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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fundamental Thermodynamic, Kinetic, and Mechanical Properties of Lithium and Its Alloys

Lithium alloying reactions are beneficial in promoting uniform plating and stripping of lithium metal in all-solid-state batteries. First-principles calculations are performed to predict thermodynamic, kinetic, and mechanical properties of lithium and several important Li–M alloys (M = Mg, Ag, Zn, Al, Ga, In, Sn, Sb, and Bi). While the Li–Mg binary system forms a solid solution, most other lithium–metal alloys prefer stoichiometric intermetallic compounds with common local motifs that enable fast Li diffusion. Lithium and Li-rich alloys exhibit an unusually flat energy landscape along paths that connect BCC to close-packed structures like FCC and HCP, with important implications for mechanical properties. Very low migration barriers for Li diffusion that rival those of superion conductors are predicted, both in pure Li and in Li–M intermetallics. However, vacancy concentration, which is crucial for substitutional diffusion, is predicted to be low in metallic Li and most Li–M intermetallics. Compounds such as B32 LiAl and LiGa as well as D0 3 Li 3 Sb and Li 3 Bi exhibit structural vacancies at higher ends of their voltage windows, which together with low migration barriers leads to exceptionally high Li mobilities. In the Li–Mg solid solution, the addition of Mg is found to decrease the vacancy tracer diffusion coefficient by an order of magnitude.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Interfacial Hydrogen-Bond Dynamics in Transition Metal Compounds

Understanding how water behaves when confined within atomic layers of active transition-metal carbides, nitrides, and carbonitrides is essential for uncovering the fundamental principles needed to engineer solid–liquid interfaces at the atomic scale. Yet, how lattice element chemistry and surface termination groups collectively regulate the structure and mobility of such interlayer water remains poorly understood. Here, we present a composition-controlled investigation of interlayer water dynamics in layered transition-metal nitride, carbide, and carbonitride systems using a systematic integration of quasi-elastic neutron scattering (QENS), ab initio molecular dynamics (AIMD) simulations, and density functional theory (DFT) calculations. QENS measurements show that nitride-rich systems host mobile, translationally diffusing water with thermally activated self-diffusion coefficients on the order of 10 –10 m 2 s –1 , whereas mixed C/N lattices confine water to localized, nontranslational motion that is insensitive to temperature. AIMD and DFT reveal that lattice C/N chemistry and surface functional group composition reshape the first hydration layer by modulating the surface electronic structure and termination-dependent hydrogen-bond networks, leading to pronounced differences in water ordering and thermal resilience. On the other hand, fully carbide systems exhibit intermediate behavior, highlighting that water mobility is not primarily controlled by the hydration level alone but by the coupling between lattice composition and surface chemistry. Overall, this study establishes how surface chemistry and lattice composition jointly control interfacial hydrogen bond dynamics, offering a mechanistic framework for designing transition-metal layered materials with tailored interfacial transport properties.

Hydration↗
DOE OSTI2022

Effects of Supercritical CO 2 Injection on the Shale Pore Structures and Mass Transport Rates

Characterizing the pore structures and transport properties of low-permeability shales is critical for evaluating these formations as potential seals or storage sites for geological CO 2 sequestration. Here, we use low-pressure gas adsorption in conjunction with nuclear magnetic resonance (NMR) to characterize the pore-size distribution of shales before and after injection of supercritical CO 2 . Nitrogen gas was used as the detecting phase for the adsorption experiments and pentane liquid was used for the NMR experiments. We also performed time-resolved NMR and gravimetric microbalance measurements to observe mass transport during desaturation. We use these data to estimate the self-diffusion coefficient of pentane and changes in the saturation state of the pore network. We analyzed samples with a range of compositions from the Wolfcamp shale before and after exposure to supercritical CO 2 for 3 days. Integrating the gas adsorption and NMR data shows how supercritical CO 2 injection alters the pore-size distribution for pore sizes <1 nm to 1 mm. Finally, our results provide insights on how the pore structure and mass transport properties of different shale lithologies may evolve during storage of supercritical CO 2 .

58 GEOSCIENCES↗
DOE OSTI2026

CO 2 Sorption in Moisture Swing Anion Exchange Resins for Direct Air Capture: Experimental Isotherm Determination and Modeling

Moisture swing (MS) sorption is a promising direct air capture (DAC) technology to achieve negative CO 2 emissions and counter global warming. Here, in this study, MS CO 2 sorption in a model MS sorbent, IRA900, was investigated. IRA900 is a macroporous commercial strong-base anion exchange resin (AER) with quaternary ammonium functional groups. A rigorous CO 2 desorption process was developed to desorb all CO 2 from the sample and obtain CO 2 sorption isotherms as a function of relative humidity, CO 2 partial pressure, and temperature. CO 2 sorption increased with decreasing relative humidity, decreasing temperature, and increasing CO 2 partial pressure. Remarkably, the total CO 2 uptake from the sorption isotherms matched the ion exchange capacity (IEC) of the material, suggesting a stoichiometry of one CO 2 molecule reacting per active site. An isotherm model for CO 2 sorption starting with empty sorption sites (i.e., with the AER in the OH – form) described the experimental data well, supporting the hypothesis that the bicarbonate-loaded AER fully unloaded to the OH – state following rigorous CO 2 desorption. CO 2 sorption kinetics were studied, and carbon diffusion coefficients were estimated as a function of %RH and CO 2 partial pressure. Overall, this study provides a roadmap for the systematic evaluation of MS CO 2 DAC materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Refined Classical Force Field for Choline Chloride and Ethylene Glycol Mixtures over Wide Composition Range

In this work, a classical force field based on the General Amber Force Field (GAFF) was refined for the simulation of choline chloride (ChCl) and ethylene glycol (EG) mixtures over a wide composition range by scaling the partial charges and van der Waals parameters. The scaling factors were derived by fitting the simulation results to only eight experimental density and viscosity data points of pure EG, and ChCl/EG mixtures at 1:2, 1:4, and 1:6 molar ratios. Using the refined force field, properties essential for electrochemical applications such as density, viscosity, self-diffusion coefficient, and ionic conductivity were calculated, and excellent agreement to experimental results was found even for compositions and temperatures not used in the fitting procedure. In addition, new experimental data for density, viscosity, and ionic conductivity are reported as a function of temperature and composition for this mixture. To the best of our knowledge, this is the first classical force field developed for the study of ChCl/EG mixtures over a composition range that includes the eutectic point. Using the new model, the liquid dynamics was studied in terms of ionic conductivity. It was found that the dynamics in ChCl/EG mixtures with ChCl mole fraction higher than 20% is similar to that of ionic liquids, high temperature molten salts, and highly concentrated water-in-salt electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2021

Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

Within the framework of Kohn–Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric–isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.

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

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

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

First-Principles-Based Machine Learning Models for Phase Behavior and Transport Properties of CO 2

In this work, we construct distinct first-principles-based machine-learning models of CO 2 , reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN and SCAN-rvv10 approximations of density functional theory. Here we employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initio molecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid phase configurations, they are able to simulate a stable interfacial system and predict vapor-liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for liquid phase and vapor-liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2020

Large-Scale Molecular Dynamics Simulation of the Dehydration of a Suspension of Smectite Clay Nanoparticles

Fine-grained sediments and sedimentary rocks play important roles in a variety of modern energy technologies from petroleum geology to geological carbon sequestration and radioactive waste management. However, despite their utility and ubiquity, many of their properties remain poorly understood. In particular, the ability to predict the permeability and mechanics of these media remains a persistent fundamental challenge in the geosciences. In the present work, we show how large-scale classical molecular dynamics (MD) simulations can help interpret the properties of fine-grained sedimentary material. All-atom simulations containing 30 discrete clay particles are utilized to understand the evolution of a clay nanoparticle suspension during its progressive dehydration. Microstructural (pore size distribution, tortuosity, anisotropy), thermodynamic (enthalpy and free energy of hydration, anion exclusion), mechanical (total suction), and transport properties (diffusion coefficient tensors of water and sodium) are calculated and compared to the experiment. Overall, our results provide new insight into the coupled chemistry, mechanics, and transport properties of disordered nanoparticle assemblages and shed light upon the important role of water films in controlling these properties.

02 PETROLEUM↗
DOE OSTI2021

Physicochemical Gas–Solid Sorption Properties of Geologic Materials Using Inverse Gas Chromatography

The goal of this study was to determine the physicochemical properties of a variety of geologic materials using inverse gas chromatography (IGC) by varying probe gas selection, temperature, carrier gas flow rate, and humidity. This is accomplished by measuring the level of interaction between the materials of interest and known probe gases. Identifying a material’s physicochemical characteristics can help provide a better understanding of the transport of gaseous compounds in different geologic materials or between different geological layers under various conditions. Our research in this study focused on measuring the enthalpy (heat) of adsorption, Henry’s constant, and diffusion coefficients of a suite of geologic materials, including two soil types (sandy clay-loam and loam), quartz sand, salt, and bentonite clay, with various particle sizes. The reproducibility of IGC measurements for geologic materials, which are inherently heterogeneous, was also assessed in comparison to the reproducibility for more homogeneous synthetic materials. This involved determining the variability of physicochemical measurements obtained from different IGC approaches, instruments, and researchers. For the investigated IGC-determined parameters, the need for standardization became apparent, including the need for application-relevant reference materials. The inherent physical and chemical heterogeneities of soil and many geologic materials can make the prediction of sorption properties difficult. Characterizing the properties of individual organic and inorganic components can help elucidate the primary factors influencing sorption interactions in more complex mixtures. This research examined the capabilities and potential challenges of characterizing the gas sorption properties of geologic materials using IGC.

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

Limiting Current in Nanostructured Block Copolymer Electrolytes

The next-generation electrolytes for lithium batteries must be able to conduct ions at sufficiently high current densities; yet this regime is rarely studied directly. The limiting current density of an electrolyte quantifies the highest possible rate of ion transport under an applied dc potential. Herein, we report on the limiting current density in twelve nanostructured polystyrene-block-poly(ethylene oxide) (PS-b-PEO, or SEO) copolymer electrolytes. We find that the limiting current at a given salt concentration increases systematically with increasing volume fraction of the PEO block (φ EO ). In contrast, the effective-medium theory, commonly used to analyze conductivity in block copolymer electrolytes, predicts that limiting current is independent of φ EO . To resolve this conundrum, the ionic conductivity, the mutual diffusion coefficient of the salt, and the steady-state current fraction of the block copolymer electrolytes were measured. These measurements enable predictions of limiting current with no adjustable parameters using the concentrated solution theory. We found quantitative agreement between experimentally measured limiting current densities and predictions based on the concentrated solution theory. This work sheds light on how to reliably measure and predict limiting current density in composite electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2021

Electrochromic Properties of Perovskite NdNiO 3 Thin Films for Smart Windows

Semiconductors with electrically tunable band gaps are of great interest in controlling transparency to electromagnetic radiation. Thin films of perovskite nickelate NdNiO 3 (NNO), a class of correlated oxides, were deposited on single-crystal (LaAlO 3 (LAO)) and polycrystalline (fluorine-doped tin oxide-coated glass (FTO)) substrates by magnetron sputtering, chemical solution deposition (CSD), and atomic layer deposition (ALD). Their electrochromic behaviors were investigated using a three-electrode setup in basic (KOH solution, pH = 12) electrolyte. During bleaching/coloration process, the proton intercalation/deintercalation and simultaneous electron compensation in the NNO lattice under electrical bias led to crossover of the material between the pristine-conducting phase (Ni 3+ ) and the strongly correlated insulating phase (Ni 2+ ), which serves as the working principle for electrochromic (tunable opacity in the visible range) behavior. Cyclic voltammetry (CV) scans demonstrate that NNO films are electrochemically stable in basic solutions for all three film deposition methods explored here. CV scans at varying rates enabled the extraction of diffusion coefficient of protons in thin film NNO, which is similar to 10 –7 cm 2 s –1 among all films studied. Large light transmittance modulation by bleaching and coloration was observed on films grown on both LAO and FTO substrates, suggesting its potential as an electrochromic material candidate for smart windows and optical shutter applications. Here, porous NNO films obtained by chemical solution deposition tend to demonstrate stronger electrochromic activity than dense films grown by sputtering or ALD.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Multidimensional Operando Study Showing the Importance of the Electrode Macrostructure in Lithium Sulfur Batteries

Lithium sulfur batteries are one of the most promising next-generation energy storage technologies, because of their impressive theoretical energy density, low materials cost, and relative safety. However, incomplete understanding of their underlying operation mechanisms has hindered their further development and commercialization. In this work, to gain a better understanding of the operation mechanisms in the lithium sulfur battery, three macroscopically different (woven and nonwoven) and microscopically similar (made from the same carbon fibers) free-standing carbon felts are used as the conductive matrices to show how the macrostructure influences the redox reactions and the electrochemical performance. Additionally, an operando radiography and simultaneous in situ electrochemical impedance spectroscopy study are performed to highlight the differences of the carbon hosts and how they compare electrochemically in a lithium sulfur battery. The electrochemical results show that the carbon host with a more open structure results in increased capacity as well as a higher diffusion coefficient. The operando radiography shows that the open structures are more conducive to efficient redox reactivity in forming solid sulfur species, mostly in the form of elongated needlelike structures identified as β-S8, near the end of charge, which is corroborated by the impedance data. The data also reveal a “breathing” mechanism, where the polysulfides are pushed to the edges during discharge and pulled back during charge. Furthermore, it is also revealed that the “breathing” process is a limiting factor in the high rate performance. Overall, this study shows that there is a close relationship between the macrostructure of the carbon matrix and the electrochemical performance of the sulfur cathode.

25 ENERGY STORAGE↗
DOE OSTI2021

Doping- and Strain-Dependent Electrolyte-Gate-Induced Perovskite to Brownmillerite Transformation in Epitaxial La 1– x Sr x CoO 3–δ Films

Much recent attention has focused on the voltage-driven reversible topotactic transformation between the ferromagnetic metallic perovskite (P) SrCoO 3-&delta; and oxygen-vacancy-ordered antiferromagnetic insulating brownmillerite (BM) SrCoO 2.5 . This is emerging as a paradigmatic example of the power of electrochemical gating (using, e.g., ionic liquids/gels), the wide modulation of electronic, magnetic, and optical properties generating clear application potential. SrCoO 3 films are challenging with respect to stability, however, and there has been little exploration of alternate compositions. Here, we present the first study of ion-gel-gating-induced P. BM transformations across almost the entire La 1-x SrxCoO 3 phase diagram (0 ≤ x ≤ 0.70), under both tensile and compressive epitaxial strain. Electronic transport, magnetometry, and operando synchrotron X-ray diffraction establish that voltage-induced P &rarr; BM transformations are possible at essentially all x, including x ≤ 0.50, where both P and BM phases are highly stable. Under small compressive strain, the transformation threshold voltage decreases from approximately +2.7 V at x = 0 to negligible at x = 0.70. Both larger compressive strain and tensile strain induce further threshold voltage lowering, particularly at low x. The P &rarr; BM threshold voltage is thus tunable, via both composition and strain. At x = 0.50, voltage-controlled ferromagnetism, transport, and optical transmittance are then demonstrated, achieving Curie temperature and resistivity modulations of similar to 220 K and at least 5 orders of magnitude, respectively, and enabling estimation of the voltage-dependent Co valence. The results are analyzed in the context of doping- and strain-dependent oxygen vacancy formation energies and diffusion coefficients, establishing that it is thermodynamic factors, not kinetics, that underpin the decrease in the threshold voltage with x, that is, with increasing formal Co valence. These findings substantially advance the practical and mechanistic understanding of this voltage-driven transformation, with fundamental and technological implications.

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

Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗