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DOE OSTI2021

Integrating static PRA information with risk informed safety margin characterization (RISMC) simulation methods

The overall objective of the project was to develop a computationally feasible and user-friendly mechanized process to integrate traditional probabilistic risk assessment (PRA) and dynamic PRA (DPRA) results. Starting with the systematic identification of items in an existing PRA that need dynamic augmentation, the project used a generic 4-loop pressurized reactor (PWR) and 3-loop PWR as example plants. Station blackout (SBO) and large break loss of coolant accident SBLOCA) were selected as the example initiating events. Using the traditional event-tree (ET)/fault-tree (FT) methodology augmented by dynamic evet tree approach, the potential consequences of the initiating events were simulated with RELAP-3D and MELCOR/RASCAL codes to cover Level 1 through Level 3 of PRA. RAVEN and ADAPT software were used to generate Level 1 simulations with RELAP-3D and Level 2/3 simulations with MELCOR (Level 2)/RASCAL (Level 3), respectively. Example branching conditions (BCs) for SBO included AC power recovery time, valve repair failure time, reactor coolant pump leak time/break size and emergency power supply duration to a total of 9. Example BCs for LOCA included off-site power recovery time, diesel generator power recovery time, auxiliary feed water system operation time, safety relief valve failure to open upon demand, reactor coolant pump seal break time and size to a total of 21. Each RELAP-3D simulation (9,587 scenarios) was labelled OK or Core Damage based on the maximum allowed peak clad temperature (2,100oF). Each MELCOR simulation (4610 scenarios) was labeled as Bin over 10rem or Bin 0-10rem based on the dose at the site boundary. The scenarios were clustered based on the criteria above using the mean shift methodology. Classical PRA (CPRA) and DPRA results were compared to identify the ET sequences that need DPRA augmentation. Several approaches were proposed for the incorporation of these sequences into CPRA using clustering with the mean shift methodology, restructuring the CPRA ETs by adding new BCs/sequences, and using the concept of a limit surface. Procedures for decision making regarding the possible consequences of an initiating event (e.g. core damage or not, site evacuation or not) were developed using a convolutional neural network (CNN), a recurrent neural network (RNN) and a transformer neural network (TNN). The project has led to two PhD degrees, three archival journal papers and five refereed conference proceedings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Electric Vehicle Supply Equipment (EVSE) Study for Vernon County, Wisconsin [Slides]

Viroqua is a town in Wisconsin with a population of approximately 4,500. The Vernon County Energy district is a local nonprofit in Viroqua that provides energy education, individualized energy consulting and coaching to residents and businesses. They have a very good relationship with our county government and wish to support their efforts. The Vernon County government is currently working on a comprehensive plan and would like to include planning for electric vehicle (EV) charging infrastructure in the plan. They are seeking guidance on logical phases for implementation and identifying the best locations for Level 2 and Level 3 EV chargers. NREL provided technical input on strategic planning for electric vehicle (EV) adoption and electric vehicle supply equipment (EVSE) expansion in Vernon County through the Clean Energy to Communities (C2C) Expert Match technical assistance program. NREL provided strategic planning support for EV expansion planning in Vernon County and siting considerations for locating EVSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Electron Microscopic Examination of an Irradiated TRISO Coated Particle of AGR-2 Experiment: AGR2-222-RS019

The transmission electron microscopic (TEM) examination of an irradiated tristructural isotropic (TRISO) coated particle from advanced gas reactor (AGR) 2 fuel Compact 2-2-2 is presented in this report. Compact 2-2-2 refers to the compact in Capsule 2 at Level 2 of Stack 2. The examination on AGR2 222 RS019 is focused on the interface between the silicon carbide (SiC) to inner pyrolytic carbon (IPyC) layer, central part of SiC, and the interface between SiC and outer pyrolytic carbon layer. The SiC grain boundary distribution and associated fission product precipitates were characterized by scanning transmission electron microscopy (STEM) energy dispersive spectroscopy (EDS), and precession electron diffraction (PED) in the transmission electron microscope. Two separate locations on SiC layer were selected for on the basis of the intact of the buffer layer in the irradiated TRISO particle. The key observation in this particle is that the compositional nature of grain boundary precipitates is more complex near IPyC/SiC layer where Pd, Pd-Ag, Pd-U containing precipitates can be observed. However, the compositions of precipitate towards outer most region of SiC mainly contain Pd. Ag has been observed in the IPyC/SiC layer in one of the two locations. Comparison of the results to previously irradiated and analyzed, Variant 3, safety tested AGR-1 TRISO particles (AGR1-433-001 and AGR1 222-RS004) showed the grain boundary distributions have some noticeable differences. The differences in coincident site lattice grain boundary and misorientation angles are possibly inherent to the fabrication process. Neutron irradiation induced void distribution has been found to be nonuniform. A high concentration of smaller voids is found at stacking faults compared to those in the SiC matrix. Larger void sizes are observed in the SiC location that is near to the broken buffer layer. This void size variation with integrity of buffer layer can potentially affect the fission product retention. However, the average void size continuously decreases along the radius of SiC in both locations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms

Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version (version 4; V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper. The IIR Level 2 algorithm has been modified in the V4 data release to improve the accuracy of the retrievals in clouds of very small (close to 0) and very large (close to 1) effective emissivities. To reduce biases at very small emissivities that were made evident in V3, the radiative transfer model used to compute clear-sky brightness temperatures over oceans has been updated and tuned for the simulations using Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data to match IIR observations in clear-sky conditions. Furthermore, the clear-sky mask has been refined compared to V3 by taking advantage of additional information now available in the V4 CALIOP 5 km layer products used as an input to the IIR algorithm. After sea surface emissivity adjustments, observed and computed brightness temperatures differ by less than ±0.2 K at night for the three IIR channels centered at 08.65, 10.6, and 12.05 µm, and inter-channel biases are reduced from several tens of Kelvin in V3 to less than 0.1 K in V4. We have also improved retrievals in ice clouds having large emissivity by refining the determination of the radiative temperature needed for emissivity computation. The initial V3 estimate, namely the cloud centroid temperature derived from CALIOP, is corrected using a parameterized function of temperature difference between cloud base and top altitudes, cloud absorption optical depth, and CALIOP multiple scattering correction factor. As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3 and increases the number of retrievals. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12.05 and 10.6 µm and at 12.05 and 08.65 µm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice habit models from the recent “TAMUice2016” database, namely the single-hexagonal-column model and the eight-element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.05 and 10.6 µm and effective diameter as derived from in situ measurements at tropical and midlatitudes during the Tropical Composition, Cloud, and Climate Coupling (TC4) and Small Particles in Cirrus Science and Operations Plan (SPARTICUS) field experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

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 Pump House at Mount Crested Butte in the East River Watershed. 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 ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples 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 and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, 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 also 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 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 OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

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 at Snodgrass Mountain. 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 SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples 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 and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, 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 also 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 OSTI2025

Comparisons of the v11.1 Orbiting Carbon Observatory‐2 (OCO‐2) X CO2 Measurements With GGG2020 TCCON

The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Early-life atomic-bomb irradiation accelerates immunological aging and elevates immune-related intracellular reactive oxygen species

Reactive oxygen species (ROS) play an important role in immune responses; however, their excessive production and accumulation increases the risk of inflammation-related diseases. Although irradiation is known to accelerate immunological aging, the underlying mechanism is still unclear. To determine the possible involvement of ROS in this mechanism, we examined 10,023 samples obtained from 3752 atomic-bomb survivors in Hiroshima and Nagasaki, who participated in repeated biennial examinations from 2008 to 2016, for the effects of aging and radiation exposure on intracellular ROS (H 2 O 2 and O 2 •- ) levels, percentages of T-cell subsets, and the effects of radiation exposure on the relationship between cell percentages and intracellular ROS levels in T-cell subsets. The cell percentages and intracellular ROS levels in T-cell subsets were measured using flow cytometry, with both fluorescently labeled antibodies and the fluorescent reagents, carboxy-DCFDA and hydroethidine. The percentages of naïve CD4 + and CD8 + T cells decreased with increasing age and radiation dose, while the intracellular O 2 •- levels in central and effector memory CD8 + T cells increased. Additionally, when divided into three groups based on the percentages of naïve CD4 + T cells, intracellular O 2 •- levels of central and effector memory CD8 + T cells were significantly elevated with the lowest radiation dose group in the naïve CD4 + T cells. Thus, the radiation exposure-induced decrease in the naïve CD4 + T cell pool size may reflect decreased immune function, resulting in increased intracellular ROS levels in central and effector memory CD8 + T cells, and increased intracellular oxidative stress.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2022

HȮ 2 + HȮ 2 : High level theory and the role of singlet channels

At low temperatures and high pressures, the HȮ 2 + HȮ 2 reaction is an important reaction in combustion. There are significant unresolved discrepancies between prior theoretical and experimental studies of this reaction. It has generally been presumed to occur as an abstraction on the triplet surface to produce H 2 O 2 + 3 O 2 . Here, we employ a combination of high-level electronic structure theory (the ANL0 composite method or multi-reference methods as appropriate), sophisticated transition state theory (vibrationally adiabatic torsions, variational, and variable reaction coordinate), and master equation analyses to predict the thermal kinetics on the H 2 O 4 surface. Notably, this analysis suggests a significant branching to HȮ 3 + ȮH near 1000 K via reaction on the singlet surface. This channel does not appear to have been considered in prior combustion models. HȮ 3 itself is a metastable complex (bound by only 3 kcal mol -1 ) that rapidly dissociates to ȮH + 3 O 2 . Thus, the net reaction for this channel, HȮ 2 + HȮ 2 → ȮH + ȮH + O 2 , converts two low reactivity HȮ 2 radicals into two highly reactive ȮH radicals. The ramifications of these newly derived rate expressions are highlighted through kinetic modeling studies of H 2 , CH 3 OH, $\textit{n}$-heptane, and isooctane oxidation; all at the high pressures of relevance to combustion devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Leafweb: Dataset in Support of Coupled Modeling of Photophysics, Photochemistry, and Biochemistry of Photosynthesis, December 2022 Release

This data set contains measurements of leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry of light, carbon dioxide (CO2), oxygen (O2), and temperature responses from 26 C3 photosynthetic and four C4 photosynthetic species measured by independent researchers in Canada, China, Finland, Netherlands, and United States in the field, garden, or greenhouse. Data were collected between 1987 and 2021, however data for individual species only provide coverage over a few hours to one year. Species include three lianas, three shrubs, two boreal deciduous trees, two boreal evergreen needle-leaf tree, three temperate deciduous trees, four tropical deciduous trees, three tropical evergreen trees, one C3 grass, three C4 grasses, and six crop varieties. These measurements are conducted according to standard protocols in gas exchange (Long and Bernacchi 2003) and PAM fluorometry (Baker 2008). The Scots pine (Pinus sylvestris) dataset contains one-year continuous fluorometry observations made at intervals of 10 or 30 minutes in the field under natural environments using Walz monitoring PAM. Measurements from all other 29 species include simultaneous PAM fluorometry and gas exchange observations. Among these 29 species, seven species were measured with light response curves only (i.e., light intensity varied systematically with ambient CO2 concentration controlled at a constant level, e.g., 400 ppm). All the other 22 species were measured with both the light response and CO2 response (i.e., ambient CO2 concentration varied systematically with light intensity controlled at a constant level, e.g., 1200 µmolm-2s-1). For most species, measurements were made with temperature controlled at ~ 25 degrees Celsius (C) with the exceptions of Scots pine (natural diurnal and seasonal variations), tomato cultivar Basket Vee (~ 21 degrees C), and cotton which contained temperature stress experiments (9 to 40 degrees C). All measurements were made at ambient O2 concentration except for the tomato cultivar Basket Vee and cotton which used two O2 levels (2 and 21%) and rice and tomato cultivar Growdena which used five O2 levels (2, 10, 21, 35, and 50%). This dataset supports the complete modeling of photosynthesis at the leaf scale, including photophysical, photochemical, and biochemical reactions. It will support research in the applications of remotely sensed solar induced chlorophyll fluorescence and the responses of energy dissipation pathways to environmental variations across species and climates. This dataset contains 260 data files in comma separate (.csv) format contained inside 11 compressed (*.zip) files. Additional metadata are provided: one data dictionary, a species information table, and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

air temperature↗

DOE OSTI2023

Be 2 C synthesis, properties, and ion-beam irradiation damage characterization

This technical report is in submission of completion of the Level 2 milestone number M2AT 23OR1101022, titled “Be 2 C synthesis, properties, and ion-beam irradiation damage characterization,” for the AT-23OR110102 “Beryllium Carbide as moderator for MSRs—ORNL” WBS number 2.04.11.01 within the larger Advanced Reactor Technologies program at Oak Ridge National Laboratory. This effort is an initial study of the viability of Be 2 C as a future neutron moderator for molten salt reactors and other high-temperature reactors. The research was awarded funding in September 2022, and this Level 2 milestone presents the work that completed by the end of July 2023. This work is a collaboration between Oak Ridge National Laboratory and the University of Michigan. This report discusses the status of this effort and planned work before the final reporting.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Catalytic Oxygen Reduction for Deep O 2 Removal from CO 2 Streams

Streams of CO 2 from various capture processes may contain several impurities of which O 2 is often overlooked as a problematic impurity. Because of its reactivity, the National Energy Technology Laboratory has recommended a stringent limit, less than 10 ppm, for the O 2 level in treated CO 2 products for safe transport, storage, and utilization. Here, in this study, a variety of catalytic oxygen reduction approaches using commercial automotive exhaust emission catalysts are evaluated for O 2 removal from CO 2 streams using different reducing agents, including H 2 , CO, CH 3 OH, and CH 4 . When the amount of reductant added into the feed is carefully controlled, H 2 , CO, or CH 3 OH can effectively remove O 2 from 1.5% to below 10 ppm with a single-reactor design (>99.93% removal efficiency), while simultaneously meeting the impurity limits for other species. With CH 4 as a reductant, it is challenging to simultaneously meet the the O 2 and CO specifications in a single-reactor design because CH 4 also reacts with CO 2 to produce high levels of CO in the stream (at several hundred ppm). A dual-reactor design is developed to enable the use of CH 4 , a readily available and low-cost reductant, for the purification of CO 2 streams to meet the specifications for O 2 and other impurities.

CO2 purification↗

DOE OSTI2023

Techno-economic analysis of carbon dioxide capture from low concentration sources using membranes

Rising carbon dioxide (CO 2 ) levels in the atmosphere lead to global warming, causing climate change. As such, carbon capture has become necessary to slow the increase and reduce CO 2 levels in the atmosphere. Point source emissions have a wide range of CO 2 concentrations, but emissions below 3% CO 2 have mostly been ignored because Carbon capture from these sources has been viewed as costly and economically unsustainable. Membrane technologies are considered the most viable solution by virtue of more energy-efficient operation. Our group at Idaho National Laboratory (INL) has developed poly[bis((2-methoxyethoxy)ethoxy)phosphazene] (MEEP)-based carbon dioxide selective membranes with CO 2 /N 2 selectivity greater than 40 and CO 2 permeability greater than 450 Barrer. To understand the economics of carbon capture, a spreadsheet-based techno-economic analysis (TEA) model was developed to consider multiple parameters, including selectivity and permeability of the membranes, performance conditions such as the number of stages, module material, electricity price, membrane price, and capital financing. The cost of carbon capture in US $\$$/metric ton was calculated at various purities and compared with other membrane processes, cryogenic capture, solvent-based capture, and pressure swing adsorption-based capture. It was determined that a MEEP-based three-stage process had a capture cost of US $\$$ 50.1/metric ton for 99.8% purity CO 2 from a 1% CO 2 feed source in nitrogen (N 2 ). In conclusion, the capture cost using the best performing Pebax-based membrane was 464% higher, cryogenic capture was 60%–140% higher, pressure swing adsorption was 55%–165% higher, and chemical absorption was -10%–110% higher than MEEP-based membrane capture, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Continued Evaluation of the Use of a Raman Spectrometer for H-Canyon Dissolver Monitoring

Remote monitoring of dissolver activities in H-Canyon can help operators avoid delays associated with excessive levels of fuel fragments remaining after a run. SRNL has proposed that effective monitoring can be achieved by using a Raman spectrometer to measure NO 2 concentrations in the offgas stream sampled from the facility stack. Prior work (SRNL-STI-2021-00451) measuring the offgas from one dissolution batch of High Flux Isotope Reactor (HFIR) fuel suggested a relationship between %NO 2 levels and fragment height. Herein, we report the results of monitoring and analysis of the dissolution of four batches of Material Test Reactor (MTR) fuel. A rigorous quantitative relationship between %NO 2 measurements and fragment heights could not be established, due to high uncertainties associated with both measurements. Uncertainties with gas measurements are associated with the %NO 2 levels in the offgas being close to the detection limit for the analyzer. Alternative gas measurement strategies are discussed which could improve sensitivity and reduce uncertainty. Limitations to the precision of the probe measurements are also discussed. It is also noted that the offgas is an average of the products from simultaneous dissolution of elements in multiple wells. Detection of a high fragment height level in an individual well may be hindered by low levels in other wells.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Atmospheric CO 2 estimates for the Miocene to Pleistocene based on foraminiferal $δ^{11}$B at Ocean Drilling Program Sites 806 and 807 in the Western Equatorial Pacific

Constraints on the evolution of atmospheric CO 2 levels throughout Earth's history are foundational to our understanding of past variations in climate. Despite considerable effort, records vary in their temporal and spatial coverage and estimates of past CO 2 levels do not always converge, and therefore new records and proxies are valuable. Here we reconstruct atmospheric CO 2 values across major climate transitions over the past 16 million years using the boron isotopic composition (δ 11 B) of planktic foraminifera from 89 samples obtained from two sites in the West Pacific Warm Pool, Ocean Drilling Program (ODP) Sites 806 and 807, measured using high-precision multi-collector inductively coupled plasma mass spectrometry. We compare our results to published data from ODP Site 872, also in the Western Equatorial Pacific, that goes back to 22 million years ago. These sites are in a region that today is near equilibrium with the atmosphere and are thought to have been in equilibrium with the atmosphere for the interval studied. We show that δ 11 B data from this region are consistent with other boron-based studies. The data show evidence for elevated pCO 2 during the Middle Miocene and Early to Middle Pliocene, and reductions in pCO 2 of ~200 ppm during the Middle Miocene Climate Transition, ~250 ppm during Pliocene Glacial Intensification and ~50 ppm during the Mid-Pleistocene Climate Transition. During the Mid-Pleistocene Transition there is a minimum pCO 2 at marine isotopic stage (MIS) 30. Our results are consistent with a coupling between pCO 2 , temperature and ice sheet expansion from the Miocene to the late Quaternary.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Trajectories for the evolution of bacterial CO 2 -concentrating mechanisms

Cyanobacteria rely on CO 2 -concentrating mechanisms (CCMs) to grow in today’s atmosphere (0.04% CO 2 ). These complex physiological adaptations require ≈15 genes to produce two types of protein complexes: inorganic carbon (Ci) transporters and 100+ nm carboxysome compartments that encapsulate rubisco with a carbonic anhydrase (CA) enzyme. Mutations disrupting any of these genes prohibit growth in ambient air. If any plausible ancestral form—i.e., lacking a single gene—cannot grow, how did the CCM evolve? Here, we test the hypothesis that evolution of the bacterial CCM was “catalyzed” by historically high CO 2 levels that decreased over geologic time. Using an E. coli reconstitution of a bacterial CCM, we constructed strains lacking one or more CCM components and evaluated their growth across CO 2 concentrations. We expected these experiments to demonstrate the importance of the carboxysome. Instead, we found that partial CCMs expressing CA or Ci uptake genes grew better than controls in intermediate CO 2 levels (≈1%) and observed similar phenotypes in two autotrophic bacteria, Halothiobacillus neapolitanus and Cupriavidus necator . To understand how CA and Ci uptake improve growth, we model autotrophy as colimited by CO 2 and HCO 3 − , as both are required to produce biomass. Our experiments and model delineated a viable trajectory for CCM evolution where decreasing atmospheric CO 2 induces an HCO 3 − deficiency that is alleviated by acquisition of CA or Ci uptake, thereby enabling the emergence of a modern CCM. This work underscores the importance of considering physiology and environmental context when studying the evolution of biological complexity.

Flamholz, Avi I.↗