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.

Geometry-Based Edge Clustering for Graph Visualization

Abstract: Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.

Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Abstract: Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

Extremely Stable Sodium Metal Batteries Enabled by Localized High-Concentration Electrolytes

Abstract: Sodium (Na) metal is a promising anode for Na-ion batteries. However, the high reactivity of Na metal with electrolytes and the low Na metal cycling efficiency have limited its practical application in rechargeable Na metal batteries. High-concentration electrolytes (HCE, ≥4 M) consisting of sodium bis(fluorosulfonyl)imide (NaFSI) and ether solvent could ensure the stable cycling of Na metal with high Coulombic efficiency but at the cost of high viscosity, poor wettability, and high salt cost. Here, we report that the salt concentration could be significantly reduced (≤1.5 M) by a hydrofluoroether as an “inert” diluent, which maintains the solvation structures of HCE, thereby forming a localized high-concentration electrolyte (LHCE). A LHCE [2.1 M NaFSI/1,2-dimethoxyethane (DME)–bis(2,2,2-trifluoroethyl) ether (BTFE) (solvent molar ratio 1:2)] enables dendrite-free Na deposition with a high Coulombic efficiency of >99%, fast charging (20C), and stable cycling (90.8% retention after 40 000 cycles) of Na∥Na...

Facile synthesized nanorod structured vanadium pentoxide for high-rate lithium batteries

Abstract: Nano-structured vanadium oxide (V2O5) is fabricated via a facile thermal-decomposition of vanadium precursor, vanadyl oxalate, which is produced by reacting micro-sized V2O5 with oxalic acid. The V2O5 nanoparticles produced by this method exhibit much better electrochemical performance than commercial micro-sized V2O5. The optimized-nanorod electrodes give the best specific discharge capacities of 270 mAh g−1 at C/2 (147 mA g−1) coupled with good cycle stability with only 0.32% fading per cycle. Even at a high rate of 4C (1176 mA g−1), the nanorod electrode still delivers 198 mAh g−1. These results suggest that the well-separated V2O5 nanorod is a good cathode material for high-rate lithium battery applications.