Deep eutectic solvents (DESs) are a class of liquids that offer great potential in alleviating some of the challenges present in today's long-term energy storage methods because they have physical properties that are favorable for storable electrolyte solutions. In this work, a series of glycols (ethylene glycol, 1,3-propanediol, 1,4-butanediol, and 1,5-pentanediol) were studied as potential hydrogen bond donors (HBD) with a common choline chloride (ChCl) as the hydrogen bond acceptor (HBA). The solvation dynamics of the prepared systems were studied by measuring the solvent reorganization response using femtosecond transient absorption spectroscopy (fs-TA). Conductivity, viscosity, density, ET(30) polarity, and dynamics of the prepared DESs were analyzed, with a particular interest in determining the effect of HBD chain length on these parameters. Classical molecular dynamics simulations were employed to investigate how the local liquid structure, solvent dynamics, and bulk solvent properties vary with changes in glycol chain length.

Directional solvent extraction (DSE) is an emerging technology capable of utilizing low-temperature heat sources (e.g., waste heat) for desalination, but the identification of high-performing directional solvents remains a significant unmet need hindering its broader implementation. Herein, we report the design of thermoresponsive ionic liquids with multicomponent ions as a powerful class of directional solvents. Our studies revealed that freshwater yield and ion rejection rates for [choline][NTf2] were greatly enhanced by the addition of [choline][OH]. Molecular simulations highlight the importance of balancing hydrophilicity and hydrophobicity through the addition of polar, protic functional groups in the development of a reliable structure-function relationship toward identifying an optimal ionic liquid-based directional solvent. The newly developed ionic liquid directional solvents may significantly advance the DSE technology to solve the global water shortage challenge.

Electrostrictive coefficients (γ) were computed for multiple ionic liquids (ILs) with molecular dynamics (MD) simulations. For ILs, γ quantifies the effect of an external electric field (EEF) expanding or contracting the IL. For all ILs studied, their molar volume increases once a strong EEF is applied. From these results, we derived structure-property relationships for the electrostrictive properties of ILs. The expansion is proportional to the applied EEF, dominated by the anion and inversely proportional to the molar volume of the IL. We rationalize the relationship with molar volume through the charge density of the ions in the IL. As molar volume increases across ILs, the charge density of a monovalent ion decreases, and charge becomes more delocalized. The decrease in charge density leads to a weaker coupling with the EEF, leading to ILs with high molar volume expanding less than ILs with low molar volumes.

Hydrotropy is a phenomenon where an amphiphilic molecule (i.e., the hydrotrope) is able to enhance the aqueous solubility of a hydrophobic solute. Understanding the molecular mechanisms behind this phenomenon is crucial to designing new hydrotropes aimed at enhancing the aqueous solubility of specific target solutes. This study investigates the hydrotropic behavior of 1,2-alkanediols in enhancing the aqueous solubility of syringic acid using molecular dynamics (MD) simulations. The analysis carried out here employs several computational methods, including Kirkwood-Buff integrals, solvation free energies, radial distribution functions, and hydrogen bonding number. The solvation free energy results reported in this work help explain the thermodynamic favorability of syringic acid solubilization in the presence of 1,2-alkanediols, aligning with experimental trends. In addition, MD simulations reveal a pronounced affinity between syringic acid and 1,2-alkanediols, particularly at low hydrotrope concentrations. This high affinity is driven by the alkyl chain of each hydrotrope when water is the main solvent, resulting in an increase in the solubility of the solute as the length of the hydrotrope alkyl chain increases. However, a shift in the solubilization mechanism is seen when water is no longer the main solvent, with the hydrogen bonding capabilities of the hydrotrope playing a larger role than its alkyl chains. Under low water concentration conditions, longer alkyl chains in the hydrotrope have difficulty forming hydrogen bonds, leading to an opposite trend compared to lower hydrotrope concentrations. This different behavior with composition results in a maximum solubility for systems with long alkyl chains at intermediate hydrotrope concentrations.

Transient nanoclusters in aqueous ZnSO 4 electrolytes are revealed with X-ray scattering and molecular dynamics simulations. These nanoclusters exhibit diverse sizes and geometries, influencing ion correlations and transport properties.

High concentration water-in-salt electrolyte (WiSE) systems can expand the electrochemical stability window of water, thereby enabling the application of water-based electrolytes in Li-ion batteries. However, the solvation structure and the dynamics of the ions are not yet fully resolved, and prior molecular-mechanics-based molecular dynamics (MMMD) simulation studies present contrasting viewpoints. In the present work, we utilize first-principles molecular dynamics (FPMD) simulations to study the structure and dynamics of high-concentration (10 and 20 m) LiTFSI electrolyte solutions at 298 and 373 K. Although computationally more expensive than the MMMD simulations, the FPMD simulations, in which the forces on the nuclei are obtained from Kohn-Sham density functional theory reflecting the instantaneous arrangement of the electron density, may offer a more accurate representation of WiSE systems where polarization and charge transfer are important. The FPMD simulations demonstrate disruption of the water hydrogen bonding environment and concurrent formation of an anionic network upon increasing the LiTFSI concentration from 10 to 20 m. However, nanoscale spatial heterogeneity is not observed. Analysis of the Li+ cation dynamics obtained from both FPMD and MMMD simulations indicates that ion transport proceeds predominantly via a mixed-mode mechanism, with contributions from both vehicular motion and hopping depending on concentration and temperature.

Electrolytes based on deep eutectic solvents (DESs) coupled with redox active organic molecules have shown potential as a versatile and energy dense electrochemical energy storage system. However, progress in these systems has been held back by a lack of understanding of the irregular behavior displayed when redox active organic molecules are transitioned from other solvent systems. In this work, the hydrogen bonding characteristics of a series of redox organic molecules were investigated through infrared spectroscopy and molecular modeling. New understanding of these interactions was then used to explain their electrochemical behavior in a DES electrolyte. A model was used to predict the behavior of new derivatives towards the design of an optimized redox organic-DES system. Hydrogen bonding between the redox molecules and the solvent was found to significantly shift the potential of a redox reaction more positive when a hydrogen bond forms at the redox active site. It was predicted that functionalizing a molecule with electron withdrawing groups to lower the electron density of the redox active functional group lowers the strength of the hydrogen bond and thus alleviates the undesirable potential shift. This hypothesis was demonstrated by the addition of nitro groups to fluorenones.

Thermophysical properties of hydrofluorocarbon (HFC) gas and ionic liquid (IL) mixtures have previously been shown to exhibit large deviations from ideal solution behavior. Understanding self-diffusivity can help elucidate the contributing dynamic and structural properties. 1H and 19F NMR pulsed-field gradient stimulated echo techniques were utilized to measure the self-diffusion coefficients of the ions of 1-ethyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)amide ([C2C1im][Tf2N]) with either difluoromethane (HFC-32) or pentafluoroethane (HFC-125) at saturation with temperatures from 25 to 75 °C and pressures to 62 bar. Molecular dynamics (MD) simulations were performed for mixtures of difluoromethane in [C2C1im][Tf2N] across a wide range of HFC compositions. MD simulations demonstrated good qualitative agreement with experiments, with better quantitative agreement at high HFC compositions. MD predicted diffusivities were generally lower and viscosities higher than experimental values, probably due to polarizability effects. Experiments and simulations illustrate that the HFC diffusivity is the highest of all mixture constituents at all investigated conditions and the cation diffuses faster than the anion. A sharp increase in the liquid self-diffusivity of all constituents was observed at HFC compositions above 80% moleHFC in the difluoromethane/IL system. From previous viscosity measurements and MD simulations, the anion generally follows Stokes-Einstein behavior while the cation and HFC exhibit increased deviations.

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho-phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o-chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. The simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these systems.

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of an open-source software for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian Process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Type III deep eutectic solvents (DESs) have drawn extensive attention over the past two decades due to their potential for applications in a wide variety of fields promised by their favorable properties. One of such applications is in energy storage due to the increasing challenge of climate change and the urgent need of the development of renewable energy techniques. Studies have been carried out over the past two decades but most of these studies focused on the liquid bulk of DESs and the solvation structure and dynamics at the electrode interface is barely known. In this work, using molecular dynamics (MD) simulations combined with recently refined force field, the choline chloride (ChCl) ethylene glycol (EG) eutectic solvents at electrode surface were systematically studied. Various ChCl/EG compositions and electrode charge densities were considered. Detailed picture of the solvation structure as well as dynamics of the solvent at the electrode surface was revealed. The electrochemical behavior was also explored. Based on the simulation results, the effect of solvent composition, the charging states of the electrode, and the electrode material on the electrochemical performance was discussed.

Molten salt reactors (MSRs) offer significant advancements in nuclear reactor safety and efficiency by operating at higher temperatures and lower pressures compared to traditional reactors. A critical aspect of MSR operation involves understanding the solubility of fission byproducts, particularly noble gases, in the molten salts used. This study employs molecular dynamics (MD) simulations to compute Henry’s law constants and enthalpies of solvation for argon and xenon in molten sodium chloride (NaCl) and potassium chloride (KCl). We developed a new pairwise potential for the noble gas and salt interactions based on first principles calculations. We then used this potential to calculate Henry’s law constants of the two gases in the molten salts, which were modeled using both a rigid ion model (RIM) and a polarizable ion model (PIM). The solubility calculations, performed using the Widom insertion method, show qualitative agreement with limited experimental data, highlighting the temperature dependence and greater solubility of both gases in KCl compared to NaCl. Additionally, free volume analysis elucidated the role of available space within the molten salts in governing solubility trends. Our findings suggest that PIM trajectories provide more reliable predictions for noble gas solubility than RIM due to their accurate density representation. These results enhance understanding of gas solubility in MSR environments, and the methods can be readily extended to other systems.

We present an efficient, general-purpose variant of the Widom test particle insertion method for computing chemical potentials of gaseous solutes in fluids or porous solids. The method is implemented in the Monte Carlo molecular simulation engine Cassandra, but receiving phase configurations are independent of this process and may be pre-sampled by other molecular simulation engines such as molecular dynamics codes. Efficiency enhancements present in this method include configurational biasing and accelerated atomic overlap detection. When applied to the estimation of Henry's law constants of atomistic difluoromethane and pentafluoroethane in ionic liquids, the accelerated overlap detection results in a speedup of more than an order of magnitude compared to conventional methods without sacrificing accuracy. We found good agreement between this method and Hamiltonian replica exchange (HREX) for Henry's law constant and absorption isotherm estimation. This embarrassingly parallel method is especially well suited for screening Henry's law constants of many small gases in the same solvents, since a liquid trajectory can be reused for as many solutes as desired.

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (T-g) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting T-g using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict T-g, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the T-g as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro-Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, T-g. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for T-g without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting T-g from density-temperature data. We believe that our proposed method would allow for more consistent predictions of T-g. We make this methodology available and open source through GitHub.

Hydrofluorocarbons are a class of fluorinated molecules used extensively in residential and industrial refrigeration systems. This study examines the potential of using adsorption processes with the silicalite-1 zeolite to separate a mixture of difluoromethane (CH2F2, HFC-32) and pentafluoroethane (CF3CF2H, HFC-125) at various concentrations. Pure adsorption data were measured using a XEMIS gravimetric microbalance, whereas binary data were determined using the Integral Mass Balance method. Grand canonical Monte Carlo molecular simulations were performed with the Cassandra package. We found that the results from molecular simulations are in satisfactory agreement with experimental loading measurements. Moreover, we show that ideal adsorbed solution theory could not quantitatively match the experimental or computational measurements of binary adsorption or selectivity. Molecular simulations show that refrigerant molecules do not have a uniform distribution in the zeolite framework.

Nanoplastics (NPs) can come into contact with humans through different means such as ingesting contaminated food or exposure to contaminated air. Recent research indicates that these NPs can act as vectors for other contaminants. Further research is still needed to determine the effects of these interactions and whether they are significant under environmental conditions. Bisphenol A (BPA) and benzophenone (BZP) are possible contaminants that could be cotransported with NPs. Even in low concentrations, BPA and BZP can act as endocrine disruptors and have been linked to several diseases. In this study, we used molecular dynamics simulations to obtain the potential of mean force (PMF) profile between a polyethylene NP and a BPA/BZP molecule. The PMF shows a minimum of -8.0 kJ mol-1 for the BPA, whereas it is -23.5 kJ mol-1 for the BZP, meaning BZP has a much greater attractive potential to polyethylene than BPA. We can infer that the higher quantity of BPA's hydrogen bonds with the water contributes to the difference between BZP and BPA. The results indicate the need to address the possibility of NPs playing a role in the cotransport and bioaccumulation of contaminants in aquatic ecosystems.

Ionic liquids (ILs) have been used in many applications, including gas separations, electrochemistry, lubrication, and catalysis. Understanding how the different properties of ILs are related to their chemical structure and composition is crucial for these applications. Experimental investigations often provide limited insights and can be tedious in exploring a range of state points. Therefore, molecular simulations have emerged as a powerful tool that not only offers a microscopic perspective but also enables rapid screening and prediction of physical properties. The accuracy of these predictions, however, depends on the quality of the intermolecular potentials (force fields) used. The widely used classical fixed charge models, such as GAFF, OPLS, and CL&P, are popular due to their simplicity and computational efficiency. However, it has been shown that the use of integer charges with these classical models leads to sluggish dynamics. The use of scaled charge models can improve the dynamics, but these mean-field approaches are unable to account for polarization effects explicitly. Several different approaches have been proposed to include polarizability in IL force fields. In this work, we follow the protocol of the CL&Pol model to develop a Drude oscillator model based on the GAFF force field (Goloviznina, K., et al. J. Chem. Theory Comput. 2019, 15, 5858). We compare the performance of the model for eight imidazolium- and pyrrolidinium-based ILs against that of other models. We find that the new model provides reasonable estimations of density, self-diffusivity, and structural properties for these ILs and suggests a relatively simple way of extending the general GAFF model to more ILs.

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

The thermal, physical, structural, and transport properties of ionic liquid (IL) electrolytes based on n-methyl-n-butylpyrrolidinium bis(trifluoromethanesulfonyl)imide [PYR14][TFSI] and Li-salts of lithium (non-afluorobutane)(trifluoromethanesulfonyl)imide [Li][IM14] and [Li][TFSI] (0 <= xLi <= 0.3), with addition of 1,1,2,2-tetrafluoroethyl 2,2,3,3-tetrafluoropropyl ether (TTE) (0 <= xTTE <= 0.45) were studied. While [IM14] increases glass transition, TTE improves conductivity over a wider liquidus range. Li+ is solvated primarily by [TFSI] with some contribution by [IM14]. However, both 7Li-19F HOESY NMR and spatial distribution functions (SDFs) derived from molecular dynamics (MD) indicate short contacts between the fluorines of TTE and Li+. This is a result of tighter anion-solvated Li+ without aggregation of the Li+ solvates, consistent with Raman measurements, thus confirming the existence of fluorous domains in the bulk which leads to improved fluidity and a Li+ diffusivity of 1.26 x 10-12 m2/s at 0 degrees C (xLi = 0.20) with a Li+transference of 0.16. Improved transport properties translated to a higher capacity of the TTE/IL electrolyte in a Li||LiFePO4 half-cell with 95mAh/g at 0.1C, compared to the IL electrolyte without TTE (60 mAh/g). This study demonstrates the tunability of solvation and transport properties in IL electrolytes by asymmetric fluorinated anions and hydrofluoroether co-solvents for low temperature Li-ion batteries.