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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Shark nanobodies with potent SARS-CoV-2 neutralizing activity and broad sarbecovirus reactivity

Despite rapid and ongoing vaccine and therapeutic development, SARS-CoV-2 continues to evolve and evade, presenting a need for next-generation diverse therapeutic modalities. Here we show that nurse sharks immunized with SARS-CoV-2 recombinant receptor binding domain (RBD), RBD-ferritin (RFN), or spike protein ferritin nanoparticle (SpFN) immunogens elicit a set of new antigen receptor antibody (IgNAR) molecules that target two non-overlapping conserved epitopes on the spike RBD. Representative shark antibody variable NAR-Fc chimeras (ShAbs) targeting either of the two epitopes mediate cell-effector functions, with high affinity to all SARS-CoV-2 viral variants of concern, including the divergent Omicron strains. The ShAbs potently cross-neutralize SARS-CoV-2 WA-1, Alpha, Beta, Delta, Omicron BA.1 and BA.5, and SARS-CoV-1 pseudoviruses, and confer protection against SARS-CoV-2 challenge in the K18-hACE2 transgenic mouse model. Structural definition of the RBD-ShAb01-ShAb02 complex enabled design and production of multi-specific nanobodies with enhanced neutralization capacity, and picomolar affinity to divergent sarbecovirus clade 1a, 1b and 2 RBD molecules. These shark nanobodies represent potent immunotherapeutics both for current use, and future sarbecovirus pandemic preparation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
DOE OSTI2024

Unraveling electrochemo-mechanical aspects of core–shell composite cathode for sulfide based all-solid-state batteries

All-solid-state lithium batteries (ASSLBs) are emerging as promising next-generation batteries for electric vehicles owing to their high energy densities and safety features. However, challenges such as inadequate material percolation and low cathode utilization often hinder their potential. This paper presents a core–shell approach to optimize the cathode active material (CAM) utilization. The resultant CAM composite showed high ionic conductivity, a highly dense microstructure with <10% porosity, and minimal stack pressure changes during electrochemical cycling. The maximum CAM utilization was achieved while effectively mitigating electrochemo-mechanical side reactions by applying a uniformly coated Li 6 PS 5 Cl solid electrolyte layer (≈500 nm) and a LiNbO 3 buffer layer (≈10 nm) onto LiNi 0.8 Mn 0.1 Co 0.1 O 2 particles (LPSCl@LNO@NMC). The engineered LPSCl@LNO@NMC composites, which incorporated a 5 wt% LPSCl coating on LNO@NMC powders, exhibited a dense microstructure that enhanced the mechanical stability at the cathode. Sulfide-based solid electrolyte (SSE)/SSE contact provided better ionic pathways within the composite and increased CAM utilization. Thus, an enhanced reversible capacity (197 mA h g -1 ) and exceptional high-rate cycling performance (86.3% capacity retention after 1000 cycles at 2C) were observed. These findings pave the way for the advancement and commercialization of high-performance ASSLBs.

25 ENERGY STORAGE↗
DOE OSTI2026

Uncovering Hidden Entanglement in Twin Beams

Proper characterization of quantum correlations in multimode optical quantum states is critical for applications in quantum information science. However, standard entanglement measurements can lead to incomplete state reconstruction and characterization. Here, we implement a resonator-based detection system that reveals entanglement between sideband modes of twin beams, achieving full tomography and retrieving often ignored quantum correlations. Unlike standard spectral measurements such as homodyne detection, resonator detection can independently address the sidebands of each beam, thereby accessing these hidden correlations. Additionally, we show how phase shifts between the carrier and the sideband modes of the involved fields redistribute information and modify the observation of entanglement for different witnesses. The ability of the resonant detection to independently address sideband modes of entangled states can contribute to enhancing the capacity for secure communication and quantum networking protocols.

Rincon Celis, Raul [University of Sao Paulo, Brazi↗
DOE OSTI2020

Broadband Nonreciprocity Enabled by Strong Coupling of Magnons and Microwave Photons

Nonreciprocity of signal transmission enhances the capacity of communication channels and protects the transmission quality against possible signal instabilities, thus becoming an important component for ensuring coherent information processing. However, nonreciprocal transmission requires breaking time-reversal symmetry (TRS), which poses challenges of both practical and fundamental character that hinder progress. Here we report an alternative scheme for achieving broadband nonreciprocity using a specially engineered hybrid microwave cavity. The TRS breaking is realized via strong coherent coupling between a selected chiral mode in the microwave cavity and a single collective spin excitation (magnon) in a ferromagnetic yttrium iron garnet sphere. The nonreciprocity in transmission is observed to span nearly a 0.5-GHz frequency band, which outperforms by two orders of magnitude the previously achieved bandwidths. Our findings suggest a promising direction for robust coherent information processing in a broad range of systems in both the classical and the quantum regime.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Modular Integration Design of LCL Circuit Featuring Field Enhancement and Misalignment Tolerance for Wireless EV Charging

One of the major challenges of high-order wireless charging circuit is to reduce the system volume and cost induced by its additional components. Based on an LCL-S circuit, this paper proposes a modular integration design, in which an identical compensation inductor and transmitter are closely coupled. A new compensation method is derived to enable zero phase angle (ZPA) and constant current (CC) output. Identical currents are fed into the same reference terminals of both inductors to enable transmission field enhancement and to eliminate reactive power flow between the inductors. A misalignment tolerant coil is used to improve the output power stability under random parking imperfection. The proposed integration design achieves enhanced transmission capacity. Experiments are carried out to verify the proposed design.

Zhang, Pengcheng↗
DOE OSTI2025

A Study on the Impact of Temperature-Dependent Ferroelectric Switching Behavior in 3D Memory Architecture

The flourishing development of neural networks that require exponentially growing amounts of data has presented an elevated demand for memory footprint. To address this, researchers have been exploring hardware accelerators with innovative memory architectures like 3D memory. These 3D memory architectures offer enhanced storage capacity and processing capabilities, at a cost of rising on-chip temperature during operation. Hafnium Zirconium Oxide (HZO) based Ferroelectric Random Access Memory (FeRAM) is a promising nonvolatile memory candidate in neural network hardware accelerators for its outstanding write performance and reliability. However, its implementation in the architecture regarding the temperature-dependent ferroelectric switching behavior has not been well studied. In this work, we study the thermal impacts on polarization switching through experimental devices and simulation results. We conduct the circuit and architecture-level simulations to showcase that one can exploit this temperature rise to reduce FeRAM's write voltage and write energy due to its unique temperature-activated polarization switching mechanisms. As the on-chip temperature increases to 351K (ambient temperature at 300K) due to neural network workloads, the access energy per bit can be reduced by 27.6% when a dynamic write voltage is applied.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tensile and fatigue characterization of multifunctional composites

This research is part of a larger effort to develop advanced self-sensing multifunctional polymer composites that are both lightweight and high-strength, while also enabling structural damage detection, fatigue cycle monitoring, and service life prediction. These multifunctional composites are particularly sought after in the automotive industry for their potential to significantly reduce vehicle weight and simultaneously provide additional functionality like condition monitoring to enhance safety. This study examines the tensile and fatigue properties of a composite material composed of acrylonitrile butadiene styrene (ABS) polymer embedded with piezoelectric barium titanate (BaTiO3) nanoparticles. The integration of BaTiO3 nanoparticles not only supplies the material with self-sensing capabilities but also influences its mechanical properties. While a high content of BaTiO3 nanoparticles is desired to enhance sensing capacity, the brittle nature of such materials causes concerns of decreased strength characteristics. To explore this, various composite samples were fabricated with nanoparticle contents ranging from 0 wt% to 20 wt%. These samples underwent tensile testing to measure their ultimate tensile strengths and Young’s moduli. Following this, fatigue tests were conducted to generate S-N curves, which are essential for understanding the material's durability under cyclic loading. The findings from these tests assess the impact of nanoparticle content on the composite’s tensile strength and fatigue life, providing essential insights that can guide the optimization and design of future self-sensing multifunctional composites. The results suggest that 5 wt% BaTiO3 provides an optimal balance between mechanical properties and nanoparticle concentration, making it a promising composition for semi-structural applications.

Bowland, Christopher [ORNL] (ORCID:000000021229431↗
DOE OSTI2022

Development and Applications of an eReaxFF Force Field for Graphitic Anodes of Lithium-Ion Batteries

Graphene is one of the most promising materials for lithium-ion battery anodes due to its superior electronic conductivity, high surface area for lithium intercalation, fast ionic diffusivity and enhanced specific capacity. A reliable description of many battery processes requires an explicit description of electrochemical interactions involving electrons. A detailed atomistic modeling of electronic conduction and non-zero voltage simulations of graphitic materials require the inclusion of an explicit electronic degree of freedom. To enable large length- and time-scale simulations of electron conduction in graphitic anodes, we developed an eReaxFF force field concept describing graphitic materials with an explicit electron. The newly developed force field, verified against quantum chemistry-based data describing, amongst others, electron affinities and equation of states, reproduces the qualitative behavior of electron conductivity in pristine and imperfect graphitic materials at different applied temperatures and voltages. In addition, excess electron localization near a defect site estimated from eReaxFF simulations agree quite well with the corresponding density functional theory calculations. Here, our eReaxFF simulations show the initiation of lithium-metal-plating driven by electron transfer from the graphene surface to the exposed lithium ions demonstrating the method’s potential for studying lithium-graphene interactions with explicit electrons and explain many unresolved electrode and electrode-electrolyte interface processes.

25 ENERGY STORAGE↗
DOE OSTI2020

Characterizing the Evolution of Trapped scCO 2 Curvature in Bentheimer and Nugget Sandstone Pore Geometry

During a Geologic Carbon Storage process, supercritical CO 2 (scCO 2 ) is subjected to a series of dynamic and static conditions where the relationship between pore geometry and the trapped scCO 2 curvature remains to be established. To mimic the dynamic process, two sandstones, Bentheimer and Nugget, were subjected to two successive drainage and imbibition (D-I) cycles and X-ray computed tomography scanned at each residual state to capture the wettability evolution at static conditions in the same pore geometry. Both sandstones contain similar grain size distributions, pore size distributions, and pore interconnectivity but differ in that the Nugget formation contains approximately half the porosity of the Bentheimer sandstone, and the pore network contains dead-end pores. scCO 2 size distributions, strain calculations, and geometric contact angle measurements were used to characterize the curvature of scCO 2 in different pore types between cycles. An increase in geometric contact angle was the greatest when advancement along the pore network of the same ganglion occurred between cycles while strain increased the most with pore-filling trapping. Moreover, Nugget sandstone results in a greater aggregated residual saturation and shows a clear increase in scCO 2 sizes with an additional D-I cycle while scCO 2 in the Bentheimer core shows a more complex response with some ganglion increasing and some decreasing in size with an additional D-I cycle. From this work, we suspect the pore geometry is playing a role in scCO 2 size distributions and use this information to suggest using water pulses to enhance trapping capacity in lower porosity sandstones.

58 GEOSCIENCES↗
DOE OSTI2022

Effects of Carbonic Acid-Rock Interactions on CO2/Brine Multiphase Flow Properties in the Upper Minnelusa Sandstones

Summary Carbon dioxide (CO2) injection into a deep saline aquifer can dissolve into formation brine and generate carbonic acid. The resulting acid can drive fluid-rock geochemical reactions. The impact of these fluid-rock geochemical reactions on porosity, permeability, and multiphase flow responses is relevant to the determination of CO2 storage capacity of deep saline aquifers. In this research, carbonic acid flooding experiments were performed on core samples consisting of poorly sorted, quartz-rich sand with laminated bedding from a possible CO2 storage target in northwest Wyoming. Complementary pre- and post-injection porosity and permeability, thin-section, Brunauer-Emmett-Teller (BET) surface area, mercury intrusion capillary pressure (MICP), and time-domain nuclear magnetic resonance (TD-NMR) measurements were conducted. Overall, both core porosity and permeability increased after a 7-day carbonic acid injection, from 6.2 to 8.4% and 1.6 to 3.7 md, respectively. We attributed these changes to carbonate mineral dissolution, which was evidenced by the effluent brine geochemistry, pore-throat size distribution (PTSD), and BET surface area. To be more specific, within the more-permeable section of core samples containing larger pore size, the permeability increment is apparent due to dolomite mineral grains and cements dissolution. However, for the lower-permeability section corresponding to the smaller pore size, mineral precipitation possibly lessened dissolution effects, leading to insignificant petrophysical properties changes. Consequently, the observed heterogeneous carbonic acid-rock interactions resulted in alterations of CO2/brine relative permeability (i.e., the initial CO2 saturation decreased and the CO2 flow capacity was enhanced). This research provides a fundamental understanding regarding effects of fluid-rock reactions on changes in static and multiphase flow properties of eolian sandstones, which lays the foundation for more accurate prediction/simulation of CO2 injection into deep saline aquifers.

Engineering↗
DOE OSTI2022

Thermal Neutron Cross Section Measurements at the RPI LINAC [Slides]

Cold polyethylene shows adequate capacity for enhancing sub-thermal neutron flux. Cold moderator system produced high quality thermal cross section measurements. Measurements were performed for polyethylene, polystyrene, Plexiglas, yttrium hydride and beryllium and compared with ENDF/B-VIII.0 TSL evaluations. All data reduction for all measurements was finalized. Future work includes completion of necessary journal publications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Coal Combustion Residual (CCR) landfills in the U.S. can lead to elevated contaminant concentrations in groundwater and runoff, particularly of arsenic, boron, and selenium. Zeolites can be readily synthesized from materials contained within the coal combustion ash impoundments and can be cation-exchanged to enhance adsorption capacity, selectivity, and reactivity. However, the optimization of zeolites becomes a daunting task when accounting for the variety of Si:Al ratios, the species of extra-framework cations present, and the zeolite pore structure. Molecular simulations provide methods to study and guide the design of zeolites for the sorption of contaminants from aqueous solution. In this work, models that predict the sorption of arsenic, boron, and selenium from water using cation-exchanged zeolites. Because of the lack of experimental adsorption data for these species of contaminants, models were parametrized to reproduce the results of first-principles calculations and then used to predict sorption for zeolites for a dataset containing 6000 combinations of zeolites and sorption conditions. Machine learning was used to train a model to predict sorption for materials in this database based on the results of the molecular simulations. Next, a genetic algorithm was used to optimize zeolites for the removal of each contaminant from aqueous solution for individual impoundment sites based on contaminant concentrations reported by the Electric Power Research Institute.

Findley, John↗
DOE OSTI2026

Analysis of Molten Salt Thermal Energy Storage Tank Sizing and Preheating

Molten Salt Thermal energy storage (TES) is essential for concentrating solar power (CSP) plants, enhancing their capacity factor and dispatchability to ultimately reduce electricity costs. This technology is becoming more important for other technologies including nuclear power, thermal-electrical storage, and concentrating solar thermal (CST). While molten salt TES tanks have been successfully deployed in lower-temperature parabolic-trough plants, the current state-of-the-art central receiver systems operating at temperatures up to 565 degrees Celsius have experienced costly and frequent failures. These failures are associated with the immaturity of the technology and problems in tank design, fabrication, commissioning and, sometimes, aggressive operation. A primary contributing factor is the absence of a dedicated technical standard for high-temperature molten nitrate TES tanks; designers must currently rely on existing codes like API 650 and ASME Section II, which are insufficient because they fail to account for the unique demands of high temperatures, thermal cycling, and transient conditions.

25 ENERGY STORAGE↗
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Heterologous expression of formate dehydrogenase enables photoformatotrophy in the emerging model microalga, Picochlorum renovo

Rising global greenhouse gas emissions and the impacts of resultant climate change necessitate development and deployment of carbon capture and conversion technologies. Amongst the myriad of bio-based conversion approaches under evaluation, a formate bio-economy has recently been proposed, wherein CO 2 -derived formate serves as a substrate for concurrent carbon and energy delivery to microbial systems. To date, this approach has been explored in chemolithotrophic and heterotrophic organisms via native or engineered formatotrophy. However, utilization of this concept in phototrophic organisms has yet to be reported. Herein, we have taken the first steps to establish formate utilization in Picochlorum renovo, a recently characterized eukaryotic microalga with facile genetic tools and promising applied biotechnology traits. Plastidial heterologous expression of a formate dehydrogenase (FDH) enabled P. renovo growth on formate as a carbon and energy source. Further, FDH expression enhanced cultivation capacity on ambient CO 2 , underscoring the potential for bypass of conventional CO 2 capture and concentration limitations. This work establishes a photoformatotrophic cultivation regime that leverages light energy-driven formate utilization. The resultant photosynthetic formate platform has widespread implications for applied phototrophic cultivation systems and the bio-economy at large.

09 BIOMASS FUELS↗
DOE OSTI2020

Chemically Cross-Linked Cellulose Nanocrystal Aerogels for Effective Removal of Cation Dye

In this study, porous aerogels were prepared by directional freeze-drying via cross-linking cellulose nanocrystals (CNCs) with poly(methyl vinyl ether-co-maleic acid) (PMVEMA) and poly(ethylene glycol) (PEG). The thermal properties and physical adsorption performance toward cation methylene blue dye of the obtained CNC aerogels were investigated. The maximum degradation temperature was increased from 324&#x000B0;C of CNCs to 355&#x000B0;C of cross-linked CNC aerogels. The dye adsorption isotherm results showed that the maximum methylene blue adsorption capacity of CNC aerogels was 116.2 mg g &#x02212;1 , according to the Langmuir model, which was mainly due to the electrostatic attractions between negatively charged carboxyl groups or sulfonate groups on the CNC aerogles and cation MB molecules. The reusability test showed that the CNC aerogels contained the same dye adsorption performance in five adsorption/desorption cycles. Overall, this study described an ideal alternative for water purification with high dye adsorption capacity and enhanced physical performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗