From Salt in Solution to Solely Ions: Solvation of Methyl Viologen in Deep Eutectic Solvents and Ionic Liquids

Structure, Microheterogeneity, and Transport Properties of Ethaline Decoded by X-ray/Neutron Scattering and MD Simulation

Ethaline, a deep eutectic solvent (DES) composed of choline chloride (ChCl)-ethylene glycol (EG) in a 1:2 molar ratio, is garnering significant interest for its wide potential applications. The nature of liquid formation and the structure of H-bonds within ethaline were investigated by X-ray scattering (XRS), neutron scattering (NS), and MD simulations. The sum of the dissociation energy barriers of Ch-EG (3.31 kJ·mol-1) and EG-Cl (4.28 kJ·mol-1) exceeds that of Ch-Cl (5.97 kJ·mol-1). This results in a more pronounced solvation of ChCl by EG compared to ChCl association, facilitating the solubilization of ChCl crystals by EG to form a DES. A partial radial distribution function (PDF) reveals that Cl- solvation is dominated by the hydroxyl group of EG, while the methylene group dominates Ch+ solvation. The spatial distribution function (SDF) shows that the distribution of EG and Cl- around Ch+ partially overlaps with that of the quaternary ammonium group. However, the center of mass distance of Ch-Cl (4.95 Å) is significantly lower than that of Ch-EG (5.65 Å), suggesting a favorable advantage for Cl- in this competition. Chain and ring structure distributions provide direct evidence of the microheterogeneity of ethaline. Hydroxyl groups on the EG promote the formation of a chain structure in ethaline, while methylene groups favor a ring structure. H-bond, carbon H-bond, and Cl- bridge bond restrict Cl- diffusion. This new understanding is crucial for a deeper comprehension of the microstructure of ethaline and for elucidating its mechanisms in applications.

Understanding the Electrode-Electrolyte Interfaces of Ionic Liquids and Deep Eutectic Solvents

Developing unconventional electrolytes such as ionic liquids (ILs) and deep eutectic solvents (DESs) has led to remarkable advances in electrochemical energy storage and conversion devices. However, the understanding of the electrode-electrolyte interfaces of these electrolytes, specifically the liquid structure and the charge/electron transfer mechanism and rates, is lacking due to the complexity of molecular interactions, the difficulty in studying the buried interfaces with nanometer-scale resolution, and the distribution of the time scales for the various interfacial events. This Feature Article outlines the standing questions in the field, summarizes some of the exciting approaches and results, and discusses our contributions to probing the electrified interfaces by electrochemical impedance spectroscopy (EIS), surface-enhanced Raman spectroscopy (SERS), and neutron reflectivity (NR). The related findings are analyzed within electrical double-layer models to provide a framework for studying ILs, DESs, and, more broadly, the concentrated hydrogen-bonded electrolytes.

Strong Replaces Weak: Design of H-Bond Interactions Enables Cryogenic Aqueous Zn Metal Batteries

Despite the numerous advantages of aqueous Zn batteries, their practical application under cryogenic conditions is hindered by the freezing of the electrolyte because the abundance of hydrogen bonds (H-bonds) between H2O molecules drives the aqueous system to transform to an orderly frozen structure. Here, a design of H-bond interactions based on the guiding ideology of "strong replaces weak" is proposed. The strong H-bonds formed between introduced eutectic components and water molecules break down the weak H-bonds in the original water molecule network, which contributes to an ultralow freezing point and a high ionic conductivity of 1.7 mS cm-1 at -40 °C. Based on multiperspective theoretical simulations and tailor-made in situ cooling Raman characterizations, it has been demonstrated that substituting weak H-bonds with strong H-bonds facilitates the structural reshaping of Zn2+ solvation and remodeling of the H-bond network in the electrolyte. Endowed with this advantage, reversible and stable Zn plating/stripping behaviors could be realized at -40 °C, and the full cells display a high discharge capacity (200 mA h g-1) at -40 °C with ∼75% capacity retention after 1000 cycles. This study will expand the design philosophy of antifreezing aqueous electrolytes and provide a perspective to promote the adoption of Zn metal batteries for cryogenic environment large-scale energy storage.

Redox-Active Eutectic Electrolyte with Viologen and Ferrocene Derivatives for Flow Batteries

Nonflammable eutectic solvents show great potential to enhance the concentrations of the redox-active materials and the cell voltages for redox flow batteries (RFBs). Herein, we report a promising redox-active eutectic electrolyte (1.5 M total redox species) with viologen and ferrocene derivatives where both of the redox reactions are reversible with a maximum open-circuit voltage of 1.35 V and an energy density of 15.1 Wh L^-1, which is relevant to large-scale energy storage. The charge-discharge (from 75 to 25% state of charge) characteristics in a flow cell (0.15 M negolyte and 0.3 M posolyte) showed that it can be cycled with consistent discharge capacity for 12 h (19 cycles), beyond which pressure-driven crossover between the posolyte and negolyte reservoirs leads to capacity decay. This study points to promising new directions toward eutectic electrolyte development for RFBs where we demonstrate increasing the polarity, functionalizing the redox molecules, and separating redox intermediates to prevent undesired side reactions can make improvements in operating cell voltage, energy density, and cyclability.

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (S _σ-profiles). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9-15-15-1). The proposed ANN was excellent in predicting the surface tension of DESs, as R ² values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.

Choline chloride and ethylene glycol based deep eutectic solvent (DES) versus hydroxyl functionalized room temperature ionic liquids (RTILs): assessing the differences in microscopic behaviour between the DES and RTILs

With the aim of understanding the differences in the behavior of deep eutectic solvents (DESs) and room temperature ionic liquids (RTILs) in terms of their structure, dynamics, and intra- and intermolecular interactions, three different ILs and one DES having similar functionalities (hydroxyl) have been investigated by using both ensembled average and single-molecule spectroscopic techniques. Specifically, for this purpose, a choline chloride based DES (ethaline) and three hydroxyl functionalized ILs (1-(2-hydroxyethyl)-3-imidazolium bis(trifluoromethanesulfonyl)imide ([OHEMIM][NTF₂]), N-(2-hydroxyl ethyl)-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide ([OHEMPy][NTf₂]), and N-(2-hydroxyethyl)-N,N-dimethylpropan-1-aminium bis(trifluoromethanesulfonyl)imide ([OHC3CH][NTf₂])) are employed and investigated by EPR, time-resolved fluorescence, NMR and FCS studies. Estimation of polarity through EPR spectroscopy has revealed that the hydroxyl ILs employed in these studies are hyper-polar (close to water) in nature, whereas the polarity of the DES is found to be close to those of aliphatic polyhydroxy-alcohols. Interestingly, both time-resolved fluorescence anisotropy and FCS studies on these systems have suggested that the hydroxyl ILs are more dynamically heterogeneous than the DES. More interestingly, PFG-NMR measurements have indicated that the fluid structure of ethaline is relatively more associated as compared to those of the ILs despite the fact that all the cations have the same hydroxyl functionalities. All these investigations have essentially demonstrated that, despite having similar functionalities, both the DES and hydroxyl ILs employed in the present study exhibit microscopic behaviours that are significantly different from each other, indicating the interplay of various intermolecular interactions within the constituent species in governing the behaviours of these solvent systems.

Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids

The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.

Radical-Pairing Interactions in a Molecular Switch Evidenced by Ion Mobility Spectrometry and Infrared Ion Spectroscopy

The digital revolution sets a milestone in the progressive miniaturization of working devices and in the underlying advent of molecular machines. Foldamers involving mechanically entangled components with modular secondary structures are among the most promising designs for molecular switch‐based applications. Characterizing the nature and dynamics of their intramolecular network following the application of a stimulus is the key to their performance. Here, we use non‐dissociative electron transfer as a reductive stimulus in the gas phase and probe the consecutive co‐conformational transitions of a donor‐acceptor oligorotaxane foldamer using electrospray mass spectrometry interfaced with ion mobility and infrared ion spectroscopy. A comparison of collision cross section distributions for analogous closed‐shell and radical molecular ions sheds light on their respective formation energetics, while variations in their respective infrared absorption bands evidence changes in intramolecular organization as the foldamer becomes more compact. These differences are compatible with the advent of radical‐pairing interactions.

Deep Eutectic Solvents: A Review of Fundamentals and Applications

Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive "designer" solvents exhibiting a host of tunable physicochemical properties. A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in this class of solvents. Complex hydrogen bonding is postulated as the root cause of their melting point depressions and physicochemical properties; to understand these hydrogen bonded networks, it is imperative to study these systems as dynamic entities using both simulations and experiments. This review emphasizes recent research efforts in order to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding of DESs. It covers recent developments in DES research, frames outstanding scientific questions, and identifies promising research thrusts aligned with the advancement of the field toward predictive models and fundamental understanding of these solvents.