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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

We perform a semi-automated search for strong gravitational lensing systems in the 9000 deg 2 Dark Energy Camera Legacy Survey (DECaLS), part of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training sample that consists of known lensing systems in the Legacy Surveys and the Dark Energy Survey as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Geothermal Deep Direct-Use for Low-Carbon Heating: A Case Study at Cornell University

Cornell University is developing a geothermal deep direct-use system to provide reliable, low carbon, and cost-competitive heating for its main campus in Ithaca, New York. This geothermal system, also referred to as Earth Source Heat, would provide the majority of the campus heating load and represents a key pillar for the university to become carbon neutral by 2035. Over the last 10 years, numerous desktop, seismic, and geophysical studies have been undertaken at Cornell to characterize the local subsurface, predict long-term reservoir performance, design the integration into the existing district heating network, and estimate capital and levelized costs. Recently, the university received funding for drilling an observatory well to obtain in-situ subsurface data (e.g., reservoir temperatures, pressures, fluid chemistry, stress field), significantly lowering subsurface uncertainty and de-risking the project. Drilling of the test well is planned for fall 2021. This paper provides an overview and status of the project, including a summary of past studies and a description of the proposed well and planned testing activities.

Cornell University↗
DOE OSTI2020

As‐Cast Ternary Organic Solar Cells Based on an Asymmetric Side‐Chains Featured Acceptor with Reduced Voltage Loss and 14.0% Efficiency

Abstract A new small‐molecule nonfullerene acceptor based on the benzo[1,2‐ b :4,5‐ b ′]dithiophene (BDT) fused central core with asymmetrical alkoxy and thienyl side chains, namely TOBDT , is designed and synthesized. The alkoxy unit helps narrow the bandgap, and thienyl side chain helps enhance the intermolecular interaction. As a result, TOBDT is suitable to match the deep‐lying highest occupied molecular orbital (HOMO) of polymer donor PM6 . Then, a strong crystalline acceptor IDIC is introduced as the third component to fabricate as‐cast nonfullerene ternary devices to achieve absorption and morphology control. Addition of IDIC not only mixes well with TOBDT but modulates the morphology of the blend film, which helps to balance the charge transport properties and reduce the photovoltage loss of ternary devices. All these contribute to synergetic improvement of J sc , V oc , and fill factor parameters, leading to a power conversion efficiency of 14.0% for the as‐cast fullerene‐free ternary device.

Chen, Xuebin↗
DOE OSTI2024

The CluMPR galaxy cluster-finding algorithm and DESI legacy survey galaxy cluster catalogue

ABSTRACT Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly lensed transients. We here present a new cluster-finding algorithm, CluMPR (Clusters from Masses and Photometric Redshifts), that exploits photometric redshifts (photo-z’s) as well as photometric stellar mass measurements. CluMPR uses a 2D binary search tree to search for overdensities of massive galaxies with similar redshifts on the sky and then probabilistically assigns cluster membership by accounting for photo-z uncertainties. We leverage the deep DESI Legacy Survey grzW1W2 imaging over one-third of the sky to create a catalogue of $\sim 300\, 000$ galaxy cluster candidates out to z = 1, including tabulations of member galaxies and estimates of each cluster’s total stellar mass. Compared to other methods, CluMPR is particularly effective at identifying clusters at the high end of the redshift range considered (z = 0.75–1), with minimal contamination from low-mass groups. These characteristics make it ideal for identifying strongly lensed high-redshift supernovae and quasars that are powerful probes of cosmology, dark matter, and stellar astrophysics. As an example application of this cluster catalogue, we present a catalogue of candidate wide-angle strongly lensed quasars in Appendix C. The nine best candidates identified from this sample include two known lensed quasar systems and a possible changing-look lensed QSO with SDSS spectroscopy. All code and catalogues produced in this work are publicly available (see Data Availability).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Spin-Charge-Lattice Coupling across the Charge Density Wave Transition in a Kagome Lattice Antiferromagnet

Understanding spin and lattice excitations in a metallic magnetic ordered system forms the basis to unveil the magnetic and lattice exchange couplings and their interactions with itinerant electrons. Kagome lattice antiferromagnet FeGe is interesting because it displays a rare charge density wave (CDW) deep inside the antiferromagnetic ordered phase that interacts with the magnetic order. Here, we use neutron scattering to study the evolution of spin and lattice excitations across the CDW transition 𝑇 CDW in FeGe. While spin excitations below ∼100 meV can be well described by spin waves of a spin-1 Heisenberg Hamiltonian, spin excitations at higher energies are centered around the Brillouin zone boundary and extend up to ∼180 meV consistent with quasiparticle excitations across spin-polarized electron-hole Fermi surfaces. Furthermore, 𝑐-axis spin wave dispersion and Fe-Ge optical phonon modes show a clear hardening below 𝑇 CDW due to spin-charge-lattice coupling but with no evidence of a phonon Kohn anomaly. By comparing our experimental results with density functional theory calculations in absolute units, we conclude that FeGe is a Hund’s metal in the intermediate correlated regime where magnetism has contributions from both itinerant and localized electrons arising from spin polarized electronic bands near the Fermi level.

charge density waves↗
DOE OSTI2020

DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1°×0.1°), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of -3.96 and -0.71 W m -2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m -2 , respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

"Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs"

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

Lee, Jonathan↗
DOE OSTI2023

Near-Cloud Atmospheric Ingredients for Deep Convection Initiation

A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.

Storm environments↗
DOE OSTI2023

Investigation of the effects of ternary deep eutectic solvent composition on pretreatment of sorghum stover

Biomass‐derived deep eutectic solvents (DESs) have been introduced as promising pretreatment and fractionation solvents because of their mild processing conditions, easy synthesis, and green solvent components from biomass. In recent DES studies, solvent‐based third constituents like water, ethanol, and others improve the processibility of typical binary DESs. However, the impacts of these components are not well understood. Here, two solvent‐based constituents, including water and ethylene glycol, were applied to 3,4‐dihydroxybenzoic acid (DHBA)‐based DES system for improving the conversion efficiency of cellulose‐rich fraction and the properties of lignin fraction. Chemical composition, enzymatic digestibility, degree of polymerization of cellulose and physicochemical properties of lignin were used to evaluate the impact of each third constituent on biomass processing. Ternary ChCl‐DHBA DESs exhibited better performances in delignification, fermentable sugar production, and preservation of β‐O‐4 ether linkage in lignin compared with binary ChCl‐DHBA DES.

09 BIOMASS FUELS↗
DOE OSTI2022

Scalable Synthesis of Monolayer Hexagonal Boron Nitride on Graphene with Giant Bandgap Renormalization

Abstract Monolayer hexagonal boron nitride (hBN) has been widely considered a fundamental building block for 2D heterostructures and devices. However, the controlled and scalable synthesis of hBN and its 2D heterostructures has remained a daunting challenge. Here, an hBN/graphene (hBN/G) interface‐mediated growth process for the controlled synthesis of high‐quality monolayer hBN is proposed and further demonstrated. It is discovered that the in‐plane hBN/G interface can be precisely controlled, enabling the scalable epitaxy of unidirectional monolayer hBN on graphene, which exhibits a uniform moiré superlattice consistent with single‐domain hBN, aligned to the underlying graphene lattice. Furthermore, it is identified that the deep‐ultraviolet emission at 6.12 eV stems from the 1s‐exciton state of monolayer hBN with a giant renormalized direct bandgap on graphene. This work provides a viable path for the controlled synthesis of ultraclean, wafer‐scale, atomically ordered 2D quantum materials, as well as the fabrication of 2D quantum electronic and optoelectronic devices.

2D heterostructures↗
DOE OSTI2022

Accelerating the discovery of battery electrode materials through data mining and deep learning models

The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.

25 ENERGY STORAGE↗
DOE OSTI2020

Optoelectronic and material properties of solution-processed Earth-abundant Cu 2 BaSn(S, Se) 4 films for solar cell applications

Copper barium thioselenostannate, Cu 2 BaSnS 4-x Se x (CBTSSe), absorbers employ low-toxicity and abundant metals while offering low-cost manufacturing options, controllable stoichiometry and band gap tunability (from 2 eV at x = 0 to 1.55 eV at x = 3). CBTSSe can therefore be considered a prospective candidate for maintaining or improving upon the advantages of already commercialized Cu(In,Ga)(S,Se) 2 (CIGSSe) and CdTe absorbers. Here, we focus on solution-deposited stoichiometric CBTSSe films with band gap of 1.59 eV (x ≈ 3) and explore the fundamental film properties. Temperature- and excitation-dependent photoluminescence studies reveal a dominant defect emission at ~1.5 eV and a second deep defect feature at 1.15 eV. From time-resolved terahertz measurements, we find a charge carrier (electron and hole sum) mobility of ~140 cm 2 /Vs—i.e., comparable to values in CIGSSe or Cu 2 ZnSnS 4-x Se x (CZTSSe)—as well as a two-component minority carrier lifetime. A longer-lived lifetime component (~2 ns) arises from bulk recombination. However, strong recombination at the (bare) surface leads to a ~50 ps lifetime, inferior to state-of-the-art CIGSSe or CZTSSe absorbers. This recombination issue may worsen for CBTSSe/CdS interfaces, due to a cliff-like band alignment with 0.6 eV band offset, as revealed by ultraviolet photoemission spectroscopy. A low number of charge carriers within the absorber further contributes to a high series resistance. Employing these films, we also report the highest performance achieved from solution-processed trigonal CBTSSe thin-film photovoltaic devices, with open circuit voltage, short-circuit current density, fill factor and efficiency of 470 mV, 14.3 mA/cm 2 , 43.6% and 2.9%, respectively. The physical measurements provided on the stoichiometric solution-processed CBTSSe absorber further point to critical areas for future improvement of CBTSSe and related photovoltaic cells in the quest for higher efficiency devices based on earth abundant metals.

14 SOLAR ENERGY↗
DOE OSTI2022

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

In this work, time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.

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

Multiplex knockout of trichome-regulating MYB duplicates in hybrid poplar using a single gRNA

As the focus for CRISPR/Cas-edited plants moves from proof-of-concept to real-world applications, precise gene manipulation will increasingly require concurrent multiplex editing for polygenic traits. A common approach for editing across multiple sites is to design one guide RNA (gRNA) per target; however, this complicates construct assembly and increases the possibility of off-target mutations. In this study, we utilized one gRNA to target MYB186, a known positive trichome regulator, as well as its paralogs MYB138 and MYB38 at a consensus site for mutagenesis in hybrid poplar (Populus tremula × P. alba INRA 717-1B4). Unexpected duplications of MYB186 and MYB138 resulted in eight alleles for the three targeted genes in the hybrid poplar. Deep sequencing and polymerase chain reaction analyses confirmed editing across all eight targets in nearly all of the resultant glabrous mutants, ranging from small indels to large genomic dropouts, with no off-target activity detected at four potential sites. This highlights the effectiveness of a single gRNA targeting conserved exonic regions for multiplex editing. Additionally, cuticular wax and whole-leaf analyses showed a complete absence of triterpenes in the trichomeless mutants, hinting at a previously undescribed role for the nonglandular trichomes of poplar.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys

Abstract We present a method for creating simulated galaxy catalogs with realistic galaxy luminosities, broadband colors, and projected clustering over large cosmic volumes. The technique, denoted Addgals (Adding Density Dependent GAlaxies to Lightcone Simulations), uses an empirical approach to place galaxies within lightcone outputs of cosmological simulations. It can be applied to significantly lower-resolution simulations than those required for commonly used methods such as halo occupation distributions, subhalo abundance matching, and semi-analytic models, while still accurately reproducing projected galaxy clustering statistics down to scales of r ∼ 100 h −1 kpc . We show that Addgals catalogs reproduce several statistical properties of the galaxy distribution as measured by the Sloan Digital Sky Survey (SDSS) main galaxy sample, including galaxy number densities, observed magnitude and color distributions, as well as luminosity- and color-dependent clustering. We also compare to cluster–galaxy cross correlations, where we find significant discrepancies with measurements from SDSS that are likely linked to artificial subhalo disruption in the simulations. Applications of this model to simulations of deep wide-area photometric surveys, including modeling weak-lensing statistics, photometric redshifts, and galaxy cluster finding, are presented in DeRose et al., and an application to a full cosmology analysis of Dark Energy Survey (DES) Year 3 like data is presented in DeRose et al. We plan to publicly release a 10,313 square degree catalog constructed using Addgals with magnitudes appropriate for several existing and planned surveys, including SDSS, DES, VISTA, Wide-field Infrared Survey Explorer, and Rubin Observatory’s Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Impact of sub-core scale heterogeneity on CO2/brine multiphase flow for geological carbon storage in the Minnelusa sandstone

CO2 geological storage in deep saline aquifers is a mitigation option for CO2 emissions due to its large storage capacity and immediate accessibility. Accurately determining the CO2-brine relative permeability curves is key to the evaluation of CO2 injectivity and sweep efficiency in reservoir simulation as well as the CO2 injection in the field. This study highlights the remarkable effects of sub-core scale heterogeneity on the CO2-brine multiphase flow properties of the Minnelusa Sandstone in Wyoming. Two unsteady state CO2-brine drainage experiments were performed on the two samples. The first sample exhibits slanted laminated structure, while the second one represents a more homogeneous sandstone system. The CO2 saturation distributions during drainage reveals that the main variation in multiphase flow properties of two core samples were attributed to the porosity distribution that leads to the capillary pressure heterogeneity. Assisted history matching was used to obtain the respective relative permeability curves, which suggests heterogeneity-dependent behavior. In addition, sensitive and uncertainty analyses indicate that physical and petro-physical properties of low-porosity and low-permeability bedding layers exert marked effects on CO2 front breakthrough time and brine production. The results presented in this study help to gain insight into the CO2-brine multiphase flow properties in heterogeneous sandstones and can pave the way for the upscaling of CO2 migration and field-scale simulation accurately. This work is funded under the Department of Energy CarbonSAFE program (awards DE-FE0031624 DE0031891).

Kou, Zuhao↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗