SEARCH · Search NASA
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.
Machine learning can be used to screen millions of compounds and unearth relationships between electronic structure, chemistry, thermodynamic stability, formability and band gap. We know how to calculate thermodynamic stability and calculate approximate band gap, but it is very complicated to estimate synthesizability.
The Regional Resiliency Assessment Program (RRAP) is a cooperative assessment of specific critical infrastructure within a designated geographic area and a regional analysis of the surrounding infrastructure that addresses a range of infrastructure resilience issues that could have regionally and nationally significant consequences. In 2018, DHS’s Cybersecurity and Infrastructure Security Agency (CISA) sponsored the Oregon Transportation Systems RRAP project in coordination with the Office of the Governor (under the oversight of the state resilience officer), the Oregon Office of Emergency Management (OEM), the Oregon Department of Transportation (ODOT), and other regional stakeholders (CISA 2021). This project focuses on assessing the impacts of a Cascadia Subduction Zone (CSZ) earthquake on state transportation systems and, in particular, how those impacts may affect the ability of emergency response efforts to move supplies into the region. The intended outcome of this analysis is the prioritization of transportation routes and modes for additional planning, investment, hardening, or other activities to enhance their resilience—and therefore, to enhance their ability to support response and recovery efforts following a CSZ earthquake. An important part of this transportation system-level assessment has been to assess the seismic vulnerability of the state highway system. In doing so, the RRAP project team used the Bridge Seismic Screening Tool (BSST) to assess, at a system-level, the potential impacts that a CSZ earthquake could have on state highway bridges (Bergerson et al. 2019).1 Argonne National Laboratory (Argonne), in collaboration with the Washington State Department of Transportation (WSDOT), originally developed the BSST as part of the 2017 Washington State Transportation Systems RRAP project, a sister project to the 2018 Oregon Transportation Systems RRAP project. Argonne updated the BSST during this more recent project in Oregon based on feedback from stakeholders and subject matter experts (SMEs) on the original version of the tool. The first step in the BSST is to assess the seismic vulnerability of roadway bridges following a CSZ earthquake to determine a projected or potential damage state. Damage states then help determine approximate reopening times for bridge crossings.2 This document provides details on the BSST methodology, the implementation of that tool to analyze the projected damage incurred in a CSZ earthquake scenario, and the determination of corresponding reopening times of interstate, state highway, and local bridges following such an event.
The purpose of this environmental calculation file (ECF) is to document the assumptions, inputs, methodology, equations, and results of a screening level ecological risk assessment (SLERA) conducted for eight of 12 representative waste sites within the newly formed 200 IA-1 Operable Unit (OU); the remaining four waste sites lacked soil data within the shallow vadose zone and were not further evaluated. The SLERA was conducted to evaluate ecological receptor exposure and potential risk using soil data for radionuclides and nonradionuclides occurring within the shallow vadose zone (0 to 15 ft) soil of the OU. The representative Hanford ecological receptors evaluated are: plants, soil invertebrates, California quail (Callipepla californica), Meadowlark (Sturnella neglecta), Killdeer (Charadrius vociferus), Red-tailed hawk (Buteo jamaicensis), Great Basin pocket mouse (Perognathus parvus), Deer mouse (Peromyscus maniculatus), Grasshopper mouse (Onychomys leucogaster), and badger (Taxidea taxus). During the SLERA, detected soil sample results from the shallow vadose zone were used to calculate Hazard Quotients (HQs) for nonradionuclides and Sum of Fractions (SOF) for radionuclides. The results of this SLERA support the preparation of DOE/RL-2020-51, 200-IA-1 Operable Unit Focused Feasibility Study.
Bio-oils contain organic acids and oxygenated compounds that can lead to corrosion issues during bio-oil processing and storage. This Laboratory Analytical Procedure (LAP) allows for rapid screening of a bio-oil's corrosivity without the need for complex equipment and long-term exposures. A robust and repeatable method for assessing the corrosivity of bio-oils is necessary in order to remove materials degradation as an obstacle to research, upgrading, use and storage of bio-oils. This LAP involves the incubation of a representative alloy, 410 stainless steel (410 SS), specimen in bio-oil over a period of 48 hours at 50 degrees C in a sealed container. The corrosive species in the bio-oil react with and deplete alloy elements such as iron (Fe) and/or chromium (Cr) from the specimen into the bio-oil solution. The depletion of Fe and Cr from the specimen results in a significant mass loss that can be recorded. The mass loss is directly correlated to the corrosivity of a bio-oil. Examples of bio-oils in scope include the ones produced by fast pyrolysis and catalytic fast pyrolysis, as well as liquids produced from hydrothermal liquefaction.
This report presents a screening level study of cost estimates for transporting CO 2 via pipeline from five separate sources to a proposed injection well near Mt Auburn IL (Christian County). The basis for the cost estimates is also presented.
A study was conducted to investigate the outgassing characteristics of four thermal filler materials. The purpose of this screening was to identify any outgassing products that might be considered reactive, specifically compounds that could result in corrosion in the systems where these materials are used. A range of compounds was observed in the sample headspaces, though most do not stand out as being known reactive species of concern. However, several halogenated compounds and sulfurous compounds- classes compounds known to facilitate corrosion reactions under certain conditions- were observed in low concentrations. The TFLEX 760 exhibited the highest total outgassing, while the GR130 had the lowest. Therm-a-Gap75 and the Si thermal grease exhibited very similar outgassing profiles. It is difficult to predict the extent to which any given compound observed in an analysis of this type might pose a risk in an actual system; factors such as temperature, system geometry, concentration, and gas conductance all play a role in the kinetics governing chemical reactions. It is recommended that the results of these analyses are shared with pertinent materials SMEs familiar with the system(s) in question to evaluate potential risks.
This engineering calculations and analysis report (ECAR) documents the calculation of screening level air dispersion factors (DFs) for use in identifying Idaho National Laboratory (INL) air pollutant sources that would not be of concern relative to state of Idaho Department of Environmental Quality (DEQ) significant impact levels for toxic air pollutants (IDAPA 2020). A DF (in units of s/m 3 ) is the maximum time-averaged model-predicted air concentration (g/m 3 ) at an ambient-air receptor location divided by a unit source release or emission rate (1 g/s). DFs were calculated for a generic pollutant released from facilities at the INL Site and the Idaho Falls Research Education Campus (REC) using the Environmental Protection Agency (EPA)-recommended AERMOD air-dispersion model (EPA 2019a) and site-specific meteorological data. The use of AERMOD for air quality analyses is specified by EPA in Appendix W of 40 CFR Part 51, Guideline on Air Quality Models, and by DEQ in their air modeling guidance (DEQ 2013). DFs were calculated for 1-hour, 3 hour, 8-hour, 24-hour, monthly, and annual averaging times.
With an objective to achieve net-zero emissions by no later than 2050, biofuels hold enormous potential as sustainable, renewable fuels that can be produced on a large scale. However, fuels of a similar type, derived from limited feedstock batch can dramatically vary in terms of their chemical structures and compositions, causing significant differences in physicochemical properties mainly influencing ignition-relevant properties. Rigorous prescreening and testing efforts are involved to evaluate fuels to comply with engines equipped with advanced combustion modes. The testing of some of these fuels implies low quantity fuel samples, access to relevant temperature and pressure conditions, high reproducibility, and a high throughput rate. In light of these considerations, a Miniaturized Ignition Screening Rapid Compression Machine (MISR) has been developed and built at the University of Illinois Chicago (UIC). Some design features include a smaller bellows-actuated driver section compared to other RCM designs, an adjustable brake section, and full optical access. The setup is designed to achieve reaction conditions (T > 600K and P < 100 bar with repetition rates of about 8 seconds per experiment). These conditions are relevant to low-to-intermediate temperature auto-ignition and can generate data for developing relevant chemical kinetics models or ignition maps. A brief insight into MISR design and its operating mechanism is shown. Also, to demonstrate the performance characteristics of the MISR, Ignition Delay (I.D) experiments for ethanol fuel at different equivalence ratios (Φ) are discussed in the context of plans with additional optical diagnostics, high-speed imaging, and gas sampling experiments.
Carbon capture and storage (CCS) is a process that captures carbon dioxide (CO2) by separating it from anthropogenic emissions sources before atmospheric release and storing that CO2 in deep geologic reservoirs. CCS is a powerful method for reducing anthropogenic CO2 which can ultimately diminish the effects of climate change. Prospective CO2 storage resource is the amount of carbon dioxide that can be stored in a given geologic formation typically given as a mass (e.g., metric tons). Obtaining accurate estimates of CO2 storage resources is necessary for governments and industries to make energy-related policy decisions. Researchers at the National Energy Technology Laboratory (NETL) under the Department of Energy (DOE) developed a methods and a tool [CO2-SCREEN (Storage prospeCtive Resource Estimation Excel aNalysis) to estimate prospective carbon storage resources for saline formations, unconventional shale formations, and residual oil zones. The methods and tool provide CO2 storage and efficiency outputs in the form of probability estimates (i.e. P10 and P90) as well as partitioning storage and efficiency estimates based on storage mechanism (total, free phase, sorbed phase, and dissolution phase). This presentation will focus on how storage efficiency is calculated based on numerical modeling efforts, how it’s applied in the storage methods and tool, and then highlighting needs for future development.
The objective of this project is to establish a new manufacturing methodology with machine learning- based high-throughput screening for the design and development of hierarchical structured, high-performance fabric electrodes for redox flow batteries (RFBs). The end goal of the project is to design and manufacture fabric electrodes for RFB applications that can provide 250 mA/cm2 current density operation for 100-cycles with 80% average energy efficiency. This was accomplished by first examining the structure-performance-property linkages of the electrodes provided by our partner, AvCarb. The electrodes’ microstructure was characterized by determining their pore size distribution, tortuosity, specific surface area, and porosity. The ohmic, charge transfer and mass transfer resistances were then calculated using electrochemical impedance spectroscopy. Carbon cloth electrodes showed the greatest resistance, which was dominated by charge transfer resistance, which we believe is related to the surface functionalization. Full cell cycling was used in order to determine the area specific resistance and energy efficiency of the cells. All of this experimental data and the results of the mathematical model (to increase the amount of inputs with parametric sweeping) were used to develop a machine learning-based model for the design of high-performance fabric electrodes. Using the results from the machine learning tool, optimized electrodes were fabricated by AvCarb. The ohmic, charge transfer and mass transfer resistances for these new electrodes were measured, and both performed better than any of the initial samples which had been provided by AvCarb.
Terrestrial enhanced weathering has emerged as a technology of interest due to its simplicity, low energy requirement, and potentially low capital investment requirement. Due to the novelty of this technology, there is limited literature exploring the techno-economics of the process. This presentation reports on a screening techno-economic analysis that examines the impact of key factors that influence terrestrial enhanced weathering performance and cost. Two cases are considered for analysis, based on the material used: (1) Naturally occurring mined igneous rock (2) Industrial waste materials (like biomass ash or cement kiln dust) The analysis considers locating the terrestrial enhanced weathering system in the Midwestern United States, because of proximity to alkaline materials, availability of farmland, and appropriate ambient conditions for weathering. For the igneous rock case, the size of the system is based on the average amount of igneous rock available from a single mine—250,000 tonnes/year. For the industrial waste case, the size of the system is based on the amount of suitable waste material produced by an industrial hub—150,000 tonnes/year.
The Screening Tool for Industrial Resilience (STIR) helps small and medium-sized manufacturing plants manage risk to their production. Developed by the Pacific Northwest National Laboratory under direction and funding from the Department of Energy’s Office of Manufacturing and Energy Supply Chains, the STIR helps manufacturers enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations. This report provides an overview of the STIR’s risk-informed, high-level approach to resilience planning, which identifies potential solutions that could enhance site resilience based on calculated major risk drivers.
This presentation reports on a screening techno-economic analysis that examines the impact of key factors that influence terrestrial enhanced weathering performance and cost. Two cases are considered for analysis based on the material used: (1) Naturally occurring mined igneous rock (2) Industrial waste materials (like biomass ash or cement kiln dust) The analysis considers locating the terrestrial enhanced weathering system in the Midwestern United States, because of proximity to alkaline materials, availability of farmland, and appropriate ambient conditions for weathering. For the igneous rock case, the size of the system is based on the average amount of igneous rock available from a single mine—250,000 tonnes/year. For the industrial waste case, the size of the system is based on the amount of suitable waste material produced by an industrial hub—150,000 tonnes/year.
National Energy Technology Laboratory (NETL) provides system-level process, cost, and market analyses on solid oxide cell (SOC) based technologies. Specifically, techno-economic analyses (TEA), market assessments, and other technology evaluations serve to guide the U.S. Department of Energy (DOE) Office of Fossil Energy and Carbon Management (FECM) Reversible Solid Oxide Fuel Cell (R-SOFC) Program technology goals and objectives. These studies are key to describing how the technologies contribute to improving domestic energy infrastructure in a clean, efficient manner. This effort seeks to elucidate the potential integration opportunities between reversible SOCs and industrial systems which would aid in SOC commercialization and deployment. These preliminary screening-level results show what opportunities exist for power generating SOFCs, hydrogen producing SOECs, and point-source carbon capture. Improvements can be seen through changes in cost of electricity, cost of hydrogen, and cost of carbon capture.
Poster on “Optimal CO2 Transport and Storage Cost Screening: Application Example” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. A major challenge to commercial scale CCS deployment from the perspective of coal and natural gas-fired power plants is understanding cost-optimal CO2 transport and viable geologic storage options. This study demonstrates unique workflows, using NETL-developed, publicly-available models and tools, to efficiently estimate optimal CO2 transport and storage (T&S) costs for each of the CO2 sources in NETL’s Carbon Capture Retrofit Databases (CCRD) for Electricity Generating Units. The results demonstrate the impact of cost-drivers on optimal T&S, and trends in optimal T&S data, based on real point sources that could be retrofitted with CO2 source technologies.
This report presents a detailed, transparent, and independent screening level techno-economic analysis and lifecycle analysis of enhanced weathering of igneous rocks and industrial waste materials. The performance, costs, and CO 2 removal potential is reported for a process spanning material acquisition, preparation, transport, application and monitoring, reporting, and verification. The base cases examined serve as a reference for informative performance and cost sensitivity analyses. This approach aims to highlight promising scenarios for enhanced weathering application and potential avenues for cost reduction.
Understanding the age of semiconductor parts being built into devices and systems is of interest for manufacturing quality control. Power spectrum analysis (PSA) is a fast, non-destructive, sensitive method for examining semiconductor parts. This talk will cover the use of multivariate analysis on both PSA data and conventional current-voltage data generated prior to PSA analysis to create algorithms that can be automated to screen semiconductor parts for aging.
A pulsed laser testing system for rapid screening of single-event latch-up (SEL) in microelectronics with real-time visualization of sensitive nodes via photo-emission is presented. We provide a detailed overview of the system, followed by SEL threshold measurements in an analog-to-digital converter (AD9240). We find that SEL thresholds are comparable to measurements taken with a low-repetition rate, fixed beam system, and demonstrate full-die scanning speeds can be an order of magnitude faster