Pacific Northwest National Laboratory

About: Pacific Northwest National Laboratory is a facility organization based out in Richland, Washington, United States. It is known for research contribution in the topics: Catalysis & Aerosol. The organization has 11581 authors who have published 27934 publications receiving 1120489 citations. The organization is also known as: PNL & PNNL.

The roles of the first and second coordination spheres in the design of molecular catalysts for H2 production and oxidation

Abstract: This tutorial review describes the development of discrete transition metal complexes as electrocatalysts for H2 formation and oxidation. The approach involves the study of thermodynamic properties of metal hydride intermediates and the design of ligands that incorporate proton relays. The work is inspired by structural features of the H2ase enzymes and should be of interest to researchers in the areas of biomimetic chemistry as well as catalyst design and hydrogen utilization.

Deep learning for computational chemistry

Abstract: The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc.

Electrochemical Properties of Mixed Conducting Perovskites La1 − x M x Co1 − y Fe y O 3 − δ (M = Sr, Ba, Ca)

Abstract: Perovskite compositions in the system La{sub 1{minus}x}M{sub x}Co{sub 1{minus}y}Fe{sub y}O{sub 3{minus}{delta}} (M = Sr, Ba, Ca) exhibited high electronic and ionic conductivity. Substantial reversible weight loss was observed at elevated temperatures as the materials became increasingly oxygen deficient. This loss of lattice oxygen at high temperatures, which tended to increase with increasing acceptor content, resulted in a decrease in the electronic conductivity. In an oxygen partial pressure gradient, oxygen flux through dense sintered membranes of these materials was highly dependent on composition and increased with increasing temperature. The increase in oxygen flux with increasing temperature was attributed to increases in the mobility and concentration of lattice oxygen vacancies. The calculated ionic conductivities of some compositions exceeded that of yttria-stabilized zirconia. This material is an attractive candidate for several important applications, including solid oxide fuel cell cathodes.

Flow-cytometric isolation of human antibodies from a nonimmune Saccharomyces cerevisiae surface display library

Abstract: A nonimmune library of 10(9) human antibody scFv fragments has been cloned and expressed on the surface of yeast, and nanomolar-affinity scFvs routinely obtained by magnetic bead screening and flow-cytometric sorting. The yeast library can be amplified 10(10)-fold without measurable loss of clonal diversity, allowing its effectively indefinite expansion. The expression, stability, and antigen-binding properties of >50 isolated scFv clones were assessed directly on the yeast cell surface by immunofluorescent labeling and flow cytometry, obviating separate subcloning, expression, and purification steps and thereby expediting the isolation of novel affinity reagents. The ability to use multiplex library screening demonstrates the usefulness of this approach for high-throughput antibody isolation for proteomics applications.