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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bioenergy sorghum’s deep roots: A key to sustainable biomass production on annual cropland

Abstract Bioenergy sorghum has high biomass yield potential, drought resilience, good nitrogen use efficiency, and a root system that contributes to the accumulation of soil organic carbon. In this study, field grown bioenergy sorghum root systems were analyzed during the growing season to characterize their depth, biomass, morphology, anatomy, and gene expression profiles. Bioenergy sorghum roots grew continuously during a 155‐day growing season producing ~175 nodal roots, accumulating ~7 Mg of dry biomass per hectare, and reaching >2 m deep in the soil profile. Nodal roots within 20 cm of the stem were 1–5 mm in diameter, whereas roots deeper in soil profiles were enriched in lateral roots with small diameters (~30–500 µm) enabling growth through soil macropores. In older field‐grown plants, roots with intact endodermal, vascular and inner root tissues were surrounded by degraded or aerenchyma‐filled epidermal and cortical cell layers. Transcriptome analysis of nodal, surface, and deep roots identified >2,500 differentially expressed genes involved in root growth, transport, adaptation, defense, and AMF–root interaction. Deep roots (180–240 cm) differentially expressed genes that regulate lateral root growth. Surface roots (0–20 cm) located mid‐row differentially expressed genes involved in nitrate transport, whereas ammonium transport genes were expressed in surface and deep roots and genes involved in phosphate transport were expressed in nodal, surface, and deep roots. Overall, bioenergy sorghum's long growing season enables root systems to grow deeper and accumulate more biomass than annual grain crops such as maize, attributes that could help restore annual cropland soil organic carbon levels and improve soil productivity. Deep roots active in nutrient transport are positioned to take‐up fertilizer leached deep into soil profiles mitigating potential nutrient run‐off. Bioenergy sorghum's large and deep root system is a key to sustainable production of biofuels, biopower, and bioproducts on annual cropland.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Potential Link Between 2020 Mentone, West Texas M5 Earthquake and Nearby Wastewater Injection: Implications for Aquifer Mechanical Properties

Abstract The M5 Mentone earthquake that occurred on March 26, 2020, was the largest event recorded over the last 2 decades in West Texas within the Delaware Basin, a U.S. major petroleum‐producing area. Also, numerous hydrofracturing and wastewater disposal wells are spread across this region. Within a 30 km distance to mainshock, eight class‐II injection wells for industrial wastewater disposal target the deep porous Ellenburger aquifer at an average rate of 1.36 × 10 6 barrel (BBL) per month during 2012–2020. Poroelastic models of fluid diffusion show these nearby injectors collectively imparted up to 80.5 kPa of Coulomb stress at the mainshock location, capable of triggering this M5 event. Assuming the Mentone event occurs when pore‐pressure increase is maximum, the time delay between peak injection and the M5 occurrence corresponds with an optimal permeability of 6.76 × 10 ‐14 m 2 for the Ellenburger aquifer layer, in agreement with independent estimates.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗
DOE OSTI2023

Mechanical, electronic, optical, piezoelectric and ferroic properties of strained graphene and other strained monolayers and multilayers: an update

Abstract This is an update of a previous review (Naumis et al 2017 Rep. Prog. Phys. 80 096501). Experimental and theoretical advances for straining graphene and other metallic, insulating, ferroelectric, ferroelastic, ferromagnetic and multiferroic 2D materials were considered. We surveyed (i) methods to induce valley and sublattice polarisation ( P ) in graphene, (ii) time-dependent strain and its impact on graphene’s electronic properties, (iii) the role of local and global strain on superconductivity and other highly correlated and/or topological phases of graphene, (iv) inducing polarisation P on hexagonal boron nitride monolayers via strain, (v) modifying the optoelectronic properties of transition metal dichalcogenide monolayers through strain, (vi) ferroic 2D materials with intrinsic elastic ( σ ), electric ( P ) and magnetic ( M ) polarisation under strain, as well as incipient 2D multiferroics and (vii) moiré bilayers exhibiting flat electronic bands and exotic quantum phase diagrams, and other bilayer or few-layer systems exhibiting ferroic orders tunable by rotations and shear strain. The update features the experimental realisations of a tunable two-dimensional Quantum Spin Hall effect in germanene, of elemental 2D ferroelectric bismuth, and 2D multiferroic NiI 2 . The document was structured for a discussion of effects taking place in monolayers first, followed by discussions concerning bilayers and few-layers, and it represents an up-to-date overview of exciting and newest developments on the fast-paced field of 2D materials.

Physics↗
DOE OSTI2021

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations

Abstract. Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol–cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit – Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener–Bergeron–Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Trophic interactions shape the spatial organization of medium-chain carboxylic acid producing granular biofilm communities

Abstract Granular biofilms producing medium-chain carboxylic acids (MCCA) from carbohydrate-rich industrial feedstocks harbor highly streamlined communities converting sugars to MCCA either directly or via lactic acid as intermediate. We investigated the spatial organization and growth activity patterns of MCCA producing granular biofilms grown on an industrial side stream to test (i) whether key functional guilds (lactic acid producing Olsenella and MCCA producing Oscillospiraceae) stratified in the biofilm based on substrate usage, and (ii) whether spatial patterns of growth activity shaped the unique, lenticular morphology of these biofilms. First, three novel isolates (one Olsenella and two Oscillospiraceae species) representing over half of the granular biofilm community were obtained and used to develop FISH probes, revealing that key functional guilds were not stratified. Instead, the outer 150–500 µm of the granular biofilm consisted of a well-mixed community of Olsenella and Oscillospiraceae, while deeper layers were made up of other bacteria with lower activities. Second, nanoSIMS analysis of 15N incorporation in biofilms grown in normal and lactic acid amended conditions suggested Oscillospiraceae switched from sugars to lactic acid as substrate. This suggests competitive-cooperative interactions may govern the spatial organization of these biofilms, and suggests that optimizing biofilm size may be a suitable process engineering strategy. Third, growth activities were similar in the polar and equatorial biofilm peripheries, leaving the mechanism behind the lenticular biofilm morphology unexplained. Physical processes (e.g., shear hydrodynamics, biofilm life cycles) may have contributed to lenticular biofilm development. Together, this study develops an ecological framework of MCCA-producing granular biofilms that informs bioprocess development.

09 BIOMASS FUELS↗
DOE OSTI2023

Surface stability and H adsorption and diffusion near surfaces of W borides: a first-principles study

Abstract Understanding the behavior of tungsten boride (W x B y ) surfaces in a fusion reactor environment is an important topic since boronization is a common wall conditioning method used in fusion Tokamaks. We report the results of density functional theory calculations that investigate the surface stability of W x B y (tetragonal I4 1 /amd -WB, hexagonal P 6 3 / mmc -WB 2 and tetragonal I4/m- W 2 B) with low-index orientations, as well as hydrogen (H) energetics near W x B y surfaces. For single element terminated W x B y surfaces, B terminated surfaces are more energetically stable than W terminated as a result of significant reconstruction of B. The H surface adsorption energy and activation energy of H diffusion penetration below W x B y surfaces are mainly related to the outer termination. Specifically, the WB(001) surface terminated with two B layers, referred to as WB(001)-T BB , has higher H adsorption affinity and lower H diffusivity on this surface than other terminations, which is controlled by the significant charge transfer from B to H. However, B atoms on the WB 2 (0001)-T BB surface decrease both H adsorption and diffusivity on the surface, but enhance H diffusion below the surface in comparison to W terminated WB 2 (0001) surface. H would be trapped and diffuse within atomic surface gaps on the WB 2 ( 2 1 ˉ 1 ˉ 0 ) surface, while H below the surface layer would jump along the [0001] direction rather than diffuse into bulk. The surface diffusion activation energy of H on the W 2 B(001) surface slightly varies with terminations. Once H crosses the surface layer of W 2 B(001) with either termination, it prefers to diffuse into the bulk, or back towards the surface, rather than move parallel to the surface. Interestingly, WB 2 (0001) and WB 2 ( 2 1 ˉ 1 ˉ 0 ) surfaces will have relatively higher H retention than the other W x B y surfaces evaluated in this work.

Yang, L. (ORCID:0000000322166071)↗
DOE OSTI2022

Comptonization by reconnection plasmoids in black hole coronae II: Electron-ion plasma

ABSTRACT We perform 2D particle-in-cell simulations of magnetic reconnection in electron-ion plasmas subject to strong Compton cooling and calculate the X-ray spectra produced by this process. The simulations are performed for trans-relativistic reconnection with magnetization 1 ≤ σ ≤ 3 (defined as the ratio of magnetic tension to plasma rest-mass energy density), which is expected in the coronae of accretion discs around black holes. We find that magnetic dissipation proceeds with inefficient energy exchange between the heated ions and the Compton-cooled electrons. As a result, most electrons are kept at a low temperature in Compton equilibrium with radiation, and so thermal Comptonization cannot reach photon energies $\sim 100\,$ keV observed from accreting black holes. Nevertheless, magnetic reconnection efficiently generates $\sim 100\,$ keV photons because of mildly relativistic bulk motions of the plasmoid chain formed in the reconnection layer. Comptonization by the plasmoid motions dominates the radiative output and controls the peak of the radiation spectrum Epk. We find Epk ∼ 40 keV for σ = 1 and Epk ∼ 100 keV for σ = 3. In addition to the X-ray peak around 100 keV, the simulations show a non-thermal MeV tail emitted by a non-thermal electron population generated near X-points of the reconnection layer. The results are consistent with the typical hard state of accreting black holes. In particular, we find that the spectrum of Cygnus X-1 is well explained by electron-ion reconnection with σ ∼ 3.

Astronomy & Astrophysics↗
DOE OSTI2022

Effect of the Nuclear Equation of State on Relativistic Turbulence-induced Core-collapse Supernovae

Abstract The nuclear equation of state (EOS) is an important component in the evolution of core-collapse supernovae. In this paper we make a survey of various EOSs in the literature and analyze their effect on spherical core-collapse models in which the effects of three-dimensional turbulence is modeled by a general relativistic formulation of Supernova Turbulence In Reduced-dimensionality (STIR). We show that the viability of the explosion is quite EOS dependent and that it best correlates with the early-time interior entropy density of the proto–neutron star. We check that this result is not progenitor dependent, although the lowest-mass progenitors show different explosion properties, due to the different pre-collapse nuclear composition. Larger central entropies also induce more vigorous proto–neutron star convection in our one-dimensional turbulence model, as well as a wider convective layer.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Experimental and modeling studies of the plasma chemistry in a humid Ar radiofrequency atmospheric pressure plasma jet

Abstract While humid atmospheric pressure plasmas are extensively modeled, reaction set validation for these conditions remains limited. We present a detailed comparison of a modelling and experimental study with a goal to elucidate the plasma chemistry in a humid Ar radiofrequency-driven atmospheric pressure plasma jet. A large group of species including radicals (H, OH, O, HO 2 ) and long-lived species (H 2 , O 2 and H 2 O 2 ) in the jet effluent was experimentally quantified by molecular beam mass spectroscopy (MBMS). MBMS measurements of H 2 O 2 , OH and H were validated by direct comparison with a liquid phase colorimetric measurement, laser-induced fluorescence (LIF) and two-photon absorption LIF respectively. While an excellent agreement was found for OH and H 2 O 2 by both techniques, a significant difference was found for H and shown to be due to boundary layer effects at the MBMS sampling substrate. The measured O, OH, HO 2 and H 2 are in good agreement with the plug model while H and O 2 were underestimated and H 2 O 2 was overestimated by the model. The accuracy of both the used reaction set and the diagnostics, as well as the observed discrepancies between the modeling and experimental results, are critically assessed. The results presented in this work enable us to identify further data needs for describing H 2 O vapor chemistry in low-temperature plasmas.

Physics↗
DOE OSTI2022

Fascinating physics at the edge of magnetic fusion devices

Abstract The physics of the processes at the edge of magnetic fusion devices is multifaceted and exhibits complex, nonlinear synergistic effects. Even though this region occupies only a small portion of the whole device, it plays a crucial role in overall plasma confinement, heat exhaust, and plasma–wall interactions. The latter affects not only the performance but also the lifetime of plasma-facing components and therefore remains an outstanding challenge for future fusion reactors. At the edge of fusion devices, researchers are dealing with phenomena including classical and anomalous plasma transport, atomic physics effects, and physics of plasma-facing material under strong irradiation by particle and energy fluxes. The diversity of edge physics makes it particularly attractive for young scientists. Working in this field, they can find endless possibilities to demonstrate their talents and creativity. This short review describes just some of the basic scrape-off layer and divertor plasma phenomena including divertor plasma detachment, intermittent bursts of anomalous cross-field plasma transport, plasma–material interactions, and dust in fusion plasmas, which are of particular interest in relation to fusion reactors.

Physics↗
DOE OSTI2024

Assessing CESM2 Clouds and Their Response to Climate Change Using Cloud Regimes

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k -means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

58 GEOSCIENCES↗
DOE OSTI2022

Fluorination‐Enhanced Surface Stability of Disordered Rocksalt Cathodes

Abstract Cation‐disordered rocksalt (DRX) oxides are a promising new class of high‐energy‐density cathode materials for next‐generation Li‐ion batteries. However, their capacity fade presents a major challenge. Partial fluorine (F) substitution into the oxygen (O) lattice appears to be an effective strategy for improving the cycling stability, but the underlying atomistic mechanism remains elusive. Here, using a combination of advanced transmission electron microscopy based imaging and spectroscopy techniques, the structural and chemical evolution upon cycling of Mn‐based DRX cathodes with an increasing F content (Li–Mn–Nb–O–F x , x = 0, 0.05, 0.2) are probed. The atomic origin behind the beneficial effect of high‐level fluorination for enhancing the surface stability of the DRX is revealed. It is discovered that, due to the reduced O redox activity while with increasing F concentration, F in the DRX lattice mitigates the formation of an O‐deficient surface layer upon cycling. For low F‐substituted DRX, the O loss near the surface results in the formation of an amorphous cathode–electrolyte interphase layer and nanoscale voids after extended cycling. Increased F concentration in the DRX lattice minimizes both O loss and the interfacial reactions between DRX and the liquid electrolyte, enhancing the surface stability of DRX. These results provide guidance on the development of next‐generation cathode materials through anion substitution.

Li, Linze↗
DOE OSTI2022

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗
DOE OSTI2022

Cloud processing and weeklong ageing affect biomass burning aerosol properties over the south-eastern Atlantic

Abstract Southern Africa produces a third of global biomass burning emissions, which have a long atmospheric lifetime and influence regional radiation balance and climate. Here, we use airmass trajectories to link different aircraft observations to investigate the evolution of biomass-burning aerosols during their westward transport from Southern Africa over the south-eastern Atlantic, where a semi-permanent stratocumulus cloud deck is located. Our results show secondary organic aerosol formation during the initial 3 days of transport, followed by decreases in organic aerosol via photolysis before reaching equilibrium. Aerosol absorption wavelength dependency decreases with ageing, due to an increase in particle size and photochemical bleaching of brown carbon. Cloud processing, including aqueous-phase reaction and scavenging, contributes to the oxidation of organic aerosols, while it strongly reduces large diameter particles and single-scattering albedo of biomass burning aerosols. Together, these processes resulted in a marine boundary layer with fewer yet more oxidized and absorbing aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗