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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Heavy metal inventory and fuel sustainability of recycling TRU in FBR design

Nuclear fuel materials from spent fuel of light water reactors have a potential to be used for destructive devices with very huge energy release or in the same time, it can be utilized as a peaceful energy or civil applications, for generating electricity, desalination of water, medical application and others applications. Several research activities showed some recycled spent fuel can be used as additional fuel loading for increasing fuel breeding capability as well as improving intrinsic aspect of nuclear non-proliferation. The present investigation intends to evaluate the composition of heavy metals inventories and fuel breeding capability in the FBR design based on the loaded fuel of light water reactor (LWR) spent fuel (SF) of 33 GWd/t with 5 years cooling time by adopting depletion code of ORIGEN. Whole core analysis of FBR design is performed by adopting and coupling codes such as SLAROM code, JOINT and CITATION codes. Nuclear data library, JFS-3-J-3.2R which is based on the JENDL 3.2 has been used for nuclear data analysis. JSFR design is the basis design reference which basically adopted 800 days cycle length for 4 batches system. Higher inventories of plutonium of MOX fuel and TRU fuel types at equilibrium composition than initial composition have been shown. Minor actinide (MA) inventory compositions obtain a different inventory trends at equilibrium composition for both fuel types. Higher Inventory of MA is obtained by MOX fuel and less MA inventory for TRU fuel at equilibrium composition than initial composition. Some different MA inventories can be estimated from the different inventory trend of americium (Am). Higher americium inventory for MOX fuel and less americium inventory for TRU fuel at equilibrium condition. Breeding ratio of TRU fuel is relatively higher compared with MOX fuel type. It can be estimated from relatively higher production of Pu-238 (through converted MA) in TRU fuel, and Pu-238 converts through neutron capture to produce Pu-239. Higher breeding ratio of MOX fuel and TRU fuel types at equilibrium condition are estimated from converted fertile material during reactor operation into fissile material of plutonium such as converted uranium fuel (converted U-238 into Pu-239) or additional converted fuel from MA into Pu-238 and changes into Pu-239 by capturing neutron. Loading LWR SF gives better fuel breeding capability and increase inventory of MA for doping material of MOX fuel; however, it requires more supply MA inventory for TRU fuel type.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

SCIENTIFIC EFFICIENCY OF GROUND-BASED TELESCOPES

I scanned the six major astronomical journals of 2008 for all 1589 papers that are based on new data obtained from ground-based optical/IR telescopes worldwide. Then I collected data on numbers of papers, citations to them in 3+ years, the most-cited papers, and annual operating costs. These data are assigned to four groups by telescope aperture. For instance, while the papers from telescopes with an aperture >7 m average 1.29 more citations than those with an aperture of 2 to <4 m, this represents a small return for a factor of four difference in operating costs. Among the 17 papers that have received {>=}100 citations in 3+ years, only half come from the large (>7 m) telescopes. I wonder why the large telescopes do so relatively poorly and suggest possible reasons. I also found that papers based on archival data, such as the Sloan Digital Sky Survey, produce 10.6% as many papers and 20.6% as many citations as those based on new data. Also, the 577.2 papers based on radio data produced 36.3% as many papers and 33.6% as many citations as the 1589 papers based on optical/IR telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

CHESS 2025: Field-collected vegetation attributes and site photos

This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (individual trees, individual shrubs, or 1-meter square plots of meadow and subshrub vegetation). Field samples were collected within 72 hours of airborne data collection using the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. Remote sensing data for the project is available on ESS-DIVE (DOI and citation to be added upon publication). Field data collected included canopy height and per-species horizontal proportional cover for meadow plots, species identity and height information for shrubs, as well as species identity, height, diameter at breast height, and health assessment information for trees. Photos of the focal site and surrounding landscape were taken for all sampling sites and are included in this archive. Green leaves or needles were collected for plant trait and foliar chemistry analysis. This data is archived separately (DOI and citation to be added upon publication). High-precision geospatial data for each sample (crown perimeter polygons for trees and shrubs, plot boundaries for meadow plots) is available here (Henderson et al., 2026). Field and remote sensing protocols largely followed those of a previous field and airborne imaging campaign performed in 2018 (described in Chadwick et al. 2020). Field data from the 2018 campaign can be found here (Chadwick et al., 2020 doi:10.15485/1618130). Because different field measurements were taken for meadow, shrub, and tree sites, data from these three sample types are archived as separate tables (chess_meadow_site_cleaned.csv, chess_shrub_site_cleaned.csv, chess_tree_site_cleaned.csv). Meadow proportional cover data is stored in a separate table (chess_meadow_cover_cleaned.csv). Taxonomy was treated identically between sample types, and the dataset shares a common set of voucher specimens (chess_voucher_IDs_cleaned.csv), as well as a single species list (chess_species_list_cleaned.csv). All taxonomic determinations were performed to the species level, and adhere to the Global Biodiversity Information Facility (GBIF) backbone taxonomy as of January 10th, 2026 (GBIF Secretariat 2023). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Tropical root traits in response to global changes from 1984 to 2023

This dataset is a compilation of tropical root traits data in response to different global changes in tropical sites, considering 23.50N and S as latitudinal boundaries. The global changes considered are warming, drought, flooding, cyclones, nitrogen addition, CO2 fertilization, and fire. This dataset contains 266 root trait observations from 93 studies across 24 tropical countries. The full citation from where the data was taken from is provided in the dataset, as well as the global change, the ecosystem type, location, coordinates, the root traits measured, and the direction of their response after the global change. Additional information such as the duration of the experiment, the intensity of the global change, the soil layers from where the roots were collected, the root orders, and the type of experiment are also shown. We obtained this dataset by performing a systematic literature review on Web of Science using standardized keywords in English, Spanish, and Portuguese (Yaffar, Lugli et al. in press).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NEWTS State-Level Database Dashboard

The National Energy Water Treatment and Speciation (NEWTS) State-Level Database Dashboard enables quick visualization and exploration of energy-related wastewater data collected from state environmental agencies and research projects across the United States. The dashboard features a subset of the NEWTS Integrated Dataset (version 1.0), which was built to provide stakeholders with a unified and standardized database to support wastewater treatment and critical minerals exploration. The full NEWTS Integrated Dataset and citations for the original data resources can be found here: https://edx.netl.doe.gov/dataset/newts-integrated-dataset-version-1-0

abandoned mine drainage↗
DOE OSTI2021

Vismia guianensis leaf age physiology in Manaus, Brazil, from July 2014 - July 2015

This data package contains net photosynthesis rates and methanol and isoprene leaf emissions as a function of light, temperature, and leaf age. Data was collected from the species V. guianensis at the NGEE Tropics site in Manaus, Brazil (BR-Ma2). This data is used in the publication "Methanol and Isoprene Emissions from the Fast Growing Tropical Pioneer Species Vismia guianensis (Aubl.) Pers. (Hypericaceae) in the Amazon Basin." Isoprene (Is) emissions by plants represent a loss of carbon and energy resources leading to the initial hypothesis that fast growing pioneer species in secondary tropical forests allocate carbon primarily to growth at the expense of isoprenoid defenses. In this study, we quantified leaf isoprene and methanol emissions from the abundant pantropical pioneer tree species Vismia guianensis and ambient isoprene concentrations above a diverse secondary forest in the central Amazon. See the below field, Dataset References, for the full citation. Included in the data package is a zip file with six datasets, three in Excel format and additional versions in CSV format. Also included are the figures from the related publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data Mining Scientific Literature Demonstrates Use of Biological and Medical Data Across Scientific Disciplines

The Protein Data Bank (PDB) was established as the 1st open access digital data resource in biology and medicine. Today, the PDB archive contains over 140,000 atomic‐level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. About 20 new structures are deposited daily. PDB data is used in basic and applied research, patent applications, discovery of lifesaving drugs, product innovation, and education. About 80% of all PDB structures deposited have a “primary citation,” which is the first paper to describe the molecule and its structure and function. In turn, these primary citations have contributed to more than 1 million publications. An initial study by Clarivate Analytics on the Nucleic Acids Research publication demonstrated that PDB data drive high‐impact research in diverse scientific fields. To build on this study, we used Python programming to perform several data mining studies to understand the impact of these structures in fundamental biology, biomedicine, and energy in the scientific literature. The scientific literature database, Web of Science, tracks scientific article metrics such as the number of times cited, subject category, year published, funding source, and keywords. Web of Science data for PDB primary citations was compiled as used as the data set. Two main investigations were performed. In the first, we examined the number of citations per article (data overall and by individual subject categories) to develop “top 10 cited structures” lists and analyzed for trends. In the second investigation, we extended our data mining analysis to consider for each main subject category, the papers with the most citations and the structures they cite, and the most frequently occuring keywords. We have observed different aspects of biological data utility in various subject areas, at times in ways unanticipated from the structures' primary citation. This research demonstrates the value of biological data from the PDB by showing the diverse ways in which the data are being used. Support or Funding Information RCSB PDB is funded by a grant (DBI‐1338415) from the National Science Foundation, the National Institutes of Health, and the US Department of Energy. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Verdiguel, Natalie↗
DOE OSTI2013

PARLO: PArallel Run-Time Layout Optimization for Scientific Data Explorations with Heterogeneous Access Pattern

Download Citation Email Print Request Permissions Save to Project The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their run-time environments. The growing gap is exacerbated by exploratory, data-intensive analytics, such as querying simulation data with multivariate, spatio-temporal constraints, which induces heterogeneous access patterns that stress the performance of the underlying storage system. Previous work addresses data layout and indexing techniques to improve query performance for a single access pattern, which is not sufficient for complex analytics jobs. We present PARLO a parallel run-time layout optimization framework, to achieve multi-level data layout optimization for scientific applications at run-time before data is written to storage. The layout schemes optimize for heterogeneous access patterns with user-specified priorities. PARLO is integrated with ADIOS, a high-performance parallel I/O middleware for large-scale HPC applications, to achieve user-transparent, light-weight layout optimization for scientific datasets. It offers simple XML-based configuration for users to achieve flexible layout optimization without the need to modify or recompile application codes. Experiments show that PARLO improves performance by 2 to 26 times for queries with heterogeneous access patterns compared to state-of-the-art scientific database management systems. Compared to traditional post-processing approaches, its underlying run-time layout optimization achieves a 56% savings in processing time and a reduction in storage overhead of up to 50%. PARLO also exhibits a low run-time resource requirement, while also limiting the performance impact on running applications to a reasonable level.

Gong, Zhenhuan↗
DOE OSTI2021

Microbial Biomass in Soils Receiving Varying Levels of Organic Inputs - A Data Compilation

This dataset contains soil microbial biomass data compiled from published laboratory studies and field manipulations of organic inputs to soils. Each study included at least three levels of a given type of organic input applied to a given type of soil. Studies were selected from the literature that had experimentally manipulated organic input rates to soils using unburned, non-synthetic material (e.g. plant litters, manures, and composts). Fifty-five (55) studies, conducted from 1997 to October 2019, were chosen and compiled into a dataset with 358 observations from 96 combinations of a given organic material applied to a given soil in agricultural systems, laboratory incubations, forests, or grasslands. • Studies were selected when biomass measurements used the chloroform fumigation-extraction procedure followed by carbon analysis, or, in two instances, studies estimated microbial biomass using phospholipid fatty acid analysis. • Studies that used fewer than three input rates or characterized microbial biomass via substrate induced respiration were excluded. • The temporal and spatial aspects of the laboratory and field studies were not essential to the analyses (Craig et al., 2021) and were not included with the measurement data. These data were used to assess the relationship between organic input amounts and microbial biomass. Complete reference citations are provided if additional information is needed. The compiled data are provided in two .csv files: (1) soil treatments and microbial biomass data and (2) literature citations. The “Study” field is the cross reference between files. Also provided is the R code for reproducing the results in the related publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Pantropical Tree Sapwood Hydraulic Properties, 1991 - 2014

This data package contains sapwood pressure-volume parameters including osmotic potential at full turgot, turgor loss point, bulk elastic modulus, and others, determined by curve fitting to raw isotherm data (paired measurements of water content and water potential). Raw data were digitized from published figures. Data are from natural tropical and subtropical forests or savannas and span rainfall gradients from deciduous dry tropical forests to everwet tropical forests, and come from the following countries: Argentina, Panama, Brazil, Costa Rica, and Madagascar. Included in the attached zip file are four CSV files. sapwood_PV_recal_headers.csv: description of variables in the two files below sapwood_PV_recal_corrected.csv: bias-corrected sapwood PV data (see Methods in the associated reference) sapwood_PV_recal_uncorrected.csv: uncorrected sapwood PV data (see Methods in the associated reference) sapwood_PV_references.csv: citation, bibliographic reference, and DOI for the published references from which data were extracted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model

This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.

biofuel sustainability↗
DOE OSTI2025

$\bar{\nu}_\mu$ charged-current $\pi^0$ data release

Data release for the NOvA muon antineutrino charged-current (CC) pi^0 cross section presented in arXiv:2511.05807. The signal for this analysis is defined as muon antineutrino CC interactions in the fiducial volume of the NOvA near detector (a 2.7 m × 2.7 m × 9.0 m region) that produce at least one pi^0 in the final state emerging from the nucleus, within the phase space of muon momentum [0.5, 2.5) GeV/c and muon angle [0, 60) degree, as described in arXiv:2511.05807. The released zip file contains two files: NOvA_NumubarCCPi0_DataRelease.root README.txt The ROOT file includes the cross-section results as well as the statistical and systematic covariance matrices for each variable used in this analysis. The README provides a detailed description of the contents of the data release. Official Flux: The flux used in this analysis is available from the NOvA Public Docs: https://publicdocs.fnal.gov/cgi-bin/ShowDocument?docid=8. File structure --- The ROOT file contains the following TDirectories: pi0p - pi^0 momentum distributions pi0dir - pi^0 angular distributions muonp - muon momentum distributions muondir - muon angular distributions Q2 - reconstructed Q^2 distributions Wmass - reconstructed W_mass distributions Each directory contains three histograms: xsec (TH1D): Cross section result cov_stat (TH2D): Statistical covariance matrix cov_syst (TH2D): Systematic covariance matrix Usage notes: - xsec gives the measured differential cross section w.r.t. the corresponding variable. - cov_stat and cov_syst provide the full covariance matrices. - The bin definitions and kinematic phase spaces follow those used in arXiv:2511.05807. Citation --- If you use these data, please cite: NOvA Collaboration, arXiv:2511.05807.

Wu, Wanwei [Pittsburgh U.] (ORCID:0000000326327215↗
DOE OSTI2016

Transportation Secure Data Center

The Transportation Secure Data Center (TSDC) at https//www.nrel.gov/tsdc provides free access to detailed transportation data from travel surveys and studies conducted across the nation. It features millions of data points including second-by-second GPS readings, vehicle characteristics, and demographics for all modes of travel. NREL screens the initial data for quality control, translates each data set into a consistent format, and interprets the data for spatial analysis. NREL processing routines add information on fuel economy and road grades and join data points to the road network. NREL maintains the TSDC in partnership with the U.S. Department of Transportation and the U.S. Department of Energy. Before viewing the data for the first time, you will be required to fill out a short registration form. If you use TSDC data in a publication, please notify NREL at tsdc@nrel.gov and include a citation consistent with the following format: "Transportation Secure Data Center." ([YEAR]). National Renewable Energy Laboratory. Accessed [DATE]: www.nrel.gov/tsdc.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Fleet DNA

Available online at www.nrel.gov/fleetdna, the Fleet DNA clearinghouse of commercial fleet vehicle operating data helps manufacturers and developers optimize vehicle designs and helps fleet managers choose advanced technologies for their fleets. Sponsored by the U.S. Department of Energy, this online tool provides data summaries and visualizations similar to real-world genetics for medium- and heavy-duty commercial fleet vehicles operating in a variety of vocations. If you use Fleet DNA data in a publication, please notify NREL at fleetdna@nrel.gov and include a citation consistent with the following format: “Fleet DNA Project Data.” ([YEAR]). National Renewable Energy Laboratory. Accessed [DATE]: www.nrel.gov/fleetdna.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Analysis of fluid fuel flow to the neutron kinetics on molten salt reactor FUJI-12

Molten Salt Reactor is a reactor are operating with molten salt fuel flowing. This condition interpret that the neutron kinetics of this reactor is affected by the flow rate of the fuel. This research analyze effect by the alteration velocity of the fuel by MSR type Fuji-12, with fuel composition LiF-BeF{sub 2}-ThF{sub 4}-{sup 233}UF{sub 4} respectively 71.78%-16%-11.86%-0.36%. Calculation process in this study is performed numerically by SOR and finite difference method use C programming language. Data of reactivity, neutron flux, and the macroscopic fission cross section for calculation process obtain from SRAC-CITATION (Standard thermal Reactor Analysis Code) and JENDL-4.0 data library. SRAC system designed and developed by JAEA (Japan Atomic Energy Agency). This study aims to observe the effect of the velocity of fuel salt to the power generated from neutron precursors at fourth year of reactor operate (last critical condition) with number of multiplication effective; 1.0155.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The Importance of Sharing Data in Systems Biology

Systems biology research spans a range of biological scales and science domains, and often requires a collaborative effort to collect and share data so that integration is possible. However, sharing data effectively is a challenging task that requires effort and alignment between collaborative partners, as well as coordination between organizations, repositories, and journals. As a community of systems biology researchers, we must get better at efficiently sharing data, and ensuring that shared data comes with the recognition and citations it deserves.

Wood-Charlson, Elisha M. (ORCID:0000000195577715)↗
DOE OSTI2026

PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE

This dataset is an open-source repository of spectral data measured at Pacific Northwest National Laboratory (PNNL). This database provides quantitative values for the complex index of refraction for seven polycyclic aromatic hydrocarbon (PAH) solids. A list of the chemicals is available in the readme file. These spectra consist of the optical constants, i.e., the real, n(ν), and imaginary, k(ν), refractive indices, over the spectral range from 7,800 to 400 cm-1 (1.28 – 25 μm). The conditions under which the individual data were acquired are described in the associated metadata files, and the user is strongly encouraged to read and understand this information to ensure the data are used appropriately for your application. Recommended Citation for Dataset Jessica M Salcido, Jeremy D. Erickson, Ashley M. Bradley, Russell G. Tonkyn, Timothy J. Johnson and Tanya L. Myers. 2026. PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE. [Data Set] PNNL DataHub. INSERT DOI License Information This work is marked with CC0 1.0: https://creativecommons.org/publicdomain/zero/1.0/. The authors do request that you appropriately cite the dataset when referencing or using the dataset.

Salcido, Jessica Marie Ortola↗