Amine, Amido, and Imido Complexes of Tantalum Supported by a Pyridine-Linked Bis(phenolate) Pincer Ligand: Ta−N π-Bonding Influences Pincer Ligand Geometry

A series of tantalum imido and amido complexes supported by a pyridine-linked bis(phenolate) ligand has been synthesized. Characterization of these complexes via X-ray crystallography reveals both C_s and C_2 binding modes of the bis(phenolate)pyridine ligand, with complexes containing two or fewer strong π-donor interactions from ancillary ligands giving C_s symmetry, whereas three strong π-donor interactions (e.g., three amido ligands or one amido ligand and one imido ligand) give C_2-symmetric binding of the bis(phenolate)pyridine ligand. DFT calculations and molecular orbital analyses of the complexes have revealed that the preference for C_s-symmetric ligand binding is a result of tantalum−phenolate π-bonding, whereas in cases where tantalum−phenolate π-bonding is overridden by stronger Ta−N π-bonding, C_2-symmetric ligand binding is preferred, likely because conformationally this is the lowest-energy arrangement. This electronically driven change in geometry indicates that, unlike analogous metallocene systems, the bis(phenolate)pyridine pincer ligand is not a strong enough π-donor to exert dominant control over the electronic and geometric properties of the complex

“Opening” a New Kind of High School: The Story of the Open High School of Utah

The use of online learning at the primary and secondary school level is growing exponentially in the United States. Much of this growth is with full-time online schools, most of which are operated by for-profit companies that use proprietary online course content. In this article we trace the development of, and philosophy behind, a full-time online school that uses open access software and open educational resources for course content. As more nations begin to put in place plans for primary and secondary education in the event of natural disasters (e.g., the Christchurch earthquakes) or pandemics (e.g., avian flu or H1N1), the availability of open online content is of critical importance

Modeling Fission Gas Release at the Mesoscale using Multiscale DenseNet Regression with Attention Mechanism and Inception Blocks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with $R^{2}$ values above 98%. The best performing network combine a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values.Comment: Submitted at Journal of Nuclear Materials, 20 pages, 10 figures, 3 table

Institutional Investors and the QE Portfolio Balance Channel

The operation of the portfolio balance channel has been emphasized by monetary policy makers as a key channel through which quantitative easing (QE) policies work. We assess whether the investment behavior of insurance companies and pension funds in the United Kingdom during the global financial crisis was consistent with such an effect by analyzing both sectoral and institution-level data. Our results suggest QE led to institutional investors shifting their portfolios away from government bonds towards corporate bonds, but did not lead to a shift into equities

Impact of grain boundary and surface diffusion on predicted fission gas bubble behavior and release in UO2_2 fuel

In this work, we quantify the impact of grain boundary (GB) and surface diffusion on fission gas bubble evolution and fission gas release in UO$_2$ nuclear fuel using simulations with a hybrid phase field/cluster dynamics model. We begin with a comprehensive literature review of uranium vacancy and xenon atom diffusivity in UO$_2$ through the bulk, along GBs, and along surfaces. In our model we represent fast GB and surface diffusion using a heterogeneous diffusivity that is a function of the order parameters that represent bubbles and grains. We find that the GB diffusivity directly impacts the rate of gas release via GB transport, and that the GB diffusivity is likely below 10$^4$ times the lower value from Olander and van Uffelen (2001). We also find that the surface diffusivity impacts bubble coalescence and mobility, and that the bubble surface diffusivity is likely below $10^{-4}$ times the value from Zhou and Olander (1984).Comment: 34 pages, 11 figures, submitted at Journal of Nuclear Materials (Under Review

The pseudophosphatase MK-STYX interacts with G3BP and decreases stress granule formation

MK-STYX [MAPK (mitogen-activated protein kinase) phospho-serine/threonine/tyrosine-binding protein] is a pseudophosphatase member of the dual-specificity phosphatase subfamily of the PTPs (protein tyrosine phosphatases). MK-STYX is catalytically inactive due to the absence of two amino acids from the signature motif that are essential for phosphatase activity. The nucleophilic cysteine residue and the adjacent histidine residue, which are conserved in all active dual-specificity phosphatases, are replaced by serine and phenylalanine residues respectively in MK-STYX. Mutations to introduce histidine and cysteine residues into the active site of MK-STYX generated an active phosphatase. Using MS, we identified G3BP1 [Ras-GAP (GTPase-activating protein) SH3 (Src homology 3) domain-binding protein-1], a regulator of Ras signalling, as a binding partner of MK-STYX. We observed that G3BP1 bound to native MK-STYX; however, binding to the mutant catalytically active form of MK-STYX was dramatically reduced. G3BP1 is also an RNA-binding protein with endoribonuclease activity that is recruited to ‘stress granules’ after stress stimuli. Stress granules are large subcellular structures that serve as sites of mRNA sorting, in which untranslated mRNAs accumulate. We have shown that expression of MK-STYX inhibited stress granule formation induced either by aresenite or expression of G3BP itself; however, the catalytically active mutant MK-STYX was impaired in its ability to inhibit G3BP-induced stress granule assembly. These results reveal a novel facet of the function of a member of the PTP family, illustrating a role for MK-STYX in regulating the ability of G3BP1 to integrate changes in growth-factor stimulation and environmental stress with the regulation of protein synthesis

Physics-based multiscale coupling for full core nuclear reactor simulation

Numerical simulation of nuclear reactors is a key technology in the quest for improvements in efficiency, safety, and reliability of both existing and future reactor designs. Historically, simulation of an entire reactor was accomplished by linking together multiple existing codes that each simulated a subset of the relevant multiphysics phenomena. Recent advances in the MOOSE (Multiphysics Object Oriented Simulation Environment) framework have enabled a new approach: multiple domain-specific applications, all built on the same software framework, are efficiently linked to create a cohesive application. This is accomplished with a flexible coupling capability that allows for a variety of different data exchanges to occur simultaneously on high performance parallel computational hardware. Examples based on the KAIST-3A benchmark core, as well as a simplified Westinghouse AP-1000 configuration, demonstrate the power of this new framework for tackling—in a coupled, multiscale manner—crucial reactor phenomena such as CRUD-induced power shift and fuel shuffle.Massachusetts Institute of Technology. Department of Nuclear Science and EngineeringIdaho National Laboratory (Contract DE-AC07-05ID14517

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing

Stepwise Conformational Stabilization of a HIV-1 Clade C Consensus Envelope Trimer Immunogen Impacts the Profile of Vaccine-Induced Antibody Responses.

Stabilization of the HIV-1 Envelope glycoprotein trimer (Env) in its native pre-fusion closed conformation is regarded as one of several requirements for the induction of neutralizing antibody (nAb) responses, which, in turn, will most likely be a prerequisite for the development of an efficacious preventive vaccine. Here, we systematically analyzed how the stepwise stabilization of a clade C consensus (ConC) Env immunogen impacts biochemical and biophysical protein traits such as antigenicity, thermal stability, structural integrity, and particle size distribution. The increasing degree of conformational rigidification positively correlates with favorable protein characteristics, leading to optimized homogeneity of the protein preparations, increased thermal stability, and an overall favorable binding profile of structure-dependent broadly neutralizing antibodies (bnAbs) and non-neutralizing antibodies (non-nAbs). We confirmed that increasing the structural integrity and stability of the Env trimers positively correlates with the quality of induced antibody responses by the immunogens. These and other data contribute to the selection of ConCv5 KIKO as novel Env immunogens for use within the European Union's H2020 Research Consortium EHVA (European HIV Alliance) for further preclinical analysis and phase 1 clinical development

PROGRESS ON GENERIC PHASE-FIELD METHOD DEVELOPMENT

In this report, we summarize our current collobarative efforts, involving three national laboratories: Idaho National Laboratory (INL), Pacific Northwest National Laboratory (PNNL) and Los Alamos National Laboatory (LANL), to develop a computational framework for homogenous and heterogenous nucleation mechanisms into the generic phase-field model. During the studies, the Fe-Cr system was chosen as a model system due to its simplicity and availability of reliable thermodynamic and kinetic data, as well as the range of applications of low-chromium ferritic steels in nuclear reactors. For homogenous nucleation, the relavant parameters determined from atomistic studies were used directly to determine the energy functional and parameters in the phase-field model. Interfacial energy, critical nucleus size, nucleation rate, and coarsening kinetics were systematically examined in two- and three- dimensional models. For the heteregoneous nucleation mechanism, we studied the nucleation and growth behavior of chromium precipitates due to the presence of dislocations. The results demonstrate that both nucleation schemes can be introduced to a phase-field modeling algorithm with the desired accuracy and computational efficiency