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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Power System Resilience Considering Hurricane Storms and Generator Step-up Transformers

Power system resilience describes the system’s ability to withstand and recover quickly from unexpected power outages due to extreme events. As society’s electrification continues at a rapid rate, it is imperative electric infrastructure is designed and operated with extra security. This work studies the impact of severe hurricane storms on the power system located along the United States’ Eastern Seaboard. Specific focus is designated to steady state system operation and loss of large generation step up power transformers. A series of phasor domain power system simulations are conducted to determine the critical point at which the system loses steady state operability. Additionally, active power load shedding and generator dispatching are studied as mitigation and recovery strategies. Results indicate generator dispatching as an effective proactive mitigation strategy and load shedding as an effective recovery strategy.

Liu, Yilu↗
DOE OSTI2020

Lessons Learned from More than Three Decades of Decommissioning and RAW Management in Slovakia - 20074

The purpose of this paper is to describe utilization of experience of JAVYS Company acquired while acting as the Slovak national decommissioning and radioactive waste management entity. JAVYS manages both the decommissioning of A1 NPP (shut down after series of accidents) and V1 NPP (shut down after standard operation) and related radioactive waste management. Besides JAVYS is responsible for management of institutional radioactive waste in Slovakia. The paper is focusing on selected successfully completed projects and related lessons learned from development of special procedures and implementation of activities within decommissioning and management of generated waste streams from cradle to grave. Recently, experience and lessons learned from all aforementioned activities are used internationally to provide assistance to other countries in order to overcome difficulties and to successfully implement various projects dealing with decommissioning and radioactive waste management. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sandia National Laboratories FY20 Progress Report

The Energetic Neutrons campaign led by Sandia National Laboratories (SNL) had a successful year testing electronic devices and printed circuit boards (PCBs) under 14 MeV neutron irradiation at OMEGA. During FY20 the Energetic Neutrons campaign increased the number and complexity of experiments, continued collaborations with external organizations, and generated knowledge that supports SNL’s National Security mission. In FY20 the Energetic Neutrons campaign was executed by an early career team led by a new PI. The SNL team members were trained to take over new responsibilities during the shot day to increase the number and complexity of experiments in the campaigns. Also, in FY20 for the first time the Energetic Neutrons campaign had a graduate student contributing with pre and post-irradiation characterizations at SNL of the semiconductor devices irradiated at OMEGA. In FY20 SNL collaborated with the Air Force Nuclear Weapons Center (AFNWC) and supported experiments related to radiation effects in semiconductor devices. SNL also gave the opportunity to ride along to Los Alamos National Laboratory and multiple scientists from MIT and LLE. SNL continued using the last two generations of the Neutron Effects Diagnostics (NEDs) to field active and passive experiments but also redesigned the latest generation of the NEDs to accommodate larger components and improve the vacuum sealing as shown in figure 1a. The redesigned NEDs allowed SNL to perform active tests of a high voltage (HV) PCB for the first time at OMEGA; where signals before, during and after the irradiation were recorded. The HV PCB installed in one of the SNL NEDs is shown in figure 1b where a 3D-printed nosecone was used to check for mechanical and electrical interference. Passive irradiations of multiple components were followed up with leakage current, gain measurements and radiation-induced defect characterization.

42 ENGINEERING↗
DOE OSTI2025

Effect of moisture ingression on material performance and particle generation in molten salts

This report summarizes activities conducted to understand and mitigate the negative consequences that moisture ingressions have upon the operations of pyroprocessing equipment. The objectives of the work conducted in FY25 were to characterize (1) the physicochemical properties of particles generated as a result of moisture ingressions, and (2) the corrosion of relevant structural materials with the introduction of moisture. Compositional and morphological analyses of the generated particles were conducted through a suite of characterization technologies, including X-ray diffraction, microscopy, and particle size analysis. The results gathered this year were compared to those obtained in FY24 when studies with oxygen ingressions were conducted on CeCl 3 -LiCl-KCl and UCl 3 -LiCl-KCl systems. Investigations involving performance evaluations of industrially relevant structural alloys were also conducted under moisture ingress conditions. In-line electrochemical monitoring of the bulk salt was augmented with microscopy of alloy samples to quantify the concentration and accumulation of corrosion products in the bulk salt. The results reported in this work provide a more holistic understanding of the effects of atmospheric ingressions on molten chloride salt chemistry such that effective redox control strategies may be implemented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Charge Concentration Limits the Hydrogen Evolution Rate in Organic Nanoparticle Photocatalysts

Colloidal organic nanoparticles have proven to be a promising class of photocatalyst for performing the Hydrogen Evolution Reaction (HER) due to their dispersibility in aqueous environments, their strong absorption within the visible region, and the tunabilty of their component materials' redox potentials. Currently, there is little understanding of how charge generation and accumulation in organic semiconductors change when these materials are formed into nanoparticles that share a high interfacial area with water, nor is it known what mechanism limits the hydrogen evolution efficiency in recent reports on organic nanoparticle photocatalysts. Herein, we use Time-Resolved Microwave Conductivity to study aqueous-soluble organic nanoparticles and bulk thin films composed of various blend ratios of the non-fullerene acceptor EH-IDTBR and conjugated polymer PTB7-Th and examine the relationship between composition, interfacial surface area, charge carrier dynamics, and photocatalytic activity. We quantitatively measure the rate of Hydrogen Evolution Reaction by nanoparticles composed of various donor:acceptor blend ratio compositions and find that the most active blend ratio displays a Hydrogen Quantum Yield of 0.83% per photon. Moreover, we find that nanoparticle photocatalytic activity corresponds directly to charge generation, and that nanoparticles have 3x more long-lived accumulated charges relative to bulk samples of the same material composition. Here these results suggest that, under our current reaction conditions, with approximately 3x solar flux, catalytic activity by these nanoparticles is limited by the concentration of electrons and holes in operando and not a finite number of active surface cites or the catalytic rate at the interface. This provides a clear design goal for the next generation of efficient photocatalytic nanoparticles.

08 HYDROGEN↗
DOE OSTI2021

Up-regulating TIPE2 alleviates inflammatory pain by suppressing microglial activation-mediated inflammatory response via inhibiting Rac1/NF-κB pathway

TNF-α-inducible protein 8-like 2 (TIPE2) is a recently discovered regulator of inflammation that can maintain immune homeostasis, exerting a significant role in the development of inflammation-related diseases. Here, we aimed to explore the role and potential regulatory mechanism of TIPE2 in the progression of inflammatory pain. In the present study, a mouse BV2 microglia cell activation-mediated inflammatory model was developed with LPS induction, and a mouse inflammatory pain model was established with complete Freund's adjuvant (CFA) injection. In vitro, the TIPE2 expression was decreased in LPS-induced BV2 cells. Overexpression of TIPE2 mitigated LPS-medicated microglial activation via decreasing nitric oxide (NO) generation and the expression of microglia marker IBA-1. Notably, increasing TIPE2 expression alleviated microglial activation-triggered expression levels and releases of proinflammatory factors such as TNF-α, IL-1β, and IL-6. Mechanism analysis verified that overexpression of TIPE2 blunted Rac1-mediated activation of NF-κB pathway following LPS stimulation. More importantly, CFA injection reduced the expression of TIPE2 in a mouse inflammatory pain model and overexpression of TIPE2 alleviated CFA-mediated pain hypersensitivity and inflammatory response, and inactivated microglia cell in vivo. Furthermore, overexpression of TIPE2 decreased Rac1 expression and suppressed the activation of NF-κB pathway in spinal cord after CFA injection. In summary, the present study revealed that overexpression of TIPE2 mitigated inflammatory pain through suppressing microglial activation-induced inflammation by inactivating Rac1/NF-κB pathway. The study provides a novel theoretical foundation for the therapy of inflammatory pain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Point Source Likelihood Framework (psl) v1.0

PSL is a software framework for localizing radiation point sources in a 3D environment using a freely moving radiation detector system. The framework assumes a single point source of radiation is in the environment and uses known detector positions and orientations along with their radiation measurements to find the most likely location of the point source. This approach to the problem enables analyses that improve upon the capability of a standard Maximum Likelihood Expectation Maximization (MLEM) approach, such as setting a source detection threshold, finding a likelihood interval for the position of the source, finding a likelihood interval for the activity (intensity) of the source, determining whether more than one point source may be present, and generating a map of Minimum Detectable Activity (MDA) for a given source type. Applications include radiological and nuclear security, consequence management, decontamination and decommissioning of nuclear facilities, and radiological emergency response.

Joshi, Tenzing↗
DOE OSTI2023

Development of Novel Small-Molecule Activators of Pyruvate Kinase Muscle Isozyme 2, PKM2, to Reduce Photoreceptor Apoptosis

Treatment options are lacking to prevent photoreceptor death and subsequent vision loss. Previously, we demonstrated that reprogramming metabolism via the pharmacologic activation of PKM2 is a novel photoreceptor neuroprotective strategy. However, the features of the tool compound used in those studies, ML-265, preclude its advancement as an intraocular, clinical candidate. This study sought to develop the next generation of small-molecule PKM2 activators, aimed specifically for delivery into the eye. Compounds were developed that replaced the thienopyrrolopyridazinone core of ML-265 and modified the aniline and methyl sulfoxide functional groups. Compound 2 demonstrated that structural changes to the ML-265 scaffold are tolerated from a potency and efficacy standpoint, allow for a similar binding mode to the target, and circumvent apoptosis in models of outer retinal stress. To overcome the low solubility and problematic functional groups of ML-265, compound 2’s efficacious and versatile core structure for the incorporation of diverse functional groups was then utilized to develop novel PKM2 activators with improved solubility, lack of structural alerts, and retained potency. No other molecules are in the pharmaceutical pipeline for the metabolic reprogramming of photoreceptors. Thus, this study is the first to cultivate the next generation of novel, structurally diverse, small-molecule PKM2 activators for delivery into the eye.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Model metamers reveal divergent invariances between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human–model discrepancy. The human recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model

Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.

diffusion model↗
DOE OSTI2025

Atomically Dispersed Ni‐N‐C Catalysts for Electrochemical CO 2 Reduction

Abstract The atomic dispersion of nickel in Ni‐N‐C catalysts is key for the selective generation of carbon monoxide through the electrochemical carbon dioxide reduction reaction (CO 2 RR). Herein, the study reports a highly selective, atomically dispersed Ni 1.0% ‐N‐C catalyst with reduced Ni loading compared to previous reports. Extensive materials characterization fails to detect Ni crystalline phases, reveals the highest concentration of atomically dispersed Ni metal, and confirms the presence of the proposed Ni‐N x active site at this reduced loading. The catalyst shows excellent activity and selectivity toward CO generation, with a faradaic efficiency for CO generation (FE CO ) of 97% and partial current density for CO (j co ) of ‐9.0 mA cm −2 at ‐0.9 V in an electrochemical H‐type cell. CO 2 RR activity and selectivity are also studied by rotating disk electrode (RDE) measurements where transport limitations can be suppressed. It is expected that the utility of these Ni‐N‐C catalysts will lie with tandem CO 2 RR reaction schemes to multi‐carbon (C 2+ ) products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Thermally Activated Circularly Polarized Photoluminescence in a 2D Hybrid Perovskite with Giant Spin Splitting

Circularly polarized light generation and detection are critical for future spin-based technologies that inter-convert circularly polarized photons and electron spins. However, detailed mechanisms in such spin-photon interfaces are often either poorly understood or operate at cryogenic temperatures since typically small energies separating spin-split electronic bands facilitate thermally driven spin depolarization. Recently, several 2D hybrid perovskites with polar achiral cations were theoretically demonstrated to exhibit conduction and valence band spin-splitting energies greatly exceeding room-temperature thermal energy, suggesting their utility as spin-photon interfaces with practical operating temperatures. Here, a strong "spin memory" effect is reported in such a polar achiral layered perovskite that enables large room-temperature circularly polarized emission anisotropy following excitation with circularly polarized light. The polarization anisotropy depends strongly on temperature (thermally activated), excitation energy, and crystal orientation with respect to the excitation source. Temperature-dependent photoconductance measurements reveal similar thermally activated carrier generation. These observations suggest a mechanism whereby giant in-plane splitting of single-particle levels protects spin-polarization of photogenerated electrons and holes before recombination. Although polarized light emission is explored in greater detail in chiral perovskites, these results reveal that even without chirality, large spin memory in polar achiral perovskites can enable spin-photon interfaces that operate at elevated temperatures.

14 SOLAR ENERGY↗
DOE OSTI2021

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning—an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Furthermore, building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Novel Approach to Map Permeability Using Passive Seismic Emission Tomography

Newly acquired magnetotelluric data and passive seismic data collected with tightly spaced geophone arrays are combined with historic drilling, active seismic, and potential fields data to generate 3-D permeability maps. A cooperative inversion methodology has been developed using active seismic, magnetotelluric, and gravity data in order to produce more robust velocity models for passive seismic data processing without requiring expensive 3-D active seismic surveys. The cooperative inversion estimates velocities from other geophysical data where no prior seismic velocity information is available at two geothermal sites in Nevada: San Emidio and Crescent Valley.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Dislocation-Activated Low Platinum-Loaded PtCu Nanoparticles Welded onto the Substrate for Practical Acidic Hydrogen Generation

Although transition metal (M) alloying with platinum (Pt) is a promising approach to reduce the Pt dosage required in the hydrogen evolution reaction (HER), the catalytic activity and durability of PtM alloy are often unsatisfactory for acidic HER, especially at high currents, due to a reduced number of highly active sites (Pt) at the atomic level and challenges in stabilizing them to prevent detachment and aggregation. Herein, we report a robust functional structure integrated electrode (D-PtCu/CF) with abundant edge dislocations and very low platinum content (0.1 mg cm -2 ) to achieve high activity and stability in acidic HER within a proton exchange membrane water electrolysis cell (PEMWE). D-PtCu/CF exhibits high Pt mass activity (10.28 A mg Pt -1 ) and excellent operational stability (negligible decay after 200 h). X-ray absorption spectroscopy and in situ electrochemical experiments reveal that the lattice distortion caused by edge dislocations in PtCu mainly affects Pt atoms by compressing strain, reducing their ability to adsorb H. At the same time, Cu atoms are subjected to tensile strain, enhancing the bonding between Cu and H. Therefore, edge dislocations not only improve the intrinsic catalytic activity of Pt atoms but also increase the number of active sites (Cu) available for hydrogen adsorption, which synergistically accelerates the kinetics of the HER reaction. Finally, the PEMWE employing the D-PtCu/CF catalytic electrode can operate stably for 100 h at an industrial-level current density of 500 mA cm -2 with 1.63 V.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica

Abstract Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in E. coli , use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeast Yarrowia lipolytica . A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide’s ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Magnesium Technology 2020

Magnesium and its alloys are being investigated widely for applications that require extraordinarily high strength-to-weight ratio, vibration damping, electromagnetic shielding, reduction in carbon footprint, sustainability improvements, and low toxicity. In the past decade or so, this drive has led to increased adoption of magnesium and its alloys in everyday products and it is no longer regarded as an esoteric material. The overarching goal of its widespread adoption and replacement of conventional materials such as steel and aluminum alloys has been restricted because of the cost of the material, limited sources of primary material, alloying constraints, high chemical reactivity, and challenges associated with its plasticity. Researchers, scientists, engineers, and economists from industry, government agencies and laboratories, and academic institutions alike are actively developing roadmaps for next-generation products and addressing these challenges as quickly as possible through unique and innovative methods. The TMS Magnesium Committee has been actively involved in providing a platform to these entities for disseminating the latest information, developments, and cutting-edge research and development, and showcasing the latest research and development trends related to magnesium and its alloys through the Magnesium Technology Symposium, which takes place every year at the TMS Annual Meeting. This proceedings volume retains the essence of this longstanding tradition. The twenty-first volume in the series, Magnesium Technology 2020, is the proceedings of the Magnesium Technology Symposium held during the 149th TMS Annual Meeting & Exhibition in San Diego, CA, February 23–27, 2020. The volume captures commentaries and papers from 15 different countries. The papers have been categorized systematically based on topics pertaining to magnesium production, casting and solidification, thermomechanical processing, deformation mechanisms, modeling, corrosion, and applications. The symposium began with keynote sessions that featured several distinguished invited speakers from government organizations and academia, who provided their perspectives on the state of the art, goals, and opportunities in magnesium alloy research and development. Dr. Carlos Tomé from Los Alamos National Laboratory, U.S. Department of Energy, discussed 3D characterization of mechanical twins across grain boundaries. This was followed by a talk on hierarchically structured ultrafine-grained magnesium alloys by Dr. Rajiv Mishra from the University of North Texas, USA, who presented an example of using friction stir additive manufacturing for implementing such microstructures at a component level. Dr. Mark Horstemeyer from Liberty University, USA, presented an overview of fatigue modeling to capture the corrosion-fatigue behavior of magnesium alloys. The final talk in this session was presented by Dr. Bin Jiang from Chongqing University, China, who presented a novel extrusion approach for achieving high-strength magnesium alloy plates.

Jordon, J. Brian↗
DOE OSTI2024

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗