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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Solvent Effects on Catalytic Reactions and Related Phenomena at Liquid-Solid Interfaces

Catalytic reactions involve the direct interaction of reactants, intermediates and products with the catalyst surface. We not only need to control the atomic structure and electronic properties of the active site, but also explore the multiple molecular interactions that occur beyond the active site; they play an essential role in altering the binding and reactivity of surface species. In liquid-phase catalysis, solvents provide additional degrees of freedom in the design of the catalytic process for desirable activity and selectivity. Here, the multi-faceted effects of solvents have a profound impact on the catalyst performance by restricting the mass transfer to the site, tuning the chemical potential of the surface species, competing for active sites, stabilizing the initial and transition states, and causing mechanistic changes by participating in the kinetically relevant elementary steps. This review addresses the different aspects of solvent effects, using a few prototype solid-liquid interfaces to illustrate these fundamental features. Recent experimental and computational studies that provide new insight at the molecular level are examined. Solvent structures in the proximity of the catalyst surface are discussed along with their influence in molecular binding and reaction at the solid-liquid interfaces. Furthermore, opportunities to alter such a solid-liquid interaction by tuning the wettability of the catalyst surfaces are explored.

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

An ab initio study of the adsorption of Eu 3+ , Pu 3+ , Am 3+ , and Cm 3+ hydroxide complexes on hematite (001) surface: Role of magnetism on adsorption

Inner-sphere hydroxide complexes of Eu 3+ , Pu 3+ , Am 3+ , and Cm 3+ adsorbed to antiferromagnetic and ferromagnetic hematite (001) surfaces were modeled by using DFT (GGA+U) calculations in order to compare the electronic properties, atomic structures, and adsorption energies between the trivalent actinide analog Eu 3+ and the corresponding actinides An 3+ . Eu 3+ forms a tridentate–binuclear surface complex on the hematite surface with an Eu–Fe distance of 3.4–3.8 Å. Eu 3+ is partially reduced upon adsorption, as indicated by the increase of original Eu 3+ spin value from 6 to ~7. Eu 3+ adsorption on the ferromagnetic surface is energetically more favorable than on the antiferromagnetic surface, while the opposite is the case for all actinides An 3+ . When retaining a similar adsorption configuration (e.g., coordination number = 6), An 3+ cations are adsorbed ~0.2 Å closer to the surface compared to Eu 3+ . Partial density of states analyses indicate ionic bonding between O ions on the hematite surface and the Eu 3+ /An 3+ adsorbates. An increasing number of bands above the Fermi energy were found when Eu 3+ and An 3+ were adsorbed to the ferromagnetic surface but not on the antiferromagnetic surface, indicating the ferromagnetic substrate becomes more semiconducting upon cation adsorption. Here, these results show that the spin configurations of the hematite (001) surface have a weak but distinct influence on the cation adsorption energy. Although the antiferromagnetic spin configuration of hematite is dominant in nature, metastable ferromagnetic domains on nanohematite grains may influence the energetics of certain cation adsorption reactions.

42 ENGINEERING↗
DOE OSTI2015

Timing is everything: Fine-tuned molecular machines orchestrate paramyxovirus entry

The Paramyxoviridae include some of the great and ubiquitous disease-causing viruses of humans and animals. In most paramyxoviruses, two viral membrane glycoproteins, fusion protein (F) and receptor binding protein (HN, H or G) mediate a concerted process of recognition of host cell surface molecules followed by fusion of viral and cellular membranes, resulting in viral nucleocapsid entry into the cytoplasm. The interactions between the F and HN, H or G viral glycoproteins and host molecules are critical in determining host range, virulence and spread of these viruses. Recently, atomic structures, together with biochemical and biophysical studies, have provided major insights into how these two viral glycoproteins successfully interact with host receptors on cellular membranes and initiate the membrane fusion process to gain entry into cells. These studies highlight the conserved core mechanisms of paramyxovirus entry that provide the fundamental basis for rational anti-viral drug design and vaccine development. - Highlights: • New structural and functional insights into paramyxovirus entry mechanisms. • Current data on paramyxovirus glycoproteins suggest a core conserved entry mechanism. • Diverse mechanisms preventing premature fusion activation exist in these viruses. • Precise spacio-temporal interplay between paramyxovirus glycoproteins initiate entry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Access to Ru(IV)–Ru(V) and Ru(V)–Ru(VI) Redox in Layered Li 7 RuO 6 via Intercalation Reactions

The push to increase the capacity of oxides to store charge as Li-ion battery cathodes has challenged our knowledge of the boundaries of redox chemistry and its relationship to the atomic structure. Layered phases with Ru are now classical models in the quest for refined theories. Current understanding dictates that the highest formal state that can be reached in them is Ru(V), after which activity centered at the oxide anions is triggered. Yet, this picture is challenged by this investigation of the intercalation chemistry of Li 7 RuO 6 . Coupling structural and spectroscopic analysis with computational simulations, we show that Li 7 RuO 6 is capable of highly reversible lithiation to Li 8 RuO 6 through the Ru(V)/Ru(IV) redox couple. Further, Li 7 RuO 6 can also undergo anodic Li deintercalation, showing clear evidence of the reversible formation of Ru(VI) with octahedral coordination, an unusual state that is not accessible in other layered oxides. The results highlight the versatility of the Ru-O bond to undergo distinct redox transitions depending on the specific layered arrangements. They enrich our understanding of redox chemistry in solids while underscoring the need for descriptions of charge compensation that reflect the nuance of covalent interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

High Substitution Rate in TiO 2 Anatase Nanoparticles with Cationic Vacancies for Fast Lithium Storage

Doping is generally used to tune and enhance the properties of metal oxides. However, their chemical composition cannot be readily modified beyond low dopant amounts without disrupting the crystalline atomic structure. In the case of anatase TiO 2 , we introduce a new solution-based chemical route allowing the composition to be significantly modified, substituting the divalent O 2- anions by monovalent F- and OH- anions resulting in the formation of cationic Ti 4+ vacancies (square) whose concentration can be controlled by the reaction temperature. The resulting polyanionic anatase has the general composition Ti 1-x-y square x+y O 2-4(x+y) F 4x (OH) 4y , reaching vacancy concentrations of up to 22%, i.e., Ti 0.78 square 0.22 O 1.12 F 0.4 (OH) 0.48 . Solid-state F-19 NMR spectroscopy reveals that fluoride ions can accommodate up to three different environments, depending on Ti and vacancies (i.e. Ti 3 -F, Ti- 2 square 1 -F, and Ti- 1 square 2 -F), with a preferential location close to vacancies. DFT calculations further confirm the fluoride/vacancy ordering. When its characteristics were evaluated as an electrode for reversible Li-ion storage, the material shows a modified lithium reaction mechanism, which has been rationalized by the occurrence of cationic vacancies acting as additional lithium hosting sites within the anatase framework. Finally, the material shows a fast discharging/charging behavior, compared to TiO 2 , highlighting the benefits of the structural modifications and paving the way for the design of advanced electrode materials, based on a defect mediated mechanism.

25 ENERGY STORAGE↗
DOE OSTI2019

Combined Theoretical and Experimental Investigations of Atomic Doping To Enhance Photon Absorption and Carrier Transport of LaFeO 3 Photocathodes

Perovskite-type lanthanum iron oxide, LaFeO 3 , is a p-type semiconductor that can achieve overall water splitting using visible light while maintaining photostability. These features make LaFeO 3 a promising photocathode candidate for various photoelectrochemical cells. Currently, the photoelectrochemical performance of a LaFeO 3 photocathode is mainly limited by considerable bulk electron-hole recombination. This study reports a combined theoretical and experimental investigation on atomic doping of LaFeO 3 , in particular, substitutional doping of La 3+ with K + , to increase its charge transport properties and decrease electron-hole recombination. The computational results show that K doping enhances not only the charge transport properties but also photon absorption below the bandgap energy of the pristine LaFeO 3 . The effect of K doping was systematically investigated by comparing the electronic and atomic structures, majority carrier density, hole-polaron formation, and optical properties of pristine and K-doped LaFeO 3 . The computational results were then verified by experimentally characterizing the crystal structures, compositions, optical properties, and photoelectrochemical properties of LaFeO 3 and K-doped LaFeO 3 electrodes. For this purpose, pristine LaFeO 3 and K-doped LaFeO 3 were prepared as high-surface-area, high-purity photoelectrodes having the same morphology to accurately and unambiguously evaluate the effect of K doping. Here, the combined computational and experimental investigations presented in this study provide useful insights into the effect of composition tuning of LaFeO 3 and other p-type oxides with a perovskite structure.

14 SOLAR ENERGY↗
DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

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

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

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

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide ab initio semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Local Order in Liquid Gallium–Indium Alloys

Liquid metals such as eutectic Ga–In alloys have low melting points and low toxicity and are used in catalysis and micro-robotics. This study investigates the local atomic structure of liquid gallium-indium alloys by a combination of density measurements, diffraction data, and Monte-Carlo simulation via the empirical potential structure refinement approach. A high-Q shoulder observed in liquid Ga is related to structural rearrangements in the second coordination shell. Structure analysis found coordination environments close to a random distribution for eutectic Ga–In alloy, while electronic effects appear to dominate the mixing enthalpy.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Thermal Conductivity Comparison of Indium Gallium Zinc Oxide Thin Films: Dependence on Temperature, Crystallinity, and Porosity

Here, the cross-plane thermal conductivity of InGaZnO (IGZO) thin films was measured using the 3ω technique from 18 to 300 K. The studied morphologies include amorphous (a-IGZO), semicrystalline (semi-c-IGZO), and c-axis-aligned single-crystal-like IGZO (c-IGZO) grown by pulsed laser deposition (PLD) as well as a-IGZO deposited by sputtering and by solution combustion processing. The atomic structures of the amorphous and crystalline films were simulated with ab initio molecular dynamics. The film quality and texturing information was assessed by X-ray diffraction and grazing incidence wide-angle X-ray scattering. X-ray reflectivity was also conducted to quantify film densities and porosities. All the high-density films exhibit an empirical power-law temperature dependence of the thermal conductivity $κ \sim T^{0.6}$ in the specified temperature range. Among the PLD dense films, semi-c-IGZO exhibits the highest thermal conductivity, remarkably exceeding both films with more order (c-IGZO) and with less order (a-IGZO) by a factor of 4. The less dense combustion-synthesized films, on the other hand, exhibited lower thermal conductivity, quantitatively consistent with a porous film using either an effective medium or percolation model. All samples are consistent with the porosity-adapted Cahill–Pohl (p-CP) model of minimum thermal conductivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Role of LiCoO 2 Surface Terminations in Oxygen Reduction and Evolution Kinetics

Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) activities of LiCoO 2 nanorods with sizes in the range from 9 to 40 nm were studied in alkaline solution. The sides of these nanorods were terminated with low-index surfaces such as (003) while the tips were terminated largely with high-index surfaces such as (104) as revealed by high-resolution transmission electron microscopy. Electron energy loss spectroscopy demonstrated that low-spin Co 3+ prevailed on the sides, while the tips exhibited predominantly high- or intermediate-spin Co 3+ . We correlated the electronic and atomic structure to higher specific ORR and OER activities at the tips as compared to the sides, which was accompanied by more facile redox of Co 2+/3+ and higher charge transferred per unit area. These findings highlight the critical role of surface terminations and electronic structures of transition metal oxides on the ORR and OER activity.

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

Evolution of Excited-State Dynamics in Periodic Au 28 , Au 36 , Au 44 , and Au 52 Nanoclusters

An understanding of the correlation between the atomic structure and optical properties of gold nanoclusters is essential for exploration of their functionalities and applications involving light harvesting and electron transfer. We report the femto-nanosecond excited state dynamics of a periodic series of face-centered cubic (FCC) gold nanoclusters (including Au 28 , Au 36 , Au 44 , and Au 52 ), which exhibit a set of unique features compared with other similar sized clusters. Molecular-like ultrafast S n → S 1 internal conversions (i.e., radiationless electronic transitions) are observed in the relaxation dynamics of FCC periodic series. Excited-state dynamics with near-HOMO–LUMO gap excitation lacks ultrafast decay component, and only the structural relaxation dominates in the dynamical process, which proves the absence of core–shell relaxation. Interestingly, both the relaxation of the hot carriers and the band-edge carrier recombination become slower as the size increases. The evolution in excited-state properties of this FCC series offers new insight into the structure-dependent properties of metal nanoclusters, which will benefit their optical energy harvesting and photocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Monolayer PtSe 2 , a New Semiconducting Transition-Metal-Dichalcogenide, Epitaxially Grown by Direct Selenization of Pt

For single-layer transition-metal dichalcogenides (TMDs) receive significant attention due to their intriguing physical properties for both fundamental research and potential applications in electronics, optoelectronics, spintronics, catalysis, and so on. Here, we demonstrate the epitaxial growth of high-quality single-crystal, monolayer platinum diselenide (PtSe2), a new member of the layered TMDs family, by a single step of direct selenization of a Pt(111) substrate. We found that a combination of atomic-resolution experimental characterizations and first-principle theoretic calculations reveals the atomic structure of the monolayer PtSe2/Pt(111). Angle-resolved photoemission spectroscopy measurements confirm for the first time the semiconducting electronic structure of monolayer PtSe2 (in contrast to its semimetallic bulk counterpart). The photocatalytic activity of monolayer PtSe2 film is evaluated by a methylene-blue photodegradation experiment, demonstrating its practical application as a promising photocatalyst. Moreover, circular polarization calculations predict that monolayer PtSe2 has also potential applications in valleytronics.

36 MATERIALS SCIENCE↗