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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

High thermoelectric performance of n-type PbTe 1-y S y due to deep lying states induced by indium doping and spinodal decomposition

Good thermoelectric materials should have high engineering figure-of-merit (ZT) eng , not peak ZT, to achieve high conversion efficiency. In this work, we achieved a good (ZT) eng by optimizing the carrier concentration to improve the room temperature ZT using deep lying dopant, indium, in PbTe 1-y S y . It was found that a room temperature ZT as high as ~0.5 and a peak ZT ~1.1 at about 673 K were obtained in Pb 0.98 In 0.02 Te 0.8 S 0.2 due to a lower thermal conductivity by alloy scattering and Spinodal decomposition. The calculated efficiency could be as high as ~12% at cold side 323 K and hot side 773 K. The approach is expected to work in other materials systems too.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Comparative evaluation of deep learning workloads for leadership-class systems

Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.

Yin, Junqi↗
DOE OSTI2021

The Deep Eutectic Solvent Precipitation Synthesis of Metastable Zn 4 V 2 O 9

A precipitation method involving a deep eutectic solvent (DES)–a mixture of hydrogen bond donor and acceptor–is used to synthesize a ternary metal oxide. Without toxic reagents, precipitates consisting of Zn 3 (OH) 2 V 2 O 7 ·nH 2 O and Zn 5 (OH) 6 (CO 3 ) 2 are obtained by simply introducing deionized H 2 O to the DES solution containing dissolved ZnO and V 2 O 5 . Manipulation of the synthetic conditions demonstrates high tunability in the size/morphology of the two-dimensional nanosheets precipitated during the dynamic equilibrium process. According to differential scanning calorimetry and high-temperature powder X-ray diffraction, Zn 3 V 2 O 8 and ZnO obtained by the annealing of the precipitate are intermediates in the reaction pathway toward metastable Zn 4 V 2 O 9 . Intimate mixing of the metal precursors achieved by the precipitation method allows access to the metastable zinc-rich vanadate with unusually rapid heat treatment. Furthermore, the UV–vis and surface photovoltage spectra reveal the presence of sub-band gap states, stemming from the reduced vanadium (V 4+ ) center. Photoelectrochemical measurements confirm weak photoanodic currents for water and methanol oxidation. For the first time, this work shows the synthesis of a metastable oxide with the DES-precipitation route and provides insight into the structure–property relationship of the zinc-rich vanadate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular Simulation of Lithium Carbonate Reactive Vapor–Liquid Equilibria Using a Deep Potential Model

We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li 2 CO 3 . The model was trained on ab initio electronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li 2 CO 3 into CO 2 residing primarily in the vapor phase and Li 2 O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO 2 partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO 2 in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. Furthermore, the present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Collapse of LiNi 1– x–y Co x Mn y O 2 Lattice at Deep Charge Irrespective of Nickel Content in Lithium-Ion Batteries

Volume variation and the associated mechanical fracture of electrode materials upon Li extraction/insertion are a main cause limiting lifetime performance of lithium-ion batteries. For LiNi 1-x-y Co x Mn y O 2 (NCM) cathodes, abrupt anisotropic collapse of the layered lattice structure at deep charge is generally considered characteristic to high Ni content and can be effectively suppressed by elemental substitution. Herein, we demonstrate the lattice collapse is a universal phenomenon almost entirely dependent on Li utilization, and not Ni content, of NCM cathodes upon delithiation. With Li removal nearing 80 mol%, very similar c-axis lattice shrinkage of around 5% occurs concurrently for NCMs synthesized in-house regardless of nickel content (90, 70, 50, or 33 mol%); meanwhile, the a-axis lattice contracts for high-Ni NCM, but it expands for low-Ni NCM. We further reveal Co-Mn co-substitution in NCM barely, if at all, affects several key structural aspects governing the lattice distortion upon delithiation. Finally, our results highlight the importance of evaluating true implications of compositional tuning on high-Ni layered oxide cathode materials to maximize their charge-storage capacities for next-generation high-energy Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Evaluation of a Stochastic Mixing Scheme in the Deep Convective Gray Zone Using a Tropical Oceanic Deep Convection Case Study

Abstract A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δ h ) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δ h = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

Significance Representations of strongly precipitating deep-convective systems in climate models are among the most important factors in their simulation. Parameterizations of these motions face the dual challenge of unclear pathways to including mesoscale organization and high sensitivity of convection to approximations of turbulent entrainment of environmental air. Ill-constrained entrainment processes can even affect global average climate sensitivity under global warming. Multiinstrument observations from the Department of Energy GoAmazon2014/5 field campaign suggest that an alternative formulation from radar-derived dominant updraft structure yields a strong relationship of precipitation to buoyancy in both mesoscale and smaller-scale convective systems. This simultaneously provides a key step toward representing the influence of mesoscale convection in climate models and sidesteps a problematic dependence on traditional entrainment rates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Microbial Life in the Deep Subsurface: Deep, Hot and Radioactive

Recent studies, motivated in part by the search for extraterrestrial life, continue to expand the recognized limits of Earth's biosphere. This work explored evidence for life a high-temperature, radioactive environment in the deep subsurface.

DeStefano, Andrea L.↗
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2016

Deep data mining in a real space: Separation of intertwined electronic responses in a lightly doped BaFe 2 As 2

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-T c superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave regime of a lightly (~1%) gold-doped BaFe 2 As 2 . Lastly, we show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗