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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Influence on Structural Loading of a Wave Energy Converter by Controlling Variable-Geometry Components and the Power Take-Off

Oceans are harsh environments and can impose significant loads on deployed structures. A wave energy converter (WEC) should be designed to maximize the energy absorbed while ensuring the operating wave condition does not exceed the failure limits of the device itself. Therefore, the loads endured by the support structure are a design constraint for the system. Furthermore, the WEC should be adaptable to different sea states. Herethis work uses a WEC-Sim model of a variable-geometry oscillating wave energy converter (VGOSWEC) mounted on a support structure simulated under different wave scenarios. A VGOSWEC resembles a paddle pitching about a fixed hinge perpendicular to the incoming wave fronts. The geometry of the VGOSWEC is varied by opening a series of controllable flaps on the pitching paddle when the structure experiences threshold loads. It is hypothesized that opening the flaps should result in load shedding at the base of the support structure by reducing the moments about the hinge axis. This work compares the hydrodynamic coefficients, natural periods, and response amplitude operators from completely closed to completely open configurations of the controllable flaps. This work shows that the completely open configuration can reduce the pitch and surge loads on the base of the support structure by as much as 80%. Increased loads at the structure’s natural period can be mitigated by an axial power take-off damping acting as an additional design parameter to control the loads at the WEC’s support structure.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

FOSWEC v2: System identification and control testing at Hinsdale Wave Research Laboratory

This report summarizes the design and execution of a wave tank test of the floating oscillating surge wave energy converter (FOSWEC) in the O.H. Hinsdale Wave Research Laboratory Directional Wave Basin at Oregon State University. This device, which uses two "flaps" that pivot about a central platform when excited by waves, has a natural frequency within the range of the waves by which it is excited. The FOSWEC was originally considered to be a 1:33 scale device, however, for the current tests, no fixed relative scale is used (i.e., the WEC is considered to be scaled for the basin’s wave environment in which it operates). The primary goal of this test was to assess the degree to which previously developed modeling, experimentation, and control design methods could be applied to a broad range of wave energy converter designs. Testing was conducted to identify a dynamic model for the impedance and excitation behavior of the device. Using these models, a series of closed loop tests were conducted using a causal impedance matching controller. This report provides a brief description of the results, as well as a summary of the device and experimental design. The results show that the methods applied to this experimental device perform well and should be broadly applicable. The data collected during testing is compressed into FOSWEC.zip. Please refer to Appendix C (pages 61-63) of the test report for descriptions of each test ID corresponding to the compressed files.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Coupled wave-current modeling for hydrodynamic load analysis of macroalgae cultivation farms

Large-scale cultivation of macroalgae is one of the most promising biofuel sources that could reduce our consumption of fossil fuels and would be particularly economically competitive when grown for the co-production of additional goods such as food and textiles. Current production of biofuel from algal biomass is limited by labor costs and lack of data on potential impacts of cultivation and harvesting on marine and coastal environments as well as a comprehensive characterization of the impact of environmental conditions on macroalgal feedstock and their bioproduct and biofuel yield and quality. Awarded by the United States (U.S.) Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program, which seeks to enable the U.S. as a global leader in the production of marine biomass, the Marine Biological Laboratory (MBL) has been developing a test system for tropical seaweed cultivation in the Gulf of Mexico and the Caribbean. An integral step in designing macroalgae cultivation farms is to adequately characterize the hydrodynamic climate at potential growth sites. In regions like the Gulf of Mexico and the Caribbean, which are highly exposed to tropical cyclones, it is critical to not only consider the day-to-day hydrodynamic conditions but to also assess the risk of extreme sea states to ensure the survivability of future macroalgae farms. Under these considerations, the Pacific Northwest National Laboratory (PNNL) is providing modeling support for MBL to inform the design and siting of the farm systems that have been proposed for their ARPA-E MARINER project. This report describes the development of a high-resolution coupled storm surge and wave model to simulate the hydrodynamics and wave climate at proposed macroalgae cultivation sites selected by MBL in Florida and Puerto Rico. Model results, including model validation, water level, current distributions, and sea states, are discussed for both selected sites in Florida and Puerto Rico coast. These simulations provide an accurate insight into the hydrodynamic conditions that a macroalgae farm is likely to experience during its operational lifetime, including current information to support fine-scale hydrodynamic load modeling, risk analysis and system design.

09 BIOMASS FUELS↗
DOE OSTI2025

MuSIKAL: Multiphysics Simulations and Knowledge Discovery through AI/ML Technologies

Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

2.2.1.410 - Significant Cost Reduction Potential for Wave Energy Conversion Devices with Variable-Geometry Modules

The Technology Commercialization Fund (TCF) award is built upon NREL's previous research exploring a bottom-fixed variable-geometry oscillating surge wave energy converter (VGOSWEC); however, unlike previous investigations, the VGOSWEC will be raised off the sea floor. Researchers believe that advances in offshore pilings and foundations, such as 3D printing, will provide low cost and easily deployable structures. There are several benefits to designing a raised VGOSWEC such as 1) reducing issues with sediment transport and environmental impacts near shore, 2) less expensive deployment and installation as work boats will not risk grounding in shallow water, and 3) deeper water deployments improve the wave resource.

oscillating surge wave energy converter↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2024

Stoichiometry–Induced Ferromagnetism in Altermagnetic Candidate MnTe

The field of spintronics has seen a surge of interest in altermagnetism due to novel predictions and many possible applications. MnTe is a leading altermagnetic candidate that is of significant interest across spintronics due to its layered antiferromagnetic structure, high Neel temperature (T N ≈ 310 K) and semiconducting properties. The results on molecular beam epitaxy (MBE) grown MnTe/InP(111) films are presented. Here, it is found that the electronic and magnetic properties are driven by the natural stoichiometry of MnTe. Electronic transport and in situ angle-resolved photoemission spectroscopy show the films are natively metallic with the Fermi level in the valence band and the band structure is in good agreement with first-principles calculations for altermagnetic spin-splitting. Neutron diffraction confirms that the film is antiferromagnetic with planar anisotropy and polarized neutron reflectometry indicates weak ferromagnetism, which is linked to a slight Mn-richness that is intrinsic to the MBE-grown samples. Further, when combined with the anomalous Hall effect, this work shows that the electronic response is strongly affected by the ferromagnetic moment. Altogether, this highlights potential mechanisms for controlling altermagnetic ordering for diverse spintronic applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

High‐Efficiency Ion‐Exchange Doping of Conducting Polymers

Abstract Molecular doping—the use of redox‐active small molecules as dopants for organic semiconductors—has seen a surge in research interest driven by emerging applications in sensing, bioelectronics, and thermoelectrics. However, molecular doping carries with it several intrinsic problems stemming directly from the redox‐active character of these materials. A recent breakthrough was a doping technique based on ion‐exchange, which separates the redox and charge compensation steps of the doping process. Here, the equilibrium and kinetics of ion exchange doping in a model system, poly(2,5‐bis(3‐alkylthiophen‐2‐yl)thieno(3,2‐b)thiophene) (PBTTT) doped with FeCl 3 and an ionic liquid, is studied, reaching conductivities in excess of 1000 S cm −1 and ion exchange efficiencies above 99%. Several factors that enable such high performance, including the choice of acetonitrile as the doping solvent, which largely eliminates electrolyte association effects and dramatically increases the doping strength of FeCl 3 , are demonstrated. In this high ion exchange efficiency regime, a simple connection between electrochemical doping and ion exchange is illustrated, and it is shown that the performance and stability of highly doped PBTTT is ultimately limited by intrinsically poor stability at high redox potential.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electro-Thermal Characterization of Dynamical VO 2 Memristors via Local Activity Modeling

We report translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO 2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemical Tuning Meets 2D Molecular Magnets

Two-dimensional (2D) magnets provoke a surge of interest in large anisotropy in reduced dimensions and are promising for next-generation information technology where dynamic magnetic tuning is essential. Until recently, the crucial metal-organic magnet Cr(pyz) 2 ∙xLiCl∙yTHF with considerable high coercivity and high-temperature magnetic order opens up a new platform to control magnetism in metal-organic materials at room temperature. Here we report an in-situ chemical tuning route to realize the controllable transformation of low-temperature magnetic order into room-temperature hard magnetism in Cr(pyz) 2 ∙xLiCl∙yTHF. The chemical tuning via electrochemical lithiation and solvation/desolvation exhibits continuously variable magnetic features from cryogenic magnetism to the room-temperature optimum performance of coercivity (H c ) of 8500 Oe and energy product of 0.6 MGOe. Such chemically flexible tunability of room-temperature magnetism is ascribed to the different degrees of lithiation and solvation that modify the stoichiometry and Cr-pyrazine coordination framework. Furthermore, the additively manufactured hybrid magnets show air stability and electromagnetic induction, providing potential applications. Our findings here suggest chemical tuning as a universal approach to control the anisotropy and magnetism of 2D hybrid magnets at room temperature, promising for data storage, magnetic refrigeration, and spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Interlayer Interactions in 1D Van der Waals Moiré Superlattices.

Studying two-dimensional (2D) van der Waals (vdW) moiré superlattices and their interlayer interactions have received surging attention after recent discoveries of many new phases of matter that are highly tunable. Different atomistic registry between layers forming the inner and outer nanotubes can also form one-dimensional (1D) vdW moiré superlattices. In this review, experimental observations and theoretical perspectives related to interlayer interactions in 1D vdW moiré superlattices are summarized. The discussion focuses on double-walled carbon nanotubes (DWNTs), a model 1D vdW moiré system, and the authors highlight the new optical features emerging from the non-trivial strong interlayer coupling effect and the unique physics in 1D DWNTs. Future directions and questions in probing the intriguing physical phenomena in 1D vdW moiré superlattices such as, correlated physics in different 1D moiré systems beyond DWNTs are proposed and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Self‐Assembled NiO Nanocrystal Arrays as Memristive Elements

Abstract Memristive switching, a nonlinear current–voltage ( I – V ) characteristic, has seen a tremendous surge in interest as an approach to achieve implementation of synaptic functions. The memristive switching behavior of self‐assembled NiO nanocrystals is investigated via scanning probe microscopy, based on first‐order reversal curve current–voltage spectroscopy. Synaptic switching is clearly observed as a direct consequence of filament growth (i.e., gradually increased conductance) in the nanocrystals. A spatial dependency of the conduction in the nanocrystals suggests that there is a localization of the switching filament. The current understanding of this localization ignores features related to local lateral variation current, which can generate an excessive local heat and temperature such as electrical dissipation. The observation of low electrical dissipation at the edge of the nanocrystals shows that less energy is wasted as heat such that the bias applied can be utilized more efficiently to assist the nucleation of the filament and thus reduces the power consumption. Electrical power dissipation is also found to scale with nanocrystal height and has spatial dependence within the nanocrystals. The combination of synaptic switching and high density of the nanocrystals demonstrate that it is feasible to exploit them to create a basic architecture for neuromorphic memory devices.

Kurnia, Fran↗
DOE OSTI2022

Insights into Heterogeneous Catalysts under Reaction Conditions by In Situ/Operando Electron Microscopy

The advancement of clean energy and environment depends strongly on the development of efficient catalysts in a wide range of heterogeneous catalytic reactions, which has benefited from transmission electron microscopic techniques in determining the atomic-scale morphologies and structures. However, it is the morphology and structure under the catalytic reaction conditions that determine the performance of the catalyst, which has captured a surge of interest in developing and applying in situ/operando transmission electron microscopic techniques in heterogeneous catalysis. The major theme of this review is to highlight some of the most recent insights into heterogeneous catalysts under the relevant reaction conditions using in situ/operando transmission electron microscopic techniques. Rather than a comprehensive overview of the basic principles of in situ/operando techniques, this review focuses on the insights into the atomic-scale/nanoscale details of various catalysts ranging from single-component to multicomponent catalysts under heterogeneous catalytic, electrocatalytic, and photocatalytic reaction conditions involving both gas–solid and liquid–solid interfaces. Here, this focus is coupled with discussions of the correlation of the atomic, molecular, and nanoscale morphology, composition, and structure with the catalytic properties under the reaction conditions, shining light on the challenges and opportunities in design of nanostructured catalysts for clean and sustainable energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sulfur Solutions: Advancing High Voltage and High Energy Lithium Batteries with Organosulfur Electrolytes

Achieving energy densities exceeding 350 Wh kg -1 while operating at elevated voltages (>4.5 V vs Li/Li + ) is attainable through judicious selection of electrochemical pairs at the cathode and anode. However, current state-of-the-art electrolytes exhibit limited stability when exposed to systems operating at or above 4.3 V. This limitation contributes to the degradation of electrode materials and raises critical safety concerns, impeding the commercialization of such systems. Consequently, there has been a notable surge in research efforts aimed at developing innovative electrolyte compositions capable of supporting high-voltage lithium-ion batteries (LIBs). A substantial portion of this research has focused on the family of organosulfur molecules, which possess high oxidative stability. Organosulfur salts also facilitate the formation of dense, ionically conductive solid electrolyte interfaces (SEI) and demonstrate excellent solubility. This article provides a comprehensive overview of the field of organosulfur electrolyte components for their applications in energy storage, encompassing solvents, alternative conducting salts, and additives. It emphasizes the idea that the deliberate design of electrolyte compositions is instrumental in controlling electrode passivation, with organosulfur-based structures historically proving advantageous in every aspect of the electrolyte. Crucially, it should be noted that many of these components are commercially available, holding significant implications for industrial applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Development of the Safe and Broad‐Spectrum Aldehyde and Ketoamide Mpro inhibitors Derived from the Constrained α, γ‐AA Peptide Scaffold

Abstract SARS‐CoV‐2 is still wreaking havoc all over the world with surging morbidity and high mortality. The main protease (M pro ) is essential in the replication of SARS‐CoV‐2, enabling itself an active target for antiviral development. Herein, we reported the design and synthesis of a new class of peptidomimetics‐constrained α, γ‐AA peptides, based on which a series of aldehyde and ketoamide inhibitors of the M pro of SARS‐CoV‐2 were prepared. The lead compounds showed excellent inhibitory activity in the FRET‐based M pro enzymatic assay not only for the M pro of SARS‐CoV‐2 but also for SARS‐CoV and MERS‐CoV, along with HCoVs like HCoV‐OC43, HCoV‐229E, HCoV‐NL63 and HKU1. The X‐ray crystallographic results demonstrated that our compounds form a covalent bond with the catalytic Cys145. They also demonstrated effective antiviral activity against live SARS‐CoV‐2. Overall, the results suggest that α, γ‐AA peptide could be a promising molecular scaffold in designing novel M pro inhibitors of SARS‐CoV‐2 and other coronaviruses.

Chemistry↗
DOE OSTI2022

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first‐responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro , hypoxia‐induced cardiac arrest in vivo , and opioid‐induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid‐induced respiratory depression, including those caused by newly emerging synthetic opioids.

Pharmacology & Pharmacy↗
DOE OSTI2025

Selective Recovery of Critical Minerals from Simulated Electronic Wastes Via Reaction‐Diffusion Coupling

Abstract Atom‐ and energy‐efficient chemical separations are urgently needed to meet the surging demand for critical materials that has strained supply chains and threatened environmental damage. In this study, we used reaction‐diffusion coupling to separate iron, neodymium, and dysprosium ions from model feedstocks of permanent magnets, which are typically found in electronic wastes. Feedstock solutions were placed in contact with a hydrogel loaded with potassium hydroxide and/or dibutyl phosphate, resulting in complex precipitation patterns as the various metal ions diffused into the reaction medium. Specifically, we observed the precipitation of up to 40 mM of iron from the feedstock, followed by the enrichment of 73 % dysprosium, and the extraction of >95 % neodymium product at a further distance from the solution‐gel interface. We designed a series of experiments and simulations to determine the relevant ion diffusivities, D Nd =5.4×10 −10 and D Dy =5.1×10 −10 m 2 /s, and precipitation rates, k Nd =1.0×10 −5 and k Dy =5.0×10 −3 m 9 mol −3 s −1 , which enabled a numerical model to be established for predicting the distribution of products in the reaction medium. Our proof‐of‐concept study validates reaction‐diffusion coupling as an effective and versatile approach for critical materials separations, without relying on ligands, membranes, resins, or other specialty chemicals.

Wang, Qingpu [Physical and Computational Sciences ↗