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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Coherent approach to two-dimensional heterolayered oxychalcogenides using molten hydroxides.

Heterolayered structures consist of two or more different types of layer and can exhibit exceptional physical properties. Rational routes to synthesize new members of such compounds are required because most of these compounds have been discovered unintentionally. So far there is no generic method to vertically stack chemically different layers to form two-dimensional compounds owing to a lack of understanding of the synthesis of these materials. Here we report the use of molten hydroxides as unconventional solutions for the rapid stacking of oxide and chalcogenide layers with precise composition control. In addition, the crystal growth of heterolayered phases can be achieved by the reaction of different components at their diffusion front in molten hydroxides. This approach creates conditions in which the building blocks for each heterolayer can coexist, enabling heterolayered structures and bypassing the challenges of traditional solid-state chemistry methods where short reactant diffusion lengths predominate. This crystal growth methodology for heterolayers is also applicable to systems that do not form congruent melts at high temperatures.

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

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

The structures of ordered defects in thiocyanate analogues of Prussian Blue

We report the structures of six new divalent transition metal hexathiocyanatobismuthate frameworks with the generic formula $\Big\{ M[Bi(SCN)_6]_\frac{2+x}{3} \Big\}$ $\{guest\}^{x+}$, M = Mn, Co, Ni and Zn. These frameworks are defective analogues of the perovskite-derived trivalent transition metal hexathiocyanatobismuthates M III [Bi(SCN) 6 ]. The defects in these new thiocyanate frameworks order and produce complex superstructures due to the low symmetry of the parent structure, in contrast to the related and more well-studied cyanide Prussian Blue analogues. Despite the close similarities in the chemistries of these four transition metal cations, we find that each framework contains a different mechanism for accommodating the lowered transition metal charge, making use of some combination of Bi(SCN) 6 3- vacancies, M Bi antisite defects, water substitution for thiocyanate, adventitious extra-framework cations and reduced metal coordination number. These materials provide an unusually clear view of defects in molecular framework materials and their variety suggests that similar richness may be waiting to be uncovered in other hybrid perovskite frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

N-Doping improves charge transport and morphology in the organic non-fullerene acceptor O-IDTBR

Molecular doping has been shown to improve the performance of various organic (opto)electronic devices. When compared to p-doped systems, research into n-doped organic small-molecules is relatively limited, primarily due to the lack of suitable dopants and the often encountered unfavourable microstructural effects. These factors have prevented the use of n-doping in a wider range of existing materials, such as non-fullerene acceptors (NFAs), that have already shown great promise for a range of (opto)electronic applications. Here, we show that several different molecular n-dopants, namely [1,2-b:2',1'-d]benzo[i][2.5]benzodiazocine potassium triflate adduct (DMBI-BDZC), tetra-n-butylammonium fluoride (TBAF) and 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI), can be used to n-dope the molecular semiconductor O-IDTBR, a promising NFA, and increase the electron field-effect mobility to >1 cm 2 V -1 s -1 . By combining complementary experimental techniques with computer simulations of doping and charge carrier dynamics, we show that improved charge transport arises from synergistic effects of n-type doping and morphological changes. Specifically, a new, previously unreported dopant-induced packing orientation results in one of the highest electron mobility values reported to-date for an NFA molecule. Overall, this work highlights the importance of dopant–semiconductor interactions and their impact on morphology, showing that dopant-induced molecular packing motifs may be generic and a key element of the charge transport enhancement observed in doped organics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Influence of solvent structure and hydrogen bonding on catalysis at solid–liquid interfaces

Solvent molecules interact with reactive species and alter the rates and selectivities of catalytic reactions by orders of magnitude. Specifically, solvent molecules can modify the free energies of liquid phase and surface species via solvation, participating directly as a reactant or co-catalyst, or competitively binding to active sites. These effects carry consequences for reactions relevant for the conversion of renewable or recyclable feedstocks, the development of distributed chemical manufacturing, and the utilization of renewable energy to drive chemical reactions. First, we describe the quantitative impact of these effects on steady-state catalytic turnover rates through a rate expression derived for a generic catalytic reaction (A → B), which illustrates the functional dependence of rates on each category of solvent interaction. Second, we connect these concepts to recent investigations of the effects of solvents on catalysis to show how interactions between solvent and reactant molecules at solid–liquid interfaces influence catalytic reactions. Furthermore, this discussion demonstrates that the design of effective liquid phase catalytic processes benefits from a clear understanding of these intermolecular interactions and their implications for rates and selectivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Templated synthesis enhances the cobalt adsorption capacity of a porous organic polymer

Divalent transition metals such as Co(II) are important targets for removal from water sources, due to their potential toxicity as well as their high value. In this study, we found that a series of porous organic polymers based on amide-linked tetraphenylmethane units are effective Co(II) ion adsorbents in aqueous solution. To increase the density of Co(II) binding sites, we then developed a templated synthesis in which the branched, rigid monomers are pre-assembled around Co(II) ions prior to polymerization. Furthermore, after polymer formation, the Co(II) template ions are removed to yield a material rich in Co(II) binding sites. Ion adsorption isotherms show that the Co(II)-templated material has an ion adsorption capacity significantly greater than those of the non-templated materials, highlighting the utility of a templated synthetic route. SEM and TEM images show the morphology of the templated polymer to be dramatically different from the non-templated polymers and to be similar in size and shape to the Co(II)–monomer precursors, emphasizing the role of the template ions in directing the formation of the resulting polymer. This guest-templated approach requires no functionalization of the generic monomer and represents a promising synthetic route to high-capacity ion adsorbents for water purification and aqueous separations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The role of complementary shape in protein dimerization

Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. Furthermore, a machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thermodynamic stability versus kinetic accessibility: Pareto fronts for programmable self-assembly

A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory: we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles' dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. Additionally, the tradeoff disappears when the number of particle types reaches a value m *, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a thermodynamic–kinetic tradeoff. However, we argue, by contrasting our coarse-grained model's predictions with those of Brownian dynamics simulations, that particles with short-ranged isotropic interactions should generically have a tradeoff, and avoiding it in larger systems will require orientation-dependent interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. Finally, we expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Insights from designing an artificial cascade catalysis system using principles from substrate channeling in enzymes

Generalizing the key requirements of highly-selective, multi-step chemical conversions involving spatially separated reaction centers remains one of the grand challenges of chemistry. Much work towards this effort has focused on decomposing multi-step conversions into their constituent reactions, whose intermediates are successively upgraded in a chemical cascade via diffusion from center to center. This approach for synthesizing more complex molecules takes its cues from biochemical networks, where near-unit conversion of even complex carbohydrates is achieved by upgrading chemical precursors via enzymatic cascades. In this computational study we examine a simple cascade involving coupled Ag and Cu catalysts that sequentially converts CO2 to CO and then CO2 and CO to reduced products, generically named CO2Product and COProduct. The system architecture is inspired by the phenomenon of biological substrate channeling, and components are examined to evaluate their effects on conversion efficiency in the cascade. Aside from a substrate channel linking two reaction centers, we find efficient cascades must also incorporate directional substrate diffusion, compartmentalization of the reaction centers, and proper timing of substrate arrival at the active center. We make explicit linkages between these requirements and chemical conversion in known biological systems, revealing additional control elements that could be incorporated.

CO 2 reduction↗
DOE OSTI2021

Test methodology for validation of multi-frequency models of renewable energy generators using small-signal perturbations

Providing trustworthy and accurate multi-frequency (or harmonic) models for renewable energy generators (REG) is an ongoing challenge for harmonic studies. There have been effective attempts to propose and design a test device to validate the harmonic models, mainly based on shunt current perturbations. However, using additional devices for perturbations is costly for converter-based test sites. This paper provides the test specifications to extend the application of the grid emulators for voltage perturbations and appropriate harmonic model validation. Besides, the effects of the sequence couplings, initial emissions, and power set-points on the test results have been overlooked in the literature. Considering these effects, this paper proposes a generic test methodology to obtain more accurate models in the sequence domain. The experimental verification of the proposed methodology is demonstrated using a 7 MVA grid emulator for testing of a 2 MVA photo-voltaic converter and a 2 MVA Type 3 wind turbine. This way, the test challenges, specifications, and recommendations are presented using the MW-scale experiments on different REGs. Furthermore, the effects of sequence couplings and initial emissions on the calculation results are investigated and compared. The proposed methodology is applicable for harmonic model validation as well as empirical modelling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

MIRACL Co‐Simulation platform for control and operation of distributed wind in microgrid

Abstract This paper presents a co‐simulation platform (CSP) developed as a part of the “Microgrids, Infrastructure Resilience and Advanced Controls Launchpad (MIRACL)” project, hereafter called MIRACL‐CSP, to allow simulation‐based testing, demonstration, and evaluations of distributed wind under various grid operating conditions. MIRACL‐CSP provides modular interactions among the power distribution system, distributed wind, and utility decision‐making framework. A hierarchical engine for large‐scale infrastructure co‐simulations (HELICS) is used as the core engine of MIRACL‐CSP to establish time and information coordination among the MIRACL‐CSP modules. In this study, MIRACL‐CSP performance is demonstrated using the IEEE 123‐node test distribution grid modelled in GridLAB‐D and the utility decision support application modelled in Python. The functionality of MIRACL‐CSP is demonstrated through various grid operational scenarios in a microgrid and networked microgrid environment. A co‐simulation study is described that demonstrated MIRACL‐CSP capabilities for the microgrid operational scenario in the IEEE 123‐node test system. MIRACL‐CSP is a generic platform that facilitates distributed wind research for different test systems, applications, and valuation of distributed wind.

17 WIND ENERGY↗
DOE OSTI2023

Characterization and dynamics of the peculiar stream Jhelum: A tentative role for the Sagittarius dwarf galaxy

Stellar streams are a promising tool to study the Milky Way’s dark matter subhalo population, as interactions with subhalos are expected to leave visible imprints in the streams in the form of substructure. However, there may be other causes of substructure. Here we studied the kinematics and the unusual morphology of the stellar stream Jhelum. Using a combination of ground-based photometry and Gaia EDR3 astrometry, we characterized the morphology of Jhelum. We combined this new data with radial velocities from the literature to perform orbit integrations of the stream in static Galactic potentials. We also carried out N-body simulations in the presence of the Sagittarius dwarf galaxy. The new data reveal a previously unreported tertiary component in the stream, as well as several gaps and a kink-like feature in its narrow component. We find that for a range of realistic Galactic potentials, no single orbit is able to reproduce Jhelum’s radial velocity data entirely. A generic property of the orbital solutions is that they share a similar orbital plane to Sagittarius and this leads to repeated encounters with the stream. Using N-body simulations that include a massive Sagittarius, we explored its effect on Jhelum, and we show that these encounters are able to qualitatively reproduce the narrow and broad components in Jhelum, as well as create a tertiary component in some cases. We also find evidence that such encounters can result in an apparent increase in the velocity dispersion of the stream by a factor up to four due to overlapping narrow and broad components. Our findings suggest that the Jhelum stream is even more complex than once thought; however, its morphology and kinematics can tentatively be explained via the interactions with Sagittarius. In this scenario, the formation of Jhelum’s narrow and broad components occurs naturally, yet some of the smaller gap-like features remain to be explained.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Early-time γ -ray constraints on cosmic-ray acceleration in the core-collapse SN 2023ixf with the Fermi Large Area Telescope

Context. While supernova remnants (SNRs) have been considered the most relevant Galactic cosmic ray (CR) accelerators for decades, core-collapse supernovae (CCSNe) could accelerate particles during the earliest stages of their evolution and hence contribute to the CR energy budget in the Galaxy. Some SNRs have indeed been associated with TeV γ -rays, yet proton acceleration efficiency during the early stages of an SN expansion remains mostly unconstrained. Aims. The multi-wavelength observation of SN 2023ixf, a Type II supernova (SN) in the nearby galaxy M 101 (at a distance of 6.85 Mpc), opens the possibility to constrain CR acceleration within a few days after the collapse of the red super-giant stellar progenitor. With this work, we intend to provide a phenomenological, quasi-model-independent constraint on the CR acceleration efficiency during this event at photon energies above 100 MeV. Methods. We performed a maximum-likelihood analysis of γ -ray data from the Fermi Large Area Telescope up to one month after the SN explosion. We searched for high-energy, non-thermal emission from its expanding shock, and estimated the underlying hadronic CR energy reservoir assuming a power-law proton distribution consistent with standard diffusive shock acceleration. Results. We do not find significant γ -ray emission from SN 2023ixf. Nonetheless, our non-detection provides the first limit on the energy transferred to the population of hadronic CRs during the very early expansion of a CCSN. Conclusions. Under reasonable assumptions, our limits would imply a maximum efficiency on the CR acceleration of as low as 1%, which is inconsistent with the common estimate of 10% in generic SNe. However, this result is highly dependent on the assumed geometry of the circumstellar medium, and could be relaxed back to 10% by challenging spherical symmetry. Consequently, a more sophisticated, inhomogeneous characterisation of the shock and the progenitor’s environment is required before establishing whether or not Type II SNe are indeed efficient CR accelerators at early times.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Infalling ultra-faint dwarfs as emissaries of the Axiverse

Recent discoveries of ultra-faint dwarf galaxies (UFDs) infalling onto the Milky Way, namely Leo K & M at r ≃ 450 kpc, considerably strengthens the case that UFDs constitute a distinct galaxy class that is inherently smaller and fainter, and metal-poorer than the classical dwarf spheroidals (dSph). This distinction is at odds with the inherent continuity of galaxy halo masses formed under scale-free gravity for any standard dark-matter (DM) model. Here, we show that distinct galaxy classes do evolve in cosmological simulations of multiple light bosons representing the “Axiverse” proposal of string theory, where a discrete mass spectrum of axions is generically predicted to span many decades in mass. In this context, the observed UFD class we show corresponds to a relatively heavy boson of 3 × 10 −21 eV, including Leo K & M, whereas a lighter axion of 10 −22 eV comprises the bulk of DM in all larger galaxies including the dSphs. Although Leo M is larger in size than Leo K, we predict its velocity dispersion to be smaller ( ≃ 1.7 km/s) than that of Leo K ( ≃ 4.5 km/s) because of the inverse de Broglie scale dependence on momentum. This scenario can be definitively tested using millisecond pulsars close to the Galactic center, where the Compton frequencies of the heavy and light bosons imprint monotone timing residuals that may be detected by the Square Kilometre Array (SKA) on timescales of approximately one week and four months, respectively.

dark matter↗