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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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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of a Low-Temperature Plasma (LTP) Ambient Ionization Source Using Temporally Resolved Monochromatic Imaging Spectrometry

The low-temperature plasma (LTP) probe is a common plasma-based source used for ambient desorption–ionization mass spectrometry (MS). While the LTP probe has been characterized in detail with MS, relatively few studies have used optical spectroscopy. In this paper, two-dimensional (2D) imaging at selected wavelengths is used to visualize important species in the LTP plasma jet. First, 2D steady-state images of the LTP plume for N 2 + (391.2 nm), He I (706.5 nm), and N 2 (337.1 nm) emissions were recorded under selected plasma conditions. Second, time-resolved 2D emission maps of radiative species in the LTP plasma jet were recorded through the use of a 200 ns detection gate and varying gate delays with respect to the LTP trigger pulse. Emission from He I, N 2 + , and N 2 in the plasma jet region was found to show a transient behavior (often referred to as plasma bullets) lasting only a few microseconds. The N 2 + and He I maps were highly correlated in spatial and temporal structure. Further, emission from N 2 showed two maxima in time, one before and one after the maximum emission for N 2 + and He I, due to an initial electronic excitation wave and ion–electron recombination, respectively. Third, the interaction of the LTP probe with a sample substrate and an electrically grounded metallic needle was studied. Emission from a fluorophore on the sample substrate showed an initial photon-induced excitation from plasma-generated photons followed by electronic excitation by other plasma species. The presence of a grounded needle near the plasma jet significantly extended the plasma jet lifetime and also generated a long-lived corona discharge on the needle. The effect of LTP operating parameters on emission spectra was correlated with mass-spectral results including reagent-ion signals. Lastly, five movies provide a side-by-side comparison of the temporal behavior of emitting species and insights into the interactions of the emission clouds with a sample surface as well as an external needle. Temporally and spatially resolved imaging provided insights into important processes in the LTP plasma jet, which will help improve analyte ion sampling in LTP–MS.

Chan, George C. -Y.↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds Developed lands Areas >0.8 km (0.5 miles) from developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 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 CCBY4.0 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↗
DOE OSTI2022

Soil physical and thermal properties from soil samples at multiple locations at Teller Road Site, Seward Peninsula, Alaska, 2019

This dataset contains results of the laboratory analysis of soil samples collected across the watershed located at miles #27 along the Teller road, near Nome, AK. The dataset includes 124 soil samples retrieved from 52 locations during the first week of August 2019. The soil samples were collected in the top 1.2 m of soil with volume known samples, analyzed with a thermal analyser to infer thermal parameters (thermal conductivity, volumetric heat capacity and thermal diffusivity), and then processed in a lab for estimation of their bulk density, water content, carbon and nitrogen content, soil texture, and elemental fractions. This dataset has one csv file and one pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Inclusion Complexes of a Metastable‐State Photoacid with High Acidity and Chemical Stability

Abstract Metastable‐state merocyanine photoacids (MCHs) have been widely applied to various chemical, material and biomedical areas to drive or control chemical processes with light. In this work, stoichiometry and association constants have been determined for inclusion complexes of a photoacid MCH1 ((E)‐3‐(2‐(2‐hydroxystyryl)‐3,3‐dimethyl‐3H‐indol‐1‐ium‐1‐yl)propane‐1‐sulfonate) withβ‐cyclodextrin (β‐CD), 2‐hydroxypropyl‐β‐CD (HP‐β‐CD),γ‐CD and HP‐γ‐CD by means of UV‐Vis absorption spectroscopic titrations. The inclusion complexes were studied to enhance acidity and chemical stability. Kinetic study showed that CDs stabilized the acidic metastable state and slowed its thermal relaxation. The acidity of the ground and metastable state (pK a GS and pK a MS ) increased upon addition of CDs. The pK a MS of [MCH1 ⋅ (γ‐CD) 2 ] is as low as 0.92 in comparison with 2.24 for MCH1, which is close to the lowest pK a MS values (1.20 and 1.03) reported previously, in which case the MCH1 was structurally modified with alkylammonium side chains. Addition of CDs also significantly enhanced the chemical stability of MCH1 against hydrolysis, which is one of the major concerns for the application of MCHs. In particular, the addition of HP‐β‐CD increased the half‐life of MCH1 in aqueous solution more than four‐fold. Moreover, the quantum chemical calculations confirmed the stoichiometry and analyzed the binding sites and hydrogen bonds of the inclusion complexes.

Chemistry↗
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Energy landscapes from cryo-EM snapshots: a benchmarking study

Abstract Biomolecules undergo continuous conformational motions, a subset of which are functionally relevant. Understanding, and ultimately controlling biomolecular function are predicated on the ability to map continuous conformational motions, and identify the functionally relevant conformational trajectories. For equilibrium and near-equilibrium processes, function proceeds along minimum-energy pathways on one or more energy landscapes, because higher-energy conformations are only weakly occupied. With the growing interest in identifying functional trajectories, the need for reliable mapping of energy landscapes has become paramount. In response, various data-analytical tools for determining structural variability are emerging. A key question concerns the veracity with which each data-analytical tool can extract functionally relevant conformational trajectories from a collection of single-particle cryo-EM snapshots. Using synthetic data as an independently known ground truth, we benchmark the ability of four leading algorithms to determine biomolecular energy landscapes and identify the functionally relevant conformational paths on these landscapes. Such benchmarking is essential for systematic progress toward atomic-level movies of continuous biomolecular function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The Iowa Tribe of Kansas and Nebraska: Advancing Clean, Resilient, and Sovereign Energy

The Iowa Tribe of Kansas and Nebraska (ITKN) is a federally recognized Native American Tribe located along the Missouri River on the border of northeast Kansas and southeastern Nebraska. There are over 800 residents (Tribal citizens and non-Tribal) who live on the reservation, as well as more than 500 people who visit or work on the reservation on a daily basis. The ITKN faces many energy challenges, including rising service costs and dozens of power outages annually that impact resident well-being and business activities on Tribal lands. Power service issues are made more challenging by the remoteness of the reservation, which is 20 miles from the nearest town. Despite this, the ITKN has a long history of cultural and economic resilience: Local self-reliance, environmental stewardship, respecting the carrying capacity of the land, and strengthening the community are Tribal communities' traditional strengths. Long-term energy goals for the ITKN are centered around achieving energy sovereignty. Priority actions include: (1) Establishing a Tribal Utility Authority (TUA) to promote social welfare and community development.; (2) Deploying renewable community microgrids with ground-mount solar arrays and sustainable energy storage systems to advance energy sovereignty, resilience, and reliability. To advance these goals, the ITKN partnered with the U.S. Department of Energy's (DOE's) Communities LEAP (Local Energy Action Program) pilot. From August 2022 to March 2024, the ITKN community coalition collaborated with technical assistance providers at DOE's National Renewable Energy Laboratory (NREL) and Sandia National Laboratories to evaluate TUA planning needs and microgrid deployment scenarios. This fact sheet provides an overview of the results and outcomes from the Communities LEAP technical assistance process.

Communities Leap↗
DOE OSTI2026

Photoinduced Charge Transfer and Vibronic Coherence in CdSe Quantum Dots with Methyl Viologen Acceptors

We show herein that photoinduced charge transfer from CdSe quantum dots (QDs) to surface-bound methyl viologen (MV 2+ ) acceptors is mediated by a vibronically coherent, nonadiabatic mechanism. Broadband multidimensional electronic spectra and an analysis of coherences show that a mixed QD–MV charge-transfer (CT) state is populated on the <50 fs time scale after optical preparation of the X3 (1P 3/2 –1P e ) state, well prior to the appearance of the one-electron photoreduced ground state (MV +• ). A partial redistribution of charge from the core of the QD to the acceptor is revealed by excited-state coherences of an out-of-plane vibrational mode local to MV 2+ and of a low-frequency mode mixing a MV 2+ mode with the longitudinal optical (LO) phonon of the QD core. The ultrafast damping of these coherences indicates that excited-state wavepackets travel from the optically prepared, Franck–Condon structure through a conical intersection to reach the CT state. These results suggest that vibronically coherent processes generating CT intermediates can be exploited to improve the efficiency of QD-based solar cells and photocatalysts.

Cadmium selenide↗
DOE OSTI2021

The $^3$He+$^5$He$\rightarrow $ $\alpha $+$\alpha $ reaction below the Coulomb barrier via the Trojan Horse Method

For the first time in an application to nuclear astrophysics, a process induced by the unstable 5 He = ( 4 He-n) nucleus, the 3 He+ 5 He→2α reaction, has been studied through the Trojan Horse Method (THM). For that purpose, the quasi-free (QF) contribution of the 9 Be( 3 He,αα) 4 He reaction was selected at E 3 He =4 MeV incident energy. The reaction was studied in a kinematically complete experiment following a recent publication, where for the quasi free contribution the momentum distribution between α and 5 He particle cluster in the 9 Be nucleus in the ground state have been extracted. The angular distribution of the QF 3 He+ 5 He→2α reaction was measured at θ cm = 78°–115°. The energy dependence of the differential cross section of the 3 He+ 5 He→2α virtual reaction was extracted in the energy range E cm = 0–650 keV. In conclusion, the total cross section obtained from the Trojan-horse method was normalized to absolute cross sections from a theoretical calculation in the energy range E cm =300–620 keV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Surface Quantitative Precipitation Estimates (SQUIRE) of Snow Water Equivalent from the Surface Atmospheric Integrated Field Laboratory

The upper Colorado River basin is the primary source of water for 40 million people. With declining snowpack in the basin, forecasting hydrological budgets in the Southwest United States is more important than ever. However, due in part, to a lack of reliable observations of precipitation in complex terrain, hydrological models struggle to assess and forecast snowpack snow water equivalent (SWE) in the upper Colorado River basin (UCRB). Therefore, the need for more reliable SWE forecasts in the UCRB motivated the U.S. Department of Energy Atmospheric Radiation Measurement Facility’s Surface Atmospheric Integrated Field Laboratory (SAIL) that occurred from June 2021 to June 2023. During SAIL, the X-band precipitation radar from Colorado State University conducted volume scans sampling the precipitation properties over the UCRB. The ARM facility developed a gridded Surface Quantitative Precipitation Estimates (SQUIRE) product from the radar observations. To do this, various daily SWE estimates from radar using the radar reflectivity factor Z e and specific differential phase K dp were compared against ground-based precipitation gauges. SWE in precipitation calculated from Wolfe and Snider’s S–Z e estimator was in best agreement with the rain gauges for the days when SWE < 12 mm. For days with SWE > 12 mm, the WSR-88D Intermountain West relationship had the best agreement with the precipitation gauges. Airborne snow depth observations show that SQUIRE captures regions of orographic enhancement in the mountains to the west and northwest of the SAIL study area, indicating that the scientific community should focus on understanding and ultimately simulating orographic atmospheric precipitation processes to improve UCRB snowpack SWE assessment and forecasting.

Hydrology↗
DOE OSTI2022

Soil moisture, physical and chemical properties coincident with airborne SAR data collections for 2017 and 2019, Seward Peninsula, Alaska

Soil samples were collected coincident with in-situ soil moisture and thaw depth measurements at NGEE Arctic study sites on the Seward Peninsula, Alaska in August 2017 and 2019 Field measurements and flights were conducted during both summers as a collaboration between the NASA ABoVE Project's Airborne SAR Campaign and the NGEE Arctic Project. Airborne overflights of L-band SAR instruments occurred during the soil sampling periods. Laboratory measurements of soil properties include bulk density, volumetric and gravimetric water content, carbon and nitrogen content, and particle size of mineral components. Soil samples processed and reported in this dataset were collected coincident with in situ measurements of soil moisture and thaw depth and airborne P-band and L-band SAR measurements as a collaboration between NGEE Arctic and NASA ABoVE. To learn more about how ABoVE protocols were applied for sampling site selection and making in situ measurements, see the following datasets: (2017: https://doi.org/10.5440/1423892 and 2019: https://doi.org/10.5440/1856042). Contained in this dataset are four .csv data files (including data dictionaries) and one zipped folder of *.pdf files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort (with some overlap with Covid-19 pandemic) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Thaw depth and dGPS locations, Utqiagvik, Alaska, 2021

Thaw depth measurements within and around warming chambers, and in ambient plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Measurements were taken at the start and end of chamber deployment, and two intermediate times during the 2021 growth season. dGPS measurements of chamber and ambient plot locations are also included. The files included in this data package are in .csv format, and include 2 data files and 3 metadata files. This data was recorded as part of the Zero Power Warming (ZPW) vegetation warming experiment. Other datasets under the Vegetation Warming Experiment include data for environmental conditions, leaf physiology, leaf traits, and landscape and plot phenocam images. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Thaw depth and dGPS locations, Utqiagvik, Alaska, 2019

Thaw depth measurements within and around warming chambers, and in ambient plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Measurements were taken at the start and end of chamber deployment, and two intermediate times during the 2019 growth season. dGPS measurements of chamber and ambient plot locations are also included. The files included in this data package are in .csv format, and include 3 data files and 3 metadata files. This data was recorded as part of the Zero Power Warming (ZPW) vegetation warming experiment. See related data files for environmental conditions, leaf physiology, leaf traits, and landscape and plot phenocam images. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 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 CCBY4.0 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↗
DOE OSTI2020

Quantum information processing with majorana bound states in superconducting circuits

In a weak link of two s-wave superconductors (SCs) coupled via a time-reversal-invariant (TRI) topological superconducting (TSC) island, a Josephson current can flow due to Cooper pairs tunneling in and out of spatially separated Majorana Kramers pairs (MKPs), which are doublets of Majorana bound states (MBSs). The sign of the resulting Josephson current is fixed by the joint parity of the four Majorana bound states that make up the MKPs on the TSC island. This parity-controlled Josephson effect can be used as a read-out mechanism for the joint parity in Majorana-based quantum computing. For a TSC island with four terminals, the SC leads can address a Majorana superconducting qubit (MSQ) formed by the charge ground states of the TSC island's terminals. Cooper pair splitting enables single-qubit operations, qubit read-out, as well as two-qubit entangling gates. Hence, TSC islands between SC leads may provide an alternative approach to superconducting quantum computation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

High event rate analysis technique for the dual-axis duo-lateral position-sensitive silicon detectors of FAUST

The dual-axis duo-lateral (DADL) position-sensitive silicon detector was developed to obtain precise position and energy information for detected charged particles. The Forward Array Using Silicon Technology (FAUST) is currently equipped with 68 DADL detectors backed by CsI(Tl) scintillators for the study of charged particle correlations in heavy-ion collisions where precise position and energy information is essential. When conventional signal processing electronics were used for the DADL detectors, a position dependence of the measured energy as well as distortions in the calculated particle positions were observed. In previous work, waveforms from the detector after preamplification were studied to better understand the features that give rise to these distortions; therein, a waveform analysis technique was developed to improve the energy resolution and linearity in position reconstruction. However, the reading and writing of waveforms for an entire detector array limits data collection rates and adds significant burden in data storage and analysis speed. In this work, the integrators of a Struck SIS3316 ADC were utilized to process 228 Th source data to develop and optimize a new analysis method that captures the benefits of the waveform analysis technique while circumventing the waveform writing requirement. This integrator method – capable of 59 keV (FWHM) energy resolution – was used in the collection of 35 MeV/nucleon 28 Si + 12 C collision data using FAUST to investigate exotic decays of highly excited highly deformed nuclei. In this data, a position resolution of 0.4 mm (FWHM) was obtained for 25 MeV α-particles; for α-particles near this energy that originate from 8 Be ground state decays, a 8 Be ground state width of 30 keV (FWHM) was obtained. The impact of the energy-dependent DADL position resolution emergent from electronic noise on the quality of excited state measurement was modeled and compared to the experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗