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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Disposition of Solids from Hydrothermal Liquefaction of Biomass: Current Understanding, Research Gaps, and Opportunities

Characteristics of solid residuals generated during hydrothermal liquefaction (HTL) of biomass are reported to establish disposition options for these residuals to reduce liability or barriers to commercialization of HTL. HTL solids from pilot scale are reported and compared to existing HTL solids literature for a variety of biomass feedstocks and HTL configurations, noting differences among literature HTL solids due to solids collection methods. Reported analyses include CHNOS, moisture, ash, inorganics by Inductively Coupled Plasma (ICP), and High Heating Value (HHV). HTL solids are determined to be substantially compatible with U.S. Environmental Protection Agency (EPA) disposal regulations within existing data. Disposition and valorization routes are identified, yet combustion as valorization is determined to be not economically beneficial. Further, research gaps are identified, which include the need for future research to be performed on HTL solids that are representative of a continuous, commercializable flowsheet; verification that specific HTL solids meet EPA disposal requirements; exploration of valorization options; and the need for invention of continuous HTL solids separations/management.

09 BIOMASS FUELS↗
DOE OSTI2021

Chloroplast SRP43 autonomously protects chlorophyll biosynthesis proteins against heat shock

The assembly of light-harvesting chlorophyll-binding proteins (LHCPs) is coordinated with chlorophyll biosynthesis during chloroplast development. The ATP-independent chaperone known as chloroplast signal recognition particle 43 (cpSRP43) mediates post-translational LHCP targeting to the thylakoid membrane and also participates in tetrapyrrole biosynthesis (TBS). How these distinct actions of cpSRP43 are controlled has remained unclear. Here, we demonstrate that cpSRP43 effectively protects several TBS proteins from heat-induced aggregation and enhances their stability during leaf greening and heat shock. While the substrate-binding domain of cpSRP43 is sufficient for chaperoning LHCPs, the stabilization of TBS clients requires the chromodomain 2 of the protein. Strikingly, cpSRP54—which activates cpSRP43’s LHCP-targeted function—inhibits the chaperone activity of cpSRP43 towards TBS proteins. High temperature weakens the interaction of cpSRP54 with cpSRP43, thus freeing cpSRP43 to interact with and protect the integrity of TBS proteins. Our data indicate that the temperature sensitivity of the cpSRP43–cpSRP54 complex enables cpSRP43 to serve as an autonomous chaperone for the thermoprotection of TBS proteins.

09 BIOMASS FUELS↗
DOE OSTI2024

Theory of Nonlinear Diffusion with a Physical Gapped Mode

In a system with one conserved charge the charge diffusion is modified by nonlinear self-interactions within an effective field theory (EFT) of diffusive fluctuations. We include the slowest ultraviolet (UV) mode, constructing a UV-regulated EFT. The relaxation time of this UV mode is protected from renormalization, as supported by experimental data in a bad metal system. Furthermore, the retarded density-density Green’s function acquires four branch points, eventually increasing the range of applicability. We discuss the fate of long time tails as well as implications for the quark gluon plasma. Published by the American Physical Society 2024

Physics↗
DOE OSTI2022

BrzostekEcologyLab/CORPSE-soil-jars

Efforts to manage soils for carbon (C) sequestration remain limited by our understanding of how differences in plant traits and microbial traits mechanistically drive soil organic C (SOC) storage. Addressing this uncertainty is particularly critical in bioenergy agriculture, due to its potential to enhance soil C and provide a C neutral fuel. As such, we examined differences between two contrasting feedstocks, Zea mays (corn) and Miscanthus x giganteus (miscanthus), in the ability of their litter to form new chemically resistant particulate SOC vs. physically protected mineral associated SOC and used this data to improve the parameterization of a microbial SOC model. We tested a hypothesized conceptual model whereby easy to decompose corn litters drive greater microbial carbon use efficiency (CUE) and the formation of more mineral associated SOC over particulate SOC than more complex miscanthus litters. To do this, we performed a soil microcosm experiment where we added 13C enriched aboveground and belowground litters to soils and traced the fate of the 13C into microbial respiration and SOC pools. We found that corn litters promoted higher microbial CUE (0.37) than miscanthus litters (0.24). In turn, corn litter formed approximately 50% more mineral associated SOC than miscanthus litters. Similarly, structurally complex root litters promoted a lower CUE and formed less mineral associated SOC than leaf and shoot litters for both crops. When we used our data to parameterize the SOC model, we found that modeling microbial trait differences uniquely allowed the model to capture the fate of litter C in SOC. Collectively, we found a robust link between litter quality, microbial efficiency, and the formation of SOC. This link bridges the empirical uncertainty in how different crops can form new soil C and provides an empirical basis for modeling SOC transformations.

Ridgeway, Joanna↗
DOE OSTI2020

Multi-Functional Distributed Fiber Sensors for Pipeline Monitoring and Methane Detections. Final Report

As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.

03 NATURAL GAS↗
DOE OSTI2022

Pantex Plant Calendar Year 2021 (Annual Site Environmental Report)

The 2021 Annual Site Environmental Report (ASER) summarizes the Pantex Plant’s status, data, and efforts for the environmental compliance, protection, and restoration programs. It has been prepared in accordance with DOE Order 231.1B, Environment, Safety and Health Reporting (DOEa), and DOE Order 458.1, Radiation Protection of the Public and the Environment (DOEb). These orders outline the requirements for environmental protection programs at DOE facilities to ensure that programs fully comply with applicable federal, state, and local environmental laws and regulations, executive orders, and DOE policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Annual Site Environmental Report: Pantex Plant

The 2022 Annual Site Environmental Report (ASER) summarizes Pantex’s status, data, and efforts for the environmental compliance, protection, and restoration programs. It has been prepared in accordance with DOE O 231.1B, Environment, Safety and Health Reporting, and DOE O 458.1, Radiation Protection of the Public and the Environment. These orders outline the requirements for environmental protection programs at DOE facilities to ensure that programs fully comply with applicable federal, state, and local environmental laws and regulations, executive orders, and DOE policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Annual Site Environmental Report for Pantex Plant, Calendar Year 2023

The 2023 Annual Site Environmental Report (ASER) summarizes Pantex’s status, data, and efforts for the environmental compliance, protection, and restoration programs. It has been prepared in accordance with DOE O 231.1B, Environment, Safety and Health Reporting, and DOE O 458.1, Radiation Protection of the Public and the Environment. These orders outline the requirements for environmental protection programs at DOE facilities to ensure that programs fully comply with applicable federal, state, and local environmental laws and regulations, executive orders, and DOE policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Technical Learning and Integration of Interns in Advanced Protection Lab Space: Enhancements to Testbed and Experiments to Improve Workflows for Producing Datasets

This report presents a successful technical learning integration of student interns in the Advanced Protection Laboratory space, located in the Grid Research Integration and Deployment Center (GRID-C) at the Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL). The Advanced Protection Laboratory was created for the primary goal of supporting DOE’s research projects and technical staff at ORNL. As a secondary goal, the space was used for collaborating with ORNL’s intern programs, providing support to the lab’s mentors and student interns. In 2024, three student interns spent a summer in the Advanced Protection lab space and were involved in the DarkNet Distributed Ledger Technology (DLT) project. The students had a great opportunity to gain hands-on experience with communication and protective relay equipment focused on information technology, data analytics, and cybersecurity. Experiences in the lab space with real equipment and software integration offer education and professional development for students, which is especially important because of a need in the energy industry to recruit highly skilled power and communication engineers.

42 ENGINEERING↗
DOE OSTI2025

Annual Site Environmental Report Pantex Plant Calendar Year 2024

The 2024 Annual Site Environmental Report (ASER) summarizes Pantex’s status, data, and efforts for the environmental compliance, protection, and restoration programs. It has been prepared in accordance with DOE Order (DOE O) 231.1B, Environment, Safety and Health Reporting, and DOE O 458.1, Radiation Protection of the Public and the Environment. These orders outline the requirements for environmental protection programs at DOE facilities to ensure that programs fully comply with applicable federal, state, and local environmental laws and regulations, executive orders, and DOE policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deepfaked online content is highly effective in manipulating people’s attitudes and intentions

In recent times, disinformation has spread rapidly through social media and news sites, biasing our (moral) judgements of other people and groups. “Deepfakes”, a new type of AI-generated media, represent a powerful new tool for spreading disinformation online. Although Deepfaked images, videos, and audio may appear genuine, they are actually hyper-realistic fabrications that enable one to digitally control what another person says or does. Given the recent emergence of this technology, we set out to examine the psychological impact of Deepfaked online content on viewers. Across seven preregistered studies (N = 2558) we exposed participants to either genuine or Deepfaked content, and then measured its impact on their explicit (self-reported) and implicit (unintentional) attitudes as well as behavioral intentions. Results indicated that Deepfaked videos and audio have a strong psychological impact on the viewer, and are just as effective in biasing their attitudes and intentions as genuine content. Many people are unaware that Deepfaking is possible; find it difficult to detect when they are being exposed to it; and most importantly, neither awareness nor detection serves to protect people from its influence. All preregistrations, data and code available at osf.io/f6ajb.

Hughes, Sean↗
DOE OSTI2020

Multi-View Convolutional Neural Network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. In order to address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

National Cancer Institute (NCI) Exposomic Linkage Protocol

The purpose of this work is to create point-level linkages of residential history data, which is provided by the Surveillance, Epidemiology, and End Results (SEER) program, to air pollution exposure data so that we can develop longitudinal measures of exposure and investigate their effects on cancer incidence, treatment response, and survival. The Louisiana, New Jersey, Kentucky, and Iowa registries were previously linked to LexisNexis residential history data through the National Cancer Institute (NCI). We will enhance the utility of the existing residential location data by geocoding addresses based on data from between 1995 and 2024 and spatially linking the locations to air pollution, indoor radon, and the US Environmental Protection Agency’s (EPA) Risk-Screening Environmental Indicators (RSEI) exposure data (Figure 1).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Molecular Layer Deposition of Crosslinked Polymeric Lithicone for Superior Lithium Metal Anodes

In this work, we for the first time developed a novel lithium-containing crosslinked polymeric material, a lithicone that enables excellent protection effects over lithium (Li) metal anodes. This new lithicone was synthesized via an accurately controllable molecular layer deposition (MLD) process, in which lithium tert -butoxide (LTB) and glycerol (GL) were used as precursors. The resultant LiGL lithicone was analyzed using a suite of characterizations. Furthermore, we found that the LiGL thichicone could serve as an exceptional polymeric protection film over Li metal anodes. Our experimental data revealed that the Li electrodes coated by this LiGL lithicone can achieve a superior cycling stability, accounting for an extremely long cyclability of >13,600 Li-stripping/plating cycles and having no failures so far in Li/Li symmetric cells at a current density of 5 mA/cm 2 and an areal capacity of 1 mAh/cm 2 . We found that, with a sufficient protection by this LiGL coating, Li electrodes could realize long-term stable cyclability with little formation of Li dendrites and solid electrolyte interphase. This novel LiGL represents a facile and effective solution to the existing issues of Li anodes and potentially paves a technically feasible route for lithium metal batteries.

Meng, Xiangbo↗
DOE OSTI2025

Physics-Informed Recurrent Neural Networks to Predict Reactor Operations of the AGN-201 Nuclear Reactor

4 page paper submitted to ANS Student conference. Summary of paper similar to the following abstract: The ability to predict how a reactor will operate, understand when anomalous conditions arise, and ensure a reactor is being operated as expected is crucial for deploying new nuclear facilities. Digital twins serve as a unique solution to recognizing reactor behavior; however, they require data to be useful. For next-generation reactors, this data may not currently be available. To explore how synthetic physics-informed reactor data can be used to predict reactor operations, a recurrent neural network was implemented for the Idaho State University AGN-201 digital twin. The goal of this work is to determine how synthetic data can be used to train a recurrent neural network model for predicting the reactor power of the AGN-201. The recurrent neural network was validated using both synthetic and real operational data. We envision this approach will help bridge the gap between the virtual and physical sides of a digital twin, where reactor physics models based on as-built data can be corrected for actual operating parameters to ensure the virtual model mirrors reality.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
DOE OSTI2021

Low-dose in vivo protection and neutralization across SARS-CoV-2 variants by monoclonal antibody combinations

Prevention of viral escape and increased coverage against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern require therapeutic monoclonal antibodies (mAbs) targeting multiple sites of vulnerability on the coronavirus spike glycoprotein. Here we identify several potent neutralizing antibodies directed against either the N-terminal domain (NTD) or the receptor-binding domain (RBD) of the spike protein. Administered in combinations, these mAbs provided low-dose protection against SARS-CoV-2 infection in the K18-human angiotensin-converting enzyme 2 mouse model, using both neutralization and Fc effector antibody functions. The RBD mAb WRAIR-2125, which targets residue F486 through a unique heavy-chain and light-chain pairing, demonstrated potent neutralizing activity against all major SARS-CoV-2 variants of concern. In combination with NTD and other RBD mAbs, WRAIR-2125 also prevented viral escape. These data demonstrate that NTD/RBD mAb combinations confer potent protection, likely leveraging complementary mechanisms of viral inactivation and clearance.

60 APPLIED LIFE SCIENCES↗