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 role of the solid electrolyte interphase layer in preventing Li dendrite growth in solid-state batteries

Abstract: Lithium (Li) metal anodes have regained intensive interest in recent years due to the ever-increasing demand for next-generation high energy battery technologies. Li metal, unfortunately, suffers from poor cycling stability and low efficiency as well as from the formation of dangerous Li dendrites, raising safety concerns. Utilizing solid-state electrolytes (SSEs) to prevent Li dendrite growth provides a promising approach to tackle the challenge. However, recent studies indicate that Li dendrites easily form at high current densities, which calls for full investigation of the fundamental mechanisms of Li dendrite formation within SSEs. Herein, the origin and evolution of Li dendrite growth through SSEs have been studied and compared by using Li6.1Ga0.3La3Zr2O12 (LLZO) and NASICON-type Li2O–Al2O3–P2O5–TiO2–GeO2 (LATP) pellets as the separators. We discover that a solid electrolyte interphase (SEI)-like interfacial layer between Li and SSE plays a critical role in alleviating the growth of dendritic Li, providing new insights into the interface between SSE and Li metal to enable future all solid-state batteries.

Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri

Abstract: We present a genome-scale metabolic model for the archaeal methanogen Methanosarcina barkeri. We characterize the metabolic network and compare it to reconstructions from the prokaryotic, eukaryotic and archaeal domains. Using the model in conjunction with constraint-based methods, we simulate the metabolic fluxes and resulting phenotypes induced by different environmental and genetic conditions. This represents the first large-scale simulation of either a methanogen or an archaeal species. Model predictions are validated by comparison to experimental growth measurements and phenotypes of M. barkeri on different substrates. The predicted growth phenotypes for wild type and mutants of the methanogenic pathway have a high level of agreement with experimental findings. We further examine the efficiency of the energy-conserving reactions in the methanogenic pathway, specifically the Ech hydrogenase reaction, and determine a stoichiometry for the nitrogenase reaction. This work demonstrates that a reconstructed metabolic network can serve as an analysis platform to predict cellular phenotypes, characterize methanogenic growth, improve the genome annotation and further uncover the metabolic characteristics of methanogenesis.

Normalization and missing value imputation for label-free LC-MS analysis.

Abstract: Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Single-Enzyme Nanoparticles Armored by a Nanometer-Scale Organic/Inorganic Network

Abstract: We have developed armored single-enzyme nanoparticles (SENs) that surround each enzyme molecule with a porous composite organic/inorganic network of less than a few nanometers thick. This approach ...

MIRAGE: Model description and evaluation of aerosols and trace gases

Abstract: [1] The Model for Integrated Research on Atmospheric Global Exchanges (MIRAGE) modeling system, designed to study the impacts of anthropogenic aerosols on the global environment, is described. MIRAGE consists of a chemical transport model coupled online with a global climate model. The chemical transport model simulates trace gases, aerosol number, and aerosol chemical component mass (sulfate, methane sulfonic acid (MSA), organic matter, black carbon (BC), sea salt, and mineral dust) for four aerosol modes (Aitken, accumulation, coarse sea salt, and coarse mineral dust) using the modal aerosol dynamics approach. Cloud-phase and interstitial aerosol are predicted separately. The climate model, based on Community Climate Model, Version 2 (CCM2), has physically based treatments of aerosol direct and indirect forcing. Stratiform cloud water and droplet number are simulated using a bulk microphysics parameterization that includes aerosol activation. Aerosol and trace gas species simulated by MIRAGE are presented and evaluated using surface and aircraft measurements. Surface-level SO2 in North American and European source regions is higher than observed. SO2 above the boundary layer is in better agreement with observations, and surface-level SO2 at marine locations is somewhat lower than observed. Comparison with other models suggests insufficient SO2 dry deposition; increasing the deposition velocity improves simulated SO2. Surface-level sulfate in North American and European source regions is in good agreement with observations, although the seasonal cycle in Europe is stronger than observed. Surface-level sulfate at high-latitude and marine locations, and sulfate above the boundary layer, are higher than observed. This is attributed primarily to insufficient wet removal; increasing the wet removal improves simulated sulfate at remote locations and aloft. Because of the high sulfate bias, radiative forcing estimates for anthropogenic sulfur given in 2001 by S. J. Ghan and colleagues are probably too high. Surface-level dimethyl sulfide (DMS) is ∼40% higher than observed, and the seasonal cycle shows too much DMS in local winter, partially caused by neglect of oxidation by NO3. Surface-level MSA at marine locations is ∼80% higher than observed, also attributed to insufficient wet removal. Surface-level BC is ∼50% lower than observed in the United States and ∼40% lower than observed globally. Treating BC as initially hydrophobic would lessen this bias. Surface-level organic matter is lower than observed in the United States, similar to BC, but shows no bias in the global comparison. Surface-level sea salt concentrations are ∼30% lower than observed, partly caused by low temporal variance of the model's 10 m wind speeds. Submicrometer sea salt is strongly underestimated by the emissions parameterization. Dust concentrations are within a factor of 3 at most sites but tend to be lower than observed, primarily because of neglect of very large particles and underestimation of emissions and vertical transport under high-wind conditions. Accumulation and Aitken mode number concentrations and mean sizes at the surface over ocean, and condensation nuclei concentrations aloft over the Pacific, are in fair agreement with observations. Concentrations over land are generally higher than observations, with mean sizes correspondingly lower than observations, especially at some European locations. Increasing the assumed size of emitted particles produces better agreement at the surface over land, and reducing the particle nucleation rate improves the agreement aloft over land.