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At least 703 records · Page 39

Charge Transport in Zirconium-Based Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are a class of crystalline porous materials characterized by inorganic nodes and multitopic organic linkers. Because of their molecular-scale porosity and periodic intraframework chemical functionality, MOFs are attractive scaffolds for supporting and/or organizing catalysts, photocatalysts, chemical-sensing elements, small enzymes, and numerous other functional-property-imparting, nanometer-scale objects. Notably, these objects can be installed after the synthesis of the MOF, eliminating the need for chemical and thermal compatibility of the objects with the synthesis milieu. Thus, postsynthetically functionalized MOFs can present three-dimensional arrays of high-density, yet well-separated, active sites. Depending on the application and corresponding morphological requirements, MOF materials can be prepared in thin-film form, pelletized form, isolated single-crystal form, polycrystalline powder form, mixed-matrix membrane form, or other forms. For certain applications, most obviously catalytic hydrolysis and electro- or photocatalytic water splitting, but also many others, an additional requirement is water stability. MOFs featuring hexa-zirconium(IV)-oxy nodes satisfy this requirement. For applications involving electrocatalysis, charge storage, photoelectrochemical energy conversion, and chemiresistive sensing, a further requirement is electrical conductivity, as embodied in electron or hole transport. As most MOFs, under most conditions, are electrically insulating, imparting controllable charge-transport behavior is both a chemically intriguing and chemically compelling challenge. Herein, we describe three strategies to render zirconium-based metal–organic frameworks (MOFs) tunably electrically conductive and, therefore, capable of transporting charge on the few nanometers (i.e., several molecular units) to few micrometers (i.e., typical dimensions for MOF microcrystallites) scale. The first strategy centers on redox-hopping between periodically arranged, chemically equivalent sites, essentially repetitive electron (or hole) self-exchange. Zirconium nodes are electrically insulating, but they can function as grafting sites for (a) redox-active inorganic clusters or (b) molecular redox couples. Alternatively, charge hopping based on linker redox properties can be exploited. Marcus’s theory of electron transfer has proven useful for understanding/predicting trends in redox-hopping based conductivity, most notably, in accounting for variations as great as 3000-fold depending on the direction of charge propagation through structurally anisotropic MOFs. In MOF environments, propagation of electronic charge via redox hopping is necessarily accompanied by movement of charge-compensating ions. Consequently, rates of redox hopping can depend on both the identity and concentration of ions permeating the MOF. In the context of electrocatalysis, an important goal is to transport electronic charge fast enough to match or exceed the inherent activity of MOF-based or MOF-immobilized catalysts. Bandlike electronic conductivity is the focus of an alternative strategy: one based on the introduction of molecular guests capable of forming donor–acceptor charge transfer complexes with the host framework. Theory again can be applied predictively to alter conductivity. A third strategy similarly emphasizes electronic conductivity, but it makes use of added bridges in the form of molecular oligomers or inorganic clusters that can then be linked to span the length of a MOF crystallite. For all strategies, retention of molecular-scale porosity is emphasized, as this property is key to many applications. Lastly, while our focus is on Zr-MOFs, the described approaches clearly are extendable to other MOF compositions, as has already been demonstrated, in part, in studies by others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spin–orbit coupling of electrons on separate lanthanide atoms of Pr 2 O 2 and its singly charged cation

Although it plays a critical role in the photophysics and catalysis of lanthanides, spin–orbit coupling of electrons on individual lanthanide atoms in small clusters is not well understood. The major objective of this work is to probe such coupling of the praseodymium (Pr) 4f and 6s electrons in Pr 2 O 2 and Pr 2 O 2 +. The approach combines mass-analyzed threshold ionization spectroscopy and spin–orbit multiconfiguration second-order quasi-degenerate perturbation theory. The energies of six ionization transitions are precisely measured; the adiabatic ionization energy of the neutral cluster is 38 045 (5) cm –1 . Most of the electronic states involved in these transitions are identified as spin–orbit coupled states consisting of two or more electron spins. The electron configurations of these states are 4f 4 6s 2 for the neutral cluster and 4 f 46 s for the singly charged cation, both in planar rhombus-type structures. Finally, the spin–orbit splitting due to the coupling of the electrons on the separate Pr atoms is on the order of hundreds of wavenumbers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine-learning predictions of the shale wells’ performance

The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). In this work, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.

03 NATURAL GAS↗

First-Principles Statistical Mechanics Study of Magnetic Fluctuations and Order–Disorder in the Spinel LiNi 0.5 Mn 1.5 O 4 Cathode

While significant magnetic interactions exist in lithium transition metal oxides, commonly used as Li-ion cathodes, the interplay between magnetic couplings, disorder, and redox processes remains poorly understood. In this work, we focus on the high-voltage spinel LiNi 0.5 Mn 1.5 O 4 (LNMO) cathode as a model system on which to apply a computational framework that uses first principles-based statistical mechanics methods to predict the finite temperature magnetic properties of materials and provide insights into the complex interplay between magnetic and chemical degrees of freedom. Density functional theory calculations on multiple distinct Ni–Mn orderings within the LNMO system, including the ordered ground-state structure (space group P4332), reveal a preference for a ferrimagnetic arrangement of the Ni and Mn sublattices due to strong antiferromagnetic superexchange interactions between neighboring Mn 4+ and Ni 2+ ions and ferromagnetic Mn–Mn and Ni–Ni couplings, as revealed by magnetic cluster expansions. These results are consistent with qualitative predictions using the Goodenough-Kanamori-Anderson rules. Simulations of the finite temperature magnetic properties of LNMO are conducted using Metropolis Monte Carlo. We find that a “semiclassical” Monte Carlo sampling method based on the Heisenberg Hamiltonian accurately predicts experimental magnetic transition temperatures observed in magnetometry measurements. This study highlights the importance of a robust computational toolkit that accurately captures the complex chemomagnetic interactions and predicts finite temperature magnetic behavior to help analyze experimental magnetic and magnetic resonance spectroscopy data acquired ex situ and operando.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Magnetic Properties of Tetravalent Pu in the Perovskites BaPuO 3 and SrPuO 3

BaPuO 3 and SrPuO 3 were synthesized, and their structures were refined in the orthorhombic space group Pbnm , a common distortion from the classic $Pm\bar{3}$m over barm cubic perovskite. Magnetic-susceptibility measurements, obtained as a function of temperature over the range of 1.8-320 K, exhibit temperature-dependent behavior, with evidence of long-range magnetic order at temperatures higher than their lanthanide and actinide analogues: BaPuO 3 below 164(1) K and SrPuO 3 below 76(1) K. Effective moments of 1.66(10)μ B for BaPuO 3 and 1.84(8)μ B for SrPuO 3 were obtained by fitting their paramagnetic susceptibilities using the Curie-Weiss law. Further, both are below the free-ion value of 2.68 μ B expected for a Pu 4+ 5 $\mid$ 4 ground level. Ab initio wave function calculations, performed at the relativistic complete active space level including spin-orbit coupling and with an embedded cluster approach that neglects interactions between Pu centers, were used to generate embedded-cluster Pu 4+ magnetic susceptibilities. The calculations agree well with experimental data at higher temperatures, providing evidence that a single-ion representation is sufficient to account for the observed paramagnetic behavior without the need to invoke charge transfer, disproportionation, strong covalent bonding, or other more complex electronic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Three rate-determining protein roles in photosynthetic O 2 -evolution addressed by time-resolved experiments on genetically modified photosystems

Light-driven water splitting by plants, algae and cyanobacteria is pivotal for global bioenergetics and biomass formation. A manganese cluster bound to the photosystem II proteins catalyzes the complex reaction at high rate, but the rate-determining factors are insufficiently understood. Here we trace the oxygen-evolution transition by time-resolved polarography and infrared spectroscopy for cyanobacterial photosystems genetically modified at two strategic sites, complemented by computational chemistry. Our results highlight three rate-determining roles of the protein environment of the metal cluster: acceleration of proton-coupled electron transfer, acceleration of substrate-water insertion after O 2 -formation, and balancing of rate-determining enthalpic and entropic contributions. Whereas in general the substrate-water insertion step may be unresolvable in time-resolved experiments, here it likely becomes traceable because of deceleration by genetic modification. Our results may stimulate new time-resolved experiments on substrate-water insertion in photosynthesis, clarification of enthalpy-entropy compensation in enzyme catalysis, and knowledge-guided development of inorganic catalyst materials.

Bioenergetics↗

Scheduling Tasks In Parallel Processing

Algorithms sought to minimize time and cost of computation. Report describes research on scheduling of computations tasks in system of multiple identical data processors operating in parallel. Computational intractability requires use of suboptimal heuristic algorithms. First algorithm called "list heuristic", variation of classical list scheduling. Second algorithm called "cluster heuristic" applied to tightly coupled tasks and consists of four phases. Third algorithm called "exchange heuristic", iterative-improvement algorithm beginning with initial feasible assignment of tasks to processors and periods of time. Fourth algorithm is iterative one for optimal assignment of tasks and based on concept called "simulated annealing" because of mathematical resemblance to aspects of physical annealing processes.

Price, Camille C.↗

The role of the host in a cooperating mainframe and workstation environment, volumes 1 and 2

In recent years, advancements made in computer systems have prompted a move from centralized computing based on timesharing a large mainframe computer to distributed computing based on a connected set of engineering workstations. A major factor in this advancement is the increased performance and lower cost of engineering workstations. The shift to distributed computing from centralized computing has led to challenges associated with the residency of application programs within the system. In a combined system of multiple engineering workstations attached to a mainframe host, the question arises as to how does a system designer assign applications between the larger mainframe host and the smaller, yet powerful, workstation. The concepts related to real time data processing are analyzed and systems are displayed which use a host mainframe and a number of engineering workstations interconnected by a local area network. In most cases, distributed systems can be classified as having a single function or multiple functions and as executing programs in real time or nonreal time. In a system of multiple computers, the degree of autonomy of the computers is important; a system with one master control computer generally differs in reliability, performance, and complexity from a system in which all computers share the control. This research is concerned with generating general criteria principles for software residency decisions (host or workstation) for a diverse yet coupled group of users (the clustered workstations) which may need the use of a shared resource (the mainframe) to perform their functions.

Kusmanoff, Antone↗

The EMSS catalog of X-ray-selected clusters of galaxies. 1: An atlas of CCD images of 41 distant clusters

An atlas of deep, wide-field R-band charge coupled device (CCD) images of a complete sample of distant, X-ray-selected clusters of galaxies is presented. These clusters are the 41 most distant (z is greater than or equal to 0.15) and most X-ray-luminous (L(sub x) is greater than or equal to 2 x 10(exp 44) ergs/s) clusters in the Einstein Observatory Extended Medium Sensitivity Survey (EMSS) catalog that are observable from Mauna Kea (delta is greater than -40 deg). The sample spans a redshift range of 0.15 is less than or equal to z is less than or equal to 0.81 and includes at least two and possibly as many as six rich clusters with z is greater than 0.5. For the most part, the data are of superior quality, with a median seeing of 0.8 sec full width half-maximum (FWHM) and coverage of at least 1 Mpc x 1 Mpc in the cluster frame (H(sub 0) = 50; q(sub 0) = 1/2). In addition, we update the available optical, X-ray, and radio data on the entire EMSS sample of 104 clusters. We outline the cluster selection criteria in detail and emphasize that X-ray-selected cluster samples may prove to be more useful for cosmological studies than optically selected samples. The EMSS cluster sample in particular can be exploited for diverse cosmological investigations, as demonstrated by the detection of evolution in the X-ray luminosity function previously reported, and more recently by the discovery of a large number of gravitationally lensed images in these clusters.

Gioia, I. M.↗

Permutation-adapted complete and independent basis for atomic cluster expansion descriptors

In many recent applications, particularly in the field of atom-centered descriptors for interatomic potentials, tensor products of spherical harmonics have been used to characterize complex atomic environments. When coupled with a radial basis, the atomic cluster expansion (ACE) basis is obtained. However, symmetrization with respect to both rotation and permutation results in an overcomplete set of ACE descriptors with linear dependencies occurring within blocks of functions corresponding to particular generalized Wigner symbols. All practical applications of ACE employ semi-numerical constructions to generate a complete, fully independent basis. While computationally tractable, the resultant basis cannot be expressed analytically, is susceptible to numerical instability, and thus has limited reproducibility. Here we present a procedure for generating explicit analytic expressions for a complete and independent set of ACE descriptors. The procedure uses a coupling scheme that is maximally symmetric w.r.t. permutation of the atoms, exposing the permutational symmetries of the generalized Wigner symbols, and yields a permutation-adapted rotationally and permutationally invariant basis (PA-RPI ACE). Theoretical support for the approach is presented, as well as numerical evidence of completeness and independence. A summary of explicit enumeration of PA-RPI functions up to rank 6 and polynomial degree 32 is provided. The PA-RPI blocks corresponding to particular generalized Wigner symbols may be either larger or smaller than the corresponding blocks in the simpler rotationally invariant basis. Finally, we demonstrate that basis functions of high polynomial degree persist under strong regularization, indicating the importance of not restricting the maximum degree of basis functions in ACE models a priori.

74 ATOMIC AND MOLECULAR PHYSICS↗

Mimas: Preliminary Evidence For Amorphous Water Ice from VIMS

We have conducted a statistical clustering analysis (1,2) on a mosaic of VIMS data cubes obtained on February 13, 2010, for Saturn s satellite Mimas. Seven VIMS cubes were geometrically projected and re-sampled to a common spatial resolution. The clustering technique consists of a partitioning algorithm coupled to a criterion that prevents sub-optimal solutions and tests for the influence of random noise in the measurements. The clustering technique is agnostic about the meaning of the clusters, and scientific interpretation requires their a posteriori evaluation. The preliminary results yielded five clusters, demonstrating that spectral variability across Mimas surface is statistically significant. The ratios of the means calculated for each of the clusters show structure within the 1.6- micron water ice band, as well as the shape and the central wavelength of the strong ice band at 2 micron, that map spatially in patterns apparently related to the topography of Mimas, in particular certain regions in and around Herschel crater. The mean spectra of the five clusters, show similarities with laboratory spectra of amorphous and crystalline H2O ice (3) that are suggestive of the presence of an amorphous ice component in certain regions of Mimas, notably on the central peak of Herschel, on the crater floor, and in faults surrounding the crater. This may represent a mixture of both ice phases, or perhaps a layer of amorphous ice on a base of crystalline ice. Another possible occurrence of amorphous ice appears southwest of Herschel, close to the south pole.

Cruikshank, Dale P.↗

Packaging of wide bandgap power electronic power stages

Design and packaging of wide bandgap (WBG) power electronic power stages are disclosed herein. An example apparatus includes a first printed circuit board (PCB) including: a first voltage phase circuit cluster; a second voltage phase circuit cluster; and a cluster of traces, the cluster of traces routed substantially perpendicular to the second voltage phase circuit cluster; a second PCB positioned below the first PCB; and a connector to connect the first PCB to the second PCB, the connector electrically coupled to the first voltage phase circuit cluster by the cluster of traces.

Singh, Brij N.↗

Possibility of 14 C cluster as a building block of medium-mass nuclei

The possibility of the 14 C cluster being a basic building block of medium-mass nuclei is discussed. Although α cluster structures have been widely discussed in the light N ≈ Z mass region, the neutron-to-proton ratio deviates from unity in the nuclei near β-stability line and in neutron-rich nuclei. Thus, more neutron-rich objects with N > Z could become the building blocks of cluster structures in such nuclei. The 14 C nucleus is strongly bound and can be regarded as such a candidate. In addition, the path to the lowest shell-model configuration at short relative distances is closed for the 14 C+ 14 C structure, contrary to the case of the 12 C+ 12 C structure; this allows an appreciable separation distance between the 14 C clusters. The recent development of the antisymmetrized quasicluster model (AQCM) allows us to utilize a j j-coupling shell-model wave function for each cluster in a simplified way. The AQCM results for the 14 C+ 14 C structure in 28 Mg are compared with the ones of cranked relativistic mean field (CRMF) calculations. Although theoretical frameworks of these two models are quite different, they give similar results for the nucleonic densities and rotational properties of the structure under investigation. The existence of a linear chain three- 14 C cluster structure in 42 Ar has also been predicted in AQCM. Furthermore, these results confirm the role of the 14 C cluster as a possible building block of cluster structures in medium-mass nuclei.

20 ≤ A ≤ 38↗

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗

Optical properties and carrier localization in the layered phosphide EuCd 2 P 2

The temperature dependence of the complex optical properties of the layered phosphide material EuCd 2 P 2 have been measured over a wide frequency range above and below T N ≃ 11.5 K for light polarized in the a-b planes. At room temperature, the optical conductivity is well described by a weak free-carrier component with a Drude plasma frequency of ≃ 1100 cm –1 and a scattering rate of 1/τ D ≃ 700 cm –1 , with the onset of interband absorptions above ≃ 2000 cm –1 . Two infrared-active E u modes are observed at ≃ 89 and 239 cm –1 . As the temperature is reduced the scattering rate decreases and the low-frequency conductivity increases slightly; however, below ≃ 50 K the conductivity decreases until at the resistivity maximum at ≃ 18 K (just below 2T N ) the spectral weight associated with free carriers is transferred to a localized excitation at ≃ 500 cm –1 . Below T N , metallic behavior is recovered. Interestingly, the E u modes are largely unaffected by these changes, with only the position of the high-frequency mode showing any signs of anomalous behavior. Finally, while several scenarios are considered, the prevailing view is that the resistivity maximum and subsequent carrier localization is due to the formation of ferromagnetic domains below ≃ 2 T N that result in spin-polarized clusters due to spin-carrier coupling.

36 MATERIALS SCIENCE↗

Confinement and Collective Escape of Active Particles

Active matter broadly covers the dynamics of self-propelled particles. While the onset of collective behavior in homogenous active systems is relatively well understood, the effect of inhomogeneities such as obstacles and traps lacks overall clarity. Here, we study how interacting, self-propelled particles become trapped and released from a trap. We have found that captured particles aggregate into an orbiting condensate with a crystalline structure. As more particles are added, the trapped condensates escape as a whole. Our results shed light on the effects of confinement and quenched disorder in active matter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗