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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Production of Fischer-Tropsch Synfuels at Nuclear Plants

A case study analysis was performed to evaluate nuclear-powered synthetic fuel production in the midwestern United States (U.S.). A Fischer-Tropsch (FT) fuel synthesis plant design was used as the basis for the analysis. The FT plant design was configured to produce a product slate consisting of diesel fuel, jet fuel, and motor gasoline blend stocks from carbon dioxide (CO 2 ) and hydrogen (H 2 ) feedstocks. The CO 2 feedstock for the FT plant was assumed to be sourced from biorefineries in the region around a Midwest light water reactor (LWR) nuclear power plant (NPP). The analysis specifies that power from the LWR is used to produce H 2 via high-temperature steam electrolysis and to operate the FT synfuel production plant. Capital costs were estimated for the FT plant while capital costs for the electrolysis plant were based on previous Idaho National Laboratory (INL) studies. In addition to labor and maintenance costs for the FT and electrolysis plants, operating costs also include the costs for CO 2 feedstock transport. An analysis was performed to determine the cost of transporting CO 2 from the distributed biorefinery sources to the centralized fuel synthesis plant as a function of the synfuel plant capacity and corresponding CO 2 demand. The primary revenue streams are associated with sales of the synthetic fuel products. The synthetic fuel products will likely follow the same market trends as the conventional fuel products. The synfuel price data was thus based on projections made by the U.S. Energy Information Administration (EIA) 2021 Annual Energy Outlook (AEO) for conventional fuel products minus federal and state taxes, as well as marketing and distribution costs. The economic analysis also considered cases that included and excluded revenues from the 2022 Inflation Reduction Act (IRA) clean hydrogen production tax credit (PTC) of $\$ $3.00/kg for the first ten years of operation. The economic analysis calculated the net present value (NPV) for cases involving steady-state synfuel production for comparison with the NPV for a business-as-usual case in which NPP continues to sell only electric power to the grid. A synfuel production “Reference Case” was considered in addition to sensitivity cases in which the plant capacity, electricity price, and synthetic fuel product prices were perturbed. The synfuel production Reference Case considered a scenario in which the electrolysis and synfuel plants utilized a combined electrical load of 1000 megawatt electrical (MWe) from the LWR with the balance of the LWR power output being sold to the electric grid. The economic analysis suggests that the synfuel production Reference Case evaluated in this analysis would lead to considerable economic potential for near-term deployment of a nuclear-based synfuel production plant. Specifically, the economic analysis suggests that the deployment of a 1000 megawatt (MW) nuclear-powered synfuel plant could result in a NPV increase of approximately $\$ $1.7 billion for a case with no clean synfuel price premium relative to conventional petroleum fuels when accounting for the additional revenues from the 2022 IRA clean hydrogen PTCs of $\$ $3/kg. Sensitivity analysis was performed to evaluate the effect of perturbation of selected model input parameters on the NPV for the synfuel production Reference Case. The sensitivity analysis indicates that the plant capacity has the largest impact on the differential NPV, with a smaller synfuel production capacity resulting in a decrease in revenue when a larger fraction of the power from the NPP is sold to the grid and a smaller fraction of the power is used to produce synthetic fuel products. The synfuel product pricing has the next largest impact on the differential NPV, with lower synfuel prices resulting in decreased NPV from decreased synfuel sales revenue while higher synfuel prices result in increased NPV from increased synfuel sales revenue. Electricity pricing has a smaller effect on the NPV than the fuel sales price since, in the Reference Case, most of the energy from the NPP is used for synfuel production and a smaller amount of the system revenues are associated with electrical power sales. However, the electricity price sensitivity does indicate that the Synfuel Integrated Energy System (IES) would have a greater NPV than the business-as-usual case (e.g., grid power sales only) when electricity market prices are low, suggesting that synfuel production could provide a strategy for decreasing the economic risks to NPPs posed by a loss of revenues attributed to falling electricity market prices.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Thermodynamic modeling of the Pd–Zn system with uncertainty quantification and its implication to tailor catalysts

Pd–Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd–Zn system, delineating phase boundaries and enumerating site occupancies within intermediate alloy phases, is essential to determining the ensembles of Pd–Zn atoms as a function of composition and temperature. Combining the present extensive first-principles calculations based on density functional theory (DFT) and available experimental data, the Pd–Zn system was remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High throughput modeling tools with uncertainty quantification, i.e., ESPEI and PyCalphad, were incorporated in the phase analysis. Here, the site occupancies across the γ phase composition region were given special attention. A four-sublattice model was used for the γ phase owing to its four Wyckoff positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT) site 8c, the octahedral (OH) site 12e, and the cuboctahedral (CO) site 24g. The site fractions of Pd and Zn calculated from the present thermodynamic model show the occupancy preference of Pd in the OT and OH sublattices in agreement with experimental observations. The force constants obtained from DFT-based phonon calculations further supports the tendency of Pd occupying the OH sublattice compared with the IT and CO sublattice. The catalytic ensembles changing from Pd monomers (Pd 1 ) to trimers (Pd 3 ) on the surface of γ phase are attributed to the increase of Pd occupancy in the OH sublattice.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications

Abstract Calculations of point defect energetics with Density Functional Theory (DFT) can provide valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches. For applications of DFT-based high-throughput computation for data-driven materials discovery, point defect properties are of interest, yet are currently excluded from available materials databases. This work presents a benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We consider three different a-posteriori correction sets implemented in an automated workflow, and evaluate the qualitative and quantitative differences among four different categories of defect information: thermodynamic transition levels, formation energies, Fermi levels, and dopability limits. We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗
DOE OSTI2025

16 O + 16 O fusion at sub-coulomb energies

A simple optical model (OPM) method using non-monotonic (NM) potentials characterized by a repulsive core from the microscopic theory of the Pauli-led energy-density functional (EDF) has been developed to investigate the 16 O + 16 O fusion at sub-Coulomb energies relevant to the oxygen burning. Here, the study involves the analysis of the experimental fusion cross-section (FCS) data in energy range 6.92 ≤ E cm ≤ 13.83 MeV, which includes the Coulomb barrier region E cm = 10.0–11.92 MeV. Apart from an excellent description of the existing FCS data in the studied energy range, the associated hindrance, characterized by the S-factor reaching a maximum and then gradually falling off at lower energies, so far suggested empirically for the system, is reproduced down to 4 MeV for the first time in the simple OPM. An S-factor maximum of S 0 = 3.15 × 10 25 MeV.b at E 0 = 7.14 MeV is observed with T ≃ 2.6 GK, which conforms to the values reported for quiescent and explosive burning. Our reaction rate, deduced from the NM potential, compares well with the Caughlan and Fowler data. Dominant partial waves implicit in the observed maximum S-factor in the studied Gamow range are also explored. Our present findings, with the success of NM potentials, suggest that the nucleus–nucleus potential is non-monotonic.

16O+16O↗
DOE OSTI2025

One- and two-body current contributions to lepton-nucleus scattering

Modeling lepton-nucleus scattering with the accuracy required to extract neutrino-oscillation parameters from long- and short-baseline experiments necessitates retaining most quantum-mechanical effects. One such effect is the interference between one- and two-body current operators in the transition currents, which has been known to enhance the cross sections, especially in transverse kinematics. Here, we incorporate such interference in the spectral function formalism, which combines relativistic currents and kinematics with an accurate description of the initial target state. Our analysis of lepton-scattering off 12 C demonstrates that interference effects appreciably enhance the transverse electromagnetic response functions and the flux-folded neutrino-nucleus cross section, in both cases, improving the agreement with experimental data. We discuss the impact on the neutrino-oscillation program and the determination of nucleon axial form factors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Data-Driven Probabilistic Voltage Risk Assessment of MiniWECC System with Uncertain PVs and Wind Generations using Realistic Data

Here, it is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks.

17 WIND ENERGY↗
DOE OSTI2026

Measurement of the W-boson angular coefficients and transverse momentum in pp collisions at s=13 TeV with the ATLAS detector

The angular distributions of Drell–Yan lepton pairs provide sensitive probes of the underlying dynamics of quantum chromodynamics (QCD) effects in vector-boson production. This paper presents for the first time the measurement of the full set of angular coefficients together with the differential cross-section as a function of the transverse momentum of the W boson, in the full phase space of the decay leptons. The measurements are performed separately for the W-$$W^-$$ and W+$$W^+$$ channels. The analysis uses proton–proton collision data recorded by the ATLAS experiment at the Large Hadron Collider in 2017 and 2018, during special low-luminosity runs with a reduced number of interactions per bunch crossings (pile-up). The data correspond to an integrated luminosity of 338 pb-1$$^{-1}$$ at a centre-of-mass energy of s=13$$\sqrt{s} = 13$$ TeV. The low pile-up environment provides excellent experimental conditions for high-precision measurements of W-boson production. All results agree with theoretical predictions incorporating finite-order QCD corrections up to order αS2$$\alpha _S^2$$.

Aad, G↗
DOE OSTI2020

Leaf-scale carbon assimilation simulated by the multi-assumption architecture & testbed for process sensitivity analysis

Raw and processed output from the Multi-Assumption Architecture & Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_walker2020; Walker et al., 2018). The dataset contains leaf-scale carbon assimilation data from three MAAT simulations and their post-processing and plotting scripts. Simulation 1, labelled "FvC_4proc_proc," is a four-process, process sensitivity analysis (Dai et al., 2017) of carbon assimilation based on the Farquhar et al. (1980) photosynthesis model, and its variant proposed by Collatz et al. (1991). Simulation 2, labelled "FvC_4proc_salt," is a 14 parameter sensitivity analysis (Saltelli et al., 2010) of carbon assimilation based on the Farquhar and Collatz photosynthesis models. Simulation 3, labelled "FvC," is a factorial combination of the alternative process representations and parameter values presented in those original papers across a factorial combination of incident photosynthetically active radiation (50-2000 µmol m-2 s-1, in increments of 50) and atmospheric CO2 concentration (50-1500 µmol m-2 s-1, in increments of 50). Additional scripts/functions for post-processing and plotting are part of the MAAT repository. These data were generated to evaluate and compare the Farquhar and Collatz models of leaf-scale photosynthesis across a range of CO2 and incident light conditions. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures; .RDS files, a binary format specific to R and read with the 'readRDS' function, to store raw MAAT output; R and bash scripts (.bs) that help to run a MAAT simulation, .XML files that are output from MAAT describing a run, and .csv files which contain the calculated sensitivity indexes published in Walker et al. (2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

58 GEOSCIENCES↗
DOE OSTI2020

Thermosteam: BioSTEAM's Premier Thermodynamic Engine

Thermosteam is a thermodynamic engine capable of solving mass and energy balances, estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. All chemical data in Thermosteam is imported from the chemicals library, an open-source compilation of data and functions for the estimation of pure component chemical and mixture properties. Thermosteam’s fast and flexible platform has enabled the evaluation of conceptual and emerging biochemical production processes. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) — capable of modeling reactors, distillation columns, heat exchangers, and other unit operations — has adopted Thermosteam as its premier thermodynamic engine. Published biorefinery designs modeled in BioSTEAM implement thermodynamic property packages created with Thermosteam, including a cornstover biorefinery for the production of cellulosic ethanol, a lipid-cane biorefinery for the co-production of ethanol and biodiesel, and a wheatstraw biorefinery for the production of cellulosic ethanol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

One and Two-Body Current Contributions to Lepton-Nucleus Scattering

Modeling lepton-nucleus scattering with the accuracy required to extract neutrino-oscillation parameters from long- and short-baseline experiments necessitates retaining most quantum-mechanical effects. One such effect is the interference between one- and two-body current operators in the transition currents, which has been known to enhance the cross-sections, especially in transverse kinematics. In this work, we incorporate such interference in the spectral function formalism, which combines relativistic currents and kinematics with an accurate description of the initial target state. Our analysis of lepton-scattering off $^{12}$C demonstrates that interference effects appreciably enhance the transverse electromagnetic response functions and the flux-folded neutrino-nucleus cross section, in both cases, improving the agreement with experimental data. We discuss the impact on the neutrino-oscillation program and the determination of nucleon axial form factors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Incorporating corrosion design constraints in desalination process optimization: A case study in mechanical vapor compression

Corrosion is an expensive and complex challenge for desalination, yet current design approaches do not explicitly account for corrosion mechanisms in process modeling and technoeconomic analysis. Here, to address this gap, we present a workflow for incorporating corrosion design constraints directly into desalination process optimization models. We develop surrogate models for general and localized corrosion metrics as functions of temperature, pH, salinity, dissolved oxygen, and material using data from OLI Systems’ Corrosion Analyzer. We then integrate these surrogates as corrosion design constraints in a cost-optimization MVC model that minimizes the levelized cost of water (LCOW). For a case study of mechanical vapor compression (MVC) treating seawater across a range of recoveries, we find dissolved oxygen (DO) is the dominant driver of localized corrosion, and thus of cost-optimal material choice and operating conditions. Reducing the DO from 8 mg/L to 0.5 mg/L reduces the LCOW by 15-35%, informing the breakeven costs for implementing DO removal or selecting highly corrosion-resistant alloys. This framework is broadly applicable across corrosion types, materials, and components and enables desalination process design that minimizes capital costs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Marine and Hydrokinetic ToolKit (MHKiT) for Data Quality Control and Analysis [Slides]

The ability to collect, ingest, condition, reduce, quality control, process, visualize, and store data in a standardized way is critical at all stages of Marine Energy (ME) research and technology/project development. MHKiT is an open-source, standardized suite of ME data processing functions that provides the ability to ingest, condition, reduce, quality control, process, visualize and store ME data. MHKiT is developed in both Python and Matlab.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Determination of short- and long-distance contributions in B 0 → K * 0 μ + μ − decays

An amplitude analysis of the B 0 → K * 0 μ + μ − decay is presented. The analysis is based on data collected by the LHCb experiment from proton-proton collisions at s = 7 , 8 and 13 TeV, corresponding to an integrated luminosity of 4.7 fb − 1 . For the first time, Wilson coefficients and nonlocal hadronic contributions are accessed directly from the unbinned data, where the latter are parametrized as a function of q 2 with a polynomial expansion. Wilson coefficients and nonlocal hadronic parameters are determined under two alternative hypotheses: the first relies on experimental information alone, while the second one includes information from theoretical predictions for the nonlocal contributions. Both models obtain similar results for the parameters of interest. The overall level of compatibility with the Standard Model is evaluated to be between 1.8 and 1.9 standard deviations when looking at the C 9 Wilson coefficient alone, and between 1.3 and 1.4 standard deviations when considering the full set of C 9 , C 10 , C 9 ′ and C 10 ′ Wilson coefficients. The ranges reflect the theoretical assumptions made in the analysis. © 2024 CERN, for the LHCb Collaboration 2024 CERN

Aaij, R. (ORCID:0000000305331952)↗
DOE OSTI2024

Nucleus++ : a new tool bridging AME and NUBASE for advancing nuclear data analysis

The newly developed software, Nucleus++ , is an advanced tool for displaying basic nuclear physics properties from NUBASE and integrating comprehensive mass information for each nuclide from Atomic Mass Evaluation. Additionally, it allows users to compare experimental nuclear masses with predictions from different mass models. Building on the success and learning experiences of its predecessor, Nucleus , this enhanced tool introduces improved functionality and compatibility. With its user-friendly interface, Nucleus++ was designed as a valuable tool for scholars and practitioners in the field of nuclear science. Finally, this article offers an in-depth description of Nucleus++ , highlighting its main features and anticipated impacts on nuclear science research.

AME↗
DOE OSTI2021

Genome Extraction from Shotgun Metagenome Sequence Data

Uncultivated Bacteria and Archaea comprise the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only recently become possible. To realize the hope of capturing Earth’s microbial genetic complement, technologies that accelerate recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform (http://www.kbase.us/). In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAG genomes, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq, and others. While portions of such capabilities are individually available from other resources, the combination of the intuitive usability, data interoperability, and integration of tools in a freely available compute resource makes KBase a uniquely powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended, with additional MAG recovery and analysis tools, via the KBase SDK (Software Development Kit).

Chivian, Dylan↗