SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This slide presentation reviews what is currently known about the solar system and the objects that make up the solar system. Information about the individual planets, comets, asteroids and moons is reviewed.
Explore the source record for details and available documents.
Not provided.
Not Available
Explore the source record for details and available documents.
The Chinese government has pledged to achieve overall carbon neutrality by 2060. Currently, the transportation sector contributes to about 10% of total greenhouse gas (GHG) emissions in China. Hence, China has created a well-defined energy vehicle development strategy to reduce GHG emissions from the transportation sector, further expanding into hydrogen vehicle technologies. In this study, the Transportation Energy Analysis Model (TEAM) investigates the potential of hydrogen internal combustion engine vehicles (H2- ICEVs) and fuel cell vehicles (FCEVs) as a reliable pathway towards the government's aspiration of carbon neutrality in the transportation sector. According to TEAM, by adopting FCEVs and H2-ICEVs in the vehicle market, hydrogen demand could reach 25% of the total light-duty transportation energy demand in 2050. Consequently, this will lead to an annual reduction of more than 35 million tons GHG compared to only counting on the electrification pathway in the decarbonization task. Besides, FCEVs would take longer to penetrate the light-duty vehicle market compared to H2-ICEVs, as the current fuel cell technology still requires much improvement to attain a competitive vehicle cost of production.
Not provided.
Not Available
Not Available
Not provided.
Complex interactions between noncoordinating residues are significant yet commonly overlooked components of macromolecular catalyst function. While these interactions have been demonstrated to impact binding affinities and catalytic rates in metalloenzymes, the roles of similar structural elements in synthetic polymeric catalysts remain underexplored. Using a model Suzuki–Miyuara cross-coupling reaction, we performed a series of systematic studies to probe the interconnected effects of metal–ligand cross-links, electrostatic interactions, and local rigidity in polymer catalysts. To achieve this, a novel bifunctional triphenylphosphine acrylamide (BisTPPAm) monomer was synthesized and evaluated alongside an analogous monofunctional triphenylphosphine acrylamide (TPPAm). In model copolymer catalysts, increased initial reaction rates were observed for copolymers untethered by Pd complexation (BisTPPAm-containing) compared to Pd-cross-linked catalysts (TPPAm-containing). Further, incorporating local rigidity through secondary structure-like and electrostatic interactions revealed nonmonotonic relationships between composition and the reaction rate, demonstrating the potential for tunable behavior through secondary-sphere interactions. Lastly, through rigorous cheminformatics featurization strategies and statistical modeling, we quantitated relationships between chemical descriptors of the substrate and reaction conditions on catalytic performance. Collectively, these results provide insights into relationships among the composition, structure, and function of protein-mimetic catalytic copolymers.
The flux synthesis of a uranium gallophosphate and a uranium gallate, Cs 4 [UO 2 Ga 2 (PO 4 ) 4 ] and Cs 2 UO 2 Ga 2 O 5 , and 4 uranium phosphates, [Rb 2 Rb 3.93 Cl 0.93 ][(UO 2 ) 5 (PO 4 ) 5 ], Rb 11 [(UO 2 ) 8 (PO 4 ) 9 ], Rb 7.6 [(UO 2 ) 8 O 8.6 F 0.4 (PO 4 ) 2 ], and Rb 6 [(UO 2 ) 5 O 2 (PO 4 ) 4 ], is reported.
Nucleation, the early stage of crystallization, is a key step in producing functional materials, but nucleation processes have yet to be understood in detail. Recent advanced characterization techniques, especially atomic force microscopy, liquid-cell transmission electron microscopy (TEM) and cryo-TEM, enable scientists to observe previously inaccessible nucleation details at the nanoscale. In this review, we summarize newly suggested non-classical nucleation models with respect to mechanistic understanding from experimental views, which highlight multi-step nucleation mechanisms. Different intermediated phases such as clusters at the pre-nucleation stage, liquid-like precursors, amorphous phases and even oligomers have been revealed, which play vital roles in nucleation and structure control. Moreover, these intermediated phases practically contribute in general to the structural variances of materials for nanoscience and nanotechnology. Overall, these studies ultimately enable us to control nucleation processes for materials synthesis.
Explore the source record for details and available documents.
X-ray absorption spectroscopy (XAS) is a powerful experimental tool to probe the local structure in materials with the core hole excitations. Here, the oxygen K-edge XAS spectra of the NaCl solution and pure water are computed by using a recently developed GW-Bethe-Salpeter equation approach, based on configurations modeled by path-integral molecular dynamics with the deep-learning technique. The neural network is trained on ab initio data obtained with strongly constrained and appropriately normed density functional theory. The observed changes in the XAS features of the NaCl solution, compared to those of pure water, are in good agreement between experimental and theoretical results. We provided detailed explanations for these spectral changes that occur when NaCl is solvated in pure water. Specifically, the presence of solvating ion pairs leads to localization of electron-hole excitons. As a result, our theoretical XAS results support the theory that the effects of the solvating ions on the H-bond network are mainly confined within the first hydration shell of ions, however beyond the shell the arrangement of water molecules remains to be comparable to that observed in pure water.
The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.