PNNL-13501 - Pacific Northwest National Laboratory

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Berkowitz, CM. June 2000. “Atmospheric chemistry modeling at <strong>PNNL</strong>: what it is and where we’re going.” Computer Sciences and Engineering Seminar Series, Richland, Washington. 210 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report Bian X, CM Berkowitz, RC Easter, JD Fast, SJ Ghan, and R Zaveri. 1999. “Development and application of an atmospheric modeling system for use on a massively parallel computer.” 1999 Annual Conference on Super- Computer, Portland, Oregon.
Study Control Number: PN99018/1346 Development of an Aquatic Bio-Optical Monitoring Buoy Dana Woodruff, Paul Farley, Karen Steinmaus Satellite algorithm development is needed in coastal and nearshore waters to take advantage of a new generation of higher resolution, recently launched satellites. This project evaluated the use of a bio-optical monitoring buoy with a complement of environmental and optical sensors to provide near real-time, remotely accessible data for algorithm development and natural resource/damage assessment of coastal and inland waters. Project Description This project developed an approach to collect bio-optical data in coastal nearshore, and inland waters using a near real-time remotely accessible bio-optical monitoring buoy designed to validate ocean color algorithms for satellites collecting data in coastal environments. The recent successful launch of the NASA Sea-Viewing Wide Fieldof-View Sensor ocean color satellite and scheduled launch of future ocean color sensors requires validation, modification, and development of ocean color algorithms for specific use in optically complex nearshore waters. In situ calibration data has traditionally been collected from shipboard platforms, requiring labor-intensive field efforts. This project will provide a streamlined, flexible approach to collect bio-optical data in geographic regions (coastal, nearshore, or inland waters) that require further algorithm development. A portable buoy with a unique complement of environmental and optical sensors will allow for a quick response monitoring capability for natural resource and/or damage assessment in inland and coastal waters. Introduction Global climate change issues have necessitated multidisciplinary assessment approaches, including the use of satellites to understand the role of oceanic phytoplankton and primary production in global ocean carbon cycling. Although coastal waters account for approximately 10% of the surface area of the global oceans, they contribute almost 40% of the total global primary production. One of the differences between coastal and oceanic primary production is thought to be partly a consequence of human-induced activity and associated impact to nearshore systems. To this end, the role of satellite monitoring in coastal waters has become increasingly important, including the ability to convert satellite-acquired data into reliable estimates of environmentally significant parameters. The use of satellite ocean color imagery in biologically productive coastal regions requires modification of standard oceanic algorithms and development of new algorithms for optical properties unique to coastal waters. Nearshore waters contain higher concentrations and greater varieties of optical constituents (chlorophyll, suspended sediments, colored dissolved organic matter) than oceanic waters. The first ocean color satellite, the Coastal Zone Color Scanner was operational between 1976 and 1986 and provided extensive data on ocean chlorophyll estimates; however, the algorithms and sensor could not adequately address coastal waters due to a limitation in gain and response of the sensor near land. The second generation of ocean color satellites recently became operational with the launch of the Sea-Viewing Wide Field-of-View Sensor satellite which has improved radiometric response and additional wavebands making it more useful in coastal waters than Coastal Zone Color Scanner was. NASA developed a well-calibrated and validated suite of algorithms for chlorophyll estimation in the open ocean. However, these algorithms are proving to be inadequate for viewing coastal regions, which are optically more complex. Rapid and accurate methods of collecting in situ absorption (chlorophyll and DOM) and backscattering (suspended particulates) data are needed to develop these new algorithms and to define regional algorithm boundaries for imagery application. Optical and environmental data that can be collected from a remote platform such as a buoy will reduce the cost significantly and will provide temporal coverage that is not available using traditional collection methods. Results and Accomplishments We evaluated the use of an aquatic bio-optical monitoring surface buoy to collect ground-truth data for satellite algorithm development in semiprotected coastal areas (Figure 1 and the schematic in Figure 2). Incorporated into the buoy platform were the following capabilities: 1) multipurpose combined satellite validation and Earth System Science 211
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Laboratory Directed Research and De
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Laboratory Directed Research and De
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Computational Science and Engineeri
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Policy and Management Sciences Asse
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Introduction Department of Energy O
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5. After reviews by the Technical a
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• The Technical Council peer revi
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methods applicable to combustion, s
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Summary Each national laboratory mu
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Study Control Number: PN0004/1411 A
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Table 1. Positive and negative ions
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m/z Arbitrary Units Figure 1. Mass
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introduce low-volatility compounds
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arrangement, but the charge is reta
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two liquid chromatographic gradient
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Integrated Microfabricated Devices
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Matrix Assisted Laser Desorption/Io
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Molecular Beam Synthesis and Charac
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temperature distribution of the des
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expected to result in significant a
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Study Control Number: PN99069/1397
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Seuss DT and KA Prather. 1999. “M
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Analysis of Receptor-Modulated Ion
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laboratory (Lu and Xie 1997; Lu et
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Automated DNA Fingerprinting Microa
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Comparison of 4 Xanthomonas Isolate
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Approach Hematopoietic (bone marrow
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Anti-Human lgG Human lgG Protein G
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Characterization of Global Gene Reg
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PCR products for immobilization on
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identify novel proteins of importan
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Immunoprecipitaton of tyrosinephosp
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Coupled NMR and Confocal Microscopy
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In the optical microscopy image, th
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Development and Applications of a M
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Figure 3. Hepa-1 cells undergoing a
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cellular processing (such as post-t
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8. Initial demonstration of an off-
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expression systems for several year
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accumulation of protein products, i
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Fungal Conversion of Agricultural W
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Fungal Conversion of Waste Glucose/
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Summary and Conclusions We demonstr
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are shown in Figure 1(a) and 1(b),
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Anchorage-Independent Growth (% con
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selection of seedlings demonstratin
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NMR-Based Structural Genomics Micha
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Solution-State Structure and Fold-C
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Plant Root Exudates and Microbial G
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98 99 100 6 7 10 3 1 12 11 15 16 17
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Summary and Conclusions • The gre
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Jones-Oliveira JB, JS Oliveira, HE
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• securely moves files to a targe
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Plenary invited lecture, “The Ele
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Figure 1. X3DGEN tetrahedral mesh o
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Figure 5a. OSO volume rendering of
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Development of Models of Cell-Signa
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SOS p 23 EGFR Professor Rodland at
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Oliveira JS, JB Jones-Oliveira, and
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Figure 2. Associating a dataset mar
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to 1) incorporate three-dimensional
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shown in Figure 1. Simulated result
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HostBuilder: A Tool for the Combina
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Invariant Discretization Methods fo
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Graphics, Inc., workstation. The co
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Mixed Hamiltonian Methods for Geoch
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guyanaite, and grimaldiite (see Fig
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in the Environmental Molecular Scie
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Simulation and Modeling of Electroc
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Summary and Conclusions We implemen
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Since the implementation of the gen
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asis of previous performance, perce
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Bioinformatics for High-Throughput
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Page 250 and 251: Application of a Crop Model The res
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tetra-scale computing, envisioned a
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Energy Technology and Management
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Figure 2. Setup for the second expe
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phase. The algorithms will be teste
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[Pb-212]/[Pb-212] final 100 10 1 0.
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Plasma Particle Dielectric Fiber El
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(species, sub-species, and sex), sp
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Figure 3. Relative risk of bone and
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Development of a Preliminary Physio
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Development of a Virtual Respirator
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Figure 3. Use of the chest cavity a
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Examination of the Use of Diazolumi
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Indoor Air Pathways to Nuclear, Che
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Source Building Release Observed re
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To illustrate the potential impact
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Non-Invasive Biological Monitoring
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Materials Science and Technology
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Figure 1. (a) - (c) Successive magn
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Component Fabrication and Assembly
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Development of a Low-Cost Photoelec
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elaxations, indicating the adequacy
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Promising chemical durability, as m
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Study Control Number : PN98028/1274
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High Power Density Solid Oxide Fuel
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Hybrid Plasma Process for Vacuum De
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Matson DW, PM Martin, WD Bennett, J
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We showed the proof-of-principle ap
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Bandgap Energy (eV) 2.9 2.85 2.8 2.
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Ultrathin Electrolyte Test Results
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Detailed Investigations on Hydrogen
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identified low energy step structur
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Development of Novel Photo-Catalyst
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-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
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Here, we perform adsorption and des
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exceed those in the all-metal devic
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Intensity (a.u.) 0 100 200 300 400
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integrated voltage (V) 0.10 0.08 0.
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The first step was to implement thi
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Spontaneous Formation of Cu2O Nano-
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Actinide Chemistry at the Aqueous-M
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Figure 5. Close-up atomic force mic
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to 300°C and 400°C. Unfortunately
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Summary and Conclusions Transmutati
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Advanced Calibration/Registration T
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Figure 1a. Violet filter Figure 1b.
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Autonomous Ultrasonic Probe for Pro
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containing nitrous oxide, an optica
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Chemical Detection Using Cavity Enh
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Figure 3 is a color rendered simula
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appropriate study has been performe
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Exploitation of Conventional Chemic
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Figures 3 and 4 illustrate the impa
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enefits of using a modified spread-
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Single Channel Signal 0.0025 0.0020
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Therefore, in order to extract the
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A next-generation technology is nee
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Wind Dir. Figure 2. Negative ion co
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Volumetric Imaging of Materials by
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amplitude and peak frequency change
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Figure 3. 13 C NMR spectrum of corn
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Approach Two flow loops, one that o
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Modification of Ion Exchange Resins
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Permanganate Treatment for the Deco
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minimized by medium exchange. After
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Figure 1. Thermogravimetric analysi
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Validation and Scale-Up of Monosacc
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Concentration (g/100g solution) Con
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Analysis of High-Volume, Hyper-Dime
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ecent figure of merit values. Shoul
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Probabilistic Methods Development f
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a) Traditional PCA-based process co
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Acronyms and Abbreviations 2-D two-
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