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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

The Hypoxic Regulator of Sterol Synthesis Nro1 Is a Nuclear Import Adaptor

Fission yeast protein Sre1, the homolog of the mammalian sterol regulatory element-binding protein (SREBP), is a hypoxic transcription factor required for sterol homeostasis and low-oxygen growth. Nro1 regulates the stability of the N-terminal transcription factor domain of Sre1 (Sre1N) by inhibiting the action of the prolyl 4-hydroxylase-like Ofd1 in an oxygen-dependent manner. The crystal structure of Nro1 determined at 2.2 {angstrom} resolution shows an all-{alpha}-helical fold that can be divided into two domains: a small N-terminal domain, and a larger C-terminal HEAT-repeat domain. Follow-up studies showed that Nro1 defines a new class of nuclear import adaptor that functions both in Ofd1 nuclear localization and in the oxygen-dependent inhibition of Ofd1 to control the hypoxic response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.

Dai, Heng↗
DOE OSTI2019

Relative Importance of Vital Rates to Population Dynamics of Wood Ducks

The wood duck (Aix sponsa) is a common and important cavity-nesting duck in North America; however, we know very little about how changes in vital rates influence population growth rate (λ). We used estimates of fertility and survival of female wood ducks from our nest-box studies in South Carolina, Alabama, and Georgia, USA, to create a stage-based matrix population model. We conducted perturbation analyses and ranked elasticity values to examine the relative importance of 17 component vital rates to λ. Female survival is influenced by nest success, so we recognized this female heterogeneity in our analyses. Four vital rates showed the greatest importance to λ. Analytic elasticities were greatest for breeding season and nonbreeding season survival of females that nested successfully, followed by nest success and female recruitment to the breeding population. Differences in female quality were important to λ. Next, we used process variation of vital rates and conducted life-stage simulation analyses (LSA) followed by variance decomposition to determine the amount of variation in λ explained by each vital rate. Female recruitment to the breeding population explained 57.7% of the variation in λ followed by nest success (11.4%), and breeding and nonbreeding season survival of females that nested successfully (9.3% and 9.4%, respectively). Together these 4 vital rates explained 88% of the variation in λ. Mean asymptotic population growth rate (λ = 0.80 ± 0.08 [SD]) from LSA revealed a declining population. Recruitment of females hatched from nest boxes was insufficient to sustain the nest-box population. Furthermore, including yearling (SY) females that were produced outside of nest boxes (i.e., immigrants) increased recruitment rates 1.5 to 2 times more than when only SY females recruited from nest boxes were included. Future research that examines how emigration and immigration interact with survival and reproduction to influence local population dynamics of wood ducks will be important for identifying the value of nest-box programs to wood duck conservation and management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Iterative Importance Sampling Algorithms for Parameter Estimation

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicability of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation leverages massively parallel computers, and we present strategies to initialize the iterations using “coarse” MCMC runs or Gaussian mixture models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Remembering the Important Things: Semantic Importance in Stream Reasoning

Reasoning and querying over data streams rely on the abil- ity to deliver a sequence of stream snapshots to the processing algo- rithms. These snapshots are typically provided using windows as views into streams and associated window management strategies. Generally, the goal of any window management strategy is to preserve the most im- portant data in the current window and preferentially evict the rest, so that the retained data can continue to be exploited. A simple timestamp- based strategy is rst-in-rst-out (FIFO), in which items are replaced in strict order of arrival. All timestamp-based strategies implicitly assume that a temporal ordering reliably re ects importance to the processing task at hand, and thus that window management using timestamps will maximize the ability of the processing algorithms to deliver accurate interpretations of the stream. In this work, we explore a general no- tion of semantic importance that can be used for window management for streams of RDF data using semantically-aware processing algorithms like deduction or semantic query. Semantic importance exploits the infor- mation carried in RDF and surrounding ontologies for ranking window data in terms of its likely contribution to the processing algorithms. We explore the general semantic categories of query contribution, prove- nance, and trustworthiness, as well as the contribution of domain-specic ontologies. We describe how these categories behave using several con- crete examples. Finally, we consider how a stream window management strategy based on semantic importance could improve overall processing performance, especially as available window sizes decrease.

RDF↗
DOE OSTI2015

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Important Factors for the Reliable and Reproducible Preparation of Non-aqueous Electrolyte Solutions for Lithium Batteries

Electrolyte solutions play a crucial role in rechargeable lithium batteries. Driven by an ever-increasing demand for better batteries, more and more stringent performance requirements are being asked of electrolytes. This demand to achieve electrolytes beyond the state-of-the-art has driven an increasing number of publications over the past decade. Nevertheless, a detailed description of electrolyte preparation is frequently missing when reporting new electrolytes for batteries, creating a barrier for others to reproduce important findings. In addition, false or misleading information in a paper may result from unintentional inaccuracies during the electrolyte preparation process. This article highlights important factors for the reliable and reproducible preparation of non-aqueous electrolyte solutions for lithium batteries, with the aim of encouraging researchers to follow more rigorous reporting standards.

Cao, Xia↗
DOE OSTI2016

Importance of hydrophobic traps for proton diffusion in lyotropic liquid crystals

The diffusion of protons in self-assembled systems is potentially important for the design of efficient proton exchange membranes. In this work, we study proton dynamics in a low-water content, lamellar phase of a sodium-carboxylate gemini surfactant/water system using computer simulations. The hopping of protons via the Grotthuss mechanism is explicitly allowed through the multi-state empirical valence bond method. We find that the hydronium ion is trapped on the hydrophobic side of the surfactant-water interface, and proton diffusion then proceeds by hopping between surface sites. The importance of hydrophobic traps is surprising because one would expect the hydronium ions to be trapped at the charged headgroups. The physics illustrated in this system should be relevant to the proton dynamics in other amphiphilic membrane systems, whenever there exist exposed hydrophobic surface regions.

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

Sodium Peroxide Dihydrate or Sodium Superoxide: The Importance of the Cell Configuration for Sodium–Oxygen Batteries

In sodium–oxygen (Na–O 2 ) batteries, multiple discharge products have been observed by different research groups. Given the fact that different materials, gas supplies, and cell configurations are used by different groups, it is a great challenge to draw a clear conclusion on the formation of the different products. Here, two different cell setups are used to investigate the cell chemistries of Na–O 2 batteries. With the same materials and gas supplies, a peroxide‐based product is observed in a glass chamber cell and a superoxide‐based product is observed in a stainless‐steel cell. Ex situ high‐energy X‐ray diffraction (HEXRD) and Raman spectroscopy are performed to investigate the structure and composition of the product. In addition, in situ XRD is used to investigate the structure evolution of the peroxide‐based product. The findings highlight the importance of the cell design and emphasize the critical environment of the formation of the discharge products of Na–O 2 batteries.

Bi, Xuanxuan↗
DOE OSTI2021

INBOUND APPOINTEE PUBLICATION Importance of Substrate-Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach of positively-charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively-charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle-fiber and particle-substrate interactions. We find an inverse correlation between particle size and attachment density to the protein nanofibers, attributed to the balance between size-dependent electrostatic particle-fiber attraction and particle-substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically-observable particle assemblies.

Guye, Kathryn N.↗
DOE OSTI2018

Structural and molecular dynamics studies of a C1-oxidizing lytic polysaccharide monooxygenase from Heterobasidion irregulare reveal amino acids important for substrate recognition

Lytic polysaccharide monooxygenases (LPMOs) are a group of recently discovered enzymes that play important roles in the decomposition of recalcitrant polysaccharides. Here, we report the biochemical, structural, and computational characterization of an LPMO from the white-rot fungus Heterobasidion irregulare (HiLPMO9B). This enzyme oxidizes cellulose at the C1 carbon of glycosidic linkages. The crystal structure of HiLPMO9B was determined at 2.1 A resolution using X-ray crystallography. Unlike the majority of the currently available C1-specific LPMO structures, the HiLPMO9B structure contains an extended L2 loop, connecting ..beta..-strands ..beta..2 and ..beta..3 of the ..beta..-sandwich structure. Molecular dynamics (MD) simulations suggest roles for both aromatic and acidic residues in the substrate binding of HiLPMO9B, with the main contribution from the residues located on the extended region of the L2 loop (Tyr20) and the LC loop (Asp205, Tyr207, and Glu210). Asp205 and Glu210 were found to be involved in the hydrogen bonding with the hydroxyl group of the C6 carbon of glucose moieties directly or via a water molecule. Two different binding orientations were observed over the course of the MD simulations. In each orientation, the active-site copper of this LPMO preferentially skewed toward the pyranose C1 of the glycosidic linkage over the targeted glycosidic bond. This study provides additional insight into cellulose binding by C1-specific LPMOs, giving a molecular-level picture of active site substrate interactions.

09 BIOMASS FUELS↗
DOE OSTI2013

Surface characterization of nanomaterials and nanoparticles: Important needs and challenging opportunities

This review examines characterization challenges inherently associated with understanding nanomaterials and the roles surface and interface characterization methods can play in meeting some of the challenges. In parts of the research community, there is growing recognition that studies and published reports on the properties and behaviors of nanomaterials often have reported inadequate or incomplete characterization. As a consequence, the true value of the data in these reports is, at best, uncertain. With the increasing importance of nanomaterials in fundamental research and technological applications, it is desirable that researchers from the wide variety of disciplines involved recognize the nature of these often unexpected challenges associated with reproducible synthesis and characterization of nanomaterials, including the difficulties of maintaining desired materials properties during handling and processing due to their dynamic nature. It is equally valuable for researchers to understand how characterization approaches (surface and otherwise) can help to minimize synthesis surprises and to determine how (and how quickly) materials and properties change in different environments. Appropriate application of traditional surface sensitive analysis methods (including x-ray photoelectron and Auger electron spectroscopies, scanning probe microscopy, and secondary ion mass spectroscopy) can provide information that helps address several of the analysis needs. In many circumstances, extensions of traditional data analysis can provide considerably more information than normally obtained from the data collected. Less common or evolving methods with surface selectivity (e.g., some variations of nuclear magnetic resonance, sum frequency generation, and low and medium energy ion scattering) can provide information about surfaces or interfaces in working environments (operando or in situ) or information not provided by more traditional methods. Although these methods may require instrumentation or expertise not generally available, they can be particularly useful in addressing specific questions, and examples of their use in nanomaterial research are presented.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Online data analysis and reduction: An important co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Data Analysis↗
DOE OSTI2015

Many-body localization in disorder-free systems: The importance of finite-size constraints

Recently it has been suggested that many-body localization (MBL) can occur in translation-invariant systems, and candidate 1D models have been proposed. We find that such models, in contrast to MBL systems with quenched disorder, typically exhibit much more severe finite-size effects due to the presence of two or more vastly different energy scales. In a finite system, this can artificially split the density of states (DOS) into bands separated by large gaps. We argue for such models to faithfully represent the thermodynamic limit behavior, the ratio of relevant coupling must exceed a certain system-size depedent cutoff, chosen such that various bands in the DOS overlap one another. Setting the parameters this way to minimize finite-size effects, we study several translation-invariant MBL candidate models using exact diagonalization. Based on diagnostics including entanglement and local observables, we observe thermal (ergodic), rather than MBL-like behavior. Our results suggest that MBL in translation-invariant systems with two or more very different energy scales is less robust than perturbative arguments suggest, possibly pointing to the importance of non-perturbative effects which induce delocalization in the thermodynamic limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Understanding the Importance of Labile Fe(III) during Fe(II)-Catalyzed Transformation of Metastable Iron Oxyhydroxides

Transformation of metastable Fe(III) oxyhydroxides is a prominent process in natural environments and can be significantly accelerated by the coexisting aqueous Fe(II) (Fe(II) aq ). Recent evidence points to the solution mass transfer of labile Fe(III) (Fe(III) labile ) as the primary intermediate species of general importance. However, a mechanistic aspect that remains unclear is the dependence of phase outcomes on the identity of the metastable Fe(III) oxyhydroxide precursor. Here, we compared the coupled evolution of Fe(II) species, solid phases, and Fe(III) labile throughout the Fe(II)-catalyzed transformation of lepidocrocite (Lp) versus ferrihydrite (Fh) at equal Fe(III) mass loadings with 0.2–1.0 mM Fe(II) aq at pH = 7.0. Similar to Fh, the conversion of Lp to product phases occurs by a dissolution–reprecipitation mechanism mediated by Fe(III) labile that seeds the nucleation of products. Though for Fh we observed a transformation to goethite (Gt), accompanied by the transient emergence and decline of Lp, for initial Lp we observed magnetite (Mt) as the main product. Further, a linear correlation between the formation rate of Mt and the effective supersaturation in terms of Fe(III) labile concentration shows that Fe(II)-induced transformation of Lp into Mt is governed by the classical nucleation theory. When Lp is replaced by equimolar Gt, Mt formation is suppressed by opening a lower barrier pathway to Gt by heterogeneous nucleation and growth on the added Gt seeds. The collective findings add to the mechanistic understanding of factors governing phase selections that impact iron bioavailability, system redox potential, and the fate and transport of coupled elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗