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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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Revisiting the influence of Ni particle size on the hydrogenation of CO 2 to CH 4 over Ni/CeO 2
Nickel nanoparticles were supported on CeO 2 and evaluated in the hydrogenation of CO 2 to CH 4 at 538 K and 1 atm. Chemisorption of H 2 was used to estimate the average Ni particle size, which ranged from 1.3 to 17 nm. The apparent activation energy for CH 4 formation and turnover frequency for CO 2 conversion was independent of Ni particle size. The smallest Ni particles (1.3 nm) were less selective to CH 4 . Comparisons to alumina-supported Ni reveal the same performance as Ni/CeO 2 when normalized to active Ni atoms counted by H 2 chemisorption. A beneficial effect of ceria relative to alumina is the enhanced reducibility of Ni as evaluated by H 2 temperature-programmed reduction. The constancy of the turnover frequency on Ni particles suggests the acid-base and/or redox properties of the support do not play a significant role in the CO 2 methanation reaction under the conditions of study, which contrasts many earlier works.
Effect of Ni particle size on the production of renewable methane from CO 2 over Ni/CeO 2 catalyst
Production of ‘renewable Methane’ has attracted renewed research interest as a fundamental probe reaction and process for CO 2 utilization through potential use in C1 fuel production and even for future space exploration technologies. CO 2 methanation is a structure sensitive reaction on Ni/CeO 2 catalysts. To precisely elucidate the size effect of the Ni metal center on the CO 2 methanation performance, we prepared 2%Ni/CeO 2 catalysts with pre-synthesized uniform Ni particles (2, 4 and 8 nm) on a high surface area CeO 2 support. Transmission electron microscopy (TEM) and ambient pressure X-ray photo spectroscopy (AP-XPS) characterization have confirmed that the catalyst structure and chemical state was uniform and stable under reaction conditions. The 8 nm sized catalyst showed superior methanation selectivity over the 4 and 2 nm counterparts, and the methanation activity in term of TOF is 10 times and 70 times higher than for the 4 and 2 nm counterparts, respectively. Here, the DRIFTS studies revealed that the larger Ni (8 nm particles) over CeO 2 efficiently facilitated the hydrogenation of the surface formate intermediates, which is proposed as the rate determining step accounting for the excellent CO 2 methanation performance.
Crystal Structures of Ternary Complexes of MEF2 and NKX2?5 Bound to DNA Reveal a Disease Related Pro
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A review of the application of 2D isotropic-anisotropic correlation NMR spectroscopy in structural studies of chalcogenide glasses
The application of solid-state high-resolution NMR spectroscopy in the structural investigation of chalcogenide glasses in Ge/As/P/Si-X (X = S, Se,Te) systems has remained challenging even for the spin-1/2 nuclides ( 29 Si, 31 P, 77 Se, 125 Te), owing to their low natural abundance (except for 31 P), slow spin-lattice relaxation rate and large CSA and chemical shift distribution induced line broadening effects. However, most of these deleterious effects can be successfully overcome in two-dimensional (2D) isotropic-anisotropic correlation NMR experiments, especially when performed at high magnetic field and in conjunction with the Car-Purcell-Meiboom-Gill (CPMG) echo train acquisition. Here, we present a short introduction to the basic principles of such experiments and review their applications over the last decade in deciphering various short- and intermediate- range structural characteristics of chalcogenide glasses in S-Se, Se-Te, Ge-Se, As-Se and Si-Se systems as well as in investigating the molecular dynamics in a P-Se supercooled liquid. We anticipate possible future applications of these 2D isotropic-anisotropic correlation NMR experiments, particularly in conjunction with density functional theory-based calculations of NMR chemical shift tensor parameters and additional signal enhancement schemes, in addressing complex structural correlations and distributions in chalcogenide glasses.
Irradiation Effects on Stability of δ-UZr2 phase in U-50 wt% Zr Alloy
U-50wt%Zr is a candidate metallic nuclear fuel with potential application in light water reactors due to its excellent thermal properties and high radiation tolerance. The Zr-rich UZr fuels possess greater swelling resistance and fission gas release characteristics compared with U-rich UZr fuels. In this current study, the δ-phase U-50wt%Zr is proton irradiated at room temperature to 1 displacement per atom (dpa) to provide insights on phase stability under irradiation conditions. High resolution characterization of Transmission Electron Microscopy (TEM) and Atom Probe Tomography (APT) characterization techniques are used to elucidate microstructural changes due to irradiation. TEM and APT results show highly oriented bcc β-Zr-rich platelet precipitates nucleating adjacent to α-U phases inside the UZr 2 matrix. Formation of this platelet morphology is characteristic of Widmänstatten structure which can be attributed to a variety of factors such as differences in thermal expansion coefficient between the phases, grain size, alloy composition, and cooling rate. The phases present are distinctly different than those observed through in situ annealing, but irradiation accelerates diffusion and phase separation kinetics. These microstructural changes in U-50wt%Zr are different from those achieved by pure thermodynamic or high temperature heavy ion irradiation experiments. Finally, our work, together with previous ones, highlight the necessity to study the U-Zr phase diagram under non-equilibrium thermodynamics conditions to support this material's deployment as a viable nuclear fuel form.
The role of Cr, P, and N solutes on the irradiated microstructure of bcc Fe
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System modeling for grid-interactive efficient building applications
Despite the abundance of research applications of system modeling in grid-interactive efficient buildings (GEBs), the transfer of those applications to real-world practices is still in its early stages. This is partially due to the lack of a summary on how system modeling should be established for a given application of GEBs. Here, we fill this gap by providing an extensive survey of the literature on system modeling for different GEBs that have been produced in the past decade. This survey involves over 300 relevant journal articles from the building community, the power system community, and the society of control. Specifically, we first identified key requirements of system modeling for GEBs based on various types of applications discussed in those publications. We then summarized the system modeling applications from those publications, in terms of their assumptions, modeling approach, and simulation. After that, we analyzed different assumptions made for various applications, the extent to which those modeling approaches satisfied the key requirements, and how those approaches can be scaled up for large-scale applications, which are quite common in GEBs. In addition, we gave insights on how to use system modeling for different applications in GEBs. At the end, we provided recommended directions for future studies to fully unleash the potentials of system modeling to support GEBs.
Systematic mapping of the conformational landscape and dynamism of soluble fibrinogen
Background: Fibrinogen is a soluble, multisubunit, and multidomain dimeric protein, which, upon its proteolytic cleavage by thrombin, is converted to insoluble fibrin, initiating polymerization that substantially contributes to clot growth. Fibrinogen contains numerous, transiently accessible “cryptic” epitopes for hemostatic and immunologic proteins, suggesting that fibrinogen exhibits conformational flexibility, which may play functional roles in its temporal and spatial interactions. Hitherto, there have been limited integrative approaches characterizing the solution structure and internal flexibility of fibrinogen. Methods: Here, utilizing a multipronged, biophysical approach involving 2 solution-based techniques, temperature-dependent hydrogen-deuterium exchange mass spectrometry and small angle X-ray scattering, corroborated by negative stain electron microscopy, we present a holistic, conformationally dynamic model of human fibrinogen in solution. Results: Our data reveal 4 major and distinct conformations of fibrinogen accommodated by a high degree of internal protein flexibility along its central scaffold. We propose that the fibrinogen structure in the solution consists of a complex, conformational landscape with multiple local minima. This is further supported by the location of numerous point mutations that are linked to dysfibrinogenemia and posttranslational modifications, residing near the identified fibrinogen flexions. Conclusion: Further, this work provides a molecular basis for the structural “dynamism” of fibrinogen that is expected to influence the broad swath of its functionally diverse macromolecular interactions and fine-tune the structural and mechanical properties of blood clots.
Automated phase segmentation and quantification of high-resolution TEM image for alloy design
In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.
Chemical design of self-propelled Janus droplets
Solubilizing, self-propelling droplets have emerged as a rich chemical platform for the exploration of active matter, but isotropic droplets rely on spontaneous symmetry breaking to sustain motion. The introduction of permanent asymmetry, e.g., in the form of a biphasic Janus droplet, has not been explored as a comprehensive design strategy for active droplets, despite the widespread use of Janus structures in motile solid particles. Here, we uncover the chemomechanical framework underlying the self-propulsion of biphasic Janus oil droplets solubilizing in aqueous surfactant. We elucidate how droplet propulsion is influenced by the degree of oil mixing, droplet shape, and oil solubilization rates for a range of oil combinations. In addition, spatiotemporal control over droplet swimming speed and orientation is demonstrated through the application of thermal gradients applied via joule heating and laser illumination. Finally, we also explore the interactions between collections of Janus droplets, including the spontaneous formation of spinning multi-droplet clusters.
Modular multi-interface nanocrystals for enhanced ethanol oxidation electrocatalysis
Electrochemical processes that utilize biomass-derived ethanol as a source of electrons and protons offer a sustainable energy strategy, yet their practical implementation is limited by sluggish ethanol oxidation reaction (EOR) kinetics and catalyst poisoning. Here, in this study, we report a modular multi-interface nanocrystal catalyst comprising core/shell Co 2 P/Pd and Pd-Au heterostructured interfaces that exhibit complementary functions for the enhanced EOR catalysis. The Co 2 P/Pd interface boosts Pd atom utilization and lowers the kinetic barriers for ethanol-to-acetate conversion, while the Pd-Au interface effectively alleviates CO poisoning caused by C–C bond cleavage of ethanol. In-depth analyses using in situ attenuated total reflectance-surface-enhanced infrared absorption spectroscopy, differential electrochemical mass spectrometry, and density functional theory calculations elucidate the mechanistic roles of these interfaces. The optimized Co 2 P/Pd-Au 0.08 nanorods achieve an excellent mass activity, underscoring the potential of modular, multi-interface nanocrystals for advancing EOR catalysis and offering a generalizable strategy for broader catalytic innovations.
Uncertainty guided online ensemble for non-stationary data streams in fusion science
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.
A chiral alkali metal capped Ni 4 cubane complex: Synthesis, structure, magnetic and catalytic bromination studies
A new chiral NaO 2 Ni 4 O 2 N 2 cluster has been developed with a distorted capped cubane-type structure. The structure was determined by single crystal X-ray diffraction. The tetradentate Schiff-base ligand was synthesized by the condensation of o-vanillin and $\textit{D}$-(-)–threo-2-amino-1-(4-nitrophenyl)-1,3-propanediol. X-ray structure reveals that the complex possesses a Ni 4 cubane cluster where sodium ion is anchored on the face of the cubane structure. Continuous shape measurement studies revealed that all the Ni centres are in distorted octahedral geometry and sodium ion adopts the spherical square pyramidal geometry. Further structural exploration reveals that the complex shows local distortions of the coordination geometries at each metal site, and these distortions govern the magnetic anisotropy associated with each Ni II ion. For the present case, an elongation ($d_{str}$ > 0) of the octahedral coordination sphere results in a local easy-plane anisotropy, with a positive $D_{Ni(II)}$ parameter. Temperature dependant magnetization measurements showed a predominant ferromagnetic interaction amongst the four Ni centres. This complex is also catalytically active towards bromination of aromatic phenolic compounds. Initial catalytic activity was verified with the conversion of phenol red to bromophenol blue. Different phenolic substances show good conversion to their corresponding bromo derivatives. The complex showed good catalytic activity towards bromination of aromatic phenolic compounds, offering up to 99% conversion with 100% selectively to their corresponding bromo derivatives.