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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

EXPERIMENTAL STUDY ON COST-EFFECTIVE ADDITIVE MANUFACTURING OF SILICA AEROGEL

The conventional aerogel insulation material manufacturing from supercritical drying suffers from issues of massive energy consumption and prolonged processing time. With the consideration of large market demand of the aerogel insulation material in the next decade, a low-cost and scalable fabrication technique is highly desired. In this paper, a direct ink writing (DIW) method is used to three-dimensionally fabricate the silica aerogel insulation material, followed by ambient pressure drying. Compared to the supercritical drying and freeze drying, the reported method significantly reduces the fabrication time and costs. The cost-effective DIW technique offers the capability to print complex hollow internal structure, coupled with the porous structure, is found to be beneficial to the thermal insulation property. The addition of fiber to the ink assures the durability of the fabricated product. The foam ink preparation methods and the printability are demonstrated in this paper, along with the printed samples for characterizing thermal insulation properties and mechanical properties.

42 ENGINEERING↗
DOE OSTI2022

Understanding Platinum Ionomer Interface Properties of Polymer Electrolyte Fuel Cells

A well-designed cathode catalyst layer with optimal ionomer distribution is critical to minimizing amount of Platinum (Pt) content in polymer electrolyte fuel cells (PEFCs). The impact of Pt loading, ionomer content and carbon support types on the catalyst/ionomer interface were also investigated at dry and wet conditions. Higher Pt loadings resulted in higher double layer capacity (C dl ) and similar electrochemical surface area (ECSA) due to well dispersed ionic phase material. Higher ionomer content resulted in higher ionic conductivity but also showed similar SO 3 − group coverage. High surface area (HSA) carbon support had larger ECSA and C dl at both dry and wet conditions, as less agglomerated Pt was well dispersed in the meso-pores of the support. Lower SO 3 − group coverages were observed for HSA carbon than for Vulcan carbon due to Pt particles being buried within the porous HSA carbon support. The effect of cell conditioning and voltage recovery on the PEFC cathode catalyst layer was shown to have minimal impact on SO 3 − group coverage despite a decrease in C dl and ECSA due to the size increase of Pt particles. At dry condition, a significant increases in SO 3 − group coverage were observed for all MEAs due to higher adsorptivity of ionomer in dry conditions.

25 ENERGY STORAGE↗
DOE OSTI2020

The Incredible Lightness of Water Vapor

The molar mass of water vapor is much less than that of dry air. This makes a moist parcel lighter than a dry parcel of the same temperature and pressure. This effect is referred to as the vapor buoyancy effect and has often been overlooked in climate studies. We propose that the vapor buoyancy effect increases Earth’s outgoing longwave radiation (OLR) and that this negative radiative effect increases with warming, stabilizing Earth’s climate. We illustrate this mechanism in an idealized tropical atmosphere, where there is no horizontal buoyancy gradient in the free troposphere. Temperature increases toward dry atmosphere columns to compensate the reduced vapor buoyancy, increasing OLR by O(1 W m −2 ) at the reference climate. In warmer climates, the temperature difference between moist and dry columns would increase as a result of increasing atmospheric water vapor, leading to enhanced radiative effect and thereby stabilizing Earth’s climate. We estimate that this feedback strength is about O(0.2 W m −2 K −1 ) in the idealized atmosphere, which compares to cloud feedback and surface albedo feedback in the current climate. We further show evidence from observations and real-gas radiative transfer calculations for a significant radiative effect of vapor buoyancy in the tropical atmosphere.

Yang, Da↗
DOE OSTI2023

Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change

Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of a Process to Separate Ensiled Corn Grain and Stover for Improved Utilization as a Biomass Feedstock

Corn grain and chopped stover were stored anaerobically at 37% to 50% (w.b.) aggregate moisture content to create a unique biomass feedstock. However, after storage, the two fractions must be separated to accommodate different conversion pathways. In this work, a modified cleaning system from a grain combine harvester was evaluated to investigate its effectiveness in separating the grain from the stover. While this system has been purposefully built for this task, there is no previous literature on separating grain at high material other than grain (MOG) to grain ratios. Using this system, material was separated into four fractions: Grain, Tailings, Heavy MOG, and Light MOG. Subsamples were collected, oven dried, and then hydrodynamically separated to quantify the grain content in the four fractions. Several different configurations of air velocities, mass flow rate, material MOG: grain ratios, and sieve types and openings were investigated. Grain capture was defined as the fraction of the total grain dry mass collected in the grain and tailings fractions. In five experiments, the grain capture effectiveness varied from 89.2% to 99.4% on a dry basis. The dry basis foreign matter (FM) in the clean grain was 0.6% to 10.8%. As a result, the use of a modified combine cleaning system has the potential to be one step in a system to fractionate corn grain from stover (i.e., MOG) in a biorefinery that uses both starch and cellulose as biomass feedstocks.

09 BIOMASS FUELS↗
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 OSTI2020

Cellular cofferdams for hydropower use. Final report

This report presents the results of a comprehensive study on a proposal to use cellular cofferdams as basis for the design and construction of water retaining structures to sustainably and cost-effectively harness hydropower. Previously, cellular cofferdams have been widely used mainly as temporary water exclusion devices to permit dry construction of in-water structures such as dams, locks, bridge footings and piers, and hydroelectric power plants. Design and construction requirements for cellular cofferdams are less stringent than for hydropower dams. To make cellular cofferdams suitable for permanent hydropower use, different design concepts that utilize cellular cofferdams as the main or core element of the water-retaining dam structure are proposed. One key design concept is the so-called “dry construction technique” in which the granular fill in cofferdam cells and the downstream berm are permanently kept dry in contrast to the wet construction technique for temporary use of cellular cofferdams. The viability of the proposed permanent cellular cofferdam design concepts is demonstrated using well-established structural and geotechnical design procedures and computational modeling. The improved performance of the proposed design concepts, particularly in combination with the dry construction technique, show cellular cofferdams have the potential to be used as basis for the construction of permanent hydropower dam structures that are versatile, with less impact on the environment, and will cost less to build than conventional hydropower dams.

13 HYDRO ENERGY↗
DOE OSTI2020

Shaker Table Test Plan

Currently, spent nuclear fuel (SNF) is stored in onsite independent spent fuel storage facilities (ISFSIs), which is a dry storage facility, at 55 nuclear power plant sites. The majority of SNF in dry storage is in welded metal canisters (2,917 canisters at the end of 2019). The canisters are loaded for storage in storage overpacks (vertical casks or horizontal storage modules) and placed on outdoor concrete pads. Because the SNF will be stored at ISFSIs for an extended period of time, there is growing concern with regards to the behavior of the SNF within these dry storage systems during earthquakes. To address these concerns, the SFWST program is considering conducting an earthquake shaker table test. The goal of this test is to determine the strains and accelerations on fuel assembly hardware and cladding during earthquakes of different magnitudes to better quantify the potential damage an earthquake could inflict on spent nuclear fuel rods. The seismic integrity of the storage system has been addressed in the past by the US Nuclear Regulatory Commission and is not the focus of this potential test. Instead the DOE would benefit from knowing the condition of the fuel cladding from storage, transportation, to disposal so that it can ascertain repository performance for the fuel and packaging in its final state. A seismic event is part of the possible loading events that the fuel could experience in its lifetime. This report proposes several earthquake shaker table tests with different degrees of complexity. Alternative 1 was defined in the FY20 work scope. Alternatives 2 and 3 were recently developed to take advantage of the NUHOMS 32PTH dry storage canister that may be available in FY21 for this test at a minimum cost to the project. The selection of the alternative(s) will depend on the available budget and the SFWST program priorities for the near future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Surface Sampling Techniques for the Canister Deposition Field Demonstration

This report describes plans for dust sampling and analysis for the multi-year Canister Deposition Field Demonstration. The demonstration will use three commercial 32PTH2 NUHOMS welded stainless steel storage canisters, which will be stored at an ISFSI site in Advanced Horizontal Storage Modules. One canister will be unheated; the other two will have heaters to achieve canister surface temperatures that match, to the degree possible, spent nuclear fuel (SNF) loaded canisters with heat loads of 10 kW and 40 kW. Surface sampling campaigns will take place on a yearly or bi-yearly basis. The goal of the planned dust sampling and analysis is to determine important environmental parameters that impact the potential occurrence of stress corrosion cracking on SNF dry storage canisters. Specifically, the size, morphology, and composition of the deposited dust and salt particles will be quantified, as well as the soluble salt load per unit area and the rate of deposition, as a function of canister surface temperature, location, time, and orientation. Sampling locations on the canister surface will nominally include 25 locations, corresponding to 5 circumferential locations at each of the 5 longitudinal locations. At each sampling location, a 2x2 sampling grid (containing 4 sample cells) will be painted onto the metal surface. During each sampling campaign, two samples at each sampling location will be collected, in a specific routine to measure both periodic (yearly or bi-yearly) and cumulative deposition rates. For each sample, a wet and a dry sample will be collected. Wet samples will be analyzed to determine the composition of the soluble salt fraction and to estimate salt loading per unit area. Dry samples will be analyzed to assess particle size, morphology, mineralogy, and identity (e.g. for floral/faunal fragments). The data generated by this proposed sampling plan will provide detailed information on dust and salt aerosol deposits on spent nuclear fuel canister surfaces. The anticipated results include information regarding particle compositions, size distributions, and morphologies, in addition to particle deposition rates as a function of canister surface location, orientation, time, and temperature. The information gathered during the Canister Deposition Field Demonstration is critical for ongoing efforts to develop a detailed understanding of the potential for stress corrosion cracking on SNF dry storage canisters

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Improved temperature control for measuring the humidity dependence of aerosol optical properties

Aerosols impact climate and cloud formation by their ability to take up water, which leads to changes in their optical properties. To better characterize the interactions of aerosol with water vapor, a humidification section has been added to the Cavity Attenuation Phase-shift Spectrometry (CAPS) instrument. The CAPS is a diode based method that provides simultaneous measurements of both aerosol total optical extinction and scattering in real-time. We are improving the humidity controlled sample line with new temperature controls. The current system suffers from particlelosses and poorly controlled humidity profiles, both of which were corrected for in the systems redesign. This improved wet-CAPS system has been integrated with a dry-CAPS to simultaneously measure the wet and dry aerosol optical properties. The two CAPS monitors were operated in a dry collection mode to ensure there were no biases between the instruments. Initial calibrations and data were collected to test the functionality of the dual CAPS system (wet + dry). The new two channel CAPS system improves our ability to measure the effects of humidity on aerosol optical properties.

58 GEOSCIENCES↗
DOE OSTI2021

Integration of the Back End of the Nuclear Fuel Cycle

Management of spent nuclear fuel and high-level radioactive waste consists of three main phases – storage, transportation, and disposal – commonly referred to as the back end of the nuclear fuel cycle. Current practice for commercial spent nuclear fuel management in the United States (US) includes temporary storage of spent fuel in both pools and dry storage systems at operating or shutdown nuclear power plants. Storage pools are filling to their operational capacity, and management of the approximately 2,200 metric tons of spent fuel newly discharged each year requires transferring older and cooler spent fuel from pools into dry storage. Unless a repository becomes available that can accept spent fuel for permanent disposal, projections indicate that the US will have approximately 136,000 metric tons of spent fuel in dry storage systems by mid-century, when the last plants in the current reactor fleet are decommissioned. Current designs for dry storage systems rely on large multi-assembly canisters, the most common of which are so-called “dual-purpose canisters” (DPCs). DPCs are certified for both storage and transportation, but are not designed or licensed for permanent disposal. The large capacity (greater number of spent fuel assemblies) of these canisters can lead to higher canister temperatures, which can delay transportation and/or complicate disposal. This current management practice, in which the utilities continue loading an ever-increasing inventory of larger DPCs, does not emphasize integration among storage, transportation, and disposal. This lack of integration does not cause safety issues, but it does lead to a suboptimal system that increases costs, complicates storage and transportation operations, and limits options for permanent disposal. This paper describes strategies for improving integration of management practices in the US across the entire back end of the nuclear fuel cycle. The complex interactions between storage, transportation, and disposal make a single optimal solution unlikely. However, efforts to integrate various phases of nuclear waste management can have the greatest impact if they begin promptly and continue to evolve throughout the remaining life of the current fuel cycle. A key factor that influences the path forward for integration of nuclear waste management practices is the identification of the timing and location for a repository. The most cost-effective path forward would be to open a repository by mid-century with the capability to directly dispose of DPCs without repackaging the spent fuel into disposalready canisters. Options that involve repackaging of spent fuel from DPCs into disposalready canisters or that delay the repository opening significantly beyond mid-century could add 10s of billions of dollars to the total system life cycle cost.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Integrated Flame Spray Process for Low Cost Production of Battery Materials for Lithium Ion Batteries and Beyond

The overall objective of this project was to develop an advanced manufacturing technology for Li-ion battery materials production at low cost and in a green chemical process using glycerol as solvent to replace water. It was aimed at developing a prototype pilot production line to reduce production cost by at least 25% to the baseline. During the course of this project, research was conducted to understand spray drying and combustion of eutectic solvent precursors of glycerol and metal salts (acetates) in the formation of mixed metal oxide powders. Reactor design and optimization were done to facilitate spray drying and combustion in powder formation and processing. Various cathode materials powders were produced, including lithium nickel-cobalt-manganese oxide (NCM) and lithium nickel-cobalt-aluminum oxide (NCA). These cathode materials powders were tested in half coin cells for their performances, with materials performances of 160 mAh/g for NCM111 and 200 mAh/g for NCA. NCA powders were also produced using a slurry spray-drying process and tested in full pouch cells. A pilot scale flame assisted spray drying reactor was constructed and demonstrated for its production rate exceeding the target of production rate of four metric tons per year. The work performed in this project has been documented in 9 peer reviewed journal publications, 8 conference presentations, and two IP disclosures/patent applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Modeling of ATR fuel in DOE Standard Canisters with Helium Backfilled Condition

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel dictates storage within helium backfilled sealed DOE standard canisters. These sealed canisters are intended for extened (>50 year) dry storage). The typical packaging configuration for the 15-foot DOE canisters places 10 ATR elements within a Type 1a basket, and three baskets are loaded within each DOE canister. During in-reactor operations and cooling pond storage conditions, oxyhydroxide layers form on the surface of the aluminum clad fuel. These layers produce hydrogen gas over time due to the fuel’s radiation field. As part of the packing procedure, the ATR fuel should be dried to remove any residual physio-/chemi- sorbed water from the surface. As testing to the effectiveness of the drying procedure is still underway, this modeling will include results at fully saturated and fully dried conditions. In previous modeling efforts, the G-value for the production of hydrogen from the oxyhydroxide layers was assumed to be in argon environments as measured by Task 2 - Oxyhydroxide Layer Radiolytic Gas Generation Resolution. Previous experimental testing showed differences in the hydrogen generated based on the gaseous environment. In the associated experimental work, Task 2 - Oxyhydroxide Layer Radiolytic Gas Generation Resolution, additional tests were completed in a helium environment, and updated G-values for the radiolytic production of hydrogen from the oxyhydroxide layers were provided. These values are 28% and 58% higher than values for argon. In addition, a change to the modeling of the oxyhydroxide radiolysis has been made from previous reports. This change assumes the dependency of the dose rate on the overall reaction rate is applied to the total weight of the sample, rather than to just the weight of the oxyhydroxide layer. This decreases the dependency of the radiolytic reaction on the thickness of the oxyhydroxide layer. For a nominal scenario of stored ATR fuel, assuming the chemi-/physio- sorbed water have been fully removed, the internal canister pressure increases to 1.61 atm over a 50 year period, with a hydrogen mole percentage of 21%. As in previous modeling, any oxygen present is in negligible amounts (<1 ppt). If a small amount residual air is present, nitric acid can form up to 1300 ppm. For a scenario with high fuel decay heat, the model shows internal pressure increasing to 2.1 atm, with 39.3 mole percentage of hydrogen. In a scenario where significant chemi-/physio- sorbed water is present within the corrosion layer, the nominal scenario shows a pressure increase to 2.54 atm, with 21.1 mole percent hydrogen. The high decay heat case shows a pressure increase to 3.18 atm with 39.9 mole percent hydrogen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Lignetics CRADA closeout report

Lignetics is one of the leading domestic producers of wood pellet fuels used for domestic heating. The company also produces pellets for other applications including animal bedding. The primary feedstock used for the production of Lignetics products is woody biomass. To improve the economics of pellet production, Lignetics is evaluating advanced preprocessing technologies developed by researchers at INL. In the proposed project, INL and Lignetics will work collaboratively to assess the feasibility of integrating INL’s advanced preprocessing technologies (fractional milling, high moisture pelleting and low temperature drying) in Lignetics production facilities. The successful completion of the project relies on the integration of the fractional milling and high moisture pelleting (HMP) process and low temperature drying to improve the economics of woody biomass pellet production for use in biofuel and bioproduct applications. In the HMP process, the biomass loses some moisture during compression and extrusion through the pellet die due frictional heat developed. Also, as pellets have a definite size and shape, they can be further dried in low temperature grain or belt dryers. Belt dryers are better suited to take advantage of low-grade and waste heat because they operate at lower temperatures than rotary dryers (Tumuluru et al.) Rotary dryers, for example, typically require inlet temperatures of 260°C, but more optimally operate around 400°C. In contrast, the inlet temperature of a belt dryer, such as a commercially available vacuum dryer, can be as low as 10°C above the ambient temperature, although more typically they operate at higher temperatures, between 90°C and 200°C. Because of their lower temperature operation, fire hazards and emissions to the air are lower for belt dryers in addition to reducing drying and overall pelleting costs, INL’s technology also has potential to reduce emissions at Lignetics facilities.

09 BIOMASS FUELS↗
DOE OSTI2022

SNF Interim Storage Canister Corrosion and Surface Environment Investigations (FY22 Status Update)

High-level purpose of this work: This report summarizes work carried out by Sandia National Laboratories (SNL) in the fiscal year 2022 (FY22) to evaluate the potential occurrence of stress corrosion cracking (SCC) on spent nuclear fuel (SNF) dry storage canisters. The U.S. currently lacks a repository for permanent disposal of SNF; thus, dry storage systems will be in use for much longer time periods than originally intended. Gap analyses by the US Department of Energy (DOE), the Nuclear Regulatory Commission (NRC), the Nuclear Waste Technical Review Board (NWTRB), and the Electric Power Research Institute (EPRI) have all determined that an improved understanding of the occurrence and risk of canister SCC is critical to demonstrating the safety of long-term dry storage. Should canister penetration by SCC occur, the containment boundary represented by the canister would be breached. A loss of the inert environment (helium) within the canister could occur and intrusion of air and moisture could react with and damage the fuel within the canister. For this reason, the DOE is funding an effort to evaluate the potential occurrence and consequences of dry storage canister SCC and to develop prevention, mitigation, and repair technologies for this degradation mechanism.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A section 110 evaluation of the 10,500-foot blast line civil effects structures and military trenches associated with the apple-2 test, area 1, nevada national security site, nye county, nevada

The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) has tasked Desert Research Institute (DRI) with identifying and evaluating additional resources that contribute to the significance of the Apple-2 Historic District as part of a Section 110 effort. Apple-2 was a 1955 atmospheric test in which the Federal Civil Defense Administration (FCDA) conducted an extensive civil effects program named Operation Cue. The objective of Operation Cue was to understand the blast and thermal effects of the test by using a simulated community of residential buildings, industrial structures, utilities, and commercial equipment arranged at various distances from ground zero (GZ). Military operations conducted by the Department of Defense (DoD) as part of the Desert Rock VI exercise were also associated with the Apple-2 test. The Apple-2 Historic District was originally defined in 2000 by Johnson and Edwards as 15 highly visible contributing elements dispersed across the landscape. Since its original recording, DRI has conducted additional archival research and this report details the identification and evaluation results of testing-related material remains at a distance of 10,500 ft from the Apple-2 GZ. Between 2017 and 2020, DRI conducted fieldwork at 10,500 ft from the Apple-2 GZ, revisited previously recorded contributing elements, and identified and evaluated newly recorded resources associated with Operation Cue as well as DoD operations. In total, DRI revisited or newly identified 15 principal resources. Of these, 13 are located within the historic district and contribute to the district’s significance. A set of military trenches and the associated material remains are discontiguous from the district, but also contribute to the district’s significance.

54 ENVIRONMENTAL SCIENCES↗