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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 OSTI2022

Development and Evaluation of Neutron Thermalization Integral Benchmarks for Advanced Reactor Applications (Final Report)

In this project, integral benchmarks that aim to examine thermal neutron scattering data for graphite (ideal and nuclear), light water, and molten salt FLiBe will be analyzed. In particular, the project aims to: 1) Develop the slowing-down-time experiment that was performed at the OREAL facility of ORNL as a graphite benchmarks; 2) Identify and examine a slowing-down-time experiment in light water as a potential benchmark; 3) Reexamine the molten salt MSRE-MSR-EXP-001 benchmark to explicitly capture the impact of TSL variations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Comparison of Tritium Dose Calculations from MACCS, UFOTRI, and ETMOD

Tritium exhibits unique environmental behavior because of its potential interactions with water and organic substances. Modeling the environmental consequences of tritium releases can be relatively complex and thus an evaluation of MACCS is needed to understand what updates, if any, are needed in MACCS to account for the behavior of tritium. We examine documented tritium releases and previous benchmarking assessments to perform a model intercomparison between MACCS and state-of-practice tritium-specific codes UFOTRI and ETMOD to quantify the difference between MACCS and state of practice models for assessing tritium consequences. Additionally, information to assist an analyst in judging whether a postulated tritium release is likely to lead to significant doses is provided.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Agro-IBIS/THMB Modeled Yield, Nitrogen Loss, and Profitability Maps for the Raccoon River Basin, Iowa, USA

This data was used to assess the potential for improving water quality in this watershed when integrating miscanthus, a bioenergy perennial grass, on to areas of low profit under current corn-soybean management and areas of high nitrogen leaching under both current and future climate conditions. This dataset includes all observations (yield, streamflow, nitrogen loads) and model simulation data used from Dr. Kelsie Ferin’s (former PhD student with Dr. Andy VanLoocke, ISU Dept. of Agronomy) dissertation and publication in Global Change Biology – Bioenergy (DOI: 10.1111/gcbb.13078). Modeled output included in this dataset includes corn, soybean, and miscanthus yields, nitrogen leaching rates, net mineralization rates, land use cover fractions with and without the inclusion of miscanthus, profitability maps, additional soil property maps, and simulated streamflow and nitrogen loads near the outlet of the Raccoon River Basin (Van Meter, Iowa) for both historical and future climate conditions. This dataset includes a mixture of netCDF, .tiff, .csv, and .xlsx files. See README.pdf for more information.

Ferin, Kelsie↗
DOE OSTI2021

The Influence of Transitional Metal Dopants on Reducing Chlorine Evolution during the Electrolysis of Raw Seawater

Electrocatalytic water splitting is a possible route to the expanded generation of green hydrogen; however, a long-term challenge is the requirement of fresh water as an electrolyzer feed. The use of seawater as a direct feed for electrolytic hydrogen production would alleviate fresh water needs and potentially open an avenue for locally generated hydrogen from marine hydrokinetic or off-shore power sources. One environmental limitation to seawater electrolysis is the generation of chlorine as a competitive anodic reaction. This work evaluates transition metal (W, Co, Fe, Sn, and Ru) doping of Mn-Mo-based catalysts as a strategy to suppress chlorine evolution while sustaining catalytic efficiency. Electrochemical evaluations in neutral chloride solution and raw seawater showed the promise of a novel Mn-Mo-Ru electrode system for oxygen evolution efficiency and enhanced catalytic activity. Subsequent stability testing in a flowing raw seawater flume highlighted the need for improved catalyst stability for long-term applications of Mn-Mo-Ru catalysts. This work highlights that elements known to be selective toward chlorine evolution in simple oxide form (e.g., RuO2) may display different trends in selectivity when used as isolated dopants, where Ru suppressed chlorine evolution in Mn-based catalysts.

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

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2021

Insights into the Physical-Chemical Properties of a CO2-Responsive Fracturing Fluid

Here we determine the phase behavior of StimuFrac, a CO2 responsive fracturing fluid, under geothermal wellbore conditions. StimuFrac is an aqueous poly(allylamine) fluid that crosslinks in the presence of CO2. StimuFrac significantly reduces the net pressure required to induce fractures, relative to other fracture fluids, and has potential to reduce water use. However, the phase behavior and equations of state to describe StimuFrac’s phase behavior remain unavailable. Here we determine the density and molar volume of the fluid as a function of geothermal relevant temperatures, pressures, and weight fractions of StimuFrac added. In general, experiments find that StimuFrac’s density decreases as temperature and pressure increase. Using these results, equations of saturated state and phase diagrams for different polymer concentrations are reported. These results are critical inputs for planned numerical simulation efforts.

Pease, Leonard F.↗
DOE OSTI2021

Systems and methods of long-duration energy storage and regeneration of energy-bearing redox pairs

Described herein are systems and methods of storing and delivering electrical using hydrogen at low-cost and for long-durations. The systems and methods use energy-bearing redox pairs that electrochemically bear energy through decoupled hydrogen and oxygen consumption and/or evolution reactions, which are typically associated with fuel cells. Each species of the energy-bearing redox pair is associated with a standard electrode potential within a water electrolysis voltage window for the electrolyte solution. Electrical energy delivery, hydrogen generation, electrolyte regeneration, or combinations thereof can be performed by logically or physically separated unit operations in a continuous manner, batch manner, or semi-batch manner facilitated by the energy-bearing redox pair.

08 HYDROGEN↗
DOE OSTI2021

Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights

Technologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large-scale computational screening can be extremely helpful for this task. This work explores the methods and details associated with such screening procedures and recommends best practices. We also shed light on the limitations of traditionally used and inexpensive to compute prescreening approaches involving geometric and energetic features to predict water adsorption behavior of porous materials. Such approaches can provide general trends to predict adsorption behavior but may lead to the overlook of potentially important structures due to the complex nature of water adsorption. Finally, this study offers insights for future water adsorption simulations to facilitate the development of optimal water adsorbents.

42 ENGINEERING↗
DOE OSTI2021

Time to Use Dendrohydrological Data in Water Resources Management?

Despite a wide consensus on the potential importance of streamflow reconstructions to water resources planning and management activities, applications of dendrohydrology have been piecemeal. This review article describes barriers to widespread use of dendrohydrological data and suggests research needs for operationalizing streamflow records reconstructed from these proxy data.

42 ENGINEERING↗
DOE OSTI2025

Hydration and Restructuring of Polar Polymer Interfaces: Implications in Antifouling and Responsive Materials

Manipulating polymer interfaces is crucial for understanding how structure influences function in applications spanning biofouling prevention to energy storage. Moreover, observing how polymers adapt their microscopic structure to changes in their local environment can reveal essential properties that govern their performance in such applications, providing key insights into how to design more effective interfaces. Here, in this study, a series of “grafting-from” polymer brushes with side chains varying in charge, are probed by sum frequency generation (SFG) and modeled using all-atom molecular dynamics (MD) simulations to elucidate how chemical makeup and charge mediate interfacial restructuring in dry versus hydrated states. Results show that charge, in progressing from nonpolar to cationic to zwitterionic, results in dramatic changes in interfacial structure and overall hydration. While net neutral systems, regardless of bulk phase polarity, show minimal interfacial water structuring, the cationic species exhibits strong bulk water signals from the surface potential. Meanwhile, the polymer brushes themselves restructure in water differently independent of charge, impacting the functional groups that are presented to the aqueous phase. Nonpolar and cationic species for instance undergo a change in alkyl group orientations to accommodate hydrating water molecules, whereas the zwitterionic polymer becomes completely disordered in water. Overall, the structure-based behavior trends presented herein have implications in antifouling applications and responsive material interfaces.

biointerface↗
DOE OSTI2020

Exploring the role of electronic structure on photo-catalytic behavior of carbon-nitride polymorphs

A fully self-consistent density-functional theory (DFT) with improved functionals is used herein to provide a comprehensive account of structural, electronic, and optical properties of C 3 N 4 polymorphs. Using our recently developed van Leeuwen-Baerends (vLB) corrected local-density approximation (LDA), we implemented LDA + vLB within full-potential N th -order muffin-tin orbital (FP-NMTO) method and show that it improves structural properties and band gaps compared to semi-local functionals (LDA/GGA). We demonstrate that the LDA + vLB predicts band-structure and work-function for well-studied 2D-graphene and bulk-Si in very good agreement with experiments, and more exact hybrid functional (HSE) calculations as implemented in the Quantum-Espresso (QE) package. The structural and electronic-structure (band gap) properties of C 3 N 4 polymorphs calculated using FP-NMTO-LDA + vLB is compared with more sophisticated hybrid-functional calculations. We also perform detailed investigation of photocatalytic behavior using QE-HSE method of C 3 N 4 polymorphs through work-function, band (valence and conduction) position with respect to water reduction and oxidation potential. Our results show $γ$-C 3 N 4 as the best candidate for photocatalysis among all the C 3 N 4 polymorphs but it is dynamically unstable at ‘zero’ pressure. We show that $γ$-C 3 N 4 can be stabilized under hydrostatic-pressure, which improves its photocatalytic behavior relative to water reduction and oxidation potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controlling matric potential in microfluidics to examine microbial dynamics in unsaturated porous media

The use of microfluidics for the study of soil microbial ecology is an emerging field. Most microfluidic studies of biological systems, however, have been performed under fully saturated conditions that are not representative of natural soil. Therefore, while microfluidics offer many unique capabilities that other methodologies cannot, they are not currently suited to address the effects of matric potential, an important variable defining the microbial moisture niche. Here, a methodology is presented that allows the user to control the aqueous conditions within microfluidic networks by manipulating matric potential using a hanging water column. The method relies on hydrophilic surface treatment of the microfluidic device using polyvinyl alcohol (PVA) and incorporating a bed of small pores at the network boundaries, which serve as a porous ceramic plate analogue (PPA). The method was validated on a simple capillary bundle and then on a more complex pore network. A water retention curve, exhibiting hysteresis, was generated for the pore network over a narrow matric potential range of 0 to – 5 kPa. Both the drainage and wetting curves were reproducible, as were the spatial configuration and the number of fragmented moisture niches in the pore network, particularly on the drainage curve. In contrast, the wetting curve exhibited greater variability in spatial configuration due to the “ink bottle effect,” where capillarity was interrupted by wider pore bodies. Ultimately, the methodology provides realistic pore-scale moisture conditions that can be easily manipulated and maintained, enabling new opportunities to explore soil biophysics and microbial biogeography in unsaturated porous media. As a brief example, images showing the localization of fluorescently tagged Pantoea sp. YR343 at −4.3 kPa are presented, highlighting bacterial distributions in water films and air-water interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗
DOE OSTI2021

How Well Do U.S. Western Water Markets Convey Economic Information?

An efficient market implies that potential gains from trade are fully captured. Achieving this requires a well-functioning market where prices reflect all available information. In the case of water rights markets, this implies that the permanent water rights transfer price reflects the sum of discounted returns to this asset (i.e., the lease price), the market interest rate, and a risk premium that reflects potential future water scarcity. Here, the purpose of this study is to assess the efficiency of western U.S. water markets by using the asset pricing model to measure how well prices reflect long-run returns to permanent water rights.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Material Design Strategies for Recovery of Critical Resources from Water

Population growth, urbanization, and decarbonization efforts are collectively straining the supply of limited resources that are necessary to produce batteries, electronics, chemicals, fertilizers, and other important products. Securing the supply chains of these critical resources via the development of separation technologies for their recovery represents a major global challenge to ensure stability and security. Surface water, groundwater, and wastewater are emerging as potential new sources to bolster these supply chains. Recently, a variety of material-based technologies have been developed and employed for separations and resource recovery in water. Judicious selection and design of these materials to tune their properties for targeting specific solutes is central to realizing the potential of water as a source for critical resources. Here, the materials that are developed for membranes, sorbents, catalysts, electrodes, and interfacial solar steam generators that demonstrate promise for applications in critical resource recovery are reviewed. In addition, a critical perspective is offered on the grand challenges and key research directions that need to be addressed to improve their practical viability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Interfacial Solar Evaporation by a 3D Graphene Oxide Stalk for Highly Concentrated Brine Treatment

In this study, we demonstrate a 3-dimensional graphene oxide (3D GO) stalk that operates near the capillary wicking limit to achieve an evaporation flux of 34.7 kg m -2 h -1 under 1 sun conditions (1 kW/m 2 ). This flux represents nearly a 100 times enhancement over a conventional solar evaporation pond. Interfacial solar evaporation traditionally uses 2D evaporators to vaporize water using sunlight, but their low evaporative water flux limits their practical applicability for desalination. Some recent studies using 3D evaporators demonstrate potential for more efficient water transfer, but the flux improvement has been marginal because of a low evaporation area index (EAI), which is defined as the ratio of the total evaporative surface area to the projected ground area. By using a 3D GO stalk with an ultrahigh EAI of 70, we achieved nearly a 20-fold enhancement over a 2D GO evaporator. The 3D GO stalk also exhibited additional advantages including omnidirectional sunlight utilization, a high evaporation flux under dark conditions from more efficient utilization of ambient heating, a dramatic increase of the evaporation rate by introducing wind, and scaling resistance in evaporating brines with a salt content of up to 17.5 wt %. This performance makes the 3D GO stalk well suited for the development of a low-cost, reduced footprint technology for zero liquid discharge in brine management applications.

3D evaporator↗
DOE OSTI2023

Decision-Making Framework to Evaluate Opportunities for Recovery of Rare Earth Elements and Critical Minerals in Produced Water Networks

Oil and gas development activities require a significant amount of water. Given the environmental impacts associated with high consumption and subsequent disposal of this water, there is a need for strategies to support effective reuse of, treatment of, and/or resource recovery from this produced water. These streams can contain appreciable concentrations of rare earth elements (REE) and critical minerals (CM) (e.g., Lithium), which are critical for many important applications in electronics, technology, manufacturing, energy, and medicine [1]. Along with REE/CM present in produced water, other waste streams such as fly ash from coal-fired power plants and acid mine discharge present other potential sources for recovery of REE/CM [2-4]. And with increases in demand for REE/CM, the need for recovery from wastewater streams is becoming even more important. There is a need to develop effective decision-making tools to evaluate the economic and environmental potential for REE/CM recovery from produced water networks while supporting the needs of drilling and hydraulic fracturing activities. In this work, we present our progress in developing a decision-making framework to efficiently design and operate produced water networks for REE/CM recovery. Our approach considers REE/CM recovery from produced water streams alongside other potential wastewater sources. The models consider the location and capacity of storage and decentralized treatment processes as well as the design and operation of pipeline and transportation networks to connect different sources and effectively schedule inventories, reuse, and recovery opportunities. We focus on the evaluation of opportunities for REE/CM recovery with treatment constraints on composition and flow. We demonstrate this framework with an illustrative case study that features diverse production sites, storage facilities, transportation, and treatment sites. This proposed framework will be deployed in an open-source software package that is compatible with the PARETO framework to deliver analysis and decision-making tools that allow stakeholders to quantitatively evaluate potential opportunities for REE/CM recovery in produced water networks.

Pulsipher, Joshua↗