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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A New Model for Simulating the Imbibition of a Wetting-Phase Fluid in a Matrix-Fracture Dual Connectivity System

The imbibition experiment is an effective approach for measuring petrophysical properties of porous media, with many such experiments performed over the past decade. Quite some empirical, analytical, and numerical models have been developed to simulate spontaneous imbibition of the wetting phase fluid into porous media, but limitations still exist. In previous studies, the imbibition process has been considered to give a piston-like displacement or the porous medium modeled as multiply-sized pores linked with bonds; both approaches fail to yield comprehensive results due to their neglect of the presence of irregular fractures or nonuniform flow paths through the matrix. By building a numerical model for simulating laboratory-scale experimental data, we performed imbibition tests on several fractured Barnett Shale samples having fractures either parallel ( P ) or transverse ( T ) to the bedding plane and used MATLAB to build a new numerical model by combining the imbibition process in fractures and the matrix using concepts from percolation theory. The experimental data show that the rocks with P -direction fractures have a more steady increase of imbibition rates than the case of T -direction one. As the shale matrix with low pore connectivity hampers the upward water movement, the imbibition rate of shales with T -direction fractures will decrease suddenly after the bottom layer in contact with water is saturated during the initial period. This wetting phase movement (WPM) model can simulate 3D porous media with 2D fractures. The rate of imbibition by fractured porous media is associated with physical parameters such as porosity and fracture distribution (e.g., the number and angle of fractures). Using Monte Carlo methods, we examined fracture parameters and predicted elapsed time and cumulative water imbibition, for the Barnett Shale samples. The results show that the rate of imbibed water mass is sensitive to the number of fractures directly connected to water source, and the connectivity between two neighboring grid cells is a key parameter for the wetting-front progression. The findings of this study can help to better understand the imbibition process with multiple influencing processes and factors in fractured-matrix rocks. Although the experiments, data simulation, and prediction results are based only on Barnett Shale samples, the model is readily applicable to imbibition tests of other fractured rocks to show the spatial and temporal behavior during a dynamic imbibition process that are not easily captured experimentally.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

A Particle-Based Conditional Sampling Scheme for the Simulation of Transport in Fractured Rock With Diffusion Into Stagnant Water and Rock Matrix

In situ experiments and field-scale characterization studies have pointed out that, in fractured crystalline media, groundwater flow is highly channelized. This implies that, at the scale of a single fracture, only part of the fracture surface area is in contact with flowing water, while the rest of in-plane water is essentially stagnant and can be accessed by solutes via molecular diffusion. Despite their importance for contaminant retention, to date, there are no numerical or analytical approaches that could be used to assess the implication of stagnant water zones on solute transport in realistic large-scale Discrete Fracture Network-based models. In this study, we present an efficient and flexible algorithm for the simulation of transport in fractured rock with diffusion into stagnant water and rock matrix. The algorithm is a generalization of a previously developed numerical framework for time domain particle tracking in sparsely fractured rock. The key of the generalization is that total time in fracture (τ f ) is first evaluated using a Monte Carlo sampling and then a second sampling is performed conditioned on τ f . The algorithm has been successfully validated against existing independent solutions and the implication of diffusion into stagnant water and secondary diffusion into the matrix has been assessed for a realistic modeling scenario. The results show that, due to diffusion into stagnant water, contaminants are more strongly retarded. This increased retention is more significant for sorbing species, as a larger number of sorption sites is accessible. A high sensitivity to the flowing channel/stagnant water zone geometry has also been observed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2022

MIS Shelf-Life Final Report for Plutonium Oxide Item CAN92 (SSR147) from Rocky Flats Analytical Laboratory Operations

A plutonium/uranium oxide material from the Material Identification and Surveillance (MIS) Program inventory has been studied to determine the gas generation and corrosion behavior in a storage environment. Sample CAN92 represents plutonium/uranium oxides stored in 3013 containers. The material originated in the analytical laboratory at Rocky Flats. This study followed over time the gas pressure and composition of a sample with nominally 0.5 wt% water in a sealed container with an internal volume scaled to 1/400th of the volume of a 3013 container. Gas compositions had been measured periodically over 5 ½ years. The maximum observed gas pressure from gas generation was 117 kPa. The increase over the initial pressure of 77.5 kPa was largely due to the generation of nitrogen and carbon dioxide with minor amounts of hydrogen and nitrous oxide. The internal components of SSR147 were inspected 6.5 years after it was removed from the array. The inside surfaces of the inner bucket exposed to the original sample for 1,937 days had an etched appearance and general corrosion. No pitting was observed on the inner bucket that held the sample.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Integrated Experimental and Modeling Study of Geochemical Reactions of Simple Fracturing Fluids with Caney Shale

Interactions between rock minerals and hydraulic fluids directly impact the geochemical and geomechanical properties of shale formations. However, the mechanisms of geochemical reactions in shale unconventional reservoirs remain poorly understood. Here, to investigate the geochemical reactions between shale and hydraulic fracturing fluids, a series of batch reactor experiments were undertaken. Three rock samples with different mineralogical compositions and three fluid samples of different compositions (deionized water, deionized water + 2% potassium chloride (KCl), and deionized water + 0.5% choline chloride (C 5 H 14 ClNO) were used. Experiments were undertaken at reservoir temperature and atmospheric pressure. Elemental compositions of effluents after 1, 3, 7, 14, 28 days were analyzed using Inductively Couple Plasma Mass Spectrometry (ICP-MS). Medical Computed Tomography (CT) scan and X-ray Fluorescence (XRF) were conducted on the entire core run to help upscale results obtained from rock-fluid interaction experiments. Geochemical modeling using a reactive simulator, TOUGHREACT, was undertaken to corroborate experimental results. Results show that lower pH triggered high dissolution rates in the rock samples, especially the carbonate components. As pH increased, the rate of dissolution declined significantly, though for most cases dissolution still continued. Observed dissolved silica concentrations were much higher than the quartz solubility, suggesting that much of the silica originates from more soluble silica polymorphs and possibly desorption from clay mineral exchange sites. Concentration of most elemental species in solution increased but aluminium and magnesium concentrations declined rapidly following initial entry into solution. Geochemical modeling corroborated the conclusions regarding mineral dissolution and precipitation observed from experiments, notably; the dissolution of calcite and oxidation of pyrite in reacted shale samples, the likely presence of silica polymorphs such as opal, chalcedony or amorphous silica in these samples, and the reduction of Al and Mg concentrations in solution by precipitation of secondary aluminosilicate phases. The de-flocculation of clay minerals during reaction implies fines migration after hydraulic fracturing. This is detrimental to reservoir productivity as clay fines are displaces and lodged within the micro and nano-fractures created during fracturing. The immediate consumption of aluminium and magnesium also has implications on blockage of hydrocarbon pathways due to precipitation of new minerals in these locations.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Drying and Analysis of Aluminum (Oxy)hydroxide Films for Dry Storage of Aluminum-Clad Spent Nuclear Fuels

In aluminum-clad spent nuclear fuels, an (oxy)hydroxide layer on the surface of the cladding hosts chemisorbed water formed during reactor and post-discharge exposure to water. Any residual water is susceptible to generating hydrogen via radiolysis, which can be a risk associated with dry fuel storage. Engineering-scale forced helium dehydration (FHD) and vacuum drying tests were conducted on mock-up fuel assemblies that included corroded aluminum surrogate plates to assess the removal of bulk and chemisorbed water. Thermogravimetric analysis was performed on samples of the surrogate plates, both undried control samples used to determine onset temperatures associated with a phase change occurring in the oxide layer and samples from drying tests used to determine the effectiveness of each drying method. Both vacuum drying and FHD processes were capable of removing bulk water. However, FHD was determined to provide additional drying capabilities, including partial removal of chemisorbed water from bayerite due to the higher temperatures during drying. In conclusion, the temperature threshold for partial dehydroxylation of the oxide layer was determined to be around 220°C, meaning any drying methods attempting to remove chemisorbed water must exceed 220°C.

chemisorbed water↗
DOE OSTI2022

MIS Shelf-Life Final Report for Plutonium Oxide Item 1000089 (SSR136 and SSR136A) from Rocky Flats Peroxide Precipitation and Calcination Process

A plutonium dioxide material from the Material Identification and Surveillance (MIS) Program inventory has been studied to determine the gas generation and corrosion behavior in a storage environment. Sample 1000089 represents product-quality plutonium oxides produced in the Rocky Flats peroxide precipitation process currently stored in 3013 containers. This study followed over time the gas pressure and composition of a sample with nominally 0.5 wt% water in a sealed container with an internal volume scaled to 1/500th of the volume of a 3013 container. Gas compositions had been measured periodically over almost 13 years. The maximum observed gas pressure was 204 kPa and was related to a temperature excursion. The maximum observed gas pressure from gas generation was 126 kPa. The increase over the initial pressure of 86.7 kPa was due to the generation of hydrogen, oxygen, and nitrogen. Carbon dioxide, methane and carbon monoxide were minor components of the headspace gas. The material exhibited unusual behavior in that the atmosphere reached flammable levels of hydrogen and oxygen within the first 30 days and remained flammable for the duration of the experiment. It has been determined that the sample inside the reactor was a mixture of the AR material, material calcined at 800 °C, and material calcined at 950 °C. The experiment was terminated in 2012 and unloaded in 2016. A new reactor, SSR136A, was loaded with the original sample freshly calcined at 950 °C. The total pressure inside SSR136A remained close to the initial value of 93 kPa. The total pressure is composed of He (steady levels after the reloading) and hydrogen which is 10% of the reactor’s total pressure. Nitrogen and oxygen compose trace amounts of the reactor headspace. The internal components of SSR136 were inspected in 2021, five years after it was removed from the array. The inside surfaces of the inner bucket exposed to the original sample for 4,576 days had an etched appearance, and pit-like features were observed in the microscopic analysis. No corrosion was observed on the SSR136A inner bucket that held the freshly calcined sample that was exposed for 1,303 days.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

Abstract Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar ( Populus nigra X P. maximowiczii ), switchgrass ( Panicum virgatum ), miscanthus ( Miscanthus giganteus ), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha −1 year −1 ) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L −1 ) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha −1 year −1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sampling and Analysis Plan for Partial Closure of Solid Waste Management Unit #16; Addendum Part I for Delineating BH-056

On July 11, 2022, Sandia National Laboratories in California (SNL/CA) submitted a Response to Regional Water Quality Control Board Comments on Soil Sampling Results for Closure of a Portion of SWMU #16 in response to the February 16,2022 San Francisco Bay Regional Water Quality Control Board’s (SFRWQCB) letter requesting supporting information for the recommended closure of 7,700 linear feet of abandoned sewer lines. On August 18, 2022, SFRWQCB further requested a Sampling and Analysis Plan (SAP) for additional “step-out” sampling to delineate the potential presence of benzidine near borehole BH-056, which is located near the former sewer line. SNL/CA is in the process of contracting Weiss Associates (Weiss) to perform and oversee the boring, sampling, analysis, and report development to determine the potential presence and extent of benzidine. This document outlines the work that is anticipated, including the development of the SAP, to complete the investigation and submit a final report to the SFRWQCB. The work proposed by Weiss provides an estimated schedule for completing the investigation and developing the addendum Part II SAP for the project. In addition, Weiss provided a preliminary estimate of the sample locations (see Attachment A) which serve as addendum Part I of the SAP requested by the SFRWQCB. The contractor will submit the addendum Part II SAP, to satisfy the SFRWQCB requirement, before proceeding with any work.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leaf Nitrogen and Carbon Content, and Leaf Mass Per Area, Kougarok Road, Seward Peninsula, Alaska, 2018

Nitrogen and carbon content, leaf mass per area (LMA) and leaf water content (LWC) of leaves sampled from locations on the Kougarok mile marker 64 NGEE Arctic site, Seward Peninsula, Alaska. Samples were collected in July 2018 from Alnus viridis, Betula nana, Betula glandulosa, Arctostaphylos alpina and Salix pulchra. This data package includes leaf sample information and trait data (*.csv). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA207_flmd.csv. See data package NGA208 "Full spectrum 350-2500 nm canopy spectral reflectance, Seward Peninsula, Alaska, 2018" for linked canopy spectral reflectance data, dGPS locations and sample photographs. 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

Riverbed and Near-Surface Water Quality Data, Hanford Reach, Columbia River, February 2021 - April 2022

This dataset contains longitudinal profiles of natural groundwater tracers (temperature, electrical conductivity (EC), and Radon-222 (Rn)) collected to identify locations of hydrologic exchange flows along a 75-km reach of the Columbia River near Richland, Washington.A jetboat was used to tow a 30 foot long, weighted tether that had one set of temperature and EC sensors just below the water surface and one set of temperature and EC sensors on the end (just above the river bed). The dataset contains profiles of temperature/EC data (resolution: 1 second) collected along the left and right banks during three different sampling events. During the final sampling event, we revisited some "deep holes" where the river was more than 30 feet deep with a weighted tether that was 75 feet long. The dataset has been processed to remove data collected when the sensors were not in the water, or when the water was too deep to keep the tether on the riverbed. We collected grab samples of water for analysis of dissolved Rn in places where the temperature/EC was different from the background river water. The Rn data was joined to the temperature/EC profiles based on the sample times.This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) installation methods; (4) February 2021 data; (5) July 2021 data; (6) April 2022 data; and (7) April 2022 deep hole data. All files are .csv format and can be opened with a spreadsheet program (such as Microsoft Excel or OpenOffice).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Non-destructive, in-situ evaluation of water presence using thermal contrast and cooled detector

Exemplary systems for detecting water include: a light source positioned to transmit thermal radiation through a sample; a lens assembly positioned to: receive the thermal radiation transmitted through the sample; and focus the transmitted thermal radiation onto a filter positioned between the lens assembly and a detector; and a cooling subsystem for cooling the filter and the detector to a temperature below that of the sample. The filter (1) selectively transmits first portions of the thermal radiation received from the lens assembly and characterized by a wavelength at least partially overlapping a predefined water absorption band and/or a predefined water absorption line; and (2) selectively blocks second portions of the thermal radiation received from the lens assembly and characterized by a wavelength outside the predefined water absorption band and/or the predefined water absorption line. Methods for detecting presence of water in a sample are also disclosed.

Bora, Mihail↗
DOE OSTI2023

Comparing π‐complexation capabilities of ionic liquids containing silver(I) and copper(I) ions by headspace single drop microextraction in combination with high‐performance liquid chromatography

Selective π‐complexation capabilities of silver(I) and copper(I) ions can be effectively facilitated in ionic liquids. To understand the effects of environmental factors that influence the π‐complexation of these metal ions with analytes, techniques that employ small volumes of ionic liquid that can be readily analyzed are desired. In this study, headspace single drop microextraction coupled with HPLC is used to investigate a diverse set of environmental factors on the metal ion‐mediated complexation with aromatic compounds in ionic liquid media. Silver(I) and copper(I) bis[(trifluoromethyl)sulfonyl]imide salts were both studied by dissolving them in the 1‐decyl‐3‐methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ionic liquid and employing the mixture as extraction media for aromatic compounds. Water and acetonitrile within the sample solution were observed to interfere with the complexation of silver(I) ions and aromatic compounds, while ethylene glycol and triethylene glycol did not. The temperature and extraction times were optimized to fully facilitate the π‐complexation capabilities of metal ions in ionic liquid media. Partition coefficients between the sample headspace and metal ion were determined using a three‐phase equilibria model. Although no discernable difference in analyte partitioning between the headspace and ionic liquid solvent was observed, analyte partition coefficients to silver(I) ion tended to be greater compared to copper(I) ion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 1. Characterizing Thermal–Hydrological–Mechanical Processes

Abstract Bentonite clay is widely used in engineered barrier systems for the permanent disposal of high‐level radioactive waste due to its low permeability, high swelling capacity, and thermal stability. However, the complex thermal‐hydrological‐mechanical (THM) processes induced by heating from decaying radioactive waste and hydration from surrounding rock can lead to heterogeneous changes that are difficult to measure and predict. This study develops an Electrical Resistivity Tomography (ERT)‐based framework for monitoring THM processes, progressing from sample‐scale to bench‐scale tests, to inform field‐scale applications. Sample‐scale tests analyzed small bentonite samples under controlled variations in water content, temperature, and porosity to establish fundamental resistivity relationships. Bench‐scale tests involved larger bentonite columns subjected to heating (up to 200°C) and hydration under controlled pressure, simulating repository conditions. ERT measurements, complemented by X‐ray CT imaging, temperature monitoring, and tracing sensors, revealed coupled THM processes, such as hydration‐induced compression, swelling, and thermal gradients, leading to complex resistivity patterns. The results demonstrate the potential of ERT for capturing THM‐induced resistivity changes, though challenges remain in upscaling and quantitative analysis. This study evaluates laboratory test capabilities and proposes future improvements for understanding THM‐induced resistivity responses. A conceptual framework for ERT implementation in field‐scale monitoring is presented, synthesizing findings from both scales and exploring how ERT data can inform long‐term modeling and reduce prediction uncertainties. Overall, this ERT‐based framework offers a robust method for monitoring bentonite buffers, aiding in early issue detection and supporting the safe long‐term disposal of radioactive waste in geological repositories, while highlighting the need for future development. Plain Language Summary Bentonite clay is crucial in engineered barrier systems (EBS) for containing high‐level radioactive waste due to its ability to absorb water, swell, seal and remain stable under high temperatures. When bentonite absorbs water and heats up from radioactive decay, it experiences complex changes in its physical and mechanical properties. Understanding these changes is important for ensuring the long‐term safety and effectiveness of EBS. This study used Electrical Resistivity Tomography (ERT), a non‐invasive method that measures electrical conductivity to monitor these changes during laboratory experiments. The ERT data revealed significant variations in resistivity corresponding to changes in water content, temperature, and density, providing detailed spatial and temporal insights into the behavior of bentonite. These findings enhance our ability to predict the long‐term performance of bentonite barriers, ensuring the safe containment of radioactive waste. By improving our understanding of bentonite's behavior, this research supports the development of more reliable and effective barrier systems for radioactive waste disposal, protecting the environment and public health. Key Points ERT monitoring was employed to capture resistivity changes in bentonite during controlled heating and hydration experiments, providing insights into THM processes ERT data reveal significant resistivity changes correlated with water content, temperature, and mechanical effects, enhancing the understanding of THM dynamics in bentonite This study explores the potential of the framework for application in field‐scale EBS monitoring, emphasizing the need for integrating additional geophysical methods for comprehensive subsurface imaging

Chen, Hang↗
DOE OSTI2025

Cobalt promotion of the InO x –TiO 2 heterojunction for dual photothermal reduction of CO 2

A series of cobalt-promoted indium–titanium composite oxides was synthesized using a microemulsion method. Their functional properties were investigated for the photothermal reduction of carbon dioxide. In this series, the indium content varied between 2.5 and 20%, while the cobalt percentage was kept constant at 4% throughout the series. In all cases, carbon monoxide formation occurred selectively through the reverse water gas shift reaction. The sample with 2.5% In maximized the synergistic use of the two energy sources, while the sample with 10% In showed the highest catalytic activity under both thermal and dual photo-thermo conditions. A physicochemical characterization was performed for all samples. The use of microscopy and X-ray absorption spectroscopy demonstrated that sub-nanometric indium entities, in combination with atomically dispersed cobalt oxide entities, achieved a balance between high thermal activity and significant synergy between light and heat in a catalytic process. The system with 10% indium is thus able to improve the thermal catalytic process through the use of light, providing an intensification procedure for the classic process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗