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Cu nanoassemblies formed transiently from cluster tetramer building blocks during reaction turn over CO 2 to methanol and hydrocarbons with leap in activity.
The High-Performance Computing of today is much different than the supercomputing resources a few decades ago. The supercomputers of the past were huge complexes of hundreds of interconnected computers that had large teams of specialists to keep them running. Today, while supercomputers like Summit (the world's most powerful supercomputer as of June 2018, installed at Oak Ridge National Laboratory) are still in high demand for extremely fast computational power (over 200,000 trillion calculations per second, or 200 petaflops), researchers at Sandia now have access to non-traditional computing resources in the way of very powerful graphics processing units (GPUs). These GPU systems have enabled an entirely new avenue of exploration for Sandia, allowing researchers to further tackle problems in energy, advanced materials, artificial intelligence, and nuclear weapons. Many teams at Sandia utilize these GPU clusters to build models through the use of machine learning. These models are faster representations of their code-driven counterparts and can often be leveraged from the exascale computing resources of traditionally larger systems with huge boosts in performance. We highlight two resources for these smaller systems: the team that builds the hardware, and the team that researches and builds the machine learning algorithms.
Abstract We present wide-field, high resolution maps of the color excess for 14 globular clusters toward the Southern Galactic bulge. The maps were derived using Gaia EDR3 astrometry and stellar photometry from the Blanco DECam Bulge Survey, which is a deep, wide-field ugriz Y photometric survey of the southern Galactic bulge. Comparisons with WISE 12 μ m images of thermal continuum emission demonstrate that the maps presented here trace interstellar extinction by dust down to 5″ scales. We use the reddening-corrected photometry of proper motion-selected cluster stars to build color–magnitude diagrams for the target globular clusters, which show residual broadening in excess of that expected from the photometric errors alone. This residual broadening is likely to be driven by star-to-star elemental abundance variations.
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The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.
The EPIC Buildings program was created to target Central New York’s (CNY) Energy Program Innovation Cluster, focused on developing energy hardware innovations for next-generation grid-interactive efficient buildings (GEB). The goals of the program were to establish and grow a sustainable regional innovation cluster for GEB in CNY and support the ambitious transition to achieve net-zero emissions by 2050.
Triphenylphosphine (PPh3)-ligated gold clusters offer promising potential applications due to their relative ease of synthesis and usefulness in forming advanced cluster architectures. While previous studies reported cationic PPh3-ligated gold clusters with core sizes of Au1 - Au4, Au6 - Au11, and Au¬13 - Au14, there has not been definitive identification by mass spectrometry of larger clusters in the Au12 - Au25¬ range. Herein, we survey a polydisperse solution of cationic PPh3-ligated gold clusters using high mass-resolution (M/?M = 60,000) electrospray ionization mass spectrometry (ESI-MS). To improve the sensitivity and mass resolution of larger clusters for unambiguous identification, we increased the number of scan averages and reduced the range of mass collection windows to 200 m/z, thereby mitigating potential mass and ion abundance bias resulting from smaller “building block” gold clusters and other solution components present in higher abundance. In addition to the previously reported clusters, we identified several new species including Au5(PPh3)5+, Au12(PPh3)9HCl2+, Au15(PPh3)9Cl2+, Au16(PPh3)10Cl22+, Au17(PPh3)113+, Au18(PPh3)102+, Au19(PPh3)10Cl2+, Au20(PPh3)12H33+, Au21(PPh3)10Cl2+, and Au22(PPh3)10Cl22+, indicating that a full range of clusters between Au1 - Au22 may be observed in a single polydisperse solution. Considering all of the observed clusters, our findings provide evidence that the “magic number” icosahedral Au13 may be the transition point in cluster growth between smaller clusters, exhibiting a 1:1 gold-to-ligand ratio, and larger clusters, wherein subsequent gold atoms are added to the core without an equal number of accompanying ligands. Our method demonstrates that reducing the range of m/z collection windows and increasing the number of scan averages can improve instrument sensitivity for cationic gold clusters and enable a more complete survey of polydisperse solutions, thereby providing new insights to guide and validate the results of other characterization methods and theoretical calculations. This work was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. MH acknowledges support from the DOE Science Undergraduate Laboratory Internship (SULI) program. HH acknowledges support from the DOE Office of Workforce Development for Teachers and Scientist (WDTS) under the Visiting Faculty Program (VFP). This work was performed using EMSL, a national scientific user facility sponsored by the DOE's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for DOE by Battelle.
Electron dynamics in water are of fundamental importance for a broad range of phenomena, but their real-time study faces numerous conceptual and methodological challenges. Here, in this paper, we introduce attosecond size-resolved cluster spectroscopy and build up a molecular-level understanding of the attosecond electron dynamics in water. We measure the effect that the addition of single water molecules has on the photoionization time delays of water clusters. We find a continuous increase of the delay for clusters containing up to four to five molecules and little change towards larger clusters. We show that these delays are proportional to the spatial extension of the created electron hole, which first increases with cluster size and then partially localizes through the onset of structural disorder that is characteristic of large clusters and bulk liquid water. These results indicate a previously unknown sensitivity of photoionization delays to electron-hole delocalization and indicate a direct link between electronic structure and attosecond photoionization dynamics. Our results offer new perspectives for studying electron-hole delocalization and its attosecond dynamics.
The lowest-energy structures of neutral and cationic Ge n M (n = 9, 10; M = Si, Li, Mg, Al, Fe, Mn, Pb, Au, Ag, Yb, Pm and Dy) clusters were studied by genetic algorithm (GA) and first-principles calculations. The calculation results show that doping of the metal atoms and Si into Ge 9 and Ge 10 clusters is energetically favorable. Most of the metal-doped Ge cluster structures can be viewed as adding or substituting metal atom on the surface of the corresponding ground-state Ge n clusters. However, the neutral and cationic FeGe 9,10 ,MnGe 9,10 and Ge 10 Al are cage-like with the metal atom encapsulated inside. Such cage-like transition metal doped Ge n clusters are shown to have higher adsorption energy and thermal stability. Our calculation results suggest that Ge 9,10 Fe and Ge 9 Si would be used as building blocks in cluster-assembled nanomaterials because of their high stabilities.
Electrides are ionic compounds where localized electrons act as anions. Because of their exotic properties in optoelectronics and catalysis, electrides have attracted considerable interest in recent years and many electrides, with anionic electrons distributed in zero-, one-, two- and three-dimensions, have been studied. In this paper, using first-principles calculations, we report a different class of inorganic electrides where Li 3 O super-alkali clusters form the building blocks of one-dimensional hierarchical structures, characterized by the presence of localized electrons with high charge density along a one-dimensional chain. We further show that the stability of this electride can be improved by embedding it inside a BN nanotube (BNNT). In addition, Li 3 O@BNNT exhibits powerful reducing ability as well as anti-ferromagnetism, highlighting the importance of nano-assembly in the development of new stable electrides with novel properties.
Superatoms are atomic clusters with tailored size and composition that mimic the chemistry of atoms in the periodic table. However, unlike the atoms whose chemistry is governed by their valence electron orbitals, the chemistry of superatoms is governed by their highest occupied molecular orbitals. In addition, due to their large size and non-spherical geometry, superatoms can promote unusual reactions and serve as the building blocks of cluster assembled materials with properties very different from conventional materials. This perspective highlights the unique role of superatoms in chemical and material sciences by focusing on superhalogens, which not only possess electron affinities larger than those of halogens but also can be stable when multiply charged. We discuss how these unique features of superhalogens enable noble gas atoms like argon to form chemical bonds at room temperature and zinc to exhibit an oxidation state of +3. Here, the advantages of using superhalogens in the synthesis of water-resistant materials for solar cells, halogen-free electrolytes for solid-state batteries, and multiferroic materials are also discussed.
Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.
The outskirts of galaxy clusters present a promising avenue for constraining cluster masses in a way that is robust to the impact of baryonic physics. We assess the accuracy to which the cluster infall regions can be used for cluster mass calibration. Building on previous work, we parametrize the velocity distribution 𝑃(𝑣r,𝑣tan|𝑟,𝑀) of dark matter halos on scales 𝑟 ≥ 5ℎ −1 Mpc as the product of the marginalized distribution 𝑃(𝑣 r |𝑟,𝑀) and the conditional distribution 𝑃(𝑣 tan |𝑣 r ,𝑟,𝑀), calibrating the radial and mass dependence of these distributions in numerical simulations. We then project our model along the line of sight to obtain accurate predictions for the distributions of line-of-sight velocities at a given projected radius and cluster mass 𝑃(𝑣 LOS |𝑅,𝑀), which we can observe with spectroscopic survey data. Furthermore, with our model, we forecast that spectra from the Dark Energy Spectroscopic Instrument can constrain cluster masses with subpercent-level precision, comparable to that of stage-IV weak lensing surveys.
This paper presents a unified communication optimization framework for sparse triangular solve (SpTRSV) algorithms on CPU and GPU clusters. The framework builds upon a 3D communication-avoiding (CA) layout of Px × Py × Pz processes that divides a sparse matrix into Pz submatrices, each handled by a Px × Py 2D grid with block-cyclic distribution. We propose three communication optimization strategies: First, a new 3D SpTRSV algorithm is developed, which trades the inter-grid communication and synchronization with replicated computation. This design requires only one inter-grid synchronization, and the inter-grid communication is efficiently implemented with sparse allreduce operations. Second, broadcast and reduction communication trees are used to reduce message latency of the intra-grid 2D communication on CPU clusters. Finally, we leverage GPU-initiated one-sided communication to implement the communication trees on GPU clusters. With these nested inter- and intra-grid communication optimization strategies, the proposed 3D SpTRSV algorithm can attain up to 3.45x speedups compared to the baseline 3D SpTRSV algorithm using up to 2048 Cori Haswell CPU cores. In addition, the proposed GPU 3D SpTRSV algorithm can achieve up to 6.5x speedups compared to the proposed CPU 3D SpTRSV algorithm with Pz up to 64. Finally it is remarkable that the proposed GPU 3D SpTRSV can scale to 256 GPUs using the Perlmutter system while the existing 2D SpTRSV algorithm can only scale up to 4 GPUs.
A novel triazole bridged cobalt fluorophosphate coordination polymer with the formula {Co 5 (1,2,4-triazole) 2 (OH) 2 (PO 3 F) 4 } n has been synthesized. Single crystal X-ray studies reveal that the compound comprises of Co 5 clusters which ultimately build the whole framework, here all cobalt centers are in octahedral geometry established from continuous shape analysis. All the cobalt centers are in Co(II) oxidation state and the results are supported by the bond valence sum (BVS) calculation. The sample has been characterized by single crystal X-ray, powder X-ray, FTIR and TGA analysis. The compound was tested for olefin catalysis performance and magnetism. Magnetization measurements reveal overall antiferromagnetic interactions among the Co(II) ions. The compound is very active in the catalytic transformation of olefins to their corresponding epoxides. In conclusion, catalytic epoxidation studies reveal that cyclohexene and styrene can be converted up to 97% with almost 65% selectivity of products with higher TON numbers.
Despite a wide range of solid electrolyte phases, most of them only exist at high temperatures. The challenge is to tailor the chemical compositions of solid electrolyte materials that yield high ionic conductivities and low activation energies at ambient temperature. This is crucial for the development of all-solid-state batteries that are both powerful and safe. Here, we report our recent works to meet this challenge by utilizing multiple mechanistic principles and clusters as the building blocks. We show that the atomic-level interactions that govern the fast-ion conduction can be optimized by incorporating polyanion dynamics, non-stoichiometry, point defects and strong ionic correlations. Specifically, two case studies of Li/Na solid electrolytes will be covered, including lithium solid electrolytes (SE) with record-high ionic conductivities at room temperature (over 100 mS/cm) and sodium SE with record-low activation energies (< 0.1 eV).
Sodium (Na) superionic conductors are the key to developing next-generation solid-state batteries with safety and low cost. However, most of the known Na-conductors exhibit limited ionic conductivities at room temperature (RT), hindering their practical applications. To meet the challenge, a series of Li- and Na-rich antiperovskite superionic conductors based on cluster ions (e.g. BH4- and BCl4-) have been theoretically developed [e.g. Fang et al. PNAS 114, 11046, 2017; ACS App. Mat. Inter. 11, 963, 2018]. These materials exhibit superior properties as solid electrolytes with greatly enhanced ionic conductivities at RT. One cluster-based solid electrolyte of such, Na3O(BH)4, has been successfully synthesized for the first time most recently and its measured RT ionic conductivity is well above 10-3 S/cm which is four orders of magnitude higher than that of its halogen counterparts Na3OX (X = Cl, Br, I) [Sun et al. J. Am. Chem. Soc. 141, 5640, 2019]. In this work, we aim to further enhance the ionic conductivity of the Na-superionic conductor by using chemical mixing according to the size effect. The study further shows the advantage of utilizing cluster ions as building blocks, introducing additional degrees of freedom into tuning the properties of superionic conductors.