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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Permanent magnet with MgB{sub 2} bulk superconductor

Superconductors with persistent zero-resistance currents serve as permanent magnets for high-field applications requiring a strong and stable magnetic field, such as magnetic resonance imaging. The recent global helium shortage has quickened research into high-temperature superconductors (HTSs)—materials that can be used without conventional liquid-helium cooling to 4.2 K. Herein, we demonstrate that 40-K-class metallic HTS magnesium diboride (MgB{sub 2}) makes an excellent permanent bulk magnet, maintaining 3 T at 20 K for 1 week with an extremely high stability (<0.1 ppm/h). The magnetic field trapped in this magnet is uniformly distributed, as for single-crystalline neodymium-iron-boron. Magnetic hysteresis loop of the MgB{sub 2} permanent bulk magnet was determined. Because MgB{sub 2} is a simple-binary-line compound that does not contain rare-earth metals, polycrystalline bulk material can be industrially fabricated at low cost and with high yield to serve as strong magnets that are compatible with conventional compact cryocoolers, making MgB{sub 2} bulks promising for the next generation of Tesla-class permanent-magnet applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Structural insights reveal a recognition feature for tailoring hydrocarbon stapled-peptides against the eukaryotic translation initiation factor 4E protein

Stapled-peptides have emerged as an exciting class of molecules which can modulate protein–protein interactions. We have used a structure-guided approach to rationally develop a set of hydrocarbon stapled-peptides with high binding affinities and residence times against the oncogenic eukaryotic translation initiation factor 4E (eIF4E) protein. Crystal structures of these peptides in complex with eIF4E show that they form specific interactions with a region on the protein-binding interface of eIF4E which is distinct from the other well-established canonical interactions. This recognition element is a major molecular determinant underlying the improved binding kinetics of these peptides with eIF4E. The interactions were further exploited by designing features in the peptides to attenuate disorder and increase helicity which collectively resulted in the generation of a distinct class of hydrocarbon stapled-peptides targeting eIF4E. This study details new insights into the molecular basis of stapled-peptide: eIF4E interactions and their exploitation to enhance promising lead molecules for the development of stapled-peptide compounds for oncology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Water-soluble saponins accumulate in drought-stressed switchgrass and may inhibit yeast growth during bioethanol production

Abstract Background Developing economically viable pathways to produce renewable energy has become an important research theme in recent years. Lignocellulosic biomass is a promising feedstock that can be converted into second-generation biofuels and bioproducts. Global warming has adversely affected climate change causing many environmental changes that have impacted earth surface temperature and rainfall patterns. Recent research has shown that environmental growth conditions altered the composition of drought-stressed switchgrass and directly influenced the extent of biomass conversion to fuels by completely inhibiting yeast growth during fermentation. Our goal in this project was to find a way to overcome the microbial inhibition and characterize specific compounds that led to this inhibition. Additionally, we also determined if these microbial inhibitors were plant-generated compounds, by-products of the pretreatment process, or a combination of both. Results Switchgrass harvested in drought (2012) and non-drought (2010) years were pretreated using Ammonia Fiber Expansion (AFEX). Untreated and AFEX processed samples were then extracted using solvents (i.e., water, ethanol, and ethyl acetate) to selectively remove potential inhibitory compounds and determine whether pretreatment affects the inhibition. High solids loading enzymatic hydrolysis was performed on all samples, followed by fermentation using engineered Saccharomyces cerevisiae . Fermentation rate, cell growth, sugar consumption, and ethanol production were used to evaluate fermentation performance. We found that water extraction of drought-year switchgrass before AFEX pretreatment reduced the inhibition of yeast fermentation. The extracts were analyzed using liquid chromatography–mass spectrometry (LC–MS) to detect compounds enriched in the extracted fractions. Saponins, a class of plant-generated triterpene or steroidal glycosides, were found to be significantly more abundant in the water extracts from drought-year (inhibitory) switchgrass. The inhibitory nature of the saponins in switchgrass hydrolysate was validated by spiking commercially available saponin standard (protodioscin) in non-inhibitory switchgrass hydrolysate harvested in normal year. Conclusions Adding a water extraction step prior to AFEX-pretreatment of drought-stressed switchgrass effectively overcame inhibition of yeast growth during bioethanol production. Saponins appear to be generated by the plant as a response to drought as they were significantly more abundant in the drought-stressed switchgrass water extracts and may contribute toward yeast inhibition in drought-stressed switchgrass hydrolysates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Impurity-generated non-Abelions

Two classes of topological superconductors and Majorana modes in condensed matter systems are known to date: one in which disorder induced by impurities strongly suppresses topological superconducting gap and is detrimental to Majorana modes, and another where Majorana fermions are protected by a disorder-robust topological superconductor gap. Observation and control of Majorana fermions and other non-Abelions often requires a symmetry of an underlying system leading to a gap in the single-particle or quasiparticle spectra. In semiconductor structures, impurities that provide charge carriers introduce states into the gap and enable conductance and proximity-induced superconductivity via the in-gap states. Thus a third class of topological superconductivity and Majorana modes emerges, in which topological superconductivity and Majorana fermions appear exclusively when impurities generate in-gap states. We show that impurity-enabled topological superconductivity is realized in a quantum Hall ferromagnet, when a helical domain wall is coupled to an 𝑠-wave superconductor. As an example of emergence of topological superconductivity in quantum Hall ferromagnets, we consider the integer quantum Hall effect in Mn-doped CdTe quantum wells. Recent experiments on transport through the quantum Hall ferromagnet domain wall in this system indicated a vital role of impurities in the conductance, but left unresolved the question whether impurities preclude generation of Majorana fermions and other non-Abelions in such systems in general. Here, solving a general quantum-mechanical problem of impurity bound states in a system of spin-orbit coupled Landau levels, we demonstrate that impurity-induced Majorana modes emerge at boundaries between topological and conventional superconducting states generated in a domain wall due to proximity to an 𝑠 superconductor. We consider both short-range disorder and a smooth random potential. The phase diagram of the system is defined by characteristic disorder, gate voltage induced angular momentum splitting of impurity levels, and by a proximity superconducting gap. The phase diagram exhibits two ranges of gate voltage with conventional superconducting order separated by a gate voltage range with topological superconductivity. We show that electrostatic control of domain walls in an integer quantum Hall ferromagnet allows manipulation of Majorana fermions. Ferromagnetic transitions in the fractional quantum Hall regime may lead to the formation and electrostatic control of higher order non-Abelian excitations.

Edge states↗
DOE OSTI2015

Photosynthetic terpene hydrocarbon production for fuels and chemicals

Summary Photosynthetic hydrocarbon production bypasses the traditional biomass hydrolysis process and represents the most direct conversion of sunlight energy into the next‐generation biofuels. As a major class of biologically derived hydrocarbons with diverse structures, terpenes are also valuable in producing a variety of fungible bioproducts in addition to the advanced ‘drop‐in’ biofuels. However, it is highly challenging to achieve the efficient redirection of photosynthetic carbon and reductant into terpene biosynthesis. In this review, we discuss four major scientific and technical barriers for photosynthetic terpene production and recent advances to address these constraints. Collectively, photosynthetic terpene production needs to be optimized in a systematic fashion, in which the photosynthesis improvement, the optimization of terpene biosynthesis pathway, the improvement of key enzymes and the enhancement of sink effect through terpene storage or secretion are all important. New advances in synthetic biology also offer a suite of potential tools to design and engineer photosynthetic terpene platforms. The systemic integration of these solutions may lead to ‘disruptive’ technologies to enable biofuels and bioproducts with high efficiency, yield and infrastructure compatibility.

Wang, Xin↗
DOE OSTI2026

Data‐Efficient Generation of Synthetic Microstructures of Polymer‐Bonded Energetic Material With Fine‐Tuned Stable Diffusion

Among current deep learning approaches for synthetic image generation, diffusion-based models stand out in terms of algorithmic stability and ability to retain high-fidelity image features with detailed resolution. Here, in this work, we employ Dreambooth, a method for fine-tuning Stable Diffusion, on X-ray CT images of microstructure of the polymer-bonded form (PBX) of a commonly used high explosive, Pentaerythritol tetranitrate (PETN), which yields generative models for creating synthetic PBX images. The models developed here represent five classes (or ‘lots’) of microstructures and demonstrate successful generation of images of each class with high fidelity, as verified by computed classification accuracy of ∼ 94% or higher. Data augmentation afforded by such image synthesis can be used to more reliably decipher underlying statistics, build processing-structure correlations, recognize off-normal structural anomalies, and identify age-related changes. Ideas related to converting image data into appropriate density mapping and performing mesoscale simulation or surrogate modeling of detonation are also discussed.

Dreambooth↗
DOE OSTI2021

Implementation of two-phase gas transport into VERA for molten salt reactor analysis

Molten salt reactors (MSRs) are a class of next-generation nuclear reactors that have received recent industrial and research interest. A generalized species transport solver was implemented in the Virtual Environment for Reactor Applications (VERA) computing suite to extend this tool to analyze liquid-fueled MSRs. This core simulator has been extended to model the transport of fission product gases into a collection of circulating gas bubbles with the purpose of removing the gases. This paper presents the governing species transport equation, along with various nuclear source terms. Development of the source term for phase migration is discussed, along with a simplified interfacial area tracking method. Finally, a case study on a simplified MSR loop is presented in which modeling parameters were varied to assess their impact on gas removal. The steady state results show that parameters such as bubble diameter, gas injection rate and mass transfer coefficient have a low to moderate effect on the fraction of xenon in the core region. Removal efficiency has the greatest effect on the fraction in the core region. After the pump bowl, bubble diameter has a minor effect on the fraction of xenon in the gas void. Finally, these results point out that increasing parameters such as mass transfer coefficient, gas injection rate, and removal efficiency drives the xenon into the circulating gas void, while decreasing bubble diameter also drives xenon into the gas void by increasing interfacial area.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Identifying point defects and ordering in the high-entropy layered oxide Li 1.5 MO 3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO 2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li 1.5 MO 3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Finally, without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structural engineering of chimeric antigen receptors targeting HLA-restricted neoantigens

Chimeric antigen receptor (CAR) T cells have emerged as a promising class of therapeutic agents, generating remarkable responses in the clinic for a subset of human cancers. One major challenge precluding the wider implementation of CAR therapy is the paucity of tumor-specific antigens. Here, we describe the development of a CAR targeting the tumor-specific isocitrate dehydrogenase 2 (IDH2) with R140Q mutation presented on the cell surface in complex with a common human leukocyte antigen allele, HLA-B*07:02. Engineering of the hinge domain of the CAR, as well as crystal structure-guided optimization of the IDH2 R140Q- HLA-B*07:02-targeting moiety, enhances the sensitivity and specificity of CARs to enable targeting of this HLA-restricted neoantigen. This approach thus holds promise for the development and optimization of immunotherapies specific to other cancer driver mutations that are difficult to target by conventional means.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

The cbb 3 -type cytochrome oxidase assembly factor CcoG is a widely distributed cupric reductase

Copper (Cu)-containing proteins execute essential functions in prokaryotic and eukaryotic cells, but their biogenesis is challenged by high Cu toxicity and the preferential presence of Cu(II) under aerobic conditions, while Cu(I) is the preferred substrate for Cu chaperones and Cu-transport proteins. These proteins form a coordinated network that prevents Cu accumulation, which would lead to toxic effects such as Fenton-like reactions and mismetalation of other metalloproteins. Simultaneously, Cu-transport proteins and Cu chaperones sustain Cu(I) supply for cuproprotein biogenesis and are therefore essential for the biogenesis of Cu-containing proteins. In eukaryotes, Cu(I) is supplied for import and trafficking by cell-surface exposed metalloreductases, but specific cupric reductases have not been identified in bacteria. It was generally assumed that the reducing environment of the bacterial cytoplasm would suffice to provide sufficient Cu(I) for detoxification and cuproprotein synthesis. Here, we identify the proposed cbb 3 -type cytochrome c oxidase ( cbb 3 -Cox) assembly factor CcoG as a cupric reductase that binds Cu via conserved cysteine motifs and contains 2 low-potential [4Fe-4S] clusters required for Cu(II) reduction. Deletion of ccoG or mutation of the cysteine residues results in defective cbb 3 -Cox assembly and Cu sensitivity. Furthermore, anaerobically purified CcoG catalyzes Cu(II) but not Fe(III) reduction in vitro using an artificial electron donor. Thus, CcoG is a bacterial cupric reductase and a founding member of a widespread class of enzymes that generate Cu(I) in the bacterial cytosol by using [4Fe-4S] clusters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Occupancy schedule development and its effect on OpenStudio prototype college building model

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). Here, the schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Quantum vacuum processes in the extremely intense light of relativistic plasma mirror sources

Abstract The advent of petawatt-class laser systems allows generating electromagnetic fields of unprecedented strength in a controlled environment, driving increasingly more efforts to probe yet unobserved processes through their interaction with the quantum vacuum. Still, the lowest intensity scale governing these effects lies orders of magnitude beyond foreseen capabilities, so that such endeavor is expected to remain extremely challenging. In recent years, however, plasma mirrors have emerged as a promising bridge across this gap, by enabling the conversion of intense infrared laser pulses into coherently focused Doppler harmonic beams lying in the X-UV range. In this work, we present predictions on the quantum vacuum signatures produced when such beams are focused to intensities between 10 24 and 10 28 W cm −2 , specifically photon–photon scattering and electron–positron pair creation. These signatures are computed via the stimulated vacuum formalism, combined with a model of perfectly focused beam built from PIC-generated harmonics spectra, and implemented on state-of-the-art massively parallel numerical tools. In view of identifying experimentally favorable configurations, we also consider the coupling of the focused harmonic beam with an auxiliary optical beam, and provide comparison with other established schemes. Our results show that a single coherently focused harmonic beam can produce as much scattered photons as two infrared pulses in head-on collision, and confirm that the coupling of the harmonic beam to an auxiliary beam gives rise to significant levels of inelastic scattering, and hence holds the potential to strongly improve the attainable signal to noise ratios in experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Galaxy bispectrum, primordial non-Gaussianity and redshift space distortions

Measurements of the non-Gaussianity of the primordial density field have the power to considerably improve our understanding of the physics of inflation. Indeed, if we can increase the precision of current measurements by an order of magnitude, a null-detection would rule out many classes of scenarios for generating primordial fluctuations. Large-scale galaxy redshift surveys represent experiments that hold the promise to realise this goal. Thus, we model the galaxy bispectrum and forecast the accuracy with which it will probe the parameter f {sub NL}, which represents the degree of primordial local-type non Gaussianity. Specifically, we address the problem of modelling redshift space distortions (RSD) in the tree-level galaxy bispectrum including f {sub NL}. We find novel contributions associated with RSD, with the characteristic large scale amplification induced by local-type non-Gaussianity. These RSD effects must be properly accounted for in order to obtain un-biased measurements of f {sub NL} from the galaxy bispectrum. We propose an analytic template for the monopole which can be used to fit against data on large scales, extending models used in the recent measurements. Finally, we perform idealised forecasts on σ {sub f} {sub N{sub L}}—the accuracy of the determination of local non-linear parameter f {sub NL}—from measurements of the galaxy bispectrum. Our findings suggest that current surveys can in principle provide f {sub NL} constraints competitive with Planck , and future surveys could improve them further.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

Here, the SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors

Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Washington Play Fairway Analysis Geothermal Heat and Permeability Potential Geodatabases

This file contains file geodatabases of the Mount St. Helens seismic zone (MSHSZ), Wind River valley (WRV) and Mount Baker (MB) geothermal play-fairway sites in the Washington Cascades. The geodatabases include input data (feature classes) and output rasters (generated from modeling and interpolation) from the geothermal play-fairway in Washington State, USA. These data were gathered and modeled to provide an estimate of the heat and permeability potential within the play-fairways based on: mapped volcanic vents, hot springs and fumaroles, geothermometry, intrusive rocks, temperature-gradient wells, slip tendency, dilation tendency, displacement, displacement gradient, max coulomb shear stress, sigma 3, maximum shear strain rate, and dilational strain rate at 200m and 3 km depth. In addition this file contains layer files for each of the output rasters. For details on the areas of interest please see the 'Phase 1 Technical Report' in the download package. This submission also includes a file with the geothermal favorability of the Washington Cascade Range based off of an earlier statewide assessment. Additionally, within this file there are the maximum shear and dilational strain rate rasters for all of Washington State.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗