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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mechanisms of internal curing water release from retentive and non-retentive superabsorbent polymers in cement paste

This paper examines the mechanisms of desorption within cement pastes for superabsorbent polymers (SAP) with different sorption kinetics: retentive and non-retentive (self-releasing). Despite the different behavior in solution, both SAP types mitigated the autogenous shrinkage of cement pastes. {sup 1}H NMR and X-ray tomography showed that the cavities formed by both SAP types were saturated until 9–11 h after mixing, while the entrained water in the cavities redistributed to the cement matrix afterwards. In particular, the non-retentive SAP might have rapidly released the absorbed water, but the released water stayed in the original cavities. Therefore, the behavior of the SAP in the pore solution may not completely determine their internal curing performance in cement pastes. In particular, the initial absorption that forms the solution-filled cavities might be more important than whether the SAP are able to retain the solution for extended periods of time or not.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A quantitative study of retention and release of deuterium and tritium during irradiation of y-LiAlO2 pellets

This study reports on the quantification of deuterium in ion-irradiated gamma-LiAlO2 pellets as a function of dose and temperature. The gamma-LiAlO2 pellets were sequentially irradiated with He+ and D2+ ions to the same fluences of 5E16, 1E17 and 2E17 He++D+/cm2 at 188 K. Additional irradiation was performed to 1E17, 2E17 and 4E17 He++D+/cm2 at 573 K. A set of the pellets irradiated at 188 K was shipped and stored at low temperatures from 80 to 132 K and characterized using time-of-flight secondary ion mass spectrometry at ~173 K. The deuterium depth profiles show a Gaussian-like distribution in the low-temperature pellets. The total deuterium retention is found to be directly proportional to the ion fluence. About 27 at.% of the implanted deuterium atoms were released from the pellet irradiated to 2E17 He++D+/cm2 at 188 K during storage at room temperature for ~1 month. Retention of the trapped or bound deuterium during ion irradiation at 573 K increases initially with ion fluence and tends to saturate at a high fluence. The amount of the released deuterium is observed to be quantitatively consistent with that of the released tritium from similar standard pellets during neutron irradiation at 573 K.

Deuterium retention and release, ion irradiation, ↗
NASA NTRS2017

Training, Retention, and Transfer of Data Entry Perceptual and Motoric Processes Over Long Retention Intervals

Subjects trained in a standard data entry task, which involved typing numbers (e.g., 5421) using their right hands. At test (6 months post-training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (8 months post-training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. For each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task. Repetition priming for the standard task reflects retention of trained numbers; for the left-hand variant reflects transfer of perceptual processes; and for the code variant reflects transfer of motoric processes. There was thus evidence for both specificity and generalizability of training data entry perceptual and motoric processes over very long retention intervals.

dat↗
NASA NTRS2019

Training, Retention, and Transfer of Data Entry Perceptual and Motor Processes Over Short and Long Retention Intervals

In 2 experiments, subjects trained in a standard data entry task, which involved typing numbers (e.g., 2147) using their right hands. At an initial test (20 min or 6 months after training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (2 days or 8 months after training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. At test, for each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task, with extended delays only slightly decreasing the magnitude of the effect. Repetition priming for the standard task reflects retention of trained numbers, for the left-hand variant reflects transfer of perceptual processes, and for the code variant reflects transfer of motoric processes. There was, thus, evidence for both specificity and generalizability of training data entry perceptual and motoric processes even over very long retention intervals.

transfer↗
DOE OSTI2020

Impact of Cr and Co on 99Tc retention in magnetite: A combined study of ab initio molecular dynamics and experiments

This work explores the effect of co-mingled dopants, Co(II) and Cr(III), on Tc(IV) incorporation and retention in magnetite when heat treated to 625 or 700 °C. Key trends in Tc retention in the high temperature regime were identified using a combination of density-functional-theory based ab initio molecular dynamics (AIMD) simulations, and batch experiments including solid phase characterization techniques, e.g. X-ray absorption spectroscopy. A stabilizing effect on Tc(IV) was observed when the number of Tc and Cr atoms are equal or when the magnetite surface is oversaturated with Tc and Cr inclusions. Here, oversaturation is hypothesized to force Cr from the magnetite surface to form a Cr2O3 phase, which may act as a protective layer that prevents Tc release. With the addition of Co, Tc(IV) is stabilized via redox processes. The presence of Cr in low concentrations interferes with this redox stabilization and Cr is preferentially stabilized as opposed to Tc. As a result, using Co as a stabilizing dopant for Tc may be compromised in the presence of Cr. When the relative concentration of Tc, Cr and Co is the same, or more Co atoms are added to high Cr incorporated systems, the formation of the Cr2O3 phase may be suppressed. Although waste streams with co-mingled Tc and Cr potentially may benefit from a Co dopant, since the formation of a Cr2O3 passivation layer may protect incorporated Tc from being released, the relative concentration of the three elements will be a critical parameter for maximizing effectiveness of this strategy.

Lee, Mal Soon↗
NASA NTRS1994

NASA records retention schedules: Procedures governing the retention, retirement, and destruction of agency records

This handbook sets forth the minimum retention periods of official records of NASA. Its provisions are applicable to NASA Headquarters and all field installations. This revised edition has been correlated to the NASA Uniform Files Index (UFI) (NHB 1442.1B), the General Records Schedules produced by the National Archives and Records Administration (NARA), and has been enlarged in scope to cover Privacy Act Systems of Records and record series previously omitted. Guidance is provided in the areas of record retirement, transfer, and retrieval from Federal Record Centers (FRC) and disposal actions. Included are provisions for making changes to these schedules by addition of new items or revision of current items. The NASA Records Retention Schedules (NRRS) were approved for NASA use by NARA, the General Services Administration, and the General Accounting Office.

Benita A Cooper↗
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

Polymer Retention Evaluation in a Heavy Oil Sand for a Polymer Flooding Application on Alaska’s North Slope

For a polymer flooding field trial in a heavy oil reservoir on Alaska’s North Slope, polymer retention is a key parameter. Because of the economic impact of retention, this parameter was extensively studied using field core material and conditions. Several important findings were noted. Polymer retention based on effluent viscosity measurements can be overestimated unless the correct (non-linear) relation between polymer concentration and viscosity is used. Polymer degradation (either mechanical or oxidative) can also lead viscosity-based measurements to overestimate retention. Inaccessible pore volume (IAPV) can be overestimated if insufficient brine is flushed through the sand between polymer banks. Around 100 pore volumes (PV) of brine may be needed to displace mobile polymer to approach a true residual resistance factor and properly measure IAPV. Even for a sand pack with kwsor = 20 mD, IAPV was zero for HPAM with molecular weight (Mw) of 18 million g/mol. Fine-grain particles (<20 μm) strongly impacted polymer retention values. Native NB#1 sand with a significant component of particles <20 μm exhibited 290 μg/g, while the same sand exhibited 28 μg/g after these small particles were removed. Polymer retention did not necessarily correlate with mineral composition. The NB#1, NB#3, and OA sands had similar elemental and clay compositions, but the NB#1 sand exhibited ~10 times higher retention than the NB#3 sand. Polymer retention did not necessarily correlate with permeability. NB#1 sand exhibited much higher retention than OA sand, even though NB#1 sand was twice as permeable as OA sand. No evidence of chromatographic separation of HPAM molecular weights was found in our experiments. Although retention tended to be greater without a residual oil saturation (than at Sor), the effect was not strong. Aging a core (with high oil saturation) at 60°C reduced HPAM retention by a factor of two. As expected, under similar conditions, polymer retention was greater for a higher Mw HPAM (18 million g/mol) than for a lower Mw HPAM (10-12 million g/mol). In many cases with high polymer retention values (e.g., 240 μg/g), polymer arrival at the end of the core was relatively quick, but achieving the injected concentration occurred gradually over many pore volumes. This effect was not due chromatographic separation of polymer molecular weights. Results from modeling of this behavior was consistent with concentrationdependent polymer retention. The form assumed for the retention function in a simulator can have an important impact on the timing and magnitude of the oil response from a polymer flood. Field-based observations can underestimate polymer retention, depending on when the tracer and polymer concentrations were measured and the assumptions made about reservoir heterogeneity.

Wang, Dongmei↗
DOE OSTI2021

Dynamic deuterium retention properties of tungsten measured using laser-induced breakdown spectroscopy

Dynamic deuterium (D) retention properties of tungsten (W) are investigated under steady-state D plasma exposure in the PISCES-A linear plasma device. In contrast to static retention, dynamic retention is quickly released from the material at the termination of the incident plasma flux, and thus in-operando laser-induced breakdown spectroscopy (LIBS) measurements have been conducted during steady-state D plasma exposure. A procedure is, first, established to extract the dynamic retention component from the in-operando LIBS D I 656.1 nm line intensity, which can contain contributions from dynamic and static retention as well as background D/D 2 gas, excited by both steady-state and laser-induced plasmas. Using the developed procedure, the dynamic D retention in W is systematically examined while scanning the following plasma exposure parameters: incident ion energy, E i , sample temperature, T s , incident ion flux, Γ i . No clear E i dependence is seen in the range of E i ~ 45–175 eV, as expected from a small variation in the average implantation depth (~3-6 nm) of D in W. The dynamic retention is found to monotonously decrease with increasing T s from 348 to 573 K, while the total static retention is reported to peak at T s ~ 500–600 K. It is revealed that the dynamic retention linearly increases with increasing Γ i , and then saturates at Γ i ≥ 0.75 × 10 21 m –2 s –1 . Possible physical mechanisms for the observed dependence of the dynamic retention on T s and Γ i are discussed. Furthermore, sequential pure He, followed by pure D, plasma exposures show that the dynamic D retention is not strongly affected by He bubbles in the near-surface region.

super-saturated surface layer↗
DOE OSTI2022

Effect of sucrose on technetium and rhenium retention during vitrification of low-activity wastes

Sucrose (C 12 H 22 O 11 ) has been used in low-activity waste (LAW) melter feeds containing large fractions of nitrates, nitrites, or both because it facilitates foam suppression and denitration. This study focused on the effect of sucrose in LAW feeds on technetium (Tc) and rhenium (Re) retention. The amount of sucrose added in feeds was varied to differentiate the carbon-to-nitrogen mole ratio (C/N ratio). The results show that larger sucrose addition (higher C/N ratio) enhances Tc and Re retention. Reducing conditions induced by sucrose decomposition and early chemical reactions between sucrose and NaNO 3 /NaNO 2 are expected to increase Tc and Re retention. However, high sucrose addition decreased sulfur (S) retention slightly because sodium sulfate decomposes in reducing conditions at lower temperature. This early sulfate decomposition can affect Tc and Re retention partly because these species can be soluble in sulfate phases. This correlation indicates that the decrease of sulfate phases in the glass by early decomposition can reduce the solubility of Tc and Re in the sulfate phases, which may increase Tc and Re retention in the glass. In addition, continuous gas evolution and vigorous foaming at the foaming temperature range of 700–900°C may influence Tc and Re retention process interrupting retention or facilitating volatilization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1997

Effects of Bioreactor Retention Time on Aerobic Microbial Decomposition of CELSS Crop Residues

The focus of resource recovery research at the KSC-CELSS Breadboard Project has been the evaluation of microbiologically mediated biodegradation of crop residues by manipulation of bioreactor process and environmental variables. We will present results from over 3 years of studies that used laboratory- and breadboard-scale (8 and 120 L working volumes, respectively) aerobic, fed-batch, continuous stirred tank reactors (CSTR) for recovery of carbon and minerals from breadboard grown wheat and white potato residues. The paper will focus on the effects of a key process variable, bioreactor retention time, on response variables indicative of bioreactor performance. The goal is to determine the shortest retention time that is feasible for processing CELSS crop residues, thereby reducing bioreactor volume and weight requirements. Pushing the lower limits of bioreactor retention times will provide useful data for engineers who need to compare biological and physicochemical components. Bioreactor retention times were manipulated to range between 0.25 and 48 days. Results indicate that increases in retention time lead to a 4-fold increase in crop residue biodegradation, as measured by both dry weight losses and CO2 production. A similar overall trend was also observed for crop residue fiber (cellulose and hemicellulose), with a noticeable jump in cellulose degradation between the 5.3 day and 10.7 day retention times. Water-soluble organic compounds (measured as soluble TOC) were appreciably reduced by more than 4-fold at all retention times tested. Results from a study of even shorter retention times (down to 0.25 days), in progress, will also be presented.

Strayer, R. F.↗