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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Precursor reaction pathway leading to BiFeO 3 formation: insights from text-mining and chemical reaction network analyses

BiFeO 3 (BFO) is a next-generation non-toxic multiferroic material with applications in sensors, memory devices, and spintronics, where its crystallinity and crystal structure directly influence its functional properties. Designing sol–gel syntheses that result in phase-pure BFO remains a challenge due to the complex interactions between metal complexes in the precursor solution. Here, we combine text-mined data and chemical reaction network (CRN) analysis to obtain novel insight into BFO sol–gel precursor chemistry. We perform text-mining analysis of 340 synthesis recipes with the emphasis on phase-pure BFO and identify trends in the use of precursor materials, including that nitrates are the preferred metal salts, 2-methoxyethanol (2 ME) is the dominant solvent, and adding citric acid as a chelating agent frequently leads to phase-pure BFO. Our CRN analysis reveals that the thermodynamically favored reaction mechanism between bismuth nitrate and 2ME interaction involves partial solvation followed by dimerization, contradicting assumptions in previous literature. We suggest that further oligomerization, facilitated by nitrite ion bridging, is critical for achieving the pure BFO phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Using Neural Networks to Identify Mixture Components in Hyperspectral Reflectance Data

Neural networks have been employed to identify materials of interest from hyperspectral data (generally imagery) based on their unique spectral signatures. This approach assumes that there is a single material that is standing out from the rest of the spectrum to be identified. However, pixels often contain more than one material, or a material of interest may itself be a mixture of multiple materials. Neural networks are only as good as the data used to train them, and it takes a great deal of work in the laboratory to identify, make, and measure all potential mixtures of interest. Thus, researchers often calculate synthetic spectra using algorithms with varying degrees of fidelity to the physics that govern the interactions between light and multiple materials. In this work, we have (1) adapted a neural network designed to identify mixture components from Raman spectroscopy to work with visible to near‐infrared reflectance data and (2) tested three common mixture algorithms to determine the most accurate and least computationally expensive method to build synthetic training datasets. With our initial test dataset, we have achieved accuracies of > 90% and found that the synthetic training dataset produced using the Hapke mixture model provides the best results.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Approximating accelerator impedances with resonator networks

It is common in the accelerator community to use the impedance of accelerator components to describe wake interactions in the frequency domain. However, it is often desirable to understand such wake interactions in the time domain in a general manner for excitations that are not necessarily Gaussian in nature. Here, the conventional method for doing this involves taking the inverse Fourier Transform of the component impedance, obtaining the Green's Function, and then convolving it with the desired excitation distribution. This method can prove numerically cumbersome, for a convolution integral must be evaluated for each individual point in time when the wake function is desired. An alternative to this method would be to compute the wake function analytically, which would sidestep the need for repetitive integration. Only a handful of cases, however, are simple enough for this method to be tenable. One of these cases is the case where the component in question is an RLC resonator, which has a closed-form analytical wake function solution. This means that a component which can be represented in terms of resonators can leverage this solution. As it happens, common network synthesis techniques may be used to map arbitrary impedance profiles to RLC resonator networks in a manner the accelerator community has yet to take advantage of. In this work, we will use Foster Canonical Resonator Networks and partial derivative descent optimization to develop a technique for synthesizing resonator networks that well approximate the impedances of real-world accelerator components. We will link this synthesis to the closed-form resonator wake function solution, giving rise to a powerful workflow that may be used to streamline beam dynamics simulations.

43 PARTICLE ACCELERATORS↗
NASA NTRS1995

A relational metric, its application to domain analysis, and an example analysis and model of a remote sensing domain

An objective and quantitative method has been developed for deriving models of complex and specialized spheres of activity (domains) from domain-generated verbal data. The method was developed for analysis of interview transcripts, incident reports, and other text documents whose original source is people who are knowledgeable about, and participate in, the domain in question. To test the method, it is applied here to a report describing a remote sensing project within the scope of the Earth Observing System (EOS). The method has the potential to improve the designs of domain-related computer systems and software by quickly providing developers with explicit and objective models of the domain in a form which is useful for design. Results of the analysis include a network model of the domain, and an object-oriented relational analysis report which describes the nodes and relationships in the network model. Other products include a database of relationships in the domain, and an interactive concordance. The analysis method utilizes a newly developed relational metric, a proximity-weighted frequency of co-occurrence. The metric is applied to relations between the most frequently occurring terms (words or multiword entities) in the domain text, and the terms found within the contexts of these terms. Contextual scope is selectable. Because of the discriminating power of the metric, data reduction from the association matrix to the network is simple. In addition to their value for design. the models produced by the method are also useful for understanding the domains themselves. They can, for example, be interpreted as models of presence in the domain.

Mcgreevy, Michael W.↗
DOE OSTI2023

Reactive Extrusion of Waste Plastics with Compatibilizer and Lightly Pyrolyzed Crumb Rubber for Asphalt Modification

Modifying asphalt is a potentially high-value application for reusing waste plastics because of the high-volume usage of asphalt in highway construction. However, simply blending hot plastics and asphalt encounters difficulties related to the poor solubility of polymers, which limits the formation of a swollen network with asphalt molecules. The polymer phases also tend to coalesce and separate from asphalt during high-temperature storage in static conditions. The present study developed an innovative process to stabilize waste plastics in asphalt and improve binder storage stability by using lightly pyrolyzed crumb rubber together with a chemical compatibilizer. Both polymers were extruded to produce a thermoplastic elastomer (TPE) for asphalt modification. The mechanical performance and chemical reactions of TPEs were characterized via tension test and Fourier transform infrared spectroscopy. The storage stability and rheological properties of modified binder blends were evaluated through laboratory asphalt stability test and dynamic shear rheometer test. Polymer phases and network structures were characterized through optical microscopy. It was found that the pyrolyzed and reactive extrusion process improved the rubber solubility and polymer interaction, and therefore the storage stability of modified binder blends. The co-existence of rigid plastic and soft rubbery regimes in an entangled network provided a promising pathway to improve the mechanical performance of asphalt binders in both high- and low-temperature domains.

Ma, Yuetan↗
DOE OSTI2020

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Characterization of the Bacterioferritin/Bacterioferritin Associated Ferredoxin Protein–Protein Interaction in Solution and Determination of Binding Energy Hot Spots

Mobilization of iron stored in the interior cavity of BfrB requires electron transfer from the [2Fe–2S] cluster in Bfd to the core iron in BfrB. A crystal structure of the Pseudomonas aeruginosa BfrB:Bfd complex revealed that BfrB can bind up to 12 Bfd molecules at 12 structurally identical binding sites, placing the [2Fe–2S] cluster of each Bfd immediately above a heme group in BfrB. We report here a study aimed at characterizing the strength of the P. aeruginosa BfrB:Bfd association using surface plasmon resonance and isothermal titration calorimetry as well as determining the binding energy hot spots at the protein–protein interaction interface. The results show that the 12 Bfd-binding sites on BfrB are equivalent and independent and that the protein–protein association at each of these sites is driven entropically and is characterized by a dissociation constant ( K d ) of approximately 3 μM. Determination of the binding energy hot spots was carried out by replacing certain residues that comprise the protein–protein interface with alanine and by evaluating the effect of the mutation on K d and on the efficiency of core iron mobilization from BfrB. The results identified hot spot residues in both proteins [L B 68 , E A 81 , and E A 85 in BfrB (superscript for residue number and subscript for chain) and Y 2 and L 5 in Bfd] that network at the interface to produce a highly complementary hot region for the interaction. The hot spot residues are conserved in the amino acid sequences of Bfr and Bfd proteins from a number of Gram-negative pathogens, indicating that the BfrB:Bfd interaction is of widespread significance in bacterial iron metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Structural basis for IL-1α recognition by a modified DNA aptamer that specifically inhibits IL-1α signaling

IL-1α is an essential cytokine that contributes to inflammatory responses and is implicated in various forms of pathogenesis and cancer. Here we report a naphthyl modified DNA aptamer that specifically binds IL-1α and inhibits its signaling pathway. By solving the crystal structure of the IL-1α/aptamer, we provide a high-resolution structure of this critical cytokine and we reveal its functional interaction interface with high-affinity ligands. The non-helical aptamer, which represents a highly compact nucleic acid structure, contains a wealth of new conformational features, including an unknown form of G-quadruplex. The IL-1α/aptamer interface is composed of unusual polar and hydrophobic elements, along with an elaborate hydrogen bonding network that is mediated by sodium ion. IL-1α uses the same interface to interact with both the aptamer and its cognate receptor IL-1RI, thereby suggesting a novel route to immunomodulatory therapeutics.

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

Layer size polydispersity in hydrated montmorillonite creates multiscale porosity networks

The aluminosilicate layers of the swelling clay mineral montmorillonite, and the saturated pores they delineate, control the mechanical properties and the transport of solutes in many natural and engineered environments. However, the structural basis of montmorillonite porosity remains poorly characterized due to the difficulty in visualizing hydrated samples in their native state. Here in this paper, we used cryogenic transmission electron microscopy (cryo-TEM) and cryo electron tomography (cryo-ET) to show that stacking defects within minimally altered, fully hydrated montmorillonite particles define multiscale porosity networks. Variations in layer lateral dimensions over tens to thousands of nanometers cause a range of topological and dynamic defects that generate pervasive curvature and introduce previously uncharacterized solute transport pathways. Observations of long-range rotational order between neighboring layers indicate that the layer-layer interactions that govern clay swelling involve three dimensional orienting forces that operate across nanoscale pores. These direct observations of the hierarchical structure of hydrated montmorillonite pore networks with nanoscale resolution reveal potentially general aspects of colloidal interactions in fluid-saturated clay minerals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Energy-efficient, Large-scale Molecular Dynamics Simulations via Hardware- and Algorithm-level Optimization

This work aims to develop a framework for energy-efficient computing that will enable molecular dynamics (MD) simulations of large-scale phenomena with atomic precision and simultaneously remove computational bottlenecks limiting the speed of MD simulations. We seek to implement such an approach through the development of surrogate models for the interatomic force calculation combined with the use of mixed numerical precision formats. For a model system of neutral atoms (only pairwise interactions), significant force calculation efficiency improvements were achieved, without detrimental effects on atomic structures or average energies, using single precision, by developing a surrogate model (deep neural network), and by quantizing this surrogate model. For a model system of charged atoms, the reciprocal-space calculation of electrostatic interactions was identified as the main bottleneck, and the development of a surrogate model should be pursued to achieve an estimated one-order-of-magnitude additional speedup.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Construction of single-crystalline supramolecular networks of perchlorinated hexa-peri-hexabenzocoronene on Au(111)

The self-assembly of the perchlorinated hexa-peri-hexabenzocoronene (PCHBC) molecules on Au(111) has been studied by a low temperature scanning tunneling microscopy (STM) combining with density functional theory based first principle calculations. Highly ordered supramolecular networks with single domains limited by the terraces are formed on Au(111) substrate. High resolution images of the PCHBC molecules, confirmed by first principle simulations, are obtained. It reveals the close-packed arrangement of the PCHBC molecules on Au(111). The calculated charge distribution of PCHBC molecules shows the existence of attractive halogen–halogen interaction between neighboring molecules. Compared with the disordered adsorption of hexa-peri-hexabenzocoronene on Au(111), we conclude that the formation of attractive ClCl interactions between neighbors is the key factor to form the highly ordered, close-packed networks. Due to the steric hindrance resulted from the peripheral chlorine atoms, the PCHBC molecule is contorted and forms the doubly concave conformation, which is different from the hexa-peri-hexabenzocoronene with a planar structure. By using this supramolecular network as a template, we deposited C{sub 60} molecules on it at room temperature with low coverage. The STM images taken at low temperature show that the C{sub 60} molecules are mono-dispersed on the networks and adsorb on top of the PCHBC molecules, forming a typical concave-convex host-guest system.

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

Self-assembly of a triply periodic continuous mesophase with Fddd symmetry in simple one-component liquids

Triply periodic continuous morphologies (networks) arising as a result of the microphase separation in block copolymer melts have so far never been observed self-assembled in systems of particles with spherically symmetric interaction. Here, we report a molecular dynamics simulation where two simple one-component liquids form upon cooling an equilibrium network with the Fddd space group symmetry. This complexity reduction in the liquid network formation in terms of the particle geometry and the number of components evidences the generic nature of this class of phase transition suggesting opportunities for producing these structures in a variety of new systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Abstract Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO 4 -like phase with both Cu 2 V 2 O 7 -like and CuV 2 O 6 -like phases, creating avenues for understanding complex photoelectrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Entanglement-fidelity limits of photonically networked atomic qubits from recoil and timing

The remote entanglement of two atomic quantum memories through photonic interactions is accompanied by atomic momentum recoil. When the interactions occur at different times, such as from the random emission over the lifetime of the atomic excited state, the difference in recoil timing can expose “which-path” information and ultimately lead to decoherence. Time-bin encoded photonic qubits can be particularly sensitive to asynchronous recoil timing. In this paper we study the limits of entanglement fidelity in atomic systems due to recoil and other timing imbalances and show how these effects can be suppressed or even eliminated through proper experimental design.

Quantum communication↗
DOE OSTI2019

Crystal structures of the naturally fused CS and cytochrome b 5 reductase ( b 5 R) domains of Ncb5or reveal an expanded CS fold, extensive CS– b 5 R interactions and productive binding of the NAD(P) + nicotinamide ring

Ncb5or (NADH-cytochrome b 5 oxidoreductase), a cytosolic ferric reductase implicated in diabetes and neurological diseases, comprises three distinct domains, cytochrome b 5 ( b 5 ) and cytochrome b 5 reductase ( b 5 R) domains separated by a CHORD–Sgt1 (CS) domain, and a novel 50-residue N-terminal region. Understanding how interdomain interactions in Ncb5or facilitate the shuttling of electrons from NAD(P)H to heme, and how the process compares with the microsomal b 5 (Cyb5A) and b 5 R (Cyb5R3) system, is of interest. A high-resolution structure of the b 5 domain (PDB entry 3lf5) has previously been reported, which exhibits substantial differences in comparison to Cyb5A. The structural characterization of a construct comprising the naturally fused CS and b 5 R domains with bound FAD and NAD + (PDB entry 6mv1) or NADP + (PDB entry 6mv2) is now reported. The structures reveal that the linker between the CS and b 5 R cores is more ordered than predicted, with much of it extending the β-sandwich motif of the CS domain. This limits the flexibility between the two domains, which recognize one another via a short β-sheet motif and a network of conserved side-chain hydrogen bonds, salt bridges and cation–Π interactions. Notable differences in FAD–protein interactions in Ncb5or and Cyb5R3 provide insight into the selectivity for docking of their respective b 5 redox partners. The structures also afford a structural explanation for the unusual ability of Ncb5or to utilize both NADH and NADPH, and represent the first examples of native, fully oxidized b 5 R family members in which the nicotinamide ring of NAD(P) + resides in the active site. Finally, the structures, together with sequence alignments, show that the b 5 R domain is more closely related to single-domain Cyb5R proteins from plants, fungi and some protists than to Cyb5R3 from animals.

Benson, David R.↗
DOE OSTI2021

Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation: Preprint

This paper presents a cyber-physical algorithm for grid interactive DER control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, we propose a cyber-physical grid-interactive DER control algorithm using the previous message strategy. The tracking and regulation capabilities of this proposed algorithm can be made immune to the asynchrony resulting from the communications network traffic. We carry out simulations to show possible numerical instabilities and sensitivities of the tracking and regulation capabilities on the proposed strategy. Our results exhibit that the uncertainties of the underlying communications infrastructure must be considered in the control algorithm for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS).

co-simulation↗