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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation. This approach circumvents the need to explicitly construct the full tensor. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical for identical-particle crystals. When applied to the calculation of internal energy and pressure-temperature curves for crystalline Cu and Ar at high (GPa) pressures, as well as the alpha-to-beta phase transition diagram of Sn, our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning, hierarchical interacting particle–neural network, and modified embedded atom method potentials,all within seconds of computation time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model to reduce the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as resting state functional connectivity derived from functional magnetic resonance imaging, into MVAR model estimation using a weighted group least absolute shrinkage and selection operator (LASSO) regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al (Neuroimage 254:119057, 2022) while resulting in models that are both more parsimonious and more accurate. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from intracranial electroencephalography (iEEG) data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach allows accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
DOE OSTI2016

The Amazon Dense GNSS Meteorological Network: A New Approach for Examining Water Vapor and Deep Convection Interactions in the Tropics

The Amazon Dense Global Navigational Satellite System (GNSS) Meteorological Network ((ADGMN) provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The ADGMN campaign consisted of two experiments: a 6-week campaign in and around Belem, which coincided with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) and a 1-yr campaign in and around Manaus. The Belem network was composed of 15 GNSS/meteorological stations that provided high-frequency (5 min) PWV data as well as surface meteorological variables For the 6-week duration of the Belem experiment, days were categorized as convective (22 days) or nonconvective (19 days) based solely on a minimum cloud-top temperature of 240 K or below over the central portion of the network and a report of precipitation at at least one site during the afternoon or evening. Here, the Manaus network commenced in April 2011 with 12 GNSS meteorological stations. Local circulations in Manaus driven by anthropogenic deforestation have, in particular, received attention.

58 GEOSCIENCES↗
DOE OSTI2025

Coupled cluster and dislocation dynamics modeling of microstructure evolution in irradiated materials

We develop here a coupled cluster and dislocation dynamics framework to study the microstructure evolution of irradiated materials. The framework not only accounts for the three dimensional diffusion of radiation-generated clusters, but also their interaction with dislocation networks and the resultant climb motion of discrete dislocations within finite crystals. The framework is solved with a superposition solution scheme, and is applied to investigate the evolution of the irradiation-induced dislocation loops in zirconium (Zr), considering the effects of various bias factors including the diffusion anisotropy difference (DAD) of interstitials and interstitial clusters, the dislocation bias of defects to discrete dislocation segments, and the production bias of defects from the radiation cascade. We find that the DAD is the most critical factor influencing the kinetics of the loop evolution in Zr, while the recombination/interaction of mobile defects can induce a strong spatial dependence of the loop evolution together with the DAD. Here, the method is also adopted to study the evolution of interstitial $\langle$a$\rangle$ and vacancy $\langle$c$\rangle$ dislocation loop ensembles consistent with the microstructure observed during irradiation-induced growth of Zr. Our findings not only reveal the spatial dependence of the size and ellipticity of the dislocation loops, but also suggest a limit on the anisotropy factor of interstitials to reproduce the co-growth of $\langle$a$\rangle$ and $\langle$c$\rangle$ loops in zirconium, in good agreement with experimental observations and other simulation results.

Bias factors↗
DOE OSTI2025

Supramolecular Support of Cuprophilic Network Bonding in 2-D Copper n -Alkanethiolates

Here, the development of heterogeneous materials, catalysts, and semiconductors is often reliant on precise control of self-assembly and crystal packing. Many new materials are initially synthesized as microcrystalline powders, making them incompatible with typical methods of structure determination, such as single-crystal X-ray diffraction. This resultant lack of structural information has made thorough investigation into the effect of metal substitution on crystal structure in metal-organic chalcogenolates (MOChas) challenging. Here, we use small molecule serial femtosecond crystallography (smSFX) to present the structures of four copper n-alkanethiolates: CuSC4, CuSC5, CuSC6, and CuSC7. Divergent patterns of alkyl chain packing are identified from microcrystalline powders via smSFX. An odd-even effect in crystal packing has been identified and attributed to different orientations of symmetry elements in the even- and odd-numbered chains. This results in minute changes in the azimuthal organization of the even-numbered chains and the network of cuprophilic interactions. Additionally, we present a synthesis of crystalline gold n-alkanethiolates to provide the first comparison between three d 10 coinage metals (Cu, Ag, and Au) and their resultant n-alkanethiolates.

Willson, Maggie C. [Univ. of Connecticut, Storrs, ↗
DOE OSTI2021

Controlling Amphiphilic Polymer Folding beyond the Primary Structure with Protein-Mimetic Di(Phenylalanine)

While methods for polymer synthesis have proliferated, their functionality pales in comparison to natural biopolymers–strategies are limited for building the intricate network of noncovalent interactions necessary to elicit complex, protein-like functions. Using a bioinspired di(phenylalanine) acrylamide (FF) monomer, we explored the impact of various noncovalent interactions in generating ordered assembled structures. Amphiphilic copolymers were synthesized that exhibit β-sheet-like local structure upon collapsing into single-chain assemblies in aqueous environments. Here, the systematic analysis of a series of amphiphilic copolymers illustrated that the global collapse is primarily driven by hydrophobic forces. Hydrogen-bonding and aromatic interactions stabilize local structure, as β-sheet-like interactions were identified via circular dichroism and thioflavin T fluorescence. Similar analysis of phenylalanine (F) and alanine-phenylalanine acrylamide (AF) copolymers found that distancing the aromatic residue from the polymer backbone is sufficient to induce β-sheet-like local structure akin to the FF copolymers; however, the interactions between AF subunits are less stable than those formed by FF. Further, hydrogen-bond donating hydrophilic monomers disrupt internal structure formed by FF within collapsed assemblies. Collectively, these results illuminate design principles for the facile incorporation of multiple facets of protein-mimetic, higher-order structure within folded synthetic polymers.

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

Hydrogen bonding structure of confined water templated by a metal-organic framework with open metal sites

Abstract Water in confinement exhibits properties significantly different from bulk water due to frustration in the hydrogen-bond network induced by interactions with the substrate. Here, we combine infrared spectroscopy and many-body molecular dynamics simulations to probe the structure and dynamics of confined water as a function of relative humidity within a metal-organic framework containing cylindrical pores lined with ordered cobalt open coordination sites. Building upon the agreement between experimental and theoretical spectra, we demonstrate that water at low relative humidity binds initially to open metal sites and subsequently forms disconnected one-dimensional chains of hydrogen-bonded water molecules bridging between cobalt atoms. With increasing relative humidity, these water chains nucleate pore filling, and water molecules occupy the entire pore interior before the relative humidity reaches 30%. Systematic analysis of rotational and translational dynamics indicates heterogeneity in this pore-confined water, with water molecules displaying variable mobility as a function of distance from the interface.

Science & Technology - Other Topics↗
DOE OSTI2012

Metaproteomics of a gutless marine worm and its symbiotic microbial community reveal unusual pathways for carbon and energy use

Low nutrient and energy availability has led to the evolution of numerous strategies for overcoming these limitations, of which symbiotic associations represent a key mechanism. Particularly striking are the associations between chemosynthetic bacteria and marine animals that thrive in nutrient-poor environments such as the deep-sea because the symbionts allow their hosts to grow on inorganic energy and carbon sources such as sulfide and CO2. Remarkably little is known about the physiological strategies that enable chemosynthetic symbioses to colonize oligotrophic environments. In this study, we used metaproteomics and metabolomics to investigate the intricate network of metabolic interactions in the chemosynthetic association between Olavius algarvensis, a gutless marine worm, and its bacterial symbionts. We propose novel pathways for coping with energy and nutrient limitation, some of which may be widespread in both free-living and symbiotic bacteria. These include (i) a pathway for symbiont assimilation of the host waste products acetate, propionate, succinate and malate, (ii) the potential use of carbon monoxide as an energy source, a substrate previously not known to play a role in marine invertebrate symbioses, (iii) the potential use of hydrogen as an energy source, (iv) the strong expression of high affinity uptake transporters, and (v) novel energy efficient steps in CO2 fixation and sulfate reduction. The high expression of proteins involved in pathways for energy and carbon uptake and conservation in the O. algarvensis symbiosis indicates that the oligotrophic nature of its environment exerted a strong selective pressure in shaping these associations.

08 HYDROGEN↗
DOE OSTI2020

Visualization of the HIV-1 Env glycan shield across scales

The dense array of N-linked glycans on the HIV-1 envelope glycoprotein (Env), known as the “glycan shield,” is a key determinant of immunogenicity, yet intrinsic heterogeneity confounds typical structure–function analysis. Here, we present an integrated approach of single-particle electron cryomicroscopy (cryo-EM), computational modeling, and site-specific mass spectrometry (MS) to probe glycan shield structure and behavior at multiple levels. We found that dynamics lead to an extensive network of interglycan interactions that drive the formation of higher-order structure within the glycan shield. This structure defines diffuse boundaries between buried and exposed protein surface and creates a mapping of potentially immunogenic sites on Env. Analysis of Env expressed in different cell lines revealed how cryo-EM can detect subtle changes in glycan occupancy, composition, and dynamics that impact glycan shield structure and epitope accessibility. Importantly, this identified unforeseen changes in the glycan shield of Env obtained from expression in the same cell line used for vaccine production. Finally, by capturing the enzymatic deglycosylation of Env in a time-resolved manner, we found that highly connected glycan clusters are resistant to digestion and help stabilize the prefusion trimer, suggesting the glycan shield may function beyond immune evasion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Long-range structural defects by pathogenic mutations in most severe glucose-6-phosphate dehydrogenase deficiency

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common blood disorder, presenting multiple symptoms, including hemolytic anemia. It affects 400 million people worldwide, with more than 160 single mutations reported in G6PD. The most severe mutations (about 70) are classified as class I, leading to more than 90% loss of activity of the wild-type G6PD. The crystal structure of G6PD reveals these mutations are located away from the active site, concentrating around the noncatalytic NADP + -binding site and the dimer interface. However, the molecular mechanisms of class I mutant dysfunction have remained elusive, hindering the development of efficient therapies. To resolve this, we performed integral structural characterization of five G6PD mutants, including four class I mutants, associated with the noncatalytic NADP + and dimerization, using crystallography, small-angle X-ray scattering (SAXS), cryogenic electron microscopy (cryo-EM), and biophysical analyses. Comparisons with the structure and properties of the wild-type enzyme, together with molecular dynamics simulations, bring forward a universal mechanism for this severe G6PD deficiency due to the class I mutations. We highlight the role of the noncatalytic NADP + -binding site that is crucial for stabilization and ordering two β-strands in the dimer interface, which together communicate these distant structural aberrations to the active site through a network of additional interactions. This understanding elucidates potential paths for drug development targeting G6PD deficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The Multicenter Aerobic Iron Respiratory Chain of Acidithiobacillus ferrooxidans Functions as an Ensemble with a Single Macroscopic Rate Constant

Electron transfer reactions among three prominent colored proteins in intact cells of Acidithiobacillus ferrooxidans were monitored using an integrating cavity absorption meter that permitted the acquisition of accurate absorbance data in suspensions of cells that scattered light. The concentrations of proteins in the periplasmic space were estimated to be 350 and 25 mg/ml for rusticyanin and cytochrome c, respectively; cytochrome a was present as one molecule for every 91 nm2 in the cytoplasmic membrane. All three proteins were rapidly reduced to the same relative extent when suspensions of live bacteria were mixed with different concentrations of ferrous ions at pH 1.5. The subsequent molecular oxygen-dependent oxidation of the multicenter respiratory chain occurred with a single macroscopic rate constant, regardless of the proteins' in vitro redox potentials or their putative positions in the aerobic iron respiratory chain. The crowded electron transport proteins in the periplasm of the organism constituted an electron conductive medium where the network of protein interactions functioned in a concerted fashion as a single ensemble with a standard reduction potential of 650 mV. The appearance of product ferric ions was correlated with the reduction levels of the periplasmic electron transfer proteins; the limiting first-order catalytic rate constant for aerobic respiration on iron was 7,400 s -1 . The ability to conduct direct spectrophotometric studies under noninvasive physiological conditions represents a new and powerful approach to examine the extent and rates of biological events in situ without disrupting the complexity of the live cellular environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Domain alternation and active site remodeling are conserved structural features of ubiquitin E1

E1 enzymes for ubiquitin (Ub) and Ub-like modifiers (Ubls) harbor two catalytic activities that are required for Ub/Ubl activation: adenylation and thioester bond formation. Structural studies of the E1 for the Ubl small ubiquitin-like modifier (SUMO) revealed a single active site that is transformed by a conformational switch that toggles its competency for catalysis of these two distinct chemical reactions. Although the mechanisms of adenylation and thioester bond formation revealed by SUMO E1 structures are thought to be conserved in Ub E1, there is currently a lack of structural data supporting this hypothesis. Here, we present a structure of Schizosaccharomyces pombe Uba1 in which the second catalytic cysteine half-domain (SCCH domain) harboring the catalytic cysteine has undergone a 106° rotation that results in a completely different network of intramolecular interactions between the SCCH and adenylation domains and translocation of the catalytic cysteine 12 Å closer to the Ub C terminus compared with previous Uba1 structures. SCCH domain alternation is accompanied by conformational changes within the Uba1 adenylation domains that effectively disassemble the adenylation active site. Importantly, the structural and biochemical data suggest that domain alternation and remodeling of the adenylation active site are interconnected and are intrinsic structural features of Uba1 and that the overall structural basis for adenylation and thioester bond formation exhibited by SUMO E1 is indeed conserved in Ub E1. Finally, the mechanistic insights provided by the novel conformational snapshot of Uba1 presented in this study may guide efforts to develop small molecule inhibitors of this critically important enzyme that is an active target for anticancer therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Defining the Synthetic Biology Supply Chain

In this article, a team of experts in synthetic biology, data analytics, and national security describe the overall supply chain surrounding synthetic biology. The team analyzes selected interactions within that network to better understand the risks raised by synthetic biology and identifies opportunities for risk mitigation. To introduce the concept, the article will briefly describe how an understanding of supply chains has been important in promoting nuclear nonproliferation objectives. The article concludes by assessing the structure and networks identified in the supply chains to reveal potential opportunities for future biodefense research and development; options for additional information exchange; and means to interdict, detect, or deter suspicious activity.

nuclear supply chain↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗