their application in other types of fuel cells, batteries, and sensors for which electrochemical reactions on the surface are similarly critical.

36 MATERIALS SCIENCE↗
DOE OSTI2013

National Open-ocean Energy Laboratory (Final Technical Report)

Under the authorization provided by Section 634 of the Energy Independence and Security Act of 2007 (P.L. 110-140), in 2009 FAU was awarded U.S. Congressionally Directed Program (CDP) funding through the U.S. Department of Energy (DOE) to investigate and develop technologies to harness the energy of the Florida Current as a source of clean, renewable, base-load power for Florida and the U.S. A second CDP award in 2010 provided additional funding in order to enhance and extend FAU’s activities. These two CDPs in 2009 and 2010 were combined into a single DOE grant, DE-EE0000319, and are the subject of this report. Subsequently, in July 2010 funding was made available under a separate contract, DE-EE0004200. Under that funding, DOE’s Wind and Water Power Program designated FAU’s state of Florida marine renewable energy (MRE) center as the Southeast National Marine Renewable Energy Center (SNMREC). This report discusses SNMREC activities funded by the DE-EE0000319 grant, but will make reference, as appropriate, to activities that require further investigation under the follow-on grant. The concept of extracting energy from the motions of the oceans has a long history. However, implementation on large scales of the technologies to effect renewable energy recovery from waves, tides, and open-ocean currents is relatively recent. DOE’s establishment of SNMREC recognizes a significant potential for ocean current energy recovery associated with the (relatively) high-speed Florida Current, the reach of the Gulf Stream System flowing through the Straits of Florida, between the Florida Peninsula and the Bahamas Archipelago. The proximity of the very large electrical load center of southeast Florida’s metropolitan area to the resource itself makes this potential all the more attractive. As attractive as this potential energy source is, it is not without its challenges. Although the technology is conceptually simple, its design and implementation in a commercially-viable fashion presents a variety of challenges. Beyond the technology itself (and, especially, the effects on the technology of the harsh oceanic environment), it is important to consider the possible environmental impacts of commercial-scale implementation of oceanic energy extraction. Further, because such implementation represents a completely new undertaking, the human resources required do not exist, so education and training programs are critical to eventual success. This project, establishing a national open-ocean energy laboratory, was designed to address each of these three challenges in a flexible framework allowing for adaptive management as the project proceeded. In particular: the technology challenge, including resource assessment, evolved during the project to recognize and address the need for a national testing facility in the ocean for small-scale prototype MRE systems developed by industry; the environmental challenge became formalized and expanded during the permitting process for such a testing facility; and the human resources/societal challenges, both in terms of the need for education and training and in terms of public acceptance of MRE, stimulated a robust outreach program far beyond that originally envisioned at SNMREC. While all of these activities at SNMREC are ongoing, a number of significant milestones (in addition to the contributions listed in the appendices) were achieved under the auspices of this award. These include: Planning and site selection for the first-phase test facility, offshore of Dania Beach, FL, including some equipment for the facility, submission of an Interim Policy Lease Application to the U.S. Department of Interior’s Bureau of Ocean Energy Management (BOEM), and completion of an Environmental Assessment by BOEM and a positive Consistency Determination by the State of Florida; Measurements using acoustic profilers of the current structure and variability in the vicinity of the site under a variety of weather conditions, seasons and time durations; Design and implementation of instrumentation for the first phase of the offshore testing facility, the wet- and top-side data acquisition systems, and shore-based analysis systems; Implementation of a laboratory-scale dynamometer system to test generators of up to 25 kW capacity using real-world (simulated) forcing; Completion of 24 months of (airborne) marine vertebrate surveys and associated analysis of sea turtle offshore activity, marine mammal vocalization research, and ocean current turbine hydrodynamic noise characterization; Development of a secondary-school (nominally grade 10) curriculum about hydrokinetic MRE, “Energy from the Oceans: The New Renewable”, and training of over 200 high-school teachers in its use and in how to educate their colleagues in application of the material in the classroom; Presentations to over 50 interested civic groups in the region on various aspects of MRE in SE Florida A series of public lectures to over 600 residents of south Florida to provide broader education on MRE. Development of an interactive kiosk for installation in local science museums. These, and other accomplishments detailed in this report contribute to a comprehensive ongoing program at the SNMREC to support the affordable, responsible, and achievable commercialization of MRE. Many of the tasks of this award are continued or will be verified with follow-on funding DE-EE0004200, and its goal: the installation of the world’s first offshore ocean current turbine testing and validation capability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Criticality Accident Alarm System Shielding Benchmark: Integral Experiment Request 498, Critical Engineering Decision 1 Report

This report supports Critical/Subcritical Experiment Decision 1 (CED-1) for Integral Experiment Request (IER) 498, a shielding benchmark produced using the Godiva IV critical assembly at the National Critical Experiments Research Center (NCERC). Shielding benchmarks are datasets collected to provide a comparison for computer models that simulate the effect of shielding on radiation transmission from a source to a detector. The purpose of a CED-1 report is to produce a conceptual design for an integral experiment and to identify the long lead-time items. The design presented in this report favors certain detailed design options such as material selections. However, alternative options can be selected as the project evolves. Dimensions are specified only as needed to evaluate feasibility and performance. Development of Criticality Accident Alarm System (CAAS) shielding benchmarks supports two main programmatic needs. The first need is to improve confidence in models of facilities that house nuclear material handling operations. Better models can support effective CAAS detector placement when criticality may result in dose, and it enables more accurate analysis of criticality accident impacts at various locations in a facility. A key quantity that can be verified is the secondary gamma production caused by neutron interactions with various materials. The second programmatic need supported by shielding benchmarks is the ongoing development of methods being integrated into nuclear simulation codes such as the Monte Carlo N-Particle (MCNP) transport code and the SCALE code system used in efforts funded by the Nuclear Criticality Safety Program (NCSP). Previous work performed under the International Criticality Safety Benchmark Experimental Program (ICSBEP) to produce shielding benchmark data is primarily limited to a set of measurements at the SILENE critical assembly at the Valduc facility in France. The experiment used an open configuration and a burst mode operation, which limited detector options to thermoluminescent dosimeters (TLDs) and activation foils. Because TLDs do not record gamma spectra, it was difficult to verify the gamma energy spectrum from the experiment. In this work, the Godiva IV assembly will be used because it is a fast system capable of producing a wide range of source strengths, including super-prompt critical bursts. The room return for the facility is expected to be substantial and challenging to characterize, so a more closed, shielded experimental configuration is considered. Furthermore, because the physics of radiation transport outside the assembly does not change between the burst mode and the delayed critical mode, it is anticipated that most measurements will be made with the Godiva IV assembly in delayed critical mode, operating within a range that will allow electronic counters to collect accurate data. Scoping calculations concluded that a workable room return shield is comprised of 40 cm of 5% borated polyethylene backed by 7 cm of lead. This configuration was shown to produce a ratio of signal–to–room return of over 100 for up to 2 mean free paths of shield sample material, and over 10 for up to 4 mean free paths of shield sample material, ensuring that useful results can be obtained. With no shielding sample in the room return shield, it was shown that the neutron and gamma spectra do not appreciably change with a change in room size. This scoping calculation does not preclude the use of tungsten powder in a binder, some other effective gamma shielding material, or a hybrid design. Dose rates for various source intensities at an unshielded detector are listed herein to inform detector selection during CED-2, and calibration chains are identified. Preliminary estimates of costs and schedules are outlined but remain to be verified. Values could double or triple as estimates become more accurate and are principally included here to demonstrate feasibility. Preliminary estimates indicate that the apparatus can be installed within a feasible schedule and budget. Two measurements per day are assumed. Producing a shielding benchmark at NCERC using the Godiva IV assembly has been shown to be feasible, with manageable uncertainties and risks. A clear, continuing need for shielding benchmark data remains present, and this report identifies and evaluates a method for creating integrated shielding benchmark datasets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Arctic Black Carbon Loading and Profile Using the Single-Particle Soot Photometer (SP2) Field Campaign Report

One of the major issues confronting aerosol climate simulations of the Arctic and Antarctic cryospheres is the lack of detailed data on the vertical and spatial distribution of aerosols with which to test these models. This is due, in part, to the inherent difficulty of conducting such measurements in extreme environments. However given the pronounced sensitivity of the polar regions to radiative balance perturbations, it is incumbent upon our community to better understand and quantify these perturbations, and their unique feedbacks, so that robust model predictions of this region can be realized. One class of under-measured radiative forcing agents in the polar region is the absorbing aerosol—black carbon and brown carbon. Black carbon (BC; also referred to as light-absorbing carbon [LAC], refractory black carbon [rBC], and soot) is second only to CO2 as a positive forcing agent. Roughly 60% of BC emissions can be attributed to anthropogenic sources (fossil fuel combustion and open-pit cooking), with the remaining fraction being due to biomass burning. Brown carbon (BrC), a major component of biomass burning, collectively refers to non-BC carbonaceous aerosols that typically possess minimal light absorption at visible wavelengths but exhibit pronounced light absorption in the near-ultraviolet (UV) spectrum. Both species can be sourced locally or be remotely transported to the Arctic region and are expected to perturb the radiative balance. The work conducted in this field campaign addresses one of the more glaring deficiencies currently limiting improved quantification of the impact of BC radiative forcing in the cryosphere: the paucity of data on the vertical and spatial distributions of BC. By expanding the Gulfstream aircraft (G-1) payload for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility-sponsored ACME-V campaign to include the Single-Particle Soot Photometer (SP2)) and leveraging the ACME-V campaign’s deployment within the Arctic Circle during the summer of 2015 (Deadhorse, Alaska [70° 12' 20" N, 148° 30' 42" W]), the truly unique opportunity presented itself to acquire profile data on BC loading at little additional cost. Since the SP2 is a particle-resolved measurement, the resulting data set provides refractory black carbon (rBC) mass loadings, size and mass distributions, and rBC-containing particle mixing state, all of which are expected to readily find value in the modeling community. As part of the ACME-V (http://www.arm.gov/campaigns/aaf2014armacmev) campaign, CO, CO2, and CH4 were also measured, providing the unique opportunity for carbon closure. We will also work closely with modelers who require such data and expect this collaboration will lead directly to a better understanding of the climate impacts of BC in the Arctic. The primary measurement objective was to acquire airborne data on the vertical and spatial distributions of refractory black carbon (rBC) loading, size and mass distribution, and particle mixing state. The primary scientific objective was to provide a targeted data set of rBC particle distributions to better understand and constrain the impact of black carbon radiative forcing in the cryosphere. The SP2-based data set during this campaign is available in the DOE-ARM archive (http://www.arm.gov/campaigns/aaf2015abclp).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Science & Technology - Other Topics↗
DOE OSTI2020

Modeling Functional Organic Chemistry in Arctic Rivers: An Idealized Siberian System

Rivers of the Arctic will become ever more important for the global climate, since they carry a majority of continental dissolved organic carbon flux into the rapidly changing polar ocean. Aqueous organics comprise a wide array of functional groups, several of which are likely to impact coastal and open water biophysical properties. Light attenuation, interfacial films, aerosol formation, gas release and momentum exchange can all be cited. We performed Lagrangian kinetic modeling for the evolution of riverine organic chemistry as the molecules in question make their way from the highlands to Arctic outlets. Classes as diverse as the proteins, sugars, lipids, re-condensates, humics, bio-tracers and small volatiles are all included. Our reduced framework constitutes an idealized northward flow driving a major hydrological discharge rate and primarily representing the Russian Lena. Mountainous, high solute and tundra sources are all simulated, and they meet up at several points between soil and delta process reactors. Turnover rates are parameterized beginning with extrapolated coastal values imposed along a limited tributary network, with connections between different terrestrial sub-ecologies. Temporal variation of our total dissolved matter most closely resembles the observations when we focus on the restricted removal and low initial carbon loads, suggesting relatively slow transformation along the water course. Thus, channel combinations and mixing must play a dominant role. Nevertheless, microbial and photochemical losses help determine the final concentrations for most species. Chemical evolution is distinct for the various functionalities, with special contributions from pre- and post-reactivity in soil and delta waters. Several functions are combined linearly to represent the collective chromophoric dissolved matter, characterized here by its absorption. Tributaries carry the signature of lignin phenols to segregate tundra versus taiga sources, and special attention is paid to the early then marine behaviors of low molecular weight volatiles. Heteropolycondensates comprise the largest percentage of reactive carbon in our simulations due to recombination/accumulation, and they tend to be preeminent at the mouth. Outlet concentrations of individual structures such as amino acids and absorbers lie above threshold values for biophysical influence, on the monolayer and light attenuation. The extent of coastal spreading is examined through targeted regional box modeling, relying on salinity and color for calibration. In some cases, plumes reach the scale of peripheral arctic seas, and amplification is expected during upcoming decades. Conclusions are mapped from the Lena to other boreal discharges, and future research questions are outlined regarding the bonding type versus mass release as permafrost degrades. Dynamic aqueous organic coupling is recommended for polar system models, from headwaters to coastal diluent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hund’s flat band in a frustrated spinel oxide

Electronic flat bands associated with quenched kinetic energy and heavy electron mass have attracted great interest for promoting strong electronic correlations and emergent phenomena such as high-temperature charge fractionalization and superconductivity. Intense experimental and theoretical research has been devoted to establishing the rich nontrivial metallic and heavy fermion phases intertwined with such localized electronic states. Here, in this study, we investigate the transition metal oxide spinel LiV 2 O 4 , an enigmatic heavy fermion compound lacking localized f orbital states. We use angle-resolved photoemission spectroscopy and dynamical mean-field theory to reveal a kind of correlation-induced flat band with suppressed interatomic electron hopping arising from intra-atomic Hund’s coupling. The appearance of heavy quasiparticles is ascribed to a proximate orbital-selective Mott state characterized by fluctuating local moments as evidenced by complementary magnetotransport measurements. The spectroscopic fingerprints of long-lived quasiparticles and their disappearance with increasing temperature further support the emergence of a high-temperature “bad” metal state observed in transport data. This work resolves a long-standing puzzle on the origin of heavy fermion behavior and unconventional transport in LiV 2 O 4 . Simultaneously, it opens a path to achieving flat bands through electronic interactions in d-orbital systems with geometrical frustration, potentially enabling the realization of exotic phases of matter such as the fractionalized Fermi liquids.

36 MATERIALS SCIENCE↗