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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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Interactive plots (in HTML/javascript) as supplemental files for the paper titled: Proteogenomic Characterization of Ovarian High-Grade Serous Cancer Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability. The plots represent the differential expression of proteins (File 1) and phosphopeptides (File 2) comparing tumor to normal tissue.
Title: Warming Permafrost Model Intercomparision Project (WrPMIP): Pan-Arctic Perturbation Warming Simulations Description: WrPMIP Pan-Arctic simulations including historial baseline and two warming simulations. Models were perturbed similarly to known experimental warming trials that have been implemented across the Pan-Arctic over the last several decades. More information about the dataset can be found at the following links: https://warmingpermafrost.nau.edu/ The dataset is available at: https://esgf-node.ornl.gov/search/project=WrPMIP&mip_era=CMIP6&activity_id=WrPMIP
The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities
The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.
Conference paper titled, Comparing microclimate weather conditions from the weather research and forecasting (WRF) model with typical meteorological year (TMY) data for building energy simulations accepted for Building Simulation 2025 Conference in Brisbane, Australia in Aug 2025.
Introduction Dimeric natural products are widespread in plants and microorganisms, which usually have complex structures and exhibit greater bioactivities than their corresponding monomers. In this study, we report five new dimeric tetrahydroxanthones, aculeaxanthones A−E ( 4−8 ), along with the homodimeric tetrahydroxanthone secalonic acid D ( 1 ), chrysoxanthones B and C ( 2 and 3 ), and 4−4’-secalonic acid D ( 9 ), from different fermentation batches of the title fungus. Methods A part of the culture was added to a total of 60 flasks containing 300 ml each of number II fungus liquid medium and culture 4 weeks in a static state at 28˚C. The liquid phase (18 L) and mycelia was separated from the fungal culture by filtering. A crude extract was obtained from the mycelia by ultrasound using acetone. To obtain a dry extract (18 g), the liquid phase combined with the crude extract were further extracted by EtOAc and concentrated in vacuo. The MIC of anaerobic bacteria was examined by a broth microdilution assay. To obtain MICs for aerobic bacteria, the agar dilution streak method recommended in Clinical and Laboratory Standards Institute document (CLSI) M07-A10 was used. Compounds 1−9 was tested against the Bel-7402, A-549 and HCT-116 cell lines according to MTT assay. Results and Discussion The structures of these compounds were elucidated on the base of 1D and 2D NMR and HR-ESIMS data, and the absolute configurations of the new xanthones 4−8 were determined by conformational analysis and time-dependent density functional theory-electronic circular dichroism (TDDFT-ECD) calculations. Compounds 1–9 were tested for cytotoxicity against the Bel-7402, A549, and HCT-116 cancer cell lines. Of the dimeric tetrahydroxanthone derivatives, only compound 6 provided cytotoxicity effect against Bel-7402 cell line (IC50, 1.96 µM). Additionally, antimicrobial activity was evaluated for all dimeric tetrahydroxanthones, including four Gram-positive bacteria including Enterococcus faecium ATCC 19434, Bacillus subtilis 168, Staphylococcus aureus ATCC 25923 and MRSA USA300; four Gram-negative bacteria, including Helicobacter pylori 129, G27, as well as 26,695, and multi drug-resistant strain H. pylori 159, and one Mycobacterium M. smegmatis ATCC 607. However, only compound 1 performed activities against H. pylori G27, H. pylori 26695, H. pylori 129, H. pylori 159, S. aureus USA300, and B. subtilis 168 with MIC values of 4.0, 4.0, 2.0, 2.0, 2.0 and 1.0 μg/mL, respectively.
Sustainable separation process development is crucial in promoting economic and environmental sustainability. Separation processes can be energy-intensive, and early-stage or emerging separation technologies can also be costly. For instance, separations in biomass-based fuels and chemicals production can account for up to 50% of the production cost, while industrial separations can consume up to 15% of the total energy used in the United States. Separations are essential to many industrial processes and play a crucial role in helping industry achieve several United Nations sustainable development goals (SDG), such as clean energy, responsible production, and climate change mitigation. Therefore, it is imperative to have a comprehensive discussion within the scientific community to identify innovative approaches for sustainable separation processes. This editorial highlights the key insights and research findings presented in this research topic titled "Future Perspectives on Separation Technologies."
This repository provides the data plotted in the figures of the article titled "Observation of Nonaxisymmetric Standard Magnetorotational Instability Induced by a Free-Shear Layer", published in the Physical Review Letters, 2025.
This data set consists of the data used to generate the figures in the paper titled "Wall conditioning effects of boron powder injection in KSTAR with a tungsten divertor" in the journal Nuclear Fusion, 2025.
This repository provides the data which is plotted in the figures of the article titled "Thermal Response of a Lithium Vapor Divertor to Cyclical Operation", published in the Journal of Fusion Energy, 2025.
Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.
The plant science corpus consists of the titles and abstracts of plant science articles in PubMed published prior to 2021 with a small number of 2021 records due to modification of records.
This replication package is a case study on automated deductive verification for Rust for practical programs. It is a companion artifact to a corresponding usability study on verification titled "Union-Find and Usability: A Case Study and Analysis of Rust Formal Verifiers". It seeks to answer the question "Can Rust developers today use Rust verifiers to verify their code?". To answer this question, the study contrasts the verification experience of two mature Rust verifiers, Creusot and Prusti, by using the tools to develop a verified implementation of union-find in Rust. The union-find implementation is based on real-world code as used in the popular egg E-graph library. The artifact consists of two different verified libraries, one using Creusot and one using Prusti. The libraries have similar Rust interfaces and high-level proofs but differ in their details: Creusot and Prusti have different annotation languages and support different proof styles. Each implementation can be verified with its respective tool and compiles as a traditional Rust development.
This dataset contains raw and processed data, as well as supplementary figures used in the paper titled "Integrated modeling of boron powder injection for real-time plasma-facing component conditioning." The data includes simulation results for boron transport and deposition in DIII-D tokamak scenarios, and processed plots. It provides insights into the effects of boron powder injection on plasma-facing component conditioning and surface composition.
This dataset includes all the raw NMR data acquired for the titled paper. This includes the 1H-17O HETCOR spectra, 1H-13C HETCOR spectra, and the 1H-13C wPDLF spectra. Other data, including cross-peak amplitudes are provided as supporing information to the main publication.
This repository contains the simulation outputs and processing scripts associated with the study of winter wheat traits across the United States, utilizing the Ecosys agroecosystem model. The dataset includes model results for both rainfed and irrigated winter wheat systems, supporting the findings presented in the manuscript titled "Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits." Data includes the original Ecosys simulation outputs (archived in .db format within the compressed .zip files) and extracted analysis data (stored in .pkl files for efficient processing). Python code for data processing and figure generation is provided in a Jupyter notebook. External Observational Datasets should refer to the following official repositories for the input and validation data used in this study. The eddy covariance data from the AmeriFlux network (https://ameriflux.lbl.gov/). Climate-forcing data of NLDAS-2 from NASA LDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data). Soil data from the Gridded Soil Survey Geographic Database (gSSURGO), available at (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database). Crop yields, planting and harvest dates from the USDA public databases (https://quickstats.nass.usda.gov/; https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx). Satellite-derived SLOPE GPP data from ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786). Land use and crop progress information from the USDA Crop Data Layer and Crop Progress and Condition Gridded Layers (https://www.nass.usda.gov/Research_and_Science/). The Ecosys model code is available online at https://github.com/jinyun1tang/ECOSYS.
This archive contains E3SM Land Model simulation results associated with the Journal of Advances in Modeling Earth Systems (JAMES) article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.
This paper summarizes the prior analysis, key findings, and recommendations from the published report titled “Initial Heliostat Supply Chain Analysis” [1]. Globally, the growing demand for concentrating solar power (CSP) technologies, primarily for electricity generation plants has been met with supply chains primarily composed of plentiful commodity materials such as steel, aluminum, and glass. Often the commodity materials can be sourced in the domestic market where power plant will be constructed. Although specialty components are required for CSP solar fields —including mirror panels used for heliostat applications—these specialty components constitute about 30-50% of total system installed costs [2]. Only a few companies and countries, including the United States, have developed the capacity to supply such specialty components. The U.S. heliostat supply chain at present is comprised of few companies (e.g., CSP developers), component suppliers, and is its infancy. By 2035, with current and aggressive solar photovoltaic (PV) capacity expansions, there is the potential for 500,000–1,500,000 direct and indirect jobs in the areas of manufacturing, installation and development, and operations and maintenance (O&M) [3]. Utilizing the CSP capacity estimations the recent NREL report [4], the construction of 39 gigawatts (GW) of CSP (assuming mainly power tower) in the U.S. could lead to approximately 195,000 manufacturing, construction, and O&M jobs. This does not include the longer-term jobs and economic impact (e.g., taxes from plant operations staff) from operating the plants once constructed. It is recommended that further CSP component and system supply chain analysis and modelling be undertaken.