Search NASA⌕ Search

SEARCH · Search NASA

Results for “data sustainability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
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 OSTI2022

Electrochemical Ammonia Compression

An electrochemical (EC) compressor is a solid-state compression device. For decades, researchers have been studying EC compressors for applications for a variety of energy systems. As the world transitions away from fossil fuel energy sources, EC compression emerges as a promising technique for energy storage, specifically energy stored in the form of pressurized ammonia. Ammonia is also a commonly used refrigerant. The present studies examine the viability of EC compression for ammonia storage and refrigeration. EC ammonia compression increases the concentration of an ammonia-hydrogen mixture via the input of electrical energy and a series of chemical reactions. A polymer electrolyte membrane separates the low-concentration side from the high-concentration side of the device. On the low-pressure (anode) side hydrogen atoms oxidize and react with ammonia molecules, forming positively charge ammonium ions. The ions traverse the membrane electrolytically. The ammonium ions are reduced and revert back to hydrogen and ammonia upon reaching the high-pressure (cathode). An external circuit provides the current needed to sustain the reactions. In this project, we studied the performance of the ammonia EC compressor under a variety of different conditions. We replicated preliminary data and using a small cell with 5 cm2 of active area. We demonstrated the operation of larger cells with 100 cm2 of active area. Further, we used a commercially available hydrogen EC compressor stack to analyze the scaled-up compressor performance. While previous experiments examined only the transient EC compressor performance, we developed test facilities that allowed the compressor to reach steady state. We analyzed the effects of pressure and current on the EC compressor performance. We measured the flow rates of gas leaving the compressors and analyzed the composition using gas chromatography. We tested methods of separating ammonia from the effluent vapor, which contained hydrogen and water vapor. We found that back diffusion adversely affected the performance, especially when we maintained high pressure lifts.

25 ENERGY STORAGE↗
DOE OSTI2022

MultiSector Dynamics: Advancing the Science of Complex Adaptive Human-Earth Systems

The field of MultiSector Dynamics (MSD) explores the dynamics and co-evolutionary pathways of human and Earth systems with a focus on critical goods, services, and amenities delivered to people through interdependent sectors. This commentary lays out core definitions and concepts, identifies MSD science questions in the context of the current state of knowledge, and describes ongoing activities to expand capacities for open science, leverage revolutions in data and computing, and grow and diversify the MSD workforce. Central to our vision is the ambition of advancing the next generation of complex adaptive human-Earth systems science to better address interconnected risks, increase resilience, and improve sustainability. This will require convergent research and the integration of ideas and methods from multiple disciplines. Understanding the tradeoffs, synergies, and complexities that exist in coupled human-Earth systems is particularly important in the context of energy transitions and increased future shocks.

Reed, Patrick↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Analyses to Inform Equitable Investment Prioritization

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. Megan Day and Liz Ross, along with their co-authors and supported by NREL's Sustainable Communities Catalyzer, identified areas where disadvantaged community indicators intersect with high potential for renewable energy deployment. Through a geospatial intersection of energy burden, environmental hazard, and sociodemographic data with the technical generation potential and levelized cost of energy for multiple renewable energy technologies, we identified trends across disadvantaged community indicators and renewable energy deployment potential. Combining metrics across several tools, including the State and Local Planning for Energy (SLOPE) platform, the Low-Income Energy Affordability (LEAD) tool, and the Environmental Justice Screening and Mapping (EJSCREEN) tool, we compiled a dataset that can be used to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantage communities.

catalyzer↗
DOE OSTI2025

A critical review and meta-analysis of energy demand, carbon footprint, and other environmental impacts from carbon fiber manufacturing

The demand for carbon fibers and carbon fiber-reinforced polymers (CFRPs) is rapidly growing due to their outstanding mechanical properties and potential to enhance sustainability, particularly for lightweighting applications. However, carbon fibers are typically produced from fossil-based feedstocks, involve energy-intensive processes, and have limited options for sustainable end-of-life management or circularity. Despite these challenges, the energy demand and lifecycle environmental implications of their production remain poorly understood. Here, we conduct a critical literature review and meta-analysis of carbon fiber manufacturing, revealing significant variations in reported energy demand, carbon footprint, and lifecycle inventory data. Our analysis makes two novel contributions. First, we identify key underlying factors driving these variations. Second, we highlight that carbon fiber, far from being a homogeneous product, has grades varying substantially in mechanical properties, end-use markets, energy intensity of manufacturing processes, and therefore environmental impacts—an aspect often underrepresented in life cycle assessments. We assert that current data are insufficient for reliably evaluating environmental impacts, posing a risk of misleading decision-making. Addressing this gap requires new lifecycle inventory datasets clearly incorporating carbon fiber heterogeneity and key influencing factors identified in this study. Additionally, we propose actionable recommendations, including a checklist, to advance sustainability in the carbon fiber sector.

CED↗
DOE OSTI2024

Low Precision and Efficient Programming Languages for Sustainable AI: Final Report for the Summer Project of 2024

This document contains all relevant material generated during the authors' summer internship at NREL in 2024. This report shows how to improve energy efficiency of a few code samples by using low-precision data types combined with mixed-precision algorithms. The main applications considered here are (i) linear system solvers using mixed precision, and (ii) neural networks using mixed precision. This report also discusses how programming languages affect energy consumption of algorithms, energy metrics for a code and tools, and the available current software and hardware infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Life Cycle Inventories and Data Gap Analysis for Rare Earth Elements: Neodymium and Dysprosium from Mining to Magnets

The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

PARETO: An open-source produced water optimization framework

PARETO is an optimization framework for onshore produced water management that is meant to empower practitioners, researchers, and policymakers to identify cost-effective and environmentally sustainable ways to manage, treat, and – when possible – beneficially reuse produced water from oil & gas operations. Given user-provided water production, demand, and transportation data, PARETO can help determine where and how to build out produced water infrastructure while simultaneously improving the coordination of water deliveries over time. As shown here, the framework is innately designed to help organizations recognize opportunities for minimizing fresh and brackish water consumption by maximizing produced water reuse in active oil & gas development areas. PARETO is Python-based and is publicly available via GitHub.

42 ENGINEERING↗
DOE OSTI2024

Effects of AI-2 quorum sensing inhibitors on mitigating bacterial contamination in bioethanol production

Here, in this paper, the activities of signal molecule AI-2 in the sole fermentation of Lactiplantibacillus plantarum and co-fermentation of Saccharomyces cerevisiae and L. plantarum were investigated. Co-fermentation of S. cerevisiae and L. plantarum was found to reduce L. plantarum to secrete AI-2. Three AI-2 quorum sensing inhibitors (DMHF (2,5-Dimethyl-4-hydroxy-3-(2H)-furanone), D-Rib ( D -Ribose), and D-Gal ( D -Galactosamine)) were added to the co-fermentation system to further interrupt the growth of L. plantarum. It was found that 0.2 g/L DMHF or 18.0 g/L D-Gal could mainly decrease the activity of AI-2 by 38 % and 36 %, respectively, and significantly inhibit the biofilm formation of L. plantarum. Moreover, the ethanol concentrations of the co-fermentation trials added with 0.2 g/L DMHF or 18.0 g/L D-Gal were 14 % and 103 % higher than that of the control, respectively, within 30 h of fermentation. This study provides basic data for the novel prevention of L. plantarum contamination in bioethanol production via quorum sensing control, which is meaningful for the environmental protection and the sustainable development of bioethanol industry.

09 BIOMASS FUELS↗
DOE OSTI2024

Global trend of methane abatement inventions and widening mismatch with methane emissions

Substantially reducing methane emissions is the fastest way to repress near-term warming and is an essential prerequisite for reaching the 1.5 °C target. However, knowledge about the global invention trend, sectoral and national distribution and international diffusion of methane-targeted abatement technologies (MTATs) remains limited. On the basis of patent data, we identify more than 175,000 MTAT inventions applied between 1990 and 2019 by 133 countries or dependent territories. Our results revealed that after sustained growth of more than fourfold, the number of global high-quality MTAT inventions declined by 3.5% annually from 2010 to 2019. The sectoral and national-level distributions of MTAT inventions and methane emissions are strongly mismatched. Additionally, the international diffusion of MTATs is 11.1% lower than that of overall climate change mitigation technologies and most transfers occur between developed countries or flow to China, South Korea and Brazil; however, other developing countries and the least developed countries are rarely involved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A high-temperature mixed potential CO gas sensor for in situ combustion control

An in situ, accurate and robust sensor that can sustain a high temperature of above 1000 °C is needed for on-site combustion monitoring because it can give real-time and local data to the control system to adjust the overall combustion efficiency. In this work, we find that nickel oxide (NiO) is a promising sensing material for CO which is a direct indicator of the status of the combustion process of a power plant. Under the conditions of 0.5–3% O 2 and 1000 °C, the fabricated yttrium-stabilized zirconia (YSZ)-based mixed potential sensor using porous NiO demonstrates good sensitivity to CO, showing a signal as high as 36 mV to 1000 ppm CO. The effects of gas transport, structure and geometry of the NiO electrode on the sensing performance are studied. Results show that fast gas transport is much beneficial to improved sensitivity. NiO having a porous structure is much more sensitive to CO than that having a dense structure. But the t90 (time to achieve 90% final signal magnitude) of the former is much longer due to slower gas diffusion inside the pores. It's worth noting that the NiO sensor exhibits a positive relationship with the CO content, opposite to other reported results of mixed potential sensors to detect CO. We find that this might be due to the electrochemical reduction, instead of oxidation of CO during the interaction with NiO at 1000 °C. Selectivity tests on how CO 2 , CH 4 and steam affect CO sensing are also demonstrated. NiO is insensitive to even 10% CO 2 . CH 4 does not shift the average value of the CO sensing response. However, it makes the sensing signal fluctuate more intensively. 2% steam exerts a great influence on NiO sensitivity to CO: it magnifies the sensitivity of the porous NiO electrode to a low CO range of 0–100 ppm, but inhibits its sensitivity to a CO range of 100 ppm to 1000 ppm. Lastly, an 11 day stability test demonstrates the promising stability property of the proposed NiO-based YSZ sensor.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Experimental and theoretical study of weakly coherent mode in I-mode edge plasmas in the EAST tokamak

The I-mode is a promising operation mode for fusion in the future, featuring high-temperature and low-density confinement, but the reason why the temperature and density are decoupled remains an important aspect to be explored. The experimental results from the experimental advanced superconducting tokamak (EAST) showed that the weakly coherent mode (WCM) is directly related to sustaining the I-mode and that the peak amplitude of the WCM is proportional to the temperature in the pedestal. Here, simulating the experimental data from EAST with the six-field model of BOUT++, we find a density perturbation close to the frequency of the WCM observed in the experiment. By testing all the physical terms in this model, we find that the density perturbation and particle transport are directly related to the drift Alfvén wave (DAW) mode. Additionally we use the SymPIC program (Xiao et al Plasma Sci. Technol. 20 110501; Phys. Plasmas 22 112504; Plasma Sci. Technol. 23 055102) to simulate the same experimental data and find that the frequency range of the WCM is close to both experimental and BOUT++ results. Therefore, the WCM of the I-mode can be considered to be driven by the DAW, which helps improve the transport of the I-mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

IDB Loader

SAND2024-08428O The IDB Loader is a database designed to hold gamma spectra. IDB is a web-accessible database of reference gamma spectra for measuring uranium (U) and plutonium (Pu) isotopic composition. The database, developed by the International Atomic Energy Agency (IAEA), provides access to well-characterized groups of gamma spectra curated by international experts in gamma spectroscopy. It was created to promote sustainability and maintenance of software used to determine the isotopic abundances of U and Pu. The IDB Loader works by uploading one set of spectral data into an existing IDB. The Data Loader package includes documentation describing the IDB's table, the format of the spectral data, and a description of the loader program. The IDB is currently hosted by the IAEA at: https://nds.iaea.org/idb Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Schwartz, Steven↗
DOE OSTI2025

The environmental impact, carbon emissions and sustainability of computing in the ATLAS experiment

ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), makes use of a large internationally-distributed computing infrastructure, including over 10 6 TB of managed data on disk and tape and almost one million simultaneously running CPU cores. Upgrades for the High-Luminosity LHC (HL-LHC) will increase the required computing resources by a factor of 3–4 by the beginning of the 2030s, and by an order of magnitude before the conclusion of data taking at the beginning of the 2040s. These resources are spread over around 100 computing sites worldwide. Efforts are underway within the experiment to evaluate and mitigate various aspects of the environmental impact of the sites, with the additional long-term goal of making recommendations to the sites that will significantly reduce the total expected environmental impact in the HL-LHC era. These efforts take several forms: building awareness in the experiment community, adjusting aspects of the computing policy, and modifications of data center configurations, either in ways that take advantage of particular features of ATLAS workloads or in generic ways that reduce the environmental impact of the computing resources. This paper describes the ongoing investigations and approaches that have already provided useful and actionable outcomes.

Aad, G. [CNRS/IN2P3] (ORCID:0000000266654934)↗
DOE OSTI2026

Data for Expression of a Bacterial Trehalose 6-Phosphate Synthase Gene otsA in Camelina sativa Seeds Promotes the Channelling of Carbon Towards Oil Accumulation

Improving seed oil yield is essential for developing Camelina sativa as a sustainable biofuel crop. Fatty acid synthesis depends on the production of acetyl-CoA from photosynthetically derived sugars. Trehalose 6-phosphate (T6P), a proxy for sucrose availability, can link sugar status to plant growth and development. Synthesised by trehalose 6-phosphate synthase (TPS) from UDP-glucose and glucose-6-phosphate, T6P plays a regulatory role in metabolism. Our previous studies on Arabidopsis transgenic lines constitutively expressing the E. coli otsA (encoding TPS) showed increased T6P levels and seed triacylglycerol, along with stunted growth. In the present study we express otsA in camelina under the control of a seed-specific Phaseolin promoter. Seeds of the resulting transgenic lines accumulated high levels of T6P, and a 15%–20% increase in total fatty acids and triacylglycerol compared to wild-type. Molecular analysis showed the transgenic seeds had reduced SnRK1 activity, elevated WRI1 protein levels, and increased the levels of WRI1 and its target genes, along with enhanced rates of fatty acid synthesis that increased seed weights relative to wild type. Notably, the increase in oil did not affect seed protein levels but did reduce the soluble metabolite fraction. Crucially, seed-specific expression of otsA mitigated the growth defects associated with constitutive otsA expression, and the transgenic lines showed normal seed development and germination. These findings demonstrate that targeted T6P modulation via seed-specific otsA expression is an effective metabolic engineering strategy to boost oil production in camelina and potentially in other oilseed crops and bioenergy crops such as energycane, sorghum and miscanthus.

Lipids↗
DOE OSTI2020

Leveraging Existing Bioenergy Data: Workshop Summary Report

The U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) invests in a diverse portfolio of technologies to ensure domestic energy security, continued economic competitiveness, environmental sustainability, and the availability of cleaner fuels and power. The mission of EERE’s Bioenergy Technologies Office (BETO) is to develop transformative and revolutionary sustainable bioenergy technologies for a prosperous nation. BETO develops technologies that convert domestic biomass and waste resources into fuels, products, and power to enable affordable energy, economic growth, and innovation in renewable energy and chemicals production. This report summarizes the input received from attendees of the public workshop sponsored by BETO on July 21–23, 2020, as well as input received from a Request for Information (RFI) run in August and September 2019.

09 BIOMASS FUELS↗