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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Three-dimensional interconnected nickel phosphide networks with hollow microstructures and desulfurization performance

Graphical abstract: Three-dimensional interconnected nickel phosphide networks with hollow microstructures and desulfurization performance. - Highlights: • Three-dimensional Ni{sub 2}P has been prepared using foam nickel as a template. • The microstructures interconnected and formed sponge-like porous networks. • Three-dimensional Ni{sub 2}P shows superior hydrodesulfurization activity. - Abstract: Three-dimensional microstructured nickel phosphide (Ni{sub 2}P) was fabricated by the reaction between foam nickel (Ni) and phosphorus red. The as-prepared Ni{sub 2}P samples, as interconnected networks, maintained the original mesh structure of foamed nickel. The crystal structure and morphology of the as-synthesized Ni{sub 2}P were characterized by X-ray diffraction, scanning electron microscopy, automatic mercury porosimetry and X-ray photoelectron spectroscopy. The SEM study showed adjacent hollow branches were mutually interconnected to form sponge-like networks. The investigation on pore structure provided detailed information for the hollow microstructures. The growth mechanism for the three-dimensionally structured Ni{sub 2}P was postulated and discussed in detail. To investigate its catalytic properties, SiO{sub 2} supported three-dimensional Ni{sub 2}P was prepared successfully and evaluated for the hydrodesulfurization (HDS) of dibenzothiophene (DBT). DBT molecules were mostly hydrogenated and then desulfurized by Ni{sub 2}P/SiO{sub 2}.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Gas sensor based on three dimensional Bi2S3 nanowires network for ammonia detection at room temperature

Highlights: • Three dimensional Bi{sub 2}S{sub 3} nanowires networks. • High response to NH{sub 3} gas. • Good selectivity. • Wide linear range. • Work at room temperature. - Abstract: A film containing three dimensional Bi{sub 2}S{sub 3} nanowires network was obtained using Bi(NO{sub 3}){sub 3} and thioacetamide as starting materials through three steps, namely the chemical synthesis of Bi{sub 2}S{sub 3}, preparation of its sol under ultrasonic condition and crystal self-assembly at 25 °C. The nanowires had diameters of 20–100 nm. The formation mechanism of the 3D nanowires network is the anisotropic growth of Bi{sub 2}S{sub 3}. The sensor based on 3D Bi{sub 2}S{sub 3} nanowires network with an excellent response to ammonia at 30 °C. The response mechanism of the sensor for ammonia is a weak coordination adsorption of ammonia molecule on the Bi{sub 2}S{sub 3} nanowires and the electron is partially transferred from ammonia molecule to bismuth (III) ion. The maximum response of the sensor to ammonia was up to 82.3 and had a good linear range from 30 to 350 ppm concentration, indicating its potential applications in the determination and leak alarm of ammonia.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network

Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries

To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations, such as discretization of a continuum elastic rod, which can be viewed as the group of rotations and translations $SE(3)$. The evolution involves not only the momenta but also the relative positions and orientations of the particles. The presence of Lie group-valued elements, such as relative positions and orientations, poses a problem for applying previously derived methods for data-based computing. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of SympNets (Jin et al., 2020) and LPNets (Eldred et al., 2024), building the neural network from phase space mappings that preserve the Lie–Poisson structure. We derive a novel system of mappings that are built into neural networks describing the evolution of such systems. We call such networks Coupled Lie–Poisson Neural Networks, or CLPNets. We consider increasingly complex examples for the applications of CLPNets, starting with the rotation of two rigid bodies about a common axis, progressing to the free rotation of two rigid bodies, and finally to the evolution of two connected and interacting $SE(3)$ components, describing the discretization of an elastic rod into two elements. Our method preserves all Casimir invariants to machine precision, preserves energy to high accuracy, and shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied (three to eighteen dimensions). Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered.

Data-based modeling↗
DOE OSTI2017

Operator splitting method for simulation of dynamic flows in natural gas pipeline networks

Here, we develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.

02 PETROLEUM↗
DOE OSTI2024

Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges

With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.

circular economy↗
DOE OSTI2013

Quantitative assessment of biological impact using transcriptomic data and mechanistic network models

Exposure to biologically active substances such as therapeutic drugs or environmental toxicants can impact biological systems at various levels, affecting individual molecules, signaling pathways, and overall cellular processes. The ability to derive mechanistic insights from the resulting system responses requires the integration of experimental measures with a priori knowledge about the system and the interacting molecules therein. We developed a novel systems biology-based methodology that leverages mechanistic network models and transcriptomic data to quantitatively assess the biological impact of exposures to active substances. Hierarchically organized network models were first constructed to provide a coherent framework for investigating the impact of exposures at the molecular, pathway and process levels. We then validated our methodology using novel and previously published experiments. For both in vitro systems with simple exposure and in vivo systems with complex exposures, our methodology was able to recapitulate known biological responses matching expected or measured phenotypes. In addition, the quantitative results were in agreement with experimental endpoint data for many of the mechanistic effects that were assessed, providing further objective confirmation of the approach. We conclude that our methodology evaluates the biological impact of exposures in an objective, systematic, and quantifiable manner, enabling the computation of a systems-wide and pan-mechanistic biological impact measure for a given active substance or mixture. Our results suggest that various fields of human disease research, from drug development to consumer product testing and environmental impact analysis, could benefit from using this methodology. - Highlights: • The impact of biologically active substances is quantified at multiple levels. • The systems-level impact integrates the perturbations of individual networks. • The networks capture the relationships between the biological mechanisms. • Four exposure experiments have been assessed to validate the methodology. • The impact results were consistent with the corresponding phenotypic measures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Dynamic traffic routing in a network with adaptive signal control

In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this work, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. Lastly, it is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗
DOE OSTI2013

The intermediate filament network protein, vimentin, is required for parvoviral infection

Intermediate filaments (IFs) have recently been shown to serve novel roles during infection by many viruses. Here we have begun to study the role of IFs during the early steps of infection by the parvovirus minute virus of mice (MVM). We found that during early infection with MVM, after endosomal escape, the vimentin IF network was considerably altered, yielding collapsed immunofluorescence staining near the nuclear periphery. Furthermore, we found that vimentin plays an important role in the life cycle of MVM. The number of cells, which successfully replicated MVM, was reduced in infected cells in which the vimentin network was genetically or pharmacologically modified; viral endocytosis, however, remained unaltered. Perinuclear accumulation of MVM-containing vesicles was reduced in cells lacking vimentin. Our data suggests that vimentin is required for the MVM life cycle, presenting possibly a dual role: (1) following MVM escape from endosomes and (2) during endosomal trafficking of MVM. - Highlights: • MVM infection changes the distribution of the vimentin network to perinuclear regions. • Disrupting the vimentin network with acrylamide decreases MVM replication. • MVM replication is significantly reduced in vimentin-null cells. • Distribution of MVM-containing vesicles is affected in MVM infected vimentin-null cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Complexity Reduction Methods for Large-Scale Spatially Explicit Biofuels Network Design

The size and complexity of energy system optimization models have increased significantly in recent years, driven by the availability of high-resolution spatial data. We present complexity reduction and solution methods that enable us to efficiently represent high-resolution spatial data in the network design of large-scale energy systems. We aim to reduce the size and enhance the computational efficiency of network design models without sacrificing solution accuracy. Specifically, we first present how to aggregate highly granular data into larger resolutions without averaging out their specific properties through a composite-curve-based approach and then develop a method to linearly represent these curves. Second, we utilize a general clustering method to determine groups of geographically proximate biomass fields and establish a single transportation arc for all of them, reducing the number of transportation-related variables while maintaining an accurate representation of the system. Finally, we introduce a two-step algorithm that decomposes large-scale network design problems into two smaller, more manageable subproblems. We demonstrate the application of our methods using a case study of switchgrass-to-biofuels network design in the eight states of the U.S. Midwest, using realistic and highly explicit spatial data.

09 BIOMASS FUELS↗
DOE OSTI2020

Isolating the Contributions of Specific Network Sites to the Diffuse Vibrational Spectrum of Interfacial Water with Isotopomer-Selective Spectroscopy of Cold Clusters

Decoding the structural information contained in the interfacial vibrational spectrum of water requires understanding how the spectral signatures of individual water molecules respond to their local hydrogen bonding environments. In this study, we isolated the contributions for the five classes of sites that differ according to the number of donor (D) and acceptor (A) hydrogen bonds that characterize each site. These patterns were measured by exploiting the unique properties of the water cluster cage structures formed in the gas phase upon hydration of a series of cations M+·(H2O)n (M = Li, Na, Cs, NH4, CH3NH3, H3O, and n = 5, 20-22). This selection of ions was chosen to systematically express the A, AD, AAD, ADD, and AADD hydrogen bonding motifs. The spectral signatures of each site were measured using two-color, IR-IR isotopomer-selective photofragmentation vibrational spectroscopy of the cryogenically cooled, mass selected cluster ions in which a single intact H2O is introduced without isotopic scrambling, an important advantage afforded by the cluster regime. The resulting patterns provide an unprecedented picture of the intrinsic line shapes and spectral complexities associated with excitation of the individual OH groups, as well as the correlation between the frequencies of the two OH groups on the same water molecule, as a function of network site. The properties of the surrounding water network that govern this frequency map are evaluated by dissecting electronic structure calculations that explore how changes in the nearby network structures, both within and beyond the first hydration shell, affect the local frequency of an OH oscillator. The qualitative trends are recovered with a simple model that correlates the OH frequency with the network-modulated local electron density in the center of the OH bond.

Yang, Nan↗
DOE OSTI2023

Thermally Induced Structural Transitions in Epoxy Thermoset Polymer Networks and Their Spectroscopic Responses

The polymer network structure of epoxy thermosets plays a significant role in its final material properties. However, the effects of mild thermal exposure on these network structures are poorly studied. In this work, wide-angle X-ray scattering was used to investigate the polymer network structure of two epoxy thermosets: homopolymerized bisphenol A (BPA) epoxy resin and BPA epoxy resin cured with a polyether amine hardener (BPA/T-403). Using density functional theory and wide-angle X-ray scattering, insights into the polymer network structure were obtained. Diffraction features were determined to originate from hardener-to-hardener molecular distance, perpendicular π–π stacking of aromatic p-phenylene rings, and the average carbon–carbon distance in the polymer. Thermal exposure was found to permanently alter these structural features for both thermosets, with an increase in the π–π stacking distance. Homopolymerized BPA had an additional decrease in the hardener-to-hardener distance. These structural alterations were found to be detectable using Fourier transform infrared spectroscopy and Raman spectroscopy, with changes in the hardener-to-hardener distance having the largest variations in the resulting spectra specifically at the aromatic and ether frequencies.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Thermally Functional Liquid Crystal Networks by Magnetic Field Driven Molecular Orientation

Aligned liquid crystal networks were synthesized by photopolymerization of liquid crystal monomers in the presence of magnetic fields. Grazing incident wide-angle X-ray scattering was used to characterize the degree of molecular alignment of mesogen chains and time-domain thermoreflectance was used to measure thermal conductivity. Liquid crystal networks with mesogenic units aligned perpendicular and parallel to the substrate exhibit thermal conductivity of 0.34 W m –1 K –1 and 0.22 W m –1 K –1 , respectively. The thermal conductivity and orientational order of liquid crystal networks vary as a function of temperature. The thermal conductivity of liquid crystal networks can be manipulated by a magnetic field at above the glass transition temperature (65 °C) where the reduced viscosity enables molecular reorientation on the time scale of 10 min.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ductile Glassy Polymer Networks Capable of Large Plastic Deformation and Heat-Induced Elastic Recovery

Many thermoplastic polymers are ductile by combining strength and large deformations. These deformations are irreversible - known as plastic deformation. Elastomers can deform reversibly but have low strength. To this end, we developed glassy and ductile polyamide networks capable of large plastic deformation (>200% strain) and high strength (~50 MPa tensile strength and ~1500 MPa Young’s modulus), similar to those of polyolefins and Nylon-66. We discovered that hydrogen bonding between meta-phthalamide groups was essential to the ductility. Since these polyamide networks are covalently bonded, we demonstrated their unique durability by repeatable elastic recovery at elevated temperatures, exhibiting indifferent tensile properties in each cycle. Furthermore, when we fixed the strain during the elastic recovery, these polyamide networks actuated stresses of 9–18 MPa, among the highest reported in shape-memory polymer actuators. Here, we envision these ductile, glassy polymer networks as promising alternatives to ductile thermoplastics, given the combined benefit of ductility and durability.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Enabling Nanoparticle Networking in Semicrystalline Polymer Matrices

Among the physical and chemical attributes of the nanocomposite components and their interactions that contribute to the ultimate material properties, nanoparticle arrangement in the matrix is a key contributing factor that has been targeted through materials choices and processing strategies in numerous previous studies. Often, the desired nanocomposite morphology contains individually dispersed and distributed nanoparticles. In this research, a phase-segregated morphology containing nanoparticle networks was studied. A model nanocomposite system composed of calcium phosphate nanoparticles and a poly(3-hydroxybutyrate) matrix was produced to understand how polymer crystallization and crystal structure can facilitate the formation of a phase-segregated morphology containing nanoparticle networks. Two chemically similar calcium phosphate nanoparticle systems with different shapes, near-spherical and nanofiber, were synthesized for use in the nanocomposites. The different shapes were used independently in nanocomposites in an attempt to understand the effect of the nanoparticle shapes on crystallization-mediated nanoparticle network formation. The resulting nanocomposites were characterized to establish the effects of component interactions on the polymer structure. Additionally from the viscoelastic properties, structure-property relationships in these materials can be defined as a function of nanoparticle shape and concentration. The results of this research suggest that when the nanocomposite components are not strongly interacting, polymer crystallization may be used as a forced assembly method for nanoparticle networks. Such a methodology has applications to the design of functional polymer nanocomposites such as biomedical implant materials and organic photovoltaic materials where judicious choice of nanoparticle-polymer pairs and control of polymer crystal nucleation and growth processes could be used to control the length scale of phase segregation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Synthesis and Structural Characterization of Magnesium Based Coordination Networks in Different Solvents

Three magnesium based metal-organic frameworks, Mg{sub 3}(3,5-PDC){sub 3}(DMF){sub 3} {center_dot} DMF [1], Mg(3,5-PDC)(H{sub 2}O) {center_dot} (H{sub 2}O) [3], and Mg4(3,5-PDC)4(DMF){sub 2}(H{sub 2}O){sub 2} {center_dot} 2DMF {center_dot} 4.5H{sub 2}O [4], and a 2-D coordination polymer, [Mg(3,5-PDC)(H{sub 2}O){sub 2}] [2] [PDC = pyridinedicarboxylate], were synthesized using a combination of DMF, methanol, ethanol, and water. Compound 1 [space group P2{sub 1}/n, a = 12.3475(5) {angstrom}, b = 11.1929(5) {angstrom}, c = 28.6734(12) {angstrom}, {beta} = 98.8160(10){sup o}, V = 3916.0(3) {angstrom}{sup 3}] consists of a combination of isolated and corner-sharing magnesium octahedra connected by the organic linkers to form a 3-D network with a 12.2 {angstrom} x 4.6 {angstrom} 1-D channel. The channel contains coordinated and free DMF molecules. In compound 2 [space group C2/c, a = 9.964(5) {angstrom}, b = 12.0694(6) {angstrom}, c = 7.2763(4) {angstrom}, {beta} = 106.4970(6){sup o}, V = 836.70(6) {angstrom}{sup 3}], PDC connects isolated seven coordinated magnesium polyhedra into a layered structure. Compound 3 [space group P6{sub 1}22, a = 11.479(1) {angstrom}, c = 14.735(3) {angstrom}, V = 1681.7(4) {angstrom}{sup 3}] (previously reported) contains isolated magnesium octahedra connected by the organic linker with each other forming a 3D network. Compound 4 [space group P2{sub 1}/c, a = 13.7442(14) {angstrom}, b = 14.2887(15) {angstrom}, c = 14.1178(14) {angstrom}, {beta} = 104.912(2){sup o}, V = 2679.2(5) {angstrom}{sup 3}] also exhibits a 3D network based on isolated magnesium octahedra with square cavities containing both disordered DMF and water molecules. The structural topologies originate due to the variable coordination ability of solvent molecules with the metal center. Water molecules coordinate with the magnesium metal centers preferably over other polar solvents (DMF, methanol, ethanol) used to synthesize the coordination networks. Despite testing multiple desolvation routes, we were unable to measure BET surface areas greater than 51.9 m{sup 2}/g for compound 1.

Banerjee, Debasis↗