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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Flux REaction TArget Prioritization (Flux RETAP) v1

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.

Czajka, Jeffrey [Battelle Memorial Institute, Paci↗
DOE OSTI2022

GROWdb US River Systems - Samples

GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

The promotional effects of cesium promoter on higher alcohol synthesis from syngas over cesium-promoted Cu/ZnO/Al2O3 catalysts

In this study, the promotional effects of cesium promoter on higher alcohol (C2+OH) synthesis from syngas over Cs-Cu/ZnO/Al2O3 catalysts were investigated using a combined experimental and theoretical density functional theory (DFT) calculation method. In the presence of cesium, the C2+OH productivity increases from 77.1 g•kgcat-1•h-1 to 157.3 g•kgcat-1•h-1 at 583 K due to the enhancement of the initial C–C bond formation. Detailed analysis of chain growth probabilities (CGPs) confirms that initial C–C bond formation is the rate-determining step in the temperature range of 543-583 K. Addition of cesium promoter significantly increases the productivities of 2-methyl-1-propanol, while the CGPs values (C3* to 2-methyl-C3*) is almost unaffected. With the assistance of cesium promoter, the CGPs of the initial C–C bond formation step (C1* to C2*) could be increased from 0.13 to 0.25 at 583 K. DFT calculations indicate that the initial C–C bond formation is mainly contributed by the HCO+HCO coupling reaction over the ZnCu(211) model surface. In the presence of the Cs2O, the stabilities of key reaction intermediates such as HCO and H2CO are enhanced which facilitates both HCO+HCO and HCO+H2CO coupling reaction steps with lower activation barriers over the Cs2O-ZnCu(211) surface. The promotional effects of cesium on the C2+OH productivity are also benefited from the competitive CH+HCO coupling reaction over CH hydrogenation that leads to lower alkane formation. In addition, Bader charge analysis suggests that the presence of cesium ions would facilitate the nucleophilic reaction between HCO and H2CO for initial C–C bond formation. This work was supported by the National Natural Science Foundation of China (No. 91545114 and No. 91545203). We appreciate the joint PhD scholarship support from the China Scholarship Council. The authors would also like to thank the support from Collaborative Innovation Center of Chemistry for Energy Materials (2011-iChEM). DM was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences. Computing time was granted by the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility located at Pacific Northwest National Laboratory (PNNL) and sponsored by DOE’s Office of Biological and Environmental Research. PNNL is a multiprogram national laboratory operated for DOE by Battelle Memorial Institute. We also appreciate the support from Sinochem Quanzhou Petrochemical Co. Ltd.

cesium↗
DOE OSTI2018

Nucleation and phase transformation pathways in electrolyte solutions investigated by in situ microscopy techniques

Identification of crystal nucleation and growth pathways is of fundamental importance for synthesis of functional materials, which requires control over size, orientation, polymorph, and hierarchical structure, often in the presence of additives used to tune the energy landscape defining these pathways. Furthermore we summarize the recent progress in application of in situ TEM and AFM techniques to monitor or even tune the pathway of crystal nucleation and growth.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Kinetics and Mechanism of the Hydrogenation of CpCr(CO)3•/[CpCr(CO)3]2 Equilibrium to CpCr(CO)3H

The kinetics of the hydrogenation of 2 CpCr(CO)3•/[CpCr(CO)3]2 to CpCr(CO)3H has been investigated. The reaction is second-order in Cr and first-order in H2, with a rate constant of 45 M 2s 1 at 25 °C in benzene. DFT calculations rule out an H2 complex as an intermediate, and suggest (a) end-on approach of H2 to one Cr of [CpCr(CO)3]2 as the Cr-Cr bond undergoes heterolytic cleavage, (b) heterolytic cleavage of the coordinated H2 between O and Cr, and (c) isomerization of the resulting O-protonated CpCr(CO)2(COH) to CpCr(CO)3H. The work at Pacific Northwest National Laboratory (PNNL) was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences; Battelle operates PNNL for DOE.

1/12/15--tjl--Added UP# 34000 per D Baxter and A G↗
DOE OSTI2014

Computational studies of adsorption in metal organic frameworks and interaction of nanoparticles in condensed phases

In this review, we describe recent efforts to systematically study nano-structured metal organic frameworks (MOFs), also known as metal organic heat carriers, with particular emphasis on their application in heating and cooling processes. We used both molecular dynamics and grand canonical Monte Carlo simulation techniques to gain a molecular-level understanding of the adsorption mechanism of gases in these porous materials. We investigated the uptake of various gases such as refrigerants R12 and R143a. We also evaluated the effects of temperature and pressure on the uptake mechanism. Our computed results compared reasonably well with available measurements from experiments, thus validating our potential models and approaches. In addition, we investigated the structural, diffusive and adsorption properties of different hydrocarbons in Ni-2(dhtp). Finally, to elucidate the mechanism of nanoparticle dispersion in condensed phases, we studied the interactions among nanoparticles in various liquids, such as n-hexane, water and methanol.

Annapureddy, HVR↗
DOE OSTI2024

Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.

Community detection, graph algorithms↗
NASA NTRS1990

Preparing technicians for engineering materials technology

A long held principle is that for every engineer and scientist there is a need for ten technicians to maximize the efficiency of the technology team for meeting needs of industry and government. Developing an adequate supply of technicians to meet the requirements of the materials related industry will be a challenge and difficult to accomplish. A variety of agencies feel the need and wish to support development of engineering materials technology programs. In a joint effort among Battelle Laboratories, the Department of Energy (DOE) and Northwest College and University Association for Science (NORCUS), the development of an engineering materials technology program for vocational programs and community colleges for the Pacific Northwest Region was recently completed. This effort has implications for a national model. The model Associate of Applied Science degree in Engineering Materials Technology shown provides a general structure. It purposely has course titles which need delimiting while also including a core of courses necessary to develop cognitive, affective and psychomotor skills with the underlining principles of math, science and technology so students have job entry skills, and so that students can learn about and adapt to evolving technology.

Jacobs, James A.↗
DOE OSTI2019

SAPO-34 Membranes for Xenon Capture from Air

Capturing Xe from gas mixtures represents one of the most challenging molecular gas separations. In this communication, we demonstrate the ability of a zeolite membrane, denoted as SAPO-34 to effectively capture Xe from air. Specifically, SAPO-34 membranes showed air permeances as high as 2.3 x 10 -7 mol/m 2 s Pa (690 GPU) and separation selectivities as high as 30.1 for a molar feed of 9:1 air/Xe. Molecular sieving, competitive adsorption, and diffusivity differences played a critical role in the overall separation performance. Membranes were air selective due to favorable molecular sieving and differences in diffusivity between gases present in mixture and Xe, which were identified as the dominant separation mechanisms. The high air permeances, and high separation selectivities, make these membranes highly appealing as a potentially less energy intensive alternative to cryogenic distillation the benchmark technology used to separate these gases. We (Pacific Northwest National Laboratory) acknowledge US Department of Energy (DOE), Office of Nuclear Energy for adsorption. P.K.T. would like to acknowledge Dr Terry Todd at Idaho National Laboratory, Dr Robert Jubin at Oakridge National Laboratory, Dr John Vienna at Pacific Northwest National Laboratory (PNNL), Kimberly Gray, Dr. Patricia Paviet and Jim Breese (DOE-NE HQ) for programmatic support. PNNL is a multi-program national laboratory operated for the US DOE by Battelle under Contract DE-AC05-76RL01830.

Wu, Ting↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2026

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2020

Correction to Reconciling Work Functions and Adsorption Enthalpies for Implicit Solvent Models: A Pt (111)/Water Interface Case Study

This errata is to correct the acknowledgment in the original published article. G.B. (partial support), V.-A.G., M.-T.N., and R.R. were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. Computer resources were provided by Research Computing at Pacific Northwest National Laboratory and the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. PNNL is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. G.B. acknowledges the EPSRC for partial support in PhD funding. The authors acknowledge the use of the IRIDIS High Performance Computing Facility (IRIDIS 5) and associated support services at the University of Southampton in the completion of this work. We are grateful to the UK Materials and Molecular Modeling Hub (Thomas HPC) for computational resources, which is partially funded by EPSRC (EP/P020194/1).

continuum solvation, metal/water interfaces, ab in↗
DOE OSTI2013

In situ DRIFTS-MS studies on the oxidation of adsorbed NH3 by NOx over a Cu-SSZ-13 zeolite

DRIFT spectroscopy combined with mass spectrometry was used to investigate the oxidation of adsorbed ammonia by NO2, NO+O2 and NO2+O2 on a copper ion exchanged SSZ 13 (Cu-SSZ-13) zeolite. Compared with both NO2 and NO, the adsorption of ammonia is much stronger on the Cu-SSZ-13 zeolite. Two adsorbed ammonia species were found over the Cu-SSZ-13 zeolite studied here; notably ammonia on Brönsted acid sites (proton) and ammonia on Lewis acid sites (copper ions). These adsorbed ammonia species present different activity profiles and selectivity to N2 during NH3 oxidation. The results obtained suggest that ammonia adsorbed onto copper ions in Cu-SSZ-13 are more active at low temperatures than proton-adsorbed NH3, and give rise to a higher selectivity to N2. The formation of N2O is associated primarily with the reaction of NOx with proton-adsorbed NH3 via the formation and subsequent thermal decomposition of NH4NO3. Financial support was provided by the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Portions of this work were performed in the Environmental Molecular Sciences Laboratory (EMSL) at Pacific Northwest National Laboratory (PNNL). The EMSL is a national scientific user facility supported by the US DOE, Office of Biological and Environmental Research. PNNL is a multi-program national laboratory operated for the US DOE by Battelle.

Cu-SSZ-13↗
DOE OSTI2014

Following the movement of Cu ions in a SSZ-13 zeolite during dehydration, reduction and adsorption: a combined in situ TP-XRD, XANES/DRIFTS study

Cu-SSZ-13 has been shown to possess high activity and superior N2 formation selectivity in the selective catalytic reduction of NOx under oxygen rich conditions. Here, a combination of synchrotron-based (XRD and XANES) and vibrational (DRIFTS) spectroscopy tools have been used to follow the changes in the location and coordination environment of copper ions in a Cu-SSZ-13 zeolite during calcinations, reduction with CO, and adsorption of CO and H2O. XANES spectra collected during these procedures provides critical information not only on the variation in the oxidation state of the copper species in the zeolite structure, but also on the changes in the coordination environment around these ions as they interact with the framework, and with different adsorbates (H2O and CO). Time-resolved XRD data indicate the movement of copper ions and the consequent variation of the unit cell parameters during dehydration. DRIFT spectra provide information about the adsorbed species present in the zeolite, as well as the oxidation states of and coordination environment around the copper ions. A careful analysis of the asymmetric T-O-T vibrations of the CHA framework perturbed by copper ions in different coordination environments proved to be especially informative. The results of this study will aid the identification of the location, coordination and oxidation states of copper ions obtained during in operando catalytic studies. Financial support was provided by the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Part of this work (sample preparation) was performed in the Environmental Molecular Sciences Laboratory (EMSL) at Pacific Northwest National Laboratory (PNNL). The EMSL is a national scientific user facility supported by the US DOE, Office of Biological and Environmental Research. PNNL is a multi-program national laboratory operated for the US DOE by Battelle. All of the spectroscopy work reported here was carried out at the National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory (BNL). NSLS is a national scientific user facility supported by the US DOE.

cation coordination↗
DOE OSTI2013

Characterization of Cu-SSZ-13 NH3 SCR Catalysts: an in situ FTIR Study

The adsorption of CO and NO over Cu-SSZ-13 zeolite catalysts, highly active in the selective catalytic reduction of NOx with NH3, was investigated by FTIR spectroscopy, and the results obtained were compared to those collected from other Cu-ion exchanged zeolites (Y,FAU and ZSM-5). At low CO pressures at room temperature (295 K) CO form monocarbonyls exclusively on the Cu+ ions, while in the presence of gas phase CO dicarbonyls on Cu+ and adsorbed CO on Cu2+ centers form, as well. At low (cryogenic) sample temperatures tricarbonyl formation on Cu+ sites was also observed. The adsorption of NO produces IR bands that can be assigned to nitrosyls bound to both Cu+ and Cu2+ centers, and NO+ species located in charge compensating cationic positions of the chabasite framework. On the reduced Cu-SSZ-13 samples the formation of N2O was also detected. The assignment of the adsorbed NOx species was aided by adsorption experiments with isotopically labeled 15NO. The movement of Cu ions from the sterically hindered six member ring position to the more accessible cavity positions as a result of their interaction with adsorbates (NO and H2O) was clearly evidenced. Comparisons of the spectroscopy data obtained in the static transmission IR system to those collected in the flow-through diffuse reflectance cell points out that care must be taken when conclusions are drawn about the adsorptive and reactive properties of metal cation centers based on a set of data collected under well defined, specific experimental conditions. Financial support was provided by the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. This work was performed in the Environmental Molecular Sciences Laboratory (EMSL) at the Pacific Northwest National Laboratory (PNNL). The EMSL is a national scientific user facility supported by the US DOE, Office of Biological and Environmental Research. PNNL is a multi-program national laboratory operated for the US DOE by Battelle.

adsorption of CO and NO↗
DOE OSTI2024

Flux Characterization of Washington State University’s Epithermal Neutron Beam Facility

The neutron flux of the epithermal neutron beamline facility at the Washington State University (WSU) Research reactor was characterized as part of a research effort between Pacific Northwest National Laboratory (PNNL) and WSU to better understand the effects that neutron interaction has on the release of hydrogen from zirconium hydride. This effort was to confirm that the spectrum was epithermal, and that the magnitude of the flux was adequate for the needs of the PNNL research efforts.

Zipperer, Travis J. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2023

Economic Impact of Pacific Northwest National Laboratory on the State of Washington in Fiscal Year 2019

Pacific Northwest National Laboratory is known for its impressive record of scientific and technical contributions to the nation. But you may not know that we are also one of Washington State’s most important economic contributors. PNNL and its operator, Battelle, have an enduring commitment to the health and growth of Washington State and our local community. When economic impact and scientific innovation converge, the results are impressive. We invite you to read more about PNNL’s economic impact in FY 2019 and the details behind these impressive figures in the full report, located at https://www.pnnl.gov/economic-impact.

99 GENERAL AND MISCELLANEOUS↗