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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Robot-based Additive Manufacturing of Lego-type Modular Molds for Wind Blades

The objective of this project is to reduce the cost and lead time of horizontal wind turbine blade mold tooling and blade transportation, while maintaining the highest standards of blade quality. The solution involves a smart-design family of modular molds that are easily transportable to fabrication sites near the place of service. Key innovations include the use of additive manufacturing (AM) to integrate conformal thermal management channels, offering enhanced control over the thermal profiles tailored to specific blade materials. This approach enables in-situ quality assurance during mold fabrication, significantly improves mold life, and allows for reuse across multiple production cycles. Ultimately, the solution aims to optimize both tooling and transportation costs, contributing to the scalability of wind turbine blade production. A significant barrier to scaling up the production of large wind turbine blades lies in the high costs associated with tooling and the transportation of blades. Traditional molds are expensive, bulky, and difficult to transport, adding considerable lead time and cost to the overall manufacturing process. Additionally, transporting blades to distant locations for final assembly further exacerbates these challenges. The project aims to address these inefficiencies by demonstrating a modularized, additive-manufactured mold that meets all necessary blade specification requirements, specifically for blade lengths between 120m and 150m.

17 WIND ENERGY↗
DOE OSTI2020

CY2019 Annual Closure Monitoring Report for Corrective Action Unit 98, Frenchman Flat, Underground Test Area, Nevada National Security Site, Nevada (January 2019–December 2019), Revision 1

Three types of monitoring are performed for CAU 98: water quality, water level, and institutional control. These are monitored to determine whether the URs remain protective of human health and the environment, and to ensure that the regulatory boundary objectives are being met. Monitoring data will be used in the future, once multiple years of data are available, to evaluate consistency with the groundwater flow and contaminant transport models because the contaminant boundaries calculated with the models are the primary basis of the UR boundaries. Six wells were sampled for water-quality monitoring in 2019. Contaminants of concern were detected only in the two source/plume wells already known to contain contamination as a result of a radionuclide migration experiment. Tritium concentrations in both of these wells, RNM-2S and UE-5n, remain above the Safe Drinking Water Act maximum contaminant level of 20,000 picocuries per liter but declined in 2019 as compared to measurements in 2018. All other contaminants of concern are below the minimum detection level plus analytical error. The water-level monitoring network includes 16 wells. Depth to water measured in 2019 is generally consistent with recent measurements for all wells. Many wells continue to exhibit a long-term downward trend in water level, though changes from 2018 to 2019 are minimal. The sharp 2016 decline in water level in Well ER-5-3-2 remains unexplained, with the lower level persisting through 2019. Rising water-level trends continue to be observed in Well ER-5-3 deep piezometer and former water supply Well WW-5A. Water supply Well WW-5B experienced a rise in water level as a result of an absence of pumping in the first part of the year (due to a mechanical problem), whereas water levels declined in WW-4 and WW-4A in response to greater pumping in 2019. Institutional control monitoring confirmed the URs are recorded in U.S. Department of Energy and U.S. Air Force land management systems, and that no activities within Frenchman Flat basin are occurring that could potentially affect the contaminant boundaries. Survey of groundwater resources in basins surrounding Frenchman Flat similarly identify no current or pending development that would indicate the need to increase monitoring activities or would otherwise cause concern for the closure decision. The URs continue to prevent exposure of the public, workers, and the environment to contaminants of concern by preventing use of potentially contaminated groundwater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

NRAP-Open-IAM Multisegmented Wellbore Reduced-Order Model

Geologic carbon storage is one of the promising strategies to mitigate climate change by reducing the emission of carbon dioxide to the atmosphere. As part of the National Risk Assessment Partnership (NRAP), a systems-level stochastic analysis tool called the open source integrated assessment model, NRAP-Open-IAM, has been developed to estimate and manage the risk of containment loss at a geological carbon sequestration site. NRAP-Open-IAM contains several wellbore leakage model components that estimate the fluid leak rate that may occur through compromised legacy wells due to the increase in pressure resulting from CO 2 injection activities. Coupled to a reservoir component model, these components estimate the leakage of CO 2 and/or brine from a storage reservoir to overlying aquifer layers and the atmosphere through legacy wells. This report presents the theoretical framework and quality testing of the multisegmented wellbore reduced-order model. The model allows for segmenting of the legacy wells passing through the overlying stratigraphy into several intervals to simulate a site’s specific stratigraphic and hydrogeologic properties. For quality assurance, the analytical model is validated against numerical reservoir flow simulations for single and multiple aquifer(s) models. The results indicate that the model accurately predicts the transport of two-phase fluids (brine and injected CO 2 ) through the well over time. A detailed description of the model helps users to understand the model and provides a basis for future improvements.

58 GEOSCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (S300): S300 Roadway Improvements - 817 Complex Soil Sampling and Analysis Plan (May 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 817 Complex (project). The purpose of the SAP was to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction in accordance with Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was developed in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The SAP was developed in accordance with the SSMP and based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Livermore Site: 4200 Block Development Project Soil Sampling and Analysis Plan (June 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Maintenance Shop Facility in the 4200 Block (project). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (Site 300): Site 300 Roadway Improvements - 836 Complex Soil Sampling and Analysis Plan (May 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 836 Complex (project). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Site 300 Roadway Improvements - 834 Complex Soil Sampling and Analysis Plan

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the 834 Complex (project) (Figure 1). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site 300 (Site 300): Site 300 Roadway Improvements - Chem Mag Loop Soil Sampling and Analysis Plan (May 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Function Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the Chem Mag Loop (project). The purpose of the SAP is to describe the procedures for collection and analysis of environmental samples and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided to EFA by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site, Site 300: Building 843 Corp Yard Redesign Soil Sampling and Analysis Plan (June 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Building 843 (B843) Corp Yard Redesign project (project). The purpose of the SAP was to identify chemicals of concern, describe the procedures for collection and analysis of environmental samples, and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows the criteria established in Lawrence Livermore National Laboratory’s (LLNL’s) Soils Screening and Management Plan (SSMP) (LLNL 2022), which is consistent with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, U.S. DOD 2000). The scope of this SAP is based on the B843 Corp Yard Redesign drawing set dated December 3, 2021.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lawrence Livermore National Laboratory Experimental Test Site, Site 300: S300 Roadway Improvements - 854 Complex Soil Sampling and Analysis Plan (June 2023)

This Soil Sampling and Analysis Plan (SAP) was prepared by the Environmental Functional Area (EFA)/Technical Services Department (TSD) of the Environment, Safety & Health (ES&H) Directorate for the Project Management Office (PMO) for the proposed Roadway Improvements Project at the Building 854 Complex (project). The purpose of the SAP was to identify chemicals of concern, describe the procedures for collection and analysis of environmental samples, and evaluation of analytical data (chemical and radiological) to determine management options of excavated soil during project construction. This SAP follows criteria established in Lawrence Livermore National Laboratory’s (LLNL) Soils Screening and Management Plan (SSMP) (LLNL 2022), which was formalized in accordance with U.S. Environmental Protection Agency (EPA) guidance for developing Data Quality Objectives for environmental data (EPA 2006) and the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) guidance (U.S. NRC, U.S. EPA, U.S. DOE, and U.S. DOD 2000). The scope of this SAP is based on preliminary design information provided by PMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Predicting RNA structure and dynamics with deep learning and solution scattering

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle x-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce a solution conformation predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep learning model, IonNet, designed for predicting Mg 2+ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg 2+ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Measurements of TRACER pre-convective conditions and mesoscale circulations using small unmanned aircraft systems (sUAS)

Improved comprehension of the physical processes governing convective cloud formation and lifecycle are of critical importance for understanding and predicting future climate states. The influence of these clouds on the planetary energy budget, including on precipitation, is significant. Things are particularly complex in coastal regimes, where gradients in aerosol particle properties, localized circulations such as sea breezes, and large population centers are found. To date, numerical models struggle to accurately represent these critical clouds and are therefore challenged to provide a realistic view on the planetary energy budget. Through the proposed research, we deployed two uncrewed aircraft systems (UAS) equipped with a variety of instruments alongside sensors deployed by the US Department of Energy Atmospheric Radiation Measurement (ARM) program for the TRACER (Tracking Aerosol Convection Interactions Experiment) field campaign. The two small UAS platforms consisted of a CU RAAVEN fixed-wing airplane and an OU CopterSonde system, with the copter collecting frequent vertical profiles of thermodynamic and kinematic variables such as temperature, pressure, wind and humidity. At the same time, the fixed-wing captured horizontal gradients of these quantities and aerosol size distribution. These systems were deployed south of the Houston metro area, in an area that is impacted by the Gulf of Mexico sea breeze on a daily basis. These observations offer enhanced and complementary perspectives to those provided by the DOE ARM Mobile Facility (AMF) which is was deployed in southeast Houston, and an ancillary site in a more rural location west of the urban Houston area. Quality-controlled versions of the UAS data were collected and posted on the DOE ARM data archive after the conclusion of the campaign where they are accessible by the research community and general public. The UAS perspective offers revolutionary insight into key spatial and temporal effects that have not been evaluated previously.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Characterization of Multiple Trichloroethene, cis-Dichloroethene and 1,1-Dichloroethene Degrading Propanotrophic Communities

Aerobic cometabolism offers a viable strategy for the remediation of chlorinated solvent plumes at oxic sites where anaerobic approaches are limited. In this study, propane-enriched mixed cultures (derived from agricultural soils and an impacted site sediment) which previously degraded 1,4-dioxane, were evaluated for their capacity to also degrade trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), and 1,1-dichloroethene (1,1-DCE) over successive transfers. Sustained biodegradation of TCE and cDCE was observed across multiple enrichments, and cultures enriched on one compound generally degraded the other. In contrast, 1,1-DCE biodegradation was restricted to a subset of cultures and removal times increased over transfers. Further, 1,1-DCE removal was absent at elevated concentrations, both trends consistent with inhibitory or toxic effects. Whole genome sequencing analyses revealed pronounced substrate-dependent selection of microbial communities, with cDCE-degrading cultures being dominated by Mycobacterium and Mycolicibacterium, whereas TCE-degrading cultures were dominated by Rhodococcus. Rhodococcus metagenome-assembled genomes (MAGs) in the TCE degrading cultures classified as R. opacus or R. wratislaviensis. 1,1-DCE degrading cultures were dominated by Pseudonocardia, although the associated MAGs contained a truncated propane monooxygenase alpha subunit. Functional gene analysis identified both group 5 (prmABCD) and putative group 6 propane monooxygenases. The following KBase narratives contain the quality controlled reads, MAGs (fasta assemblies) and the prokka annotations for each assembly TCE Site 1A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254918) TCE Soil 2A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254919) TCE Soil T3 A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254920) TCE Soil T4 A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254921) cDCE Site 1A 1B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254915) cDCE Soils T2 A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254927) cDCE Soil T3 A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254928) cDCE Soil 4A and B Propanotrophic MAGs (https://narrative.kbase.us/narrative/254942) 1,1-DCE T2 T3 Propanotrophic MAGs (https://narrative.kbase.us/narrative/254903)

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hydropower Infrastructure – LAkes, Reservoirs, and RIvers (HILARRI)

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2024) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2024) These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

13 HYDRO ENERGY↗
DOE OSTI2019

Mechanistic Study of Nucleation Enhancement in Atomic Layer Deposition by Pretreatment with Small Organometallic Molecules

Thermal atomic layer deposition (ALD) of metals on metal oxide surfaces typically suffers from nucleation delays that result in poor-quality films. The poor nucleation may be caused by a lack of suitable chemisorption sites on the oxide surface, which are needed for metal nucleation to occur. In this work, we demonstrate that prefunctionalizing the surface with a single monolayer of small organometallic molecules from the vapor phase can lead to a significant increase in surface coverage of the metal deposited by ALD. This process is demonstrated for Pt ALD from (methylcyclopentadienyl)trimethylplatinum (MeCpPtMe 3 ) and O 2 , with nucleation enhanced almost 3-fold at 100 ALD cycles after the pretreatment. We hypothesize that the high coverage of the organometallic molecule provides an alternative chemisorption mechanism for the platinum precursor and thus leads to an increase in its uptake. The proposed chemisorption mechanism is robust across several organometallic molecule pretreatments and could potentially be exploited for other organometallic-based metal ALD processes. This chemisorption mechanism was probed using in situ quadrupole mass spectrometry (QMS). The growth of the platinum deposits was investigated in depth through scanning electron microscopy (SEM) and grazing incidence small-angle X-ray scattering (GISAXS). These studies show that the pretreatment also results in the improved wettability of Pt nanoparticles (NPs). The improved wettability is likely to affect the Pt diffusion properties, further contributing to the enhancement observed on the treated substrates. In addition, GISAXS and SEM studies indicate the growth of larger, denser, and more highly ordered Pt NPs at early cycle numbers, which subsequently coalesce into continuous and pinhole-free films. Surface pretreatment by organometallic molecules therefore introduces a potential route to achieve improved nucleation and growth of ultrathin films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Reparameterizing Litter Decomposition Using a Simplified Monte Carlo Method Improves Litter Decay Simulated by a Microbial Model and Alters Bioenergy Soil Carbon Estimates

Litter decomposition determines soil organic matter (SOM) formation and plant-available nutrient cycles. Therefore, accurate model representation of litter decomposition is critical to improving soil carbon (C) projections of bioenergy feedstocks. Soil C models that simulate microbial physiology (i.e., microbial models) are new to bioenergy agriculture, and their parameterization is often based on small datasets or manual calibration to reach benchmarks. Here, we reparameterized litter decomposition in a microbial soil C model (CORPSE - Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) using the continental-scale Long-term Inter-site Decomposition Experiment Team (LIDET) dataset which documents decomposition across a range of litter qualities over a decade. Here, we conducted a simplified Monte Carlo simulation that constrained parameter values to reduce computational costs. The LIDET-derived parameters improved modeled C and nitrogen (N) remaining, decomposition rates, and litter mean residence times as compared to Baseline parameters. We applied the LIDET litter decomposition parameters to a microbial bioenergy model (Fixation and Uptake of Nitrogen – Bioenergy Carbon, Rhizosphere, Organisms, and Protection) to examine soil C estimates generated by Baseline and LIDET parameters. LIDET parameters increased estimated soil C in bioenergy feedstocks, with even greater increases under elevated plant inputs (i.e., by increasing residue, N fertilization). This was due to the integrated effects of plant litter quantity, quality, and agricultural practices (tillage, fertilization). Collectively, we developed a simple framework for using large-scale datasets to inform the parameterization of microbial models that impacts projections of soil C for bioenergy feedstocks.

54 ENVIRONMENTAL SCIENCES↗