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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Antiphase-Boundary-Engineered Domain Switching in a (110)-Oriented BiFeO 3 Film

A ferroelectric domain wall memory device exploits the presence (or absence) of domain walls as the basis for information storage. Although structural defects strongly influence ferroelectric domain wall nucleation and growth, this correlation is far from being fully understood. For example, a single defect can play opposing roles: it can act as a nucleation site to initiate domain switching or, conversely, serve as a pinning center inhibiting domain wall motion. This is particularly the case for antiphase boundaries (APBs), whose influence on switching remains relatively unexplored despite being ubiquitous in oxide perovskite ferroelectrics. Here, we use aberration-corrected scanning transmission microscopy and in situ transmission electron microscopy under an applied bias to extensively characterize the influence of an APB on the switching behavior in a (110)-oriented BiFeO 3 thin film. We demonstrate that deterministic nucleation occurs in the proximity of a vertically oriented APB, following which domain growth occurs laterally in a symmetric fashion. Atomic-scale structural analysis reveals that the shear operation vector for the APB is 1/2[110]; this half-unit-cell shift extends over a few nanometers, implying that the APB plane is inclined toward the imaging direction. The symmetric switching observation is attributed to local inhomogeneous lattice strain and a reduction of polarization caused by the APB. These results demonstrate that an extended structural defect can be used as an active switching element rather than a stumbling block in reprogrammable domain-wall-based devices. Ultimately, this will expand the capabilities of ferroelectric thin films for future electronic devices based on ferroelectric domain walls.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Directing Matter: Toward Atomic-Scale 3D Nanofabrication

Here we report that enabling memristive, neuromorphic, and quantum based computing as well as efficient mainstream energy storage and conversion technologies requires next generation of materials customized at the atomic scale. This requires full control of atomic arrangement and bonding in three dimensions. The last two decades witnessed substantial industrial, academic, and government research efforts directed towards this goal through various lithographies and scanning probe based methods. These technologies emphasize 2D surface structures, with some limited 3D capability. Recently, a range of focused electron and ion based methods have demonstrated compelling alternative pathways to achieving atomically precise manufacturing of 3D structures in solids, liquids, and at interfaces. Electron and ion microscopies offer a platform that can simultaneously observe dynamic and static structures at the nano and atomic scales, and also induce structural rearrangements and chemical transformation. The addition of predictive modeling or rapid image analytics and feedback enables guiding these in a controlled manner. Here, we review the recent results that used focused electron and ion beams to create free-standing nanoscale 3D structures, radiolysis and the fabrication potential with liquid precursors, epitaxial crystallization of amorphous oxides with atomic layer precision, as well as visualization and control of individual dopant motion within a 3D crystal lattice. These works lay the foundation for new approaches to directing nanoscale level architectures and offer a potential roadmap to full 3D atomic control in materials. Lastly, in this perspective we lay out the gaps that currently constrain the processing range of these platforms, reflect on indirect requirements, such as the integration of large scale data analysis with theory, and discuss future prospects of these technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Electrochemical Conversion of Biologically Produced Muconic Acid: Key Considerations for Scale-Up and Corresponding Technoeconomic Analysis

We present muconic acid, an unsaturated diacid that can be produced from cellulosic sugars and lignin monomers by fermentation, emerges as a promising intermediate for the sustainable manufacture of commodity polyamides and polyesters including Nylon-6,6 and polyethylene terephthalate (PET). Current conversion schemes consist in the biological production of cis,cis-muconic acid using metabolically engineered yeasts and bacteria, and the subsequent diversification to adipic acid, terephthalic acid, and their derivatives using chemical catalysts. In some instances, conventional precious metal catalysts can be advantageously replaced by base metal electrocatalysts. Here, we show the economic relevance of utilizing a hybrid biological–electrochemical conversion scheme to convert glucose to trans-3-hexenedioic acid (t3HDA), a monomer used for the synthesis of bioadvantaged Nylon-6,6. Potential roadblocks to biological and electrochemical integration in a single reactor, including electrocatalyst deactivation due to biogenic impurities and low faradaic efficiency inherent to side reactions in complex media, have been studied and addressed. In this study, t3HDA was produced with 94% yield and 100% faradaic efficiency. With consideration of the high t3HDA yield and faradaic efficiency, a technoeconomic analysis was developed on the basis of the current yield and titer achieved for muconic acid, the figures of merit defined for industrial electrochemical processes, and the separation of the desired product from the medium. On the basis of this analysis, t3HDA could be produced for approximately $2.00 kg –1 . The low cost for t3HDA is a primary factor of the electrochemical route being able to cascade biological catalysis and electrocatalysis in one pot without separation of the muconic acid intermediate from the fermentation broth.

3-Hexenedioic acid↗
DOE OSTI2014

Band Gap Engineering and Layer-by-Layer Band Gap Mapping of Selenium-doped Molybdenum Disulfide

Ternary two-dimensional dichalcogenide alloys exhibit compositionally modulated electronic structure and hence, control of dopant concentration within each layer of these layered compounds provides a powerful way to modify their properties. The challenge then becomes quantifying and locating the dopant atoms within each layer in order to better understand and fine-tune the desired properties. Here we report the synthesis of selenium substitutionally doped molybdenum disulfide atomic layers, with a broad range of selenium concentrations, resulting in band gap modulations of over 0.2 eV. Atomic scale chemical analysis using Z-contrast imaging provides direct maps of the dopant atom distribution in individual MoS2 layers and hence a measure of the local band gaps. Furthermore, in a bilayer structure, the dopant distribution of each layer is imaged independently. We demonstrate that each layer in the bilayer contains similar doping levels, randomly distributed, providing new insights into the growth mechanism and alloying behavior in two-dimensional dichalcogenide atomic layers. The results show that growth of uniform, ternary, two-dimensional dichalcogenide alloy films with tunable electronic properties is feasible.

Gong, Yongji↗
DOE OSTI2021

Regional Subsidence Analysis Through a Multi-Scale Modeling Framework Based on Breakage Mechanics

Although poroelastic models are often used to explain the delay between subsurface fluid depletion and ground subsidence, inelastic compaction involving permanent changes of rock microstructure may exacerbate hydro-mechanical coupling, thus influencing the interpretation of measurements and long-term forecasts. Here, a multi-scale modeling approach is discussed, which accounts for the inherent connection between rock microstructure, hydraulic conductivity, and pore compaction. A constitutive model built within the framework of breakage mechanics is proposed to link the hydraulic conductivity of granular rocks with inelastic deformations and changes in grading caused by injection-depletion cycles at stress levels far from yielding. The proposed model has been incorporated into large-scale simulation frameworks, thus enabling the spatiotemporal mapping of regional subsidence through a hybrid, semi-analytical approach. Numerical results based on this strategy show that the model allows isolating near-field and far-field effects into the computation of land subsidence and can generate forecasts for different modeling scenarios (e.g., elastic and inelastic compaction, constant permeability, and concurrent change of compressibility and permeability). In particular, examples of simulations for the case of the Groningen gas field are discussed, showing the model capabilities to use both field measurements and laboratory tests for the generation of reasonable subsidence maps, without expensive computational costs. Results indicate that ignoring coupled inelastic effects has major consequences on the predicted timescale of subsidence. Specifically, while all the model scenarios produced similar long-term ground settlements, those ignoring breakage-dependent permeability changes result in a variation of the temporal window of residual subsidence of the order of several decades.

58 GEOSCIENCES↗
DOE OSTI2023

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Structural diverseness of neurons between brain areas and between cases

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of schizophrenia and control cases. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that (1) neuron structures are significantly dissimilar between brain areas and that (2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases. The schizophrenia cases showed a thin and tortuous neuronal network compared with the controls, suggesting that the neuron structure is associated with the disorder. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluating the economic impact of water scarcity in a changing world

Abstract Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Increasing large wildfires over the western United States linked to diminishing sea ice in the Arctic

Abstract The compound nature of large wildfires in combination with complex physical and biophysical processes affecting variations in hydroclimate and fuel conditions makes it difficult to directly connect wildfire changes over fire-prone regions like the western United States (U.S.) with anthropogenic climate change. Here we show that increasing large wildfires during autumn over the western U.S. are fueled by more fire-favorable weather associated with declines in Arctic sea ice during preceding months on both interannual and interdecadal time scales. Our analysis (based on observations, climate model sensitivity experiments, and a multi-model ensemble of climate simulations) demonstrates and explains the Arctic-driven teleconnection through regional circulation changes with the poleward-shifted polar jet stream and enhanced fire-favorable surface weather conditions. The fire weather changes driven by declining Arctic sea ice during the past four decades are of similar magnitude to other leading modes of climate variability such as the El Niño-Southern Oscillation that also influence fire weather in the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. Further, the complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

QUBO formulations for training machine learning models

Abstract Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Excitingly, we observed possible sequestration of metabolites recently suggested to have immune modulating roles. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Model-guided analysis suggested that alterations in metabolism prioritized other activities necessary for pathogenesis instead, such as lipopolysaccharide biosynthesis.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

Evidence for interfacial geometric interactions at metal–support interfaces and their influence on the electroactivity and stability of Pt nanoparticles

Supported metals are widely used as industrial catalysts wherein the supports affect catalytic performances remarkably through either electronic and/or geometric interactions with the metals, and/or providing interfacial synergistic sites. Herein, we observed evidence for interfacial geometric interactions (IGIs) at Pt–C interfaces through a combination of atomic-scale structural analysis, in situ X-ray absorption fine structure, and electrochemical measurements for Pt/C model systems. The IGI has a long-range attribute and affects Pt surface atoms not only at interfacial perimeters but also adjacent to interfaces. The affected Pt surface atoms are proposed to contribute enhanced activity and stability for the oxygen reduction reaction through retarding the formation of strongly adsorbed oxygenated intermediates. Our work provides some necessary information for a better understanding of support effects and catalytically active surface sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Open-source framework for power system transmission and distribution dynamics co-simulation

The promise of the smart grid entails more interactions between the transmission and distribution networks, and there is an immediate need for tools to provide the comprehensive modelling and simulation required to integrate operations at both transmission and distribution levels. Existing electromagnetic transient simulators can perform simulations with integration of transmission and distribution systems, but the computational burden is high for large-scale system analysis. For transient stability analysis, currently there are only separate tools for simulating transient dynamics of the transmission and distribution systems. In this paper, we introduce an open source co-simulation framework “Framework for Network Co-Simulation” (FNCS), together with the decoupled simulation approach that links existing transmission and distribution dynamic simulators through FNCS. FNCS is a middleware interface and framework that manages the interaction and synchronization of the transmission and distribution simulators. Preliminary testing results show the validity and capability of the proposed open-source co-simulation framework and the decoupled co-simulation methodology.

Huang, Renke↗
DOE OSTI2015

Non-destructive assessment of the polarity of GaN nanowire ensembles using low-energy electron diffraction and x-ray photoelectron diffraction

We investigate GaN nanowire ensembles spontaneously formed in plasma-assisted molecular beam epitaxy by non-destructive low-energy electron diffraction (LEED) and x-ray photoelectron diffraction (XPD). We show that GaN nanowire ensembles prepared on AlN-buffered 6H-SiC(0001{sup ¯}) substrates with well-defined N polarity exhibit similar LEED intensity-voltage curves and angular distribution of photo-emitted electrons as N-polar free-standing GaN layers. Therefore, as in the case of GaN layers, LEED and XPD are found to be suitable techniques to assess the polarity of GaN nanowire ensembles on a macroscopic scale. The analysis of GaN nanowire ensembles prepared on bare Si(111) allows us to conclude that, on this non-polar substrate, the majority of nanowires is also N-polar.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Dynamics of dipole- and valence bound anions in iodide-adenine binary complexes: A time-resolved photoelectron imaging and quantum mechanical investigation

Dipole bound (DB) and valence bound (VB) anions of binary iodide-adenine complexes have been studied using one-color and time-resolved photoelectron imaging at excitation energies near the vertical detachment energy. The experiments are complemented by quantum chemical calculations. One-color spectra show evidence for two adenine tautomers, the canonical, biologically relevant A9 tautomer and the A3 tautomer. In the UV-pump/IR-probe time-resolved experiments, transient adenine anions can be formed by electron transfer from the iodide. These experiments show signals from both DB and VB states of adenine anions formed on femto- and picosecond time scales, respectively. Analysis of the spectra and comparison with calculations suggest that while both the A9 and A3 tautomers contribute to the DB signal, only the DB state of the A3 tautomer undergoes a transition to the VB anion. The VB anion of A9 is higher in energy than both the DB anion and the neutral, and the VB anion is therefore not accessible through the DB state. Experimental evidence of the metastable A9 VB anion is instead observed as a shape resonance in the one-color photoelectron spectra, as a result of UV absorption by A9 and subsequent electron transfer from iodide into the empty π-orbital. In contrast, the iodide-A3 complex constitutes an excellent example of how DB states can act as doorway state for VB anion formation when the VB state is energetically available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗