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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tuning acidity in silica-overcoated oxides for hydroalkoxylation

Mixed oxides are ubiquitious solid acid catalysts for a number of transformations. Here, the chemistry of mixed oxide interfaces was experimentally probed by constructing model SiO 2 on MO x interfaces using a liquid-phase, stoichiometrically-controlled deposition of tetraethyl orthosilicate (TEOS). Catalytically active Brønsted sites were generated in four series of overcoated SiO 2 on metal oxide materials prepared by depositing TEOS onto Al 2 O 3 , anatase TiO 2 , Nb 2 O 5 , and ZrO 2 . Pyridine DRIFTS studies indicate that the acidity of deposited silanol groups was enhanced by interaction with the metal oxide surface, suggesting that pseudo-bridging silanol type structures were formed. Trimethylphosphine oxide (TMPO) 31 P NMR experiments showed that Brønsted acid strength varied in the overcoated materials, with increasing strength in the following order: SiO 2 /ZrO 2 < SiO 2 /TiO 2 < SiO 2 /Nb 2 O 5 < SiO 2 /Al 2 O 3 . This order correlated with the Lewis acidity and chemical hardness of the underlying metal oxides. The catalytic performance of the materials was evaluated in the liquid-phase hydroalkoxylation of dihydropyran with n-octanol, revealing that activity was highly dependent on SiO 2 loading, which impacted the density of acid sites, and scaled exponentially with Brønsted acid strength, determined by the core metal oxide identity. Brønsted acid strength appears to be the key driver of the materials’ activity, with the highest areal rate of 2.2 mmol / m 2 – h being measured over SiO 2 /Al 2 O 3 . Furthermore, this study shows that SiO 2 -overcoated materials are a systematically tunable class of catalysts that can be applied to acid catalyzed reactions.

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

Force-Modulated Selectivity of the Rhodium-Catalyzed Hydroformylation of 1-Alkenes

Here we quantify the regio- chemo-, and enantioselectivity of the hydroformylation of 1-alkenes catalyzed by Rh(I) complexes containing chiral macrocyclic biaryl bis(phosphine) ligands as a function of mechanical force applied to the biaryl backbone of these ligands. Extension forces increase the regioselectivity of the hydroformylation of 1-octene from l:b = 1.2 to 1.9 and decrease the chemoselectivity for C 9 aldehyde relative to 2-octene from 96% to 20% across a ~230 pN change in applied force. Extension forces decrease the enantioselectivity of the hydroformylation of styrene from 28% to 18% ee across a similar force range. Variable temperature 1 H and 31 P NMR analysis of five-coordinate rhodium complexes of the form (P–P)Rh(CO) 2 H established structures in which the bisphosphine ligand occupies one equatorial and one apical position in the trigonal bipyramid with rapid interconversion of the equatorial and apical phosphorus atoms ($ΔG^‡_{223 \text{K}}$ = 9.6–9.9 kcal/mol). Neither the isomeric composition nor fluxional behavior of these complexes was detectably perturbed across a ~300 pN change in applied force.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Nanoparticle O–H Bond Dissociation Free Energies from Equilibrium Measurements of Cerium Oxide Colloids

A novel equilibrium strategy for measuring the hydrogen atom affinity of colloidal metal oxide nanoparticles is presented. Reactions between oleate-capped cerium oxide nanoparticle colloids (nanoceria) and organic proton-coupled electron transfer (PCET) reagents are used as a model system. Nanoceria redox changes, or hydrogen loadings, and overall reaction stoichiometries were followed by both 1 H NMR and X-ray absorption near-edge spectroscopies. Furthermore, these investigations revealed that, in many cases, reactions between nanoceria and PCET reagents reach equilibrium states with good mass balance. Each equilibrium state is a direct measure of the bond strength, or bond dissociation free energy (BDFE), between nanoceria and hydrogen. Further studies, including those with larger nanoceria, indicated that the relevant bond is a surface O–H. Thus, we have measured surface O–H BDFEs for nanoceria—the first experimental BDFEs for any nanoscale metal oxide. Remarkably, the measured CeO–H BDFEs span 13 kcal mol –1 (0.56 eV) with changes in the average redox state of the nanoceria colloid. Possible chemical models for this strong dependence are discussed. We propose that the tunability of ceria BDFEs may be important in explaining its effectiveness in catalysis. More generally, metal oxide BDFEs have been used as predictors of catalyst efficacy that, traditionally, have only been accessible by computational methods. These results provide important experimental benchmarks for metal oxide BDFEs and demonstrate that the concepts of molecular bond strength thermochemistry can be applied to nanoscale materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Instability of an electron-plasma shear layer in an externally imposed strain flow

The E x B shear instability of a two-dimensional (2D) filament (i.e., a thin, rectangular strip perpendicular to the magnetic field) of magnetized pure electron plasma is investigated experimentally in the presence of an externally imposed strain flow. Data are acquired using a specialized Penning–Malmberg trap in which strain flows can be applied in 2D by biasing segmented electrodes surrounding the plasma. The E x B drift dynamics are well-described by the Drift-Poisson equations, which are isomorphic to the 2D Euler equations describing ideal fluids. Thus, the experimental results correspond to the Rayleigh instability of a shear layer in a 2D ideal fluid, where the electron density is analogous to the fluid vorticity. Shear layers are prepared by stretching initially axisymmetric electron vortices using a strong, applied strain flow. The data at early times are in quantitative agreement with a linear model which extends Rayleigh’s work to account for the influence of an external strain flow. In the presence of weak strain, the system approximately maintains a phase relationship that corresponds to an instantaneous Rayleigh eigenmode. The instability develops into the nonlinear regime later in time and at smaller spatial scales as the strain rate is increased. A secondary vortex pairing instability is observed, but it is suppressed when the strain-to-vorticity ratio exceeds roughly 0.025. In this way, vorticity transport perpendicular to the filament is diminished due to the applied strain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Possibilities of using practical limitations of an eavesdropper in quantum cryptography

An important advantage of quantum cryptography over classical cryptography consists in the fact that the security of the transmitted keys is not related to the assumptions about the eavesdropper capabilities and is guaranteed by the laws of nature. Nevertheless, in some situations it makes sense to consider some reasonable assumptions about the eavesdropper capabilities, which can increase the secret key distribution rate. Methods are proposed for legitimate users to employ some practical limitations, and attacks are constructed that the eavesdropper can apply under the conditions of these limitations. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Superconductivity-induced spectral weight transfer due to quantum geometry

Optical spectral weight transfer associated with the onset of superconductivity at high-energy scales compared with the superconducting gap has been observed in several systems such as high-T c cuprates. While there are still debates on the origin of this phenomenon, a consensus is that it is due to strong correlation effects beyond the BCS theory. Here, we show that there is another route to a nonzero spectral weight transfer based on the quantum geometry of the conduction band in multiband systems. Further, we discuss applying this idea to the cuprates and twisted multilayer graphene.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Electron beam treatment for the removal of 1,4-dioxane in water and wastewater

Electron beam (e-beam) treatment uses accelerated electrons to form oxidizing and reducing radicals when applied to water without the use of external chemicals. In this study, electron beam treatment was used to degrade 1,4-dioxane in several water matrices. Removal improved in the progressively cleaner water matrices and removals as high as 94% to 99% were observed at a dose of 2.3 kGy in secondary effluent. 1,4-dioxane removal was confirmed to be primarily through hydroxyl radical oxidation. The calculated electrical energy per order was found to be 0.53, 0.26, and 0.08 kWh/m 3 /order for secondary effluent (Avg. total organic carbon (TOC) 9.25 mg/L), granular activated carbon effluent (TOC 3.46 mg/L), and ultrapure water, respectively, with a 70% generation and transfer efficiency applied.

1,4-dioxane↗
DOE OSTI2022

The Bouger's Law Shell Ionospheric Transfer Function

Ionospheric transfer function (ITF) algorithms determine the effects of the ionosphere on an electromagnetic (EM) radio-frequency (RF) signal as it propagates through. In this report, the Bouger’s law shell model is outlined. This ITF is very similar to the Snell’s Law Shell ITF; however, in this formulation, plasma parameters in the ionosphere are allowed to change radially. This algorithm is expressed in the frequency domain. In this way, it is applied as linear time invariant (LTI) filter function. Signals in this report are assumed to have only a single component (i.e. x, y or z in a rectangular coordinate system). Multi-component signals can be treated simply by applying the specific ITF to each component separately.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Reactivity and Structural Dynamics of Supported Metal Nanoclusters Using Electron Microscopy, in-situ X-Ray Spectroscopy, Electronic Structure Theories, and Molecular Dynamics Simulations

This research will provide new methods to determine the structures and compositions of heterogeneous catalysts as they exist in reaction conditions. The new tools will make it possible to understand how extremely small material structures—ones in a size range spanning from single atoms to larger polyatomic clusters—make it possible to carry out highly selective chemical transformations required to synthesize useful materials as well as for efficient energy production and utilization. The tools will make it possible to see how the atomic structures of catalytic materials evolve over time under the conditions of their use, and in this way provide understandings to guide the design of new energy-efficient catalysts. The work will make it possible to answer long standing questions that include the following. How does catalyst structure help guide molecules along specific reaction pathways? What are the important intermediates involved in these transformations? How do the structures and reactivity of catalytic materials, as well as the chemistry of their interfaces, vary with reaction conditions? Is it possible to develop new catalytic materials using new knowledge of the active structures and reaction mechanisms? The work will provide heretofore unavailable experimental tools that can be applied in advanced programs of research addressing such questions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Preparation of asymmetric porous materials

As asymmetric porous film structure formed by depositing a porous material film on a flexible substrate, and applying an anisotropic stress to the porous media on the flexible substrate, where the anisotropic stress results from a stress such as an applied mechanical force, a thermal gradient, and an applied voltage, to form an asymmetric porous material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. Furthermore, the groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Life on the Edge: Microbes in Rock Varnish

The newly discovered high concentrations of manganese on Mars open up new possibilities for habitability on that planet [1, 2]. On Earth, there is a close association between Mn deposits and the presence of Mn- and Fe- oxidizing microbes [3-7]; as such, Mn is considered a principal biosignature for Mars [8]. The most common terrestrial Mn-rich surface material on Earth is desert varnish, a dark, shiny coating on rocks in arid locations. Microorganisms occupy various niches within rock varnish, and it is probable that the concentration of Mn in many rock varnishes is mediated by microbial activity [6, 9, 10]; however, the relationship between microbes and varnish remains a source of long-standing controversy [11]. Do microorganisms drive the formation of varnish, oxidizing Mn and Fe to gain energy, or do they instead utilize varnish as a habitat? This long-standing question has important implications for our understanding of how life on Earth has evolved to capture and harness energy from the physical environment. Additionally, it will greatly influence the search on Mars for the signatures of life that may be present in rock varnish cannot be definitively identified on Mars without first being identified on Earth. Here we explored the microbial species and processes involved in the habitation of rock varnish and identified organic biosignatures that, in concert with trace element and mineralogy, could be used to distinguish the biogenic and abiogenic origins of terrestrial Mn-rich surfaces so that we may then apply the knowledge gained in this work to current Martian datasets from ChemCam. Each of these goals has important implications for our understanding of how life on Earth has evolved to capture and harness energy from the physical environment. A combinatorial experimental approach, utilizing microscopy, high-throughput DNA sequencing, culturing and physiological assays, were used to characterize the microbial communities inhabiting varnished rocks across the Western U.S. Microbial communities inhabiting varnish were shaped by both location (larger contribution) and rock type. However, we found that the unique varnish environment selects for a core group of radiation- and desiccation-tolerant microorganisms across landscapes and rock types. The bacteria comprising this core group include a who’s who of radiation resistant organisms including Rubrobacter spp., Deinococcus spp., Hymenobaceter spp., and Chroococcidiopsis spp. Among the fungi, potential lichen-forming members of the Lecanoromycetes and rock-inhabiting Dothideomycetes were common. Importantly, we found that two of the survival mechanisms employed by these microorganisms are likely key drivers of Mn oxide formation and the biogenesis of varnish. First, we showed that the key cyanobacteria inhabiting the varnish accumulate copious amounts of low molecular weight Mn2+-conjugates, that are critical for their ability to withstand the toxic effects of reactive oxygen species generated upon radiation exposure. Secondly, the microbial community inhabiting varnished rocks were found to be particularly adept at producing siderophores, which are high-affinity chelating compounds secreted by bacteria and fungi that serve to sequester Fe2+ (and Mn2+) from oxidizing environments for transport into cells as essential micronutrients for growth and survival. When tightly bound to organic ligands, Mn2+ is susceptible to oxidation to Mn3+-ligand under atmospheric oxygen and at slightly basic pH levels. Further, Mn3+-ligand can be oxidized to MnO2 (manganese oxide) when it comes into contact with reactive oxygen species (e.g., superoxide radical) formed during the photolysis of water during periods of high UV exposure. The data points towards a scenario whereby cyanobacterial biofilms, and associated radiation resistant, heterotrophic bacteria, comprise the central hub of the varnish community. These communities depend on light and water to slowly establish on the rock surface where they accumulate and concentrate Mn for survival. When the biofilms dry out, mineral rinds rich in Mn oxides form which serve as a habitat for the successive rounds of microbial growth and Mn oxide formation. Copious production and excretion of siderophores, which are extremely stable in the environment, coats the rock surface with a longlived catalyst for Mn oxidation. By discovering these two completely novel mechanisms for biogenic varnish formation, we have gained the underlying knowledge necessary to develop a suite of organic and inorganic biosignatures to distinguish biogenic and abiogenic origins of Mnrich surfaces on Earth or Mars.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Datasets for DOE 2023 Communities LEAP

This data is aligned to eligibility criteria outlined in the United States Department of Energy (DOE) 2023 Communities LEAP (Local Energy Action Program). Please visit the LEAP website (https://www.energy.gov/communitiesLEAP/communities-leap) to learn more about LEAP and gain additional contextual information for how these data may be used. The data provided approximates how the eligibility criteria apply at the census tract level across the United States. This EDX submission provides access to information pertaining to each of the four eligibility criteria outlined (average energy burden, percent low income, communities with a historic economic dependence on fossil fuel industrial facilities, and disadvantaged communities) for all census tracts within the 50 U.S. States, the District of Columbia (D.C.), and Puerto Rico. This information can be access in a detailed excel spreadsheet or through the linked interactive web application (https://arcgis.netl.doe.gov/portal/apps/experiencebuilder/experience/?id=2a77f443d72b4a4d82474b3ffe33b8cd). Please note that while these data are provided at the census tract level, census tracts do not necessarily have the same physical boundaries as a community but were used as they provide the closest proxy based on publicly available information collected using an empirically robust method. U.S. territories are not listed but are eligible to apply to Communities LEAP. As stated in the Opportunity Announcement, applying communities should describe how they meet the eligibility criteria in their application even if these data do not specifically show that they are eligible.

2023↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗