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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Recent advances in Redox flow cell storage systems

Attention is given to recent data pertaining to flow cell storage system-related features as well as the state of Redox membrane technology. In addition, the state of the technology as it relates to the two application areas of storage for photovoltaic/wind and distributed energy storage for electrical utility applications is addressed. Also covered are the cost and life advantages of Redox systems as well as a discussion of such cells as the open circuit voltage cell, the rebalance cell, and the trim cell. Finally, it is concluded that the main thrust of membrane development will be to reduce the interaction between the chloro complex of iron in the ferric state since this is the major factor in membrane area resistivity.

Thaller, L. H.↗
DOE OSTI2024

The CCS-EJ-SJ Database: A Tool for Addressing Justice Challenges in Carbon Capture and Storage

At CCUS 2024, within Theme 8: ESG and Stakeholder Engagement, the poster presentation on "The CCS-EJ-SJ Database: A Tool for Addressing Justice Challenges in Carbon Capture and Storage" highlights the innovative database designed to integrate environmental, social, energy, and economic justice into CCS projects. The CCS-EJ-SJ Database serves as a comprehensive resource for stakeholders, offering an interactive dashboard that provides access to essential data across various justice themes. It incorporates established initiatives like the Justice40 Initiative and tools such as the DOE's Disadvantaged Community Reporter, enhancing its utility and credibility. This presentation will highlight the CCS-EJ-SJ database's framework, the consequential research products, and its pivotal role in offering crucial insights into CCS opportunities, ultimately expediting sustainable, environmentally considerate, and socially responsible efforts.

White, Casey↗
DOE OSTI2022

Life Cycle Assessment for Closed-Loop Pumped Hydropower Energy Storage in the United States

The federal government has initiated an aggressive set of policies to achieve a net-zero carbon emission goal for the electricity sector by 2050. As a result, rapid growth in deployment of renewable energy technologies is expected. Most commercially mature technologies are temporally variable and do not provide grid inertia, while renewable technologies with high projected deployment have intermittent generation methods. Energy storage technologies are needed to both dispatch power on-demand and help provide the needed grid inertia. Pumped storage hydro (PSH) is a well-established technology that has gained renewed interest in recent years offering energy-balancing, grid stability, control of electrical network frequency, and large-scale storage capacity. For widespread adoption of PSH, more information is needed regarding its current life cycle environmental impacts. The objective of this study is to perform a full life cycle assessment (LCA) of new closed-loop PSH in the U.S. The functional unit for this study is 1 kWh of electrical power delivered to the grid and the base case project lifetime is 80 years. The life cycle inventory for this project accounts for all material and energy flows associated with the green-field construction, operation, maintenance, and decommissioning of a closed-loop PSH plant in the U.S. Collected data represents a range of potential PSH specifications and geographic locations coming from all prospective closed-loop PSH installations in the U.S. with data available. In addition, existing PSH installations are used to provide assumptions for inventory inputs. Results presented will include the global warming potential (GWP IPCC 100a) and Energy Return on Investment (EROI) from our base case (average PSH installation) as well as from scenario analyses and model sensitivity. These results will be compared to the impacts from existing PSH sites and alternate storage technologies. Methods align with the assumptions and guidelines put in place by previous PSH LCAs to ensure an accurate comparison with the results from this report.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2020

Sister Rod Destructive Examinations (FY20) Appendix A: Full Length Rod Heat Treatments (FHT)

As a part of the US DOE-NE High Burnup Spent Fuel Data Project, ORNL is performing destructive examinations (DEs) of high burnup (HBU) (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4, low-tin Zircaloy-4, ZIRLO®, and M5®. The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding are being tested. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data will support SNF dry storage licensing and relicensing activities.This report documents the status of the ORNL Phase 1 DE activities related to full length rod heat treatments (FHT) applied to selected sister rods in Phase 1 of the sister rod test program. The results of this work and detailed examinations are summarized in the Sister Rod DE Status Report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Challenges and strategies for probing the composite interface of PEM electrolyzers and fuel cells using operando AP-XPS

Left: cross-section schematic of a membrane electrode assembly, the working electrode changing state with applied potential. Center: the operando cell design that enables snapshot data acquisition during trajectory movement. Right: resulting spectra. Understanding the surface chemistry of electrocatalyst systems under operando conditions is central to revealing the electrocatalytic cell's working mechanisms. Determination of these catalytic processes on a molecular scale and the involved components is fundamental to streamlining material design for energy conversion and storage applications. X-ray photoelectron spectroscopy (XPS) is an established technique used to study the chemical and electronic states of materials. While the surface sensitivity of XPS is typically high, use of tender X-ray energies and technical advancements have allowed for the direct probing of solid–vapor and solid–liquid interfaces. However, protocols and documentation of experimental considerations for operando XPS probing of working electrolyzers and fuel cells remain scarce. Herein, we report an approach for the study of working polymer electrolyte membrane (PEM) electrolysis cells using ambient pressure X-ray photoelectron spectroscopy (AP-XPS). This approach directly probes the composite electrode surface on the membrane electrode assembly (MEA) in 100% relative humidity to establish a meaningful liquid layer for electrocatalysis. We carry out a systematic investigation from the cell constituent components to a fully assembled working operando electrolytic system and establish a method for AP-XPS study of the complex composite MEA, providing recommendations for data acquisition and component analysis.

Hamlyn, Rebecca↗
DOE OSTI2024

Community Solar and Community Solar+Storage: A Roadmap of Barriers and Solutions for Commercial Systems in NYC

Sustainable CUNY worked with decision makers and subject matter experts (SME's) to identify the barriers to and solutions for advancing commercial Community Solar (CS) and CS+Storage (CS+S) in urban areas. This roadmap captures the key challenges and solutions identified by New York City (NYC) stakeholders, including the Real Estate Board of New York (REBNY), through a collaborative process. Solar, as well as storage, are among the fastest growing energy segments in the United States, with CS, also known as Community Distributed Generation (CDG), gaining popularity with those who may not own or have access to a viable roof. Urban areas like NYC, which have a large population of renters, are particularly well suited for CS projects where credits from the power produced by a large remote installation are offered on a subscription basis to residents or businesses in the community. However, CS and CS+S projects have stalled at the doorstep of many cities. Host site owners, particularly those with large rooftops, have been slow to commit to installing CS due to competing rooftop usage and programs, limited knowledge about incentives, lack of economic data, and a complicated implementation process.

14 SOLAR ENERGY↗
DOE OSTI2022

Energy Northwest – Horn Rapids Solar and Storage: A Techno-economic Assessment

In 2017, as part of the second round of funding from the Washington State Clean Energy Fund, the Washington State Department of Commerce granted up to $\$3$ million in matching funds to develop an estimated $6.5 million project that deployed a 4-MW, 20-acre solar generating array of photovoltaic (PV) panels coupled with a 1 MW/4 MWh (nameplate capacity) lithium-iron-phosphate battery energy storage system (BESS) in Richland, Washington. The combination of PV and BESS will provide a predictable, renewable generating source and will also serve as a training ground for solar and battery technicians throughout the nation. This report documents the techno-economic assessment of the integrated system, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, case studies, and analysis results.

14 SOLAR ENERGY↗
NASA NTRS1996

Geotail MCA Plasma Wave Investigation Data Analysis

The goals of this program include identifying, studying, and understanding the source, movement, and dissipation of plasma mass, momentum, and energy between the Sun and Earth. The GEOTAIL spacecraft was built by the Japanese Institute of Space and Aeronautical Science and has provided extensive measurements of entry, storage, acceleration, and transport in the geomagnetic tail. Due to the GEOTAIL trajectory, which kept the spacecraft passing into the deep tail, GEOTAIL also made 'magnetopause skimming passes' which allowed measurements in the outer magnetosphere, magnetopause, bow shock, and upstream solar wind regions as well as in the lobe, magnetosheath, boundary layers, and central plasma sheet regions of the tail. In late 1994, after spending nearly 30 months primarily traversing the deep tail region, GEOTAIL began its near Earth phase where apogee was reduced first to about 50 Re and later to 30 Re and perigee was decreased to about 10 Re. The WIND spacecraft was launched on November 1, 1994, and the POLAR spacecraft was launched on February 24, 1996. These successful launches have dramatically increased the opportunities for GEOTAIL and the GGS spacecraft to conduct global research.

Anderson, Roger R.↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Ambipolar Electrochemistry of Pre‐Intercalated Ti 3 C 2 T x MXene in Ionic Liquid Electrolyte

Abstract Cation intercalation with or without redox remains the dominant charge storage mechanism for two‐dimensional (2D) Ti 3 C 2 T x MXene. Anion‐based charge storage remains unexplored due to intrinsic negative surface charge of MXenes preventing spontaneous intercalation of anions and irreversible oxidation of Ti at anodic potentials in aqueous electrolytes. In this work, we report on the ambipolar electrochemical behavior of the Ti 3 C 2 T x in ionic liquid electrolyte over a 2.5 V electrochemically stable window. The experiments are conducted on a thin Ti 3 C 2 T x film current collector coated with an electroactive layer of small flakes (∼150 nm) of Ti 3 C 2 T x pre‐intercalated with 1‐ethyl‐3‐methylimidazolium bis‐(trifluoromethylsulfonyl)‐imide (EMIM‐TFSI) ionic liquid. Couples of redox peaks with a very small potential separation during the voltage sweep are observed at high negative (−0.75 V vs. Ag wire) and high positive (+0.75 V vs. Ag wire) potentials. Our experimental electrochemical data combined with density functional theory (DFT) calculations suggest feasibility of pseudo‐intercalation of TFSI anions between Ti 3 C 2 T x flakes. This study provides a pathway for elucidating anion intercalation for different MXene chemistries in solvent‐free electrolytes, which can lead to development of MXene based energy storage devices with improved performance.

Electrochemistry↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

mCDR and carbon sequestration in various macroalgae products (FY22 Seedling Report)

Marine approaches to CDR (mCDR) are gaining recognition and substantial funding in the United States. Macroalgae farming has been identified by several highly recognized organizations as a potential carbon capture strategy, and significant research investments have been made in this area. However, much of the existing literature on the potential of macroalgae cultivation and product development as an mCDR strategy has not adequately considered the permanence of carbon captured, due to the status of the industry. Pacific Northwest National Laboratory (PNNL) has been interested in exploring the potential connections between macroalgae farming, mCDR, and marine energy. In October 2021, PNNL conducted a literature review of life cycle analyses (LCAs) from macroalgae products to assess the carbon capture potential and permanence. This project leverages the previous work done based on LCA for carbon sequestration and permanence in macroalgae and expands it to include additional approaches to monitoring, reporting, and verification (MRV), and opportunities for marine energy. This report provides a collection of the findings throughout this FY22 Seedling for the U.S. Department of Energy’s Water Power Technology Office, structured by the following tasks as defined by the initial proposal: Task 1: Update rankings of macroalgae products based on expanded literature review of carbon sequestration and value of temporary storage. Task 2: Assess potential changes to net climate impacts measured in LCAs of kelp products if marine energy was integrated in processing / harvesting. Task 3: Develop recommendations for standardized assessment of carbon capture potential of biological products and define data collection requirements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Grid Value Propositions for Tidal Generation Resources - a Temporal Analysis

As the energy sector experiences a paradigm shift towards cleaner and greener generation sources, the need for evaluating the applicability of marine energy for grid-based applications has intensified. This is because marine energy (ocean, wave and tidal) may be utilized to provide unique benefits to electric grid operations, especially in conjunction with other established renewable technologies (such as wind and solar) or technologies like energy storage. In this work, we focus our attention on some specific characteristics of two types of marine energy resources i.e. tidal and ocean current based resources. Specifically, we perform a comprehensive temporal analysis using a suite of metrics on resource data to characterize the inherent predictability of the resource across various time scales of interest. We also investigate the correlation of tidal power generation profiles with energy demand and comment on the effectiveness of having such resources in the overall generation portfolio.

Bhattacharya, Saptarshi↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
DOE OSTI2025

Q1-2024 Solar Cost Benchmarks

Each year, the U.S. Department of Energy’s (DOE) Solar Energy Technologies Office (SETO) and its national laboratory partners develop cost benchmarks for U.S. solar photovoltaic (PV) systems. These benchmarks track progress toward reducing solar costs and guide R&D priorities. Unlike typical studies that report only $/W, SETO uses intrinsic units (e.g., $/m² for mounting structures) to better capture how technology improvements such as module efficiency would impact system costs. This allows flexible modeling where inputs can vary significantly to assess cost sensitivity. Costs are reported in two ways: Minimum Sustainable Price (MSP): Long term, financially viable price under stable market conditions. Modeled Market Price (MMP): Actual market price, influenced by short term distortions such as tariffs or subsidies. Three national labs collect cost data from industry stakeholders, ensuring no duplication in outreach to stakeholders. Data reflects real transactions (primarily from Q1) and is weighted based on the number of sources per cost element. The PV System Cost Model (PVSCM) divides total installed system cost into eight categories: 1. Module (PV) 2. Inverter 3. Energy Storage System (ESS) 4. Structural BOS (SBOS) 5. Electrical BOS (EBOS) 6. Fieldwork 7. Office work 8. Other (developer/EPC costs) The first five are hardware costs, while the last three are soft costs. Each category includes fixed and variable cost components, where “size” depends on context (e.g., manufacturing capacity for modules vs. system capacity for installation costs). Variable costs are expressed using appropriate intrinsic units. The model reflects the owner’s upfront overnight capital cost, excluding tax credits. Tariffs and subsidies are treated as temporary market distortions affecting MMP but not MSP. PVSCM is implemented in Excel, where cost elements are aggregated into total system cost. Additional sheets handle unit conversions and operation & maintenance (O&M), with O&M costs levelized over the system’s lifetime.

14 SOLAR ENERGY↗
NASA NTRS1982

A low-power photovoltaic system with energy storage for radio communications: Description and design methodology

A low power photovoltaic system was constructed with approximately 500 amp hours of battery energy storage to provide power to an emergency amateur radio communications center. The system can power the communications center for about 72 hours of continuous nonsun operation. Complete construction details and a design methodology algorithm are given with abundant engineering data and adequate theory to allow similar systems to be constructed, scaled up or down, with minimum design effort.

Chapman, C. P.↗
NASA NTRSDate not supplied

Designing Molten Salt Eutectics: A Combined Thermodynamic Modeling and Machine Learning Approach

Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.

Thermodynamics↗