Effects of Acid Dissociation and Ionic Solutions on the Aggregation of 2-Pyrone-4,6-dicarboxylic Acid
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Physical networks formed by ionizable polymers with ionic clusters as crosslinks are controlled by coupled dynamics that transcend from ionic clusters through chain motion to macroscopic response. Here, the coupled dynamics, across length scales, from the ionic clusters to the networks in toluene swollen polystyrene sulfonate networks, were directly correlated, as the electrostatic environment of the physical crosslinks was altered. The multiscale insight is attained by coupling neutron spin echo measurements with molecular dynamics simulations, carried out to times typical of relaxation of polymers in solutions. The experimental dynamic structure factor is in outstanding agreement with the one calculated from computer simulations, as the networks are perturbed by elevating the temperature and changing the electrostatic environment. In toluene, the long-lived clusters remain stable over hundreds of ns across a broad temperature range, while the polymer network remains dynamic. In conclusion, though the size of the clusters changes as the dielectric constant of the solvent is modified through the addition of ethanol, they remain stable but morph, enhancing the polymer chain dynamics.
Renewable energy is increasingly relying on optimized electrolytes and interfaces. In this work, Tween 20 and sodium chloride are selected as a model system to reveal the effects of surfactants on salt crystallization in the context of ionic conductivity and interface optimization. At a varied crystallization speed and mix ratio, it is demonstrated that the resultant solution-grown ionic crystalline complexes can achieve a highly tunable ion transport with a controllable crystalline interface. X-ray diffraction results rule out the possibility of polymorphism in the NaCl/Tween 20 systems, which further supports the importance of an optimized crystalline network for optimizing permittivity or ionic conductivity. Raman mapping and machine learning techniques are used to perform semantic segmentation on highly heterogeneous NaCl/Tween 20 complexes. Furthermore, FTIR measurements demonstrate that inter- and intra-molecular interactions play critical roles in the formation of these crystals. This work lays a foundation toward future optimization of such complex ion systems for a specific salt or crystallization modifier in energy storage or ion transport applications.
The electrochemical stability window (ESW) defines the voltage range within which an electrolyte remains stable against oxidation and reduction, serving as a key descriptor for ionic liquid (IL)-based systems. The ESWs of 15 ILs with diverse cation-anion combinations were systematically investigated, both in their neat forms and as binary mixtures with three representative solvents, i.e., propylene carbonate (PC), ethylene glycol (EG), and tetrahydrofuran (THF). Cyclic voltammetry on a glassy carbon electrode determined anodic and cathodic stability limits, revealing that ESW protection in IL-solvent mixtures strongly depends on solvent polarity, proticity, and IL compatibility. PC, with a high dielectric constant and aprotic character, broadened anodic limits and moderately stabilized cathodic limits. THF maintained compact interfacial structure, offering additional cathodic protection, whereas EG, being protic, reduced ESWs via proton-coupled electron transfer and hydrogen evolution. The solvent protection trend followed by PC > THF > EG, with bulky, hydrophobic cations ([HMIM] + , [BMPYRR] + ) and weakly coordinating anions ([NTF 2 ] - , [DCA] - , [DEP] - ) showing the highest ESW protection for 50 mol% IL in these solvents. Among the neat ILs, [EMIM][DEP], [HMIM][PF 6 ], and [BMPYRR][DCA] showed wide ESWs of ∼5.5 V. These results provide valuable insight into solvent-modulated electrochemical stability, enabling rational design of IL-based electrolytes for energy storage and electrosynthesis.
Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. Here, the model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.
Highly selective ion separations are vital for producing pure salts, and membrane-based separations are promising alternatives to conventional ion-separation techniques. Our previous work demonstrated that simple pressure-driven flow through negatively charged isoporous membranes can separate Li+ and K+ with selectivities as high as 70 in dilute solutions. The separation mechanism relies on spontaneously arising streaming potentials that induce electromigration, which opposes advection and separates cations based on differences in their electrophoretic mobilities. Although the separation technique is simple, this work shows that high selectivities are possible only with careful consideration of experimental conditions including transmembrane pressure, solution ionic strength, the K+/Li+ ratio in the feed, and the extent of concentration polarization. Separations conducted with a rotating membrane show Li+/K+ selectivities as high as 150 with a 1000 rpm membrane rotation rate, but the selectivity decreases to 1.3 at 95 rpm. These results demonstrate the benefits and necessity of quantitative control of concentration polarization in highly selective separations. Increases in solution ionic strength or the K+/Li+ feed ratio can also decrease selectivities more than an order of magnitude.
We characterize a concentrated 7.3 m CaCl 2 solution, combining neutron diffraction with chloride isotopic substitution (Cl-NDIS) in null water and molecular dynamics (MD) simulations. We elucidate the solution structure, thermodynamic properties, and extent of ion pairing previously suggested as concentration-dependent and often not observed at lower concentrations. Our Cl-NDIS measurements designate the solvent-shared ion pairing as dominant and the contact ion pairing (CIP) as insignificant even under conditions close to the solubility limit. The MD models parameterized against neutron diffraction with calcium isotopic substitution (Ca-NDIS) overestimate CIP despite successfully reproducing most of the Cl-NDIS signal. This drawback originates from the fact that Ca 2+ –Cl - interactions were primarily “hidden” in the Ca-NDIS signal due to overlapping with Ca 2+ –O w and Ca 2+ –H w contributions to the total scattering. Contrary, MD models with moderate CIP and possessing generally good performance at high concentrations fail to reproduce the NDIS measurements accurately. Therefore, the electronic polarization, introduced in most of the recent MD models via scaling ionic charges, resolves some but not all parameterization drawbacks. We conclude that despite improving the quality of MD models “on average,” the question “which model is the best” has not been answered but replaced by the question “which model is better for a given research.” An overall “good” model can still be inappropriate or, in some instances, “bad” and, unfortunately, produce erroneous results. Additionally, the accurate interpretation of several NDIS datasets, complemented by MD simulations, can prevent such mistakes and help identify the strengths, weaknesses, and convenient applications for corresponding computational models.
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Abstract The ionic liquid 1-ethyl-3-methylimidazolium methanesulfonate ([EMIM][MeSO 3 ]) has been considered as a promising alternative desiccant to triethylene glycol and lithium bromide commonly used in the industry. In this paper, the water activity coefficient of this binary system was measured from 303 K to 363 K with water concentration from 18% to 92%. The interaction energies between the ionic liquid molecules ( $${g}_{22}$$ g 22 ) and between the ionic liquid and water molecules ( $${g}_{12}$$ g 12 ) for the [EMIM][MeSO 3 ]/water binary system were determined from the water activity coefficient data using the Non-Random Two-Liquid (NRTL) model. The magnitude of the interaction energy between the [EMIM][MeSO 3 ] and water molecules ( $${g}_{12}$$ g 12 ) was found to be in the range of 45~49 kJ/mol, which was about 20% larger than that between the water molecules ( $${g}_{11}$$ g 11 ) in the [EMIM][MeSO 3 ]/water system. The large ( $${g}_{12}$$ g 12 ) can explain many observed macroscopic thermodynamic properties such as strong hygroscopicity in the ionic liquid [EMIM][MeSO 3 ]. These interaction energies were used to determine the heat of desorption of the [EMIM][MeSO 3 ]/water system, and the obtained heat of desorption was in good agreement with that calculated from the conventional Clausius-Clapeyron Equation.
Photoelectron spectroscopy experiments in ionic solutions reveal important electronic structure information, in which the interaction between hydrated ions and water solvent can be inferred. Based on many-body perturbation theory with GW approximation, we theoretically compute the quasiparticle electronic structure of chloride anion solution, which is modeled by path-integral abinitio molecular dynamics simulation by taking account the nuclear quantum effects (NQEs). The electronic levels of hydrated anion as well as water are determined and compared with the recent experimental photoelectron spectra. It is found that NQEs improve the agreement between theoretical prediction and experiment because NQEs effectively weaken the hybridization of the between the Cl – anion and water. Furthermore, our study indicates that NQEs plays a small but non-negligible role in predicting the electronic structure of the aqueous solvation of ions of the Hofmeister series.
Electrolytes featuring negatively charged polymers, such as nonaqueous polyelectrolyte solutions and polymerized ionic liquids, are currently under investigation as potential high cation transference number (t+) electrolytes for lithium-ion batteries. Herein, we use coarse-grained molecular dynamics simulations to characterize the Onsager transport coefficients of polyelectrolyte solutions as a function of chain length and concentration. For all the systems studied, we find that the rigorously computed transference number is substantially lower than that approximated by the ideal solution (Nernst-Einstein) equations typically used to characterize these systems due to the presence of strong anion-anion and cation-anion correlations. None of the polyelectrolyte solutions achieve t+ greater than that of the conventional binary salt electrolyte, with some solutions having negative t+. Overall, this work demonstrates that the Nernst-Einstein assumption does not provide a physically meaningful estimate of the transference number in these solutions and calls into question the expectation of polyelectrolytes to exhibit a high cation transference number.
This research investigates the reductive removal of technetium-99 ({sup 99}Tc) by zero valent iron (ZVI) from low activity waste (LAW) off-gas condensate simulant as a secondary treatment after recovery from vitrification planned at the Hanford Tank Waste Treatment and Immobilization Plant (WTP). Due to its high volatility, only a fraction of Tc{sup (VII)} will be incorporated into glass waste forms. Volatilized Tc will be captured by an off-gas treatment system with current plans to recycle off-gas condensate back to the vitrification facility. The scheme with off-gas recycling will increase Tc loading in the glass waste, but will also increase the concentrations of sulfate, halides, and other problematic constituents impeding overall LAW processing and increasing volume of the glass product. This study focuses on Tc{sup (VII)} removal by ZVI as a feasible pathway to minimize off-gas condensate recycling, which may result in a reduction in the volumes of LAW waste to be immobilized and subsequent cost savings. ZVI is an established treatment agent for redox-active contaminants such as trichloroethylene, nitrate, arsenic, chromium, phenol, and others. It is a commercially available and cost-effective material. Our previous experiments showed that ZVI is very efficient for the reductive removal of {sup 99}Tc. In this work we studied ZVI oxidation under aerobic conditions at pH 7 with 0.1 M Na{sub 2}SO{sub 4} solution (ionic strength, IS, 0.3 M) in the presence and absence of {sup 99}Tc. The concentration of {sup 99}Tc and the changes in pH, dissolved oxygen (DO), and oxidation-reduction potential (ORP) of the simulated solutions were monitored over 8 days. The formation of iron oxide phases was probed by X-ray diffraction (XRD). Obtained results suggest that ZVI contact time with {sup 99}Tc containing solutions for 6 hours resulted in nearly complete removal of {sup 99}Tc from the aqueous phase. XRD analysis showed that the oxidation of ZVI is rapid with formation of magnetite (Fe{sub 3}O{sub 4}) with minor percentage of goethite and maghemite. This work is a part of a larger set of studies investigating the feasibility of {sup 99}Tc reductive removal from the LAW off-gas condensate. (authors)
The propensity for ion-pairing can often dictate the thermodynamic and kinetic properties of electrolyte solutions. Fast and accurate estimates of ion-pairing can thus be extremely valuable for supplementing design and screening efforts for novel electrolytes. We introduce an efficient cluster model to estimate the local ion-pair potential-of-mean-force between ionic solutes in electrolytes. The model incorporates an ion-pair and a few layers of explicit solvent in a gas-phase cluster and leverages an enhanced sampling approach to achieve high efficiency and accuracy. We employ harmonic restraints to prevent solvent escape from the cluster and restrict sampling of large inter-ion distances. We develop a cluster ion-pair sampling tool that implements our cluster model and demonstrate its potential utility for screening simple and poly-electrolyte systems.
Abstract Salt water is ubiquitous, playing crucial roles in geological and physiological processes. Despite centuries of investigations, whether or not water’s structure is drastically changed by dissolved ions is still debated. Based on density functional theory, we employ machine learning based molecular dynamics to model sodium chloride, potassium chloride, and sodium bromide solutions at different concentrations. The resulting reciprocal-space structure factors agree quantitatively with neutron diffraction data. Here we provide clear evidence that the ions in salt water do not distort the structure of water in the same way as neat water responds to elevated pressure. Rather, the computed structural changes are restricted to the ionic first solvation shells intruding into the hydrogen bond network, beyond which the oxygen radial-distribution function does not undergo major change relative to neat water. Our findings suggest that the widely cited pressure-like effect on the solvent in Hofmeister series ionic solutions should be carefully revisited.
In this paper, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.
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