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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multi-site evaluation of the LN34 pan-lyssavirus real-time RT-PCR assay for post-mortem rabies diagnostics

Rabies is a fatal zoonotic disease that requires fast, accurate diagnosis to prevent disease in an exposed individual. The current gold standard for post-mortem diagnosis of human and animal rabies is the direct fluorescent antibody (DFA) test. While the DFA test has proven sensitive and reliable, it requires high quality antibody conjugates, a skilled technician, a fluorescence microscope and diagnostic specimen of sufficient quality. The LN34 pan-lyssavirus real-time RT-PCR assay represents a strong candidate for rabies post-mortem diagnostics due to its ability to detect RNA across the diverse Lyssavirus genus, its high sensitivity, its potential for use with deteriorated tissues, and its simple, easy to implement design. Here, we present data from a multi-site evaluation of the LN34 assay in 14 laboratories. A total of 2,978 samples (1,049 DFA positive) from Africa, the Americas, Asia, Europe, and the Middle East were tested. The LN34 assay exhibited low variability in repeatability and reproducibility studies and was capable of detecting viral RNA in fresh, frozen, archived, deteriorated and formalin-fixed brain tissue. The LN34 assay displayed high diagnostic specificity (99.68%) and sensitivity (99.90%) when compared to the DFA test, and no DFA positive samples were negative by the LN34 assay. The LN34 assay produced definitive findings for 80 samples that were inconclusive or untestable by DFA; 29 were positive. Five samples were inconclusive by the LN34 assay, and only one sample was inconclusive by both tests. Furthermore, use of the LN34 assay led to the identification of one false negative and 11 false positive DFA results. Together, these results demonstrate the reliability and robustness of the LN34 assay and support a role for the LN34 assay in improving rabies diagnostics and surveillance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Variable Active Galactic Nuclei in the Galaxy Evolution Explorer Time Domain Survey

We searched the northern hemisphere fields of the Galaxy Evolution Explorer Time Domain Survey for galaxies with UV variability indicative of active galactic nuclei (AGNs). We identified 48 high-probability candidate AGNs from a parent sample of 1819 galaxies in the NASA Sloan Atlas catalog. We further characterized these systems using optical spectroscopic diagnostics, Wide-field Infrared Survey Explorer IR color selection criteria, and spectral energy distribution modeling. Of the 48 candidates, eight were identified as AGNs from optical emission lines, two were identified by their IR colors, and 28 were identified through spectral energy decomposition. Observational biases of each selection method are discussed in connecting these AGN subsamples to another. By selecting AGNs based on UV variability, we also identified six low-mass AGN candidates, all of which would have been missed by spectroscopic selection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Nisqually River, WA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of glacial freeze/thaw cycle-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Nisqually River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Erpe River, Germany

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of wastewater treatment plant effluent induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Erpe River, Germany. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in September 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Columbia River in Washington, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of the upstream Priest Rapids Dam-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Columbia River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Jordan River, Israel

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of groundwater pumping induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Jordan River, Israel. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the East Fork Poplar Creek in Tennessee, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of wastewater treatment plant effluent induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the East Fork Poplar Creek, TN, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2021

Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field of view under consideration by a factor of ≈2.5. We find that the improved photometric precision per exposure due to longer exposure time for LCO images combined with the greater time sampling of LCO photometry enables us to increase the total number of detections in this field of view. Each LCO image covers a field of view of 26' × 26' and observes a region close to the Galactic plane (b = -9.°4) abundant in stars with an average stellar density of ≈8 arcmin{sup -2}. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, three candidate eclipsing binaries of type EA, and seven candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color–magnitude diagram as a powerful classification tool for variable-star studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Altamaha River in Georgia, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of tidally induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Altamaha River, GA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in October 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour. This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors. Please note that this data package was originally published in November 2019. It was updated in July 2020. See the readme file in the data package for details. None of the data values were altered. The units were updated in one column of the main metadata file and in one of the methods codes descriptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Post-fire time series of sensor and geochemistry sample data from surface water, groundwater, precipitation, soil, and vegetation across Oak Creek watershed, Washington

This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Stream sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2 in 2024, affecting the five study sites at varying burn severities. Each site is equipped with YSI EXO2 sondes logging sub-hourly throughout the year, and grab samples are collected approximately every six weeks. YSI sondes are used to measure temporally resolved proxies for groundwater inputs (specific conductivity) and organic matter (fluorescent dissolved organic matter; fDOM) along with basic water quality and depth. Grab samples of surface water, groundwater, and precipitation are analyzed for water stable isotopes and conductivity to understand endmembers for hydrologic mixing Grab samples of surface water, groundwater, soil water, and litter/vegetation/soil leachates are analyzed for organic matter composition measured by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) to understand organic matter dynamics. Game camera photos are provided in a separate data package available at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018598. Future versions of this dataset will include time series data from YSI EXO2 sondes (fDOM, dissolved oxygen, temperature, depth, specific conductance, turbidity, pH), BaroTROLL sensors (air temperature and barometric pressure), rain gauges (precipitation), and data from the soil and vegetation samples. Because this study is ongoing, this data package will be updated regularly to include newly collected data and the additional data types. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data; (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) a data checks report; (5) file-level metadata; (6) data dictionary; (7) field metadata; (8) readme; (9) international generic sample number (IGSN) mapping file; and (10) field protocols. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) stable water isotopes and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Biogeochemistry↗
DOE OSTI2017

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 - ), total acid (H + ), neodymium (Nd 3+ ), sodium (Na + ), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Co-optimization of CO 2 -EOR and Storage Processes under Geological Uncertainty

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2 –EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Analysis of air mass trajectories to explain observed variability of tritium in precipitation at the Southern Sierra Critical Zone Observatory, California, USA

Understanding the behavior of tritium, a radioactive isotope of hydrogen, in the environment is important to evaluate the exposure risk of anthropogenic releases, and for its application as a tracer in hydrology and oceanography. To understand and predict the variability of tritium in precipitation, HYSPLIT air mass trajectories were analyzed for 16 aggregate precipitation samples collected over a 2 year period at irregular intervals at a research site located at 2000 m elevation in the southern Sierra Nevada (California, USA). Attributing the variation in tritium to specific source areas confirms the hypothesis that higher latitude or inland sources bring higher tritium levels in precipitation than precipitation originating in the lower latitude Pacific Ocean. In this case, the source of precipitation accounts for 79% of the variation observed in tritium concentrations. In conclusion, air mass trajectory analysis is a promising tool to improve the predictions of tritium in precipitation at unmonitored locations and thoroughly understand the processes controlling transport of tritium in the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

“Non-hydrolytic” sol–gel synthesis of molybdenum sulfides

Non-hydrolytic sol–gel reactions provide a low temperature solution based synthetic approach to solid-state materials. In this paper, reactions between molybdenum chloride and hexamethyldisilthiane in chloroform were explored, which gave access to both MoS{sub 2} and Mo{sub 2}S{sub 3} after heat treatment of as-recovered amorphous samples to 600–1000 °C. Interesting morphologies were obtained for MoS{sub 2}, ranging from fused spherical particles to well-defined nanoplatelets and nanoflakes. Both 2H- and 3R-MoS{sub 2} were observed, which formed thin hexagonal and triangular platelets, respectively. The platelets exhibited thicknesses of 10–30 nm, which corresponds to 15–50 MoS{sub 2} layers. No attempts to prevent agglomeration were made, however, well separated platelets were observed for many samples. Heating at 1000 °C led to formation of Mo{sub 2}S{sub 3} for samples that showed well-defined MoS{sub 2} at lower temperatures, while less crystalline samples had a tendency to retain the MoS{sub 2} structure. - Graphical abstract: Overlay of variable pressure X-ray diffraction data of Al{sub 2}Mo{sub 3}O{sub 12} collected in a diamond anvil cell. Both subtle and discontinuous phase transitions are clearly observed. - Highlights: • Molybdenum sulfides were prepared by non-hydrolytic sol–gel chemistry. • Nanocrystalline 3R-MoS{sub 2} and 2H-MoS{sub 2}, and microcrystalline Mo{sub 2}S{sub 3} were obtained. • Particle morphology correlated strongly with crystalline phases. • Ultrathin platelets with limited tendency to agglomerate were recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Rapid Microfluidic Neptunium Extraction Using a Supported Liquid Membrane Module

Extraction of neptunium from acidic matrices is important for its quantification, but its complex redox chemistry can cause variable yields. This study develops a microfluidic redox extraction for rapidly separating neptunium from submilliliter samples, achieving up to 90% process yield in less than 10 min for samples as small as 100 μL, with over 97% steady-state yield achieved after 20 min. It uses a supported liquid membrane module loaded with 30 vol % tributyl phosphate in n-dodecane, which performs forward- and back-extractions in a single, continuous step. Neptunium is first oxidized to +6 for extraction and then reduced during stripping. Bromate was selected as an oxidant over permanganate for its greater compatibility with the organic phase, achieving complete oxidation in under 30 s. Ascorbic acid and hydrogen peroxide were both effective reductants. Finally, the system’s high yield and rapid kinetics make it promising for future separations from complex mixtures.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Progressing emergent constraints on future climate change

In recent years, an evaluation technique for Earth System Models (ESMs) has arisen—emergent constraints (ECs)—which rely on strong statistical relationships between aspects of current climate and future change across an ESM ensemble. Combining the EC relationship with observations could reduce uncertainty surrounding future change. Here, we articulate a framework to assess ECs, and provide indicators whereby a proposed EC may move from a strong statistical relationship to confirmation. The primary indicators are verified mechanisms and out-of-sample testing. Confirmed ECs have the potential to improve ESMs by focusing attention on the variables most relevant to climate projections. Looking forward, there may be undiscovered ECs for extremes and teleconnections, and ECs may help identify climate system tipping points.

54 ENVIRONMENTAL SCIENCES↗