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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 OSTI2021

Advancing Electrolyte Solution Chemistry and Interfacial Electrochemistry of Divalent Metal Batteries

Divalent metal (Mg, Ca, etc.) battery chemistries potentially provide a sustainable long-term technical solution for large-scale energy storage because of high natural abundance of divalent metal elements in the earth crust. Good progress has been made on materials especially electrolyte development in the past years; however, significant challenges exist, particularly the very limited fundamental understanding on electrolyte solution chemistry and interfacial electrochemistry. In this perspective, we review and discuss key discoveries and understanding of divalent battery chemistry with a focus on electrolyte-dependent interfacial electrochemistry of divalent metal anodes. A concise review of electrolyte development, operando studies of the electrified interfaces and unique charge-transfer process is provided; the knowledge gaps and future research directions are discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2024

Impact of electrolyte solutions on carbon dioxide fixation in single chamber Al–CO 2 battery

Governments and research & development (R&D) organizations are actively initiating various programs and research strategies for CO 2 capture, its utilization, and integration with long duration energy storage from renewable sources worldwide. In line with the carbon capture goals, here we report a novel electrochemical Al-CO 2 battery cell, that can simultaneously capture CO 2 and convert it into value-added products, in addition to long-duration energy generation and storage. Here, this innovative approach employs cost-effective Al metal as an anode and an in-house synthesized Ni–Fe based bimetallic double hydroxide catalyst as the cathode, with meticulously optimized compositions and morphologies. We explore the impact of different aqueous electrolyte solutions compositions on the cell performance, demonstrating up to 10 h of stable long duration energy storage with a stable voltage profile. The cell exhibits low polarization even at high current densities of up to 12 mA cm -2 and maintains stable cycling over 500 h. Through Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray Diffraction and X-ray photoelectron spectroscopy (XPS) analysis, we determined that the discharge product is either NaAlCO 3 (OH) 2 or KAlCO 3 (OH) 2 , distinct from the Al 2 (CO 3 ) 3 typically reported in conventional Al–CO 2 batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations

This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. Finally, while direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Correlations between Molecular Structure, Solvation Topology, and Transport Properties of Aqueous Organic Flow Battery Electrolyte Solutions

Aqueous organic redox flow batteries (AORFBs) are considered promising technologies for storing energy generated from renewable resources. However, designing organic electrolyte molecules is limited by gaps between fundamental understanding of coupling between solvation structure and dynamics, and macroscopic transport properties like viscosity. Herein, we used molecular dynamics simulations to understand correlations between ionic molecular structures, ion clustering, and transport properties in 2,3-dihydrophenazine (2,3-DHP), a promising AORFB anolyte. We show that experimentally measured viscosity can be reproduced from simulations at relevant concentrations and that the asymmetric structure of 2,3-DHP leads to unique inhomogeneity in the solvation topology. However, order parameters and metrics need to be developed for better correlations over spatiotemporal scales, with careful consideration of the inhomogeneity of organic anolyte molecules. In conclusion, we show that the increased size and asymmetry of the anolyte leads to breakdown of assumptions within methods for determining ion transport mechanisms previously developed for Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

The variation in self-organized anode plasma pattern structure with solution electrolyte type in 1 atm DC glow discharge

Abstract Plasma self-organization on anode surfaces in 1 atm DC glow discharges remains poorly understood. This effort aims to elucidate the nature of self-organization through the experimental study of resulting patterns on liquid anode surfaces with 13 different electrolytes and thus improves our understanding of the underlying physical processes that give rise to self-organization by investigating electrolyte sensitivity. Self-organization pattern formation and behavior were studied as a function of discharge current, solution ionic strength, and their chemical property evaluation. The response of the patterns to variation in these parameters was measured using an imaging camera and optical emission spectroscopy. Observed pattern characteristic length scales for all of the electrolytes were ranged from 2 to 13 mm and typically increased with current over the investigated range of 20–80 mA. Complex self-organized pattern structures not reported to date were also observed. The parameters associated with pattern formation and morphology complexity are discussed and summarized.

Physics↗
DOE OSTI2022

Extending the low-temperature operation of sodium metal batteries combining linear and cyclic ether-based electrolyte solutions

Nonaqueous sodium-based batteries are ideal candidates for the next generation of electrochemical energy storage devices. However, despite the promising performance at ambient temperature, their low-temperature (e.g., < 0 °C) operation is detrimentally affected by the increase in the electrolyte resistance and solid electrolyte interphase (SEI) instability. Here, to circumvent these issues, we propose specific electrolyte formulations comprising linear and cyclic ether-based solvents and sodium trifluoromethanesulfonate salt that are thermally stable down to –150 °C and enable the formation of a stable SEI at low temperatures. When tested in the Na||Na coin cell configuration, the low-temperature electrolytes enable long-term cycling down to –80 °C. Via ex situ physicochemical (e.g., X-ray photoelectron spectroscopy, cryogenic transmission electron microscopy and atomic force microscopy) electrode measurements and density functional theory calculations, we investigate the mechanisms responsible for efficient low-temperature electrochemical performance. We also report the assembly and testing between –20 °C and –60 °C of full Na||Na 3 V 2 (PO 4 ) 3 coin cells. The cell tested at –40 °C shows an initial discharge capacity of 68 mAh g –1 with a capacity retention of approximately 94% after 100 cycles at 22 mA g –1 .

25 ENERGY STORAGE↗
DOE OSTI2021

Circuit Analysis of Ionizing Surface Potential Measurements of Electrolyte Solutions

Surface potential measurement values of the gas-liquid interface can be ambiguous despite the numerous electrochemical approaches used for quantification of the reported values. Calibration and normalization methods are not standardized, which often undermines the robustness of the reported values. Surface potential instrumentation and data interpretation also varies significantly across literature. Here, we propose a circuit model for an ionizing surface potential method based on the alpha decay of a radioactive americium-241 electrode. We evaluate the robustness of the circuit model for quantifying the surface potential at the air-aqueous interface. We then show successful validation of our circuit model through determination of the surface tension of the air-electrolyte interface with comparison to respective surface tension literature values. This validation reveals the reliability of surface potential measurements using the americium-241 ionizing method. We also report the surface potential difference of the air/water interface to be –0.49 V ± 0.01 V consistent with hydrogens of water pointing toward the air phase.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Influences of Alkali Metal Cation Interactions on N2 Coordination to Iron(I) Beta-Diketiminate Complexes in Electrolyte Solutions

N2 functionalization reactions often use stable, isolable N2-bound complexes and low temperatures or highly activated reagents. However, little work has focused on systems where N2 binds weakly, and the N2 binding equilibrium can be modulated. Here, we describe analysis of surprising trends in alkali metal cation solvation on the formation of an anionic iron(I) beta-diketiminate complex and the subsequent N2 binding equilibrium. Variable temperature UV-vis spectroscopy shows that increased ionic strength in the coordinating solvent tetrahydrofuran promotes N2 coordination. Additionally, less solvated alkali metal cations in noncoordinating 2-methyltetrahydrofuran (MeTHF) stabilize N2 coordination, likely through the formation of contact ion pairs in solution that have enhanced N2 binding. Ionic strength has little effect on the energetics of N2 coordination to these proposed contact ion pairs in MeTHF. When the electrochemical analysis is done at –78 °C, there is an irreversible reduction of iron(II) and an anodically shifted oxidation, which is attributed to the formation of the spectroscopically observed N2 complex. Finally, bulk electrolysis is used to verify the electrochemical formation of the N2-bound anionic iron(I) complex. These studies demonstrate a new strategy for promoting N2 coordination by changing solution properties, which is relevant to homogeneous electrochemical N2 reduction methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thermodynamic modeling of aqueous lithium salt solutions with association electrolyte nonrandom two-liquid activity coefficient model

The high charge density of lithium ion and the resulting strong association phenomena make thermodynamic modeling of aqueous lithium electrolyte solutions extremely challenging. In this study, the association electrolyte nonrandom two-liquid activity coefficient model of Lin et al. (AIChE J. 2022, 68(2), e17422) is utilized to correlate and predict thermodynamic properties and solubility behavior of aqueous single electrolyte solutions of LiCl, LiBr, LiI, and LiNO 3 , and their mixed electrolyte solutions. Capturing self-association of water, cross-association of ion and water for hydration, and cross-association of cation and anion for ion-pairing, the association model accurately represents the literature experimental data up to saturation concentrations and at the temperature ranging from 263 K to 523 K. Here, this study further investigated the effect of anions of the lithium salts, and re-confirmed that the order of solution non-ideality as LiI > LiBr > LiCl > LiNO 3 because the anions with stronger association strengths are more likely to form ion pairs and thus lower the mean ionic activity coefficients.

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

Toward a First-Principles Framework for Predicting Collective Properties of Electrolytes

Conspectus Given the universal importance of electrolyte solutions, it is natural to expect that we have a nearly complete understanding of the fundamental properties of these solutions (e.g., the chemical potential) and that we can therefore explain, predict, and control the phenomena occurring in them. In fact, reality falls short of these expectations. But, recent advances in the simulation and modeling of electrolyte solutions indicate that it should soon be possible to make progress toward these goals. In this Account, we will discuss the use of first-principles interaction potentials based in quantum mechanics (QM) to enhance our understanding of electrolyte solutions. Specifically, we will focus on the use of quantum density functional theory (DFT) combined with molecular dynamics simulation (DFT-MD) as the foundation for our approach. The overarching concept is to understand and accurately reproduce the balance between local or short-ranged (SR) structural details and long-range (LR) correlations, allowing the prediction of the thermodynamics of both single ions in solution as well as the collective interactions characterized by activity/osmotic coefficients. In doing so, relevant collective motions and driving forces characterized by chemical potentials can be determined. Here, in this Account, we will make the case that understanding electrolyte solutions requires a faithful QM representation of the SR nature of the ion–ion, ion–water, and water–water interactions. However, the number of molecules that is required for collective behavior makes the direct application of high-level QM methods that contain the best SR physics untenable, making methods that balance accuracy and efficiency a practical goal. Alternatives such as continuum solvent models (CSMs) and empirically based classical molecular dynamics have been extensively employed to resolve this problem but without yet overcoming the fundamental issue of SR accuracy. We will demonstrate that accurately describing the SR interaction is imperative for predicting both intrinsic properties, namely, at infinite dilution, and collective properties of electrolyte solutions. DFT has played an important role in our understanding of condensed phase systems, e.g., bulk liquid water, the air–water interface, ions in bulk, and at the air–water interface. This approach holds huge promise to provide benchmark calculations of electrolyte solution properties that will allow for the development and improvement of more efficient methods, as well as an enhanced understanding of fundamental phenomena. However, the standard protocol using the generalized gradient approximation with van der Waals (vdW) correction requires improvement in order to achieve a high level of quantitative accuracy. Simply simulating with higher level DFT functionals may not be the best route considering the significant computational cost. Alternative methods of incorporating information from higher levels of QM should be explored; e.g., using force matching techniques on small clusters, where high level benchmark calculations are possible, to develop ideal correction terms to the DFT functional is a promising possibility. We argue that DFT with statistical mechanics is becoming an increasingly useful framework enabling the prediction of collective electrolyte properties.

Duignan, Timothy T.↗