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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Pennsylvania Department of Environmental Protection (PA DEP) 26r Detailed Produced Water Compositions (version 1.0)

A database of geochemical compositions of aqueous species in produced water reported to the PA DEP. Samples were collected between mid-2012 to early-2020. Data from publicly-available PA DEP 26r reports were scraped from pdf files and cumulated into tabular spreadsheet format for >1000 produced water streams from Marcellus wells in Pennsylvania. In addition to providing the original values, the NETL NEWTS team has reformatted the dataset to allow sample streams to be easily copied into OLI Studio and Geochemist WorkBench (GWB) software for modeling the geochemistry and the recovery of critical minerals, such as lithium, from these produced water streams. In addition, a version of the dataset has been included with predictions for some missing values in the original dataset using machine learning techniques within CoDaRT software, a public ML software developed by the Nation Energy Technology Laboratory. We have made the Input into CoDaRT and one example output from CoDaRT available in this dataset.

Aqueous Chemistry↗
DOE OSTI2025

Pennsylvania Department of Environmental Protection (PA DEP) 26r Detailed Produced Water Compositions (version 2.0)

A database of geochemical compositions of aqueous species in produced water reported to the PA Department of Environmental Protection (PA DEP). Samples were collected between late-2010 to late-2024. Data from publicly available PA DEP 26r reports were scraped from pdf files and cumulated into tabular spreadsheet format for >3,000 produced water streams from Marcellus Shale wells in Pennsylvania. In addition to providing the original values, the NETL NEWTS team has reformatted the dataset to allow sample streams to be easily copied into OLI Studio and Geochemist WorkBench (GWB) software for modeling the geochemistry and the recovery of critical minerals, such as lithium, from these produced water streams. ***This dataset is an updated version of the PA DEP 26r Detailed Produced Water Compositions (version 1.0) dataset, providing expanded spatial and temporal coverage.***

Aqueous Chemistry↗
DOE OSTI2025

Incorporating corrosion design constraints in desalination process optimization: A case study in mechanical vapor compression

Corrosion is an expensive and complex challenge for desalination, yet current design approaches do not explicitly account for corrosion mechanisms in process modeling and technoeconomic analysis. Here, to address this gap, we present a workflow for incorporating corrosion design constraints directly into desalination process optimization models. We develop surrogate models for general and localized corrosion metrics as functions of temperature, pH, salinity, dissolved oxygen, and material using data from OLI Systems’ Corrosion Analyzer. We then integrate these surrogates as corrosion design constraints in a cost-optimization MVC model that minimizes the levelized cost of water (LCOW). For a case study of mechanical vapor compression (MVC) treating seawater across a range of recoveries, we find dissolved oxygen (DO) is the dominant driver of localized corrosion, and thus of cost-optimal material choice and operating conditions. Reducing the DO from 8 mg/L to 0.5 mg/L reduces the LCOW by 15-35%, informing the breakeven costs for implementing DO removal or selecting highly corrosion-resistant alloys. This framework is broadly applicable across corrosion types, materials, and components and enables desalination process design that minimizes capital costs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH 2 )O-C(=O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Structural regulation-induced Li-electron disentanglement for stabilized oxygen redox of Li-excess disordered rock-salt cathode materials

Since the discovery of its electrochemical activity, Li-excess disordered rock-salt (DRX) cathode material has received worldwide attention as it sets up a new way to exploit oxygen redox beyond the conventional layered structure with late-3d transition metals. However, the intricate structure-function relationship in the disordered lattice of the DRX material fogs the researcher's lens on the underlying redox mechanisms. Here, in this study, we employ a synergistic approach combining neutron total scattering with reverse Monte Carlo modeling and density functional theory calculations to unravel the landscape of oxygen redox reactions in DRX. Redox activities are evaluated in diverse oxygen clusters (OLi x TM 6-x ) and the spatial distribution of these clusters in the model DRX structure (Li 1.16 Ti 0.37 Ni 0.37 Nb 0.1 O 2 and Li 1.2 Ti 0.35 Ni 0.35 Nb 0.1 O 1.8 F 0.2 ) is explicitly determined. The results unveil that by regulating the short-range ordering between cations, fluorine atoms can effectively decouple the location of Li extraction and electron depletion. Such disentanglement between the Li reservoir and electron reservoir in the DRX lattice could play a pivotal role in protecting the oxidized oxygen and preserving the lattice framework during cycling. Through a tentatively designed non-fluorinated DRX oxide realizing similar Li-electron decoupling, an obvious enhancement of the cycling capability can be achieved without compromising the capacity release.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Weakly-Supervised, Multitask Deep Learning Framework for Shadow Mitigation in Remote Sensing Imagery

We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.

Couwenhoven, Scott↗
DOE OSTI2020

Materials Data on Li8Mn13Fe3O32 by Materials Project

Li8Mn13Fe3O32 is Spinel-derived structured and crystallizes in the triclinic P1 space group. The structure is three-dimensional. there are eight inequivalent Li1+ sites. In the first Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–63°. There are a spread of Li–O bond distances ranging from 1.98–2.08 Å. In the second Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with three FeO6 octahedra and corners with nine MnO6 octahedra. The corner-sharing octahedra tilt angles range from 55–63°. There are a spread of Li–O bond distances ranging from 1.98–2.06 Å. In the third Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–64°. There are a spread of Li–O bond distances ranging from 1.99–2.07 Å. In the fourth Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–63°. There are a spread of Li–O bond distances ranging from 1.98–2.05 Å. In the fifth Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–63°. There are a spread of Li–O bond distances ranging from 1.98–2.05 Å. In the sixth Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–63°. There are a spread of Li–O bond distances ranging from 1.99–2.07 Å. In the seventh Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with two FeO6 octahedra and corners with ten MnO6 octahedra. The corner-sharing octahedra tilt angles range from 56–63°. There are a spread of Li–O bond distances ranging from 1.98–2.07 Å. In the eighth Li1+ site, Li1+ is bonded to four O2- atoms to form LiO4 tetrahedra that share corners with three FeO6 octahedra and corners with nine MnO6 octahedra. The corner-sharing octahedra tilt angles range from 55–63°. There are a spread of Li–O bond distances ranging from 1.98–2.07 Å. There are thirteen inequivalent Mn+3.62+ sites. In the first Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There is two shorter (1.93 Å) and four longer (1.96 Å) Mn–O bond length. In the second Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There is two shorter (1.93 Å) and four longer (1.96 Å) Mn–O bond length. In the third Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There is two shorter (1.93 Å) and four longer (1.96 Å) Mn–O bond length. In the fourth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.93–1.98 Å. In the fifth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.96–2.23 Å. In the sixth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.96–2.22 Å. In the seventh Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra and edges with six MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.98–2.16 Å. In the eighth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.93–1.97 Å. In the ninth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.93–1.97 Å. In the tenth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.96–2.23 Å. In the eleventh Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.96–2.23 Å. In the twelfth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, an edgeedge with one FeO6 octahedra, and edges with five MnO6 octahedra. There are a spread of Mn–O bond distances ranging from 1.93–1.98 Å. In the thirteenth Mn+3.62+ site, Mn+3.62+ is bonded to six O2- atoms to form MnO6 octahedra that share corners with six LiO4 tetrahedra, edges with two FeO6 octahedra, and edges with four MnO6 octahedra. There is two shorter (1.93 Å) and four longer (1.96 Å) Mn–O bond length. There are three inequivalent Fe3+ sites. In the first Fe3+ site, Fe3+ is bonded to six O2- atoms to form FeO6 octahedra that share corners with six LiO4 tetrahedra and edges with six MnO6 octahedra. There are a spread of Fe–O bond distances ranging from 2.03–2.08 Å. In the second Fe3+ site, Fe3+ is bonded to six O2- atoms to form FeO6 octahedra that share corners with six LiO4 tetrahedra and edges with six MnO6 octahedra. There are a spread of Fe–O bond distances ranging from 2.03–2.08 Å. In the third Fe3+ site, Fe3+ is bonded to six O2- atoms to form FeO6 octahedra that share corners with six LiO4 tetrahedra and edges with six MnO6 octahedra. There are a spread of Fe–O bond distances ranging from 2.03–2.08 Å. There are thirty-two inequivalent O2- sites. In the first O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form distorted OLiMn2Fe trigonal pyramids that share corners with two OLiMn3 tetrahedra, corners with five OLiMn2Fe trigonal pyramids, and edges with two OLiMn2Fe trigonal pyramids. In the second O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+, two Mn+3.62+, and one Fe3+ atom. In the third O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the fourth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the fifth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the sixth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the seventh O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe tetrahedra. In the eighth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the ninth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the tenth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe tetrahedra. In the eleventh O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twelfth O2- site, O2- is bonded to one Li1+ and three Mn+3.62+ atoms to form distorted OLiMn3 tetrahedra that share corners with three OLiMn3 tetrahedra, corners with four OLiMn2Fe trigonal pyramids, and an edgeedge with one OLiMn3 tetrahedra. In the thirteenth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe tetrahedra. In the fourteenth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the fifteenth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form distorted OLiMn2Fe trigonal pyramids that share corners with three OLiMn3 tetrahedra, corners with five OLiMn2Fe trigonal pyramids, and edges with two OLiMn2Fe tetrahedra. In the sixteenth O2- site, O2- is bonded to one Li1+ and three Mn+3.62+ atoms to form distorted OLiMn3 tetrahedra that share corners with three OLiMn2Fe tetrahedra, corners with five OLiMn2Fe trigonal pyramids, and an edgeedge with one OLiMn3 tetrahedra. In the seventeenth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe tetrahedra. In the eighteenth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the nineteenth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twentieth O2- site, O2- is bonded to one Li1+ and three Mn+3.62+ atoms to form distorted OLiMn3 tetrahedra that share corners with three OLiMn3 tetrahedra, corners with five OLiMn2Fe trigonal pyramids, and an edgeedge with one OLiMn3 tetrahedra. In the twenty-first O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the twenty-second O2- site, O2- is bonded to one Li1+ and three Mn+3.62+ atoms to form distorted OLiMn3 tetrahedra that share corners with two OLiMn3 tetrahedra, corners with five OLiMn2Fe trigonal pyramids, and an edgeedge with one OLiMn3 tetrahedra. In the twenty-third O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twenty-fourth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twenty-fifth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twenty-sixth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form distorted OLiMn2Fe trigonal pyramids that share corners with four OLiMn3 tetrahedra, corners with four OLiMn2Fe trigonal pyramids, and edges with two OLiMn2Fe trigonal pyramids. In the twenty-seventh O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the twenty-eighth O2- site, O2- is bonded in a distorted rectangular see-saw-like geometry to one Li1+ and three Mn+3.62+ atoms. In the twenty-ninth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form a mixture of distorted edge and corner-sharing OLiMn2Fe trigonal pyramids. In the thirtieth O2- site, O2- is bonded to one Li1+, two Mn+3.62+, and one Fe3+ atom to form distorted OLi

36 MATERIALS SCIENCE↗
DOE OSTI2024

NEWTS Argonne Geothermal Geochemical Database with CoDART

A database of geochemical compositions of aqueous species in potential geothermal resources. The NETL NEWTS team has formatted the original Argonne V2 database for Charge Balance, Input into OLI Studio, and Input into GWB. (Geochemist WorkBench) In addition, some missing species in the original database were predicted using machine learning techniques within CoDart software, a public ML software developed by the Nation Energy Technology Laboratory. We have made the Input into CoDart and one example output from CoDart available in this dataset.

Aqueous Chemistry↗
DOE OSTI2020

Data product development for cold cloud and precipitation process analysis/Snow regime classifications from the NSA snow product

This grant funded work focused first on developing a data product for cold-cloud precipitation and second, under a continuation, on examining distinct snowfall regimes occurring at the North Slope Alaska (NSA) facility. The overarching goal under the first phase was to provide a data product that quantifies the vertically- and temporally-resolved properties of cold, precipitating clouds and that will enable future analyses of the processes that control their formation and evolution. The data product derives from observations at the NSA Utqiagvik (BRW) and Oliktok Point (OLI) sites, its algorithm utilizing radar observations supplemented with in situ and other collocated remote sensing measurements to constrain an optimal estimation retrieval.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluations of the Fates of Alkali Metals, Actinides, Mercury, and Iodine During DWPF Recycle Diversion

The fates of alkali metals, actinides, mercury, and iodine in the Defense Waste Processing Facility Recycle Diversion process (as currently conceptualized) have been evaluated through paper studies based on available knowledge of the chemistry, physical properties, solubility, and volatility of the various species involved. The effect of pH in the range from 9 to 13 has been discussed. Recommendations for additional studies to close technology gaps have been provided, many of which are contingent upon the results of pending testing and sample characterization efforts. There is uncertainty in the amounts of soluble actinides passing through the process filter, though the bulk of the actinides should be captured on the filter with the Recycle Collection Tank solids and the total amounts of actinides should be relatively low. The Recycle Collection Tank pH could impact the fraction of actinides reaching the evaporator, but the primary factors determining the actinide fate are expected to be the amount of CO 2 sorption from air sparging or, for certain actinides (such as plutonium), oxidation and/or sorption to MnO 2 solids from permanganate additions to destroy the glycolate anion. Process optimization could minimize the amounts of actinides passing the filter. Depending upon the levels of mercury observed in recycle stream samples and because of the volatility of mercury, the evaporator should be designed with the capability to remove dense mercury phases from the condensate to avoid exceeding ETP WAC limits. The facility design must be adequate to transfer dense mercury phases and testing to confirm mercury transfer is needed. Simulant containing mercury is recommended for both filtration and evaporation testing. OLI Modeling of the various recycle streams is recommended to provide insight on the fate of iodine. Iodine-spiked simulants are recommended for upcoming evaporation tests. The pro) ect should consider the likelihood and impact of NAS scale formation in the evaporators. Process optimization may be needed to minimize the accumulation of NAS scale and possibly the sorption of actinides in the evaporator. Actual waste testing of the Recycle Diversion filtration and evaporation should include the analysis of actinides, mercury, and iodine to determine their partitioning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Highly Efficient Regeneration Module for Carbon Capture Systems in NGCC Applications

The objective of this project is to design, fabricate, and test a highly efficient regeneration module capable of providing an ultra-lean absorption solution that is required for capturing CO 2 from dilute sources at 95% or better efficiency. By integrating this advanced regenerator module with SRI International’s Mixed Salt Process (MSP) absorption modules, SRI expects to demonstrate significant progress toward a reduction in cost of capture versus the DOE reference natural gas combined cycle (NGCC) plant with carbon capture. SRI designed, built, and tested an advanced stripper to enhance the performance of SRI’s MSP for CO 2 capture – a transformational ammonia-based solvent technology – for natural gas (NG) power sources. The testing of the advanced stripper for MSP was conducted at an SRI site using a simulated flue gas stream equivalent to about 10 kWe. The research work included modeling of the advanced stripper and integrating it with the MSP absorbers; studying the strategies for producing very highly alkaline lean solvent with minimized emissions; operating the stripper with advanced heat integration to improve process efficiencies; and collecting critically important data for a detailed techno-economic analysis (TEA). The project tasks were designed to address concerns relating to scale-up and integration of the technology to NG power plants—more specifically, to maximize the carbon capture efficiency achievable with MSP and identify pathways to achieve higher capture efficiencies and ultimately zero net carbon emissions. SRI teamed up with a process modeling company (OLI Systems), a process and chemical engineering company (Trimeric Corporation), and a cost-sharing commercial partner (Baker-Hughes – a leading multinational company that designs, manufactures, and services transformative energy technologies) to execute the project. The research findings will accelerate the MSP development and pave the way for the technology to reach the DOE’s goal, and ultimately commercialization of the MSP technology for low-cost CO 2 capture from NGCC flue gas and other dilute CO 2 sources.

03 NATURAL GAS↗
DOE OSTI2025

Effect of Anoxic Iron Corrosion on WIPP Brine Geochemistry FY23 Final Report (U)

A 280-day study was completed to evaluate the effect of zero-valent iron (Fe 0 ) on the Waste Isolation Pilot Plant (WIPP) brine geochemistry under anticipated reducing conditions. Hydrogen (H 2 ) gas is expected to be present in the repository after closure due to the anoxic corrosion of a vast quantity of iron contained in the waste forms disposed at WIPP; therefore, a background argon atmosphere containing H 2 was chosen for this study. WIPP groundwater brine pH and E h will impact the mobility and fate of plutonium within the repository. Modeling and laboratory results for Castile WIPP brine indicate that equilibrium E h values relative to the standard hydrogen electrode (SHE) are 40 mV more reducing (i.e., more negative) than those for Salado WIPP brine (-480 mV vs. -440 mV, respectively) because of the higher pH of the Castile brine (pH 9.3 for Castile vs. pH 8.8 for Salado). The E h and pH data were corrected for the effects of high ionic strength. The experimental results for both brines are consistent with thermodynamic predictions using OLI Systems' Mixed Solvent Electrolyte chemical equilibrium model. The measured and corrected pH and E h data from this study are provided in Table ES-1 and Table ES-2, respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Impact of europium and samarium on growth and metabolism of two anaerobic wastewater microorganisms: Sporacetigenium mesophilum and Clostridium sporogenes

Increases in environmental fluxes of rare earth elements (REEs) stemming from increased demand for these technology critical elements may affect microbial ecosystem functions important for pollutant degradation and nutrient cycling. Here, this study investigates the responses of Sporacetigenium mesophilum and Clostridium sporogenes, two anaerobic fermenters, to varying concentrations of two REE, namely europium and samarium. Eu and Sm are adjacent lanthanides but differ significantly in their redox behavior. We tested Eu and Sm concentrations ranging from < 1 to 600 µM. Despite the close phylogenetic relatedness of the two bacteria, we observed species-specific as well as REE-specific and incubation time-dependent sensitivities. S. mesophilum exhibited pronounced inhibition of hydrogen production and growth with exposure to ≥ 60 µM of Eu, but with 6 µM Eu, a hormetic effect was observed—hydrogen production was enhanced relative to the control. With Sm, the only impact observed on S. mesophilum was inhibition at the highest concentration (600 µM) tested. For C. sporogenes, growth inhibition was observed only at 600 µM Eu or Sm, and no hormesis was observed with either REE. This study offers both significant benefits and novelty by addressing the emerging environmental concern of REE pollution, particularly the effects of Eu and Sm on anaerobic microorganisms relevant to wastewater treatment. It provides valuable insights into REE impacts under anaerobic conditions, which are essential for understanding potential disruptions to organic matter degradation and nutrient cycling processes.

60 - APPLIED LIFE SCIENCES↗
DOE OSTI2024

Techno-economic analysis of renewable energy generation at the South Pole

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. Here, this study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all -diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources: Preprint

Considerations for electrifying school buses are presented with an analysis of battery sizing to match bus driving requirements. The charging and vehicle-to-building dispatch of the electric school buses were optimized to evaluate the potential to reduce the impact of the bus charging on the school's electric utility bill. Distributed energy resources (DER) and flexible building loads were also considered with the school bus electrification to evaluate the further reduction in energy costs with enhanced system integration and optimized dispatch. The effect of degradation on the school bus batteries was analyzed to determine if the smart charging and vehicle-to-building battery operation decreases the life of the battery. The results show that there is an opportunity to mitigate the increase of electric utility bills with improved charging controls and bi-directionally operating the school bus batteries to reduce building demand charges. The battery degradation analysis using dispatch with optimized charging and discharging shows acceptable battery life.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗