Viscosity of Ionic Liquids: An Extensive Database and a New Group Contribution Model Based on a Feed-Forward Artificial Neural Network

Modeling of ionic liquids viscosity via advanced white-box machine learning

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9 MPa) and temperatures (253.15-573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Viscosity of Ionic Liquids: Theories and Models

Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Electron Density Learning of Z-Bonds in Ionic Liquids and Its Application

Ionic liquids (ILs) exhibit fascinating properties due to special Z-bonds and have been widely used in electrochemical systems. The local Z-bond networks potentially cause a discrepancy in electrochemical properties. Understanding the correlations between the Z-bond energy (EZ-bond) and the electrochemical properties is helpful to identify appropriate ILs. It is difficult to estimate the correlations from single density functional theory calculations or molecular dynamic simulations. In this work, a machine learning model targeting the electronic density (ρBCP) of Z-bonds has been trained successfully, as expected for use in systems above the nanoscale size. The connection between the EZ-bond and the electrochemical potential window in ILs@TiO2, as well as that between the EZ-bond and the charge carrier mobility in ILs-PEDOT:Tos@SiO2, was separately investigated. This study highlights an efficient model for predicting ρBCP in nanoscale systems and anticipates exploring the connection between Z-bonds and the electrochemical properties of IL-based systems.

Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods

Ionic liquids (ILs) have drawn much attention due to their extensive applications and environment-friendly nature. Refractive index prediction is valuable for ILs quality control and property characterization. This paper aims to predict refractive indices of pure ILs and identify factors influencing refractive index changes. Six chemical structure-based machine learning models called eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Adaptive Boosting-Decision Tree (Ada-DT), and Adaptive Boosting-Support Vector Machine (Ada-SVM) were developed to achieve this goal. An enormous dataset containing 6098 data points of 483 different ILs was exploited to train the machine learning models. Each data point's chemical substructures, temperature, and wavelength were considered for the models' inputs. Including wavelength as input is unprecedented among predictions done by machine learning methods. The results show that the best model was CatBoost, followed by XGBoost, LightGBM, Ada-DT, CNN, and Ada-SVM. The R² and average absolute percent relative error (AAPRE) of the best model were 0.9973 and 0.0545, respectively. Comparing this study's models with the literature shows two advantages regarding the dataset's abundance and prediction accuracy. This study also reveals that the presence of the -F substructure in an ionic liquid has the most influence on its refractive index among all inputs. It was also found that the refractive index of imidazolium-based ILs increases with increasing alkyl chain length. In conclusion, chemical structure-based machine learning methods provide promising insights into predicting the refractive index of ILs in terms of accuracy and comprehensiveness.

Carbon dioxide solubility in choline chloride-based deep eutectic solvents under diverse conditions

CONTEXT

Global warming is a severe problem experiencing the climate crisis due to rising CO₂ emissions. Deep eutectic solvents (DESs) have recently attracted a lot of attention as potential absorbents to mitigate carbon dioxide CO₂ emissions because of their large CO₂ capacities and stability under diverse conditions. Designing a potent DES requires knowledge of molecular-level understanding including structure, dynamics, and interfacial properties in DESs. In this study, we investigate the CO₂ sorption and diffusion in different DESs at different temperatures and pressure using molecular dynamics (MD) simulations. Our results demonstrate that CO₂ molecules preferentially concentrate at the CO₂-DES interface, and the diffusion of CO₂ in bulk DESs increases with increasing pressure and temperature. The solubility of CO₂ in the three DESs increases as ChCL-ethylene glycol < ChCL-urea < ChCL-glycerol at high pressure (58.6 bar).

METHODS

The initial configuration for MD simulations included DES and CO₂ and produced the solvation box using PACKMOL software. The geometries are optimized in the Gaussian 09 software at the theoretical level of B3LYP/6-311 + G*. The partial atomic charges were fitted to an electrostatic surface potential using the CHELPG method. MD simulations were carried out by using the NAMD version 2.13 software. VMD software was used to take the snapshots. TRAVIS software is used to determine spatial distribution functions.

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (106-1010s-1) for which substantial shear thinning is observed under pressures P∈0.1-955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435×6 dimensions and the intermolecular atom pair orientation tensors of 61×6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P=0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P≥400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.

VOCs absorption from gas streams using deep eutectic solvents - A review

Volatile organic compounds (VOCs) are one of the most severe atmospheric pollutants. They are mainly emitted into the atmosphere from anthropogenic sources such as automobile exhaust, incomplete fuel combustion, and various industrial processes. VOCs not only cause hazards to human health or the environment but also adversely affect industrial installation components due to their specific properties, i.e., corrosive and reactivity. Therefore, much attention is being paid to developing new methods for capturing VOCs from gaseous streams, i.e., air, process streams, waste streams, or gaseous fuels. Among the available technologies, absorption based on deep eutectic solvents (DES) is widely studied as a green alternative to other commercial processes. This literature review presents a critical summary of the achievements in capturing individual VOCs using DES. The types of used DES and their physicochemical properties affecting absorption efficiency, available methods for evaluating the effectiveness of new technologies, and the possibility of regeneration of DES are described. In addition, critical comments on the new gas purification methods and future perspectives are included.

Insights into structure-property relationships in ionic liquids using cyclic perfluoroalkylsulfonylimides

Room temperature ionic liquids of cyclic sulfonimide anions ncPFSI (ring size: n = 4-6) with the cations [EMIm]+ (1-ethyl-3-methylimidazolium), [BMIm]+ (1-butyl-3-methylimidazolium) and [BMPL]+ (BMPL = 1-butyl-1-methylpyrrolidinium) have been synthesized. Their solid-state structures have been elucidated by single-crystal X-ray diffraction and their physicochemical properties (thermal behaviour and stability, dynamic viscosity and specific conductivity) have been assessed. In addition, the ion diffusion was studied by pulsed field gradient stimulated echo (PFGSTE) NMR spectroscopy. The decisive influence of the ring size of the cyclic sulfonimide anions on the physicochemical properties of the ILs has been revealed. All ILs show different properties compared to those of the non-cyclic TFSI anion. While these differences are especially distinct for ILs with the very rigid 6cPFSI anion, the 5-membered ring anion 5cPFSI was found to result in ILs with relatively similar properties. The difference between the properties of the TFSI anion and the cyclic sulfonimide anions has been rationalized by the rigidity (conformational lock) of the cyclic sulfonimide anions. The comparison of selected IL properties was augmented by MD simulations. These highlight the importance of π+-π+ interactions between pairs of [EMIm]+ cations in the liquid phase. The π+-π+ interactions are evident for the solid state from the molecular structures of the [EMIm]+-ILs with the three cyclic imide anions determined by single-crystal X-ray diffraction.

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.

Synthesis and Behavior of Hexamethylenetetramine-Based Ionic Liquids as an Active Ingredient in Latent Curing Formulations with Ethylene Glycol for DGEBA

The paper presents the preparation of new ionic liquids based on hexamethylenetetramine with bis(trifluoromethanesulfonyl)imide and dicyanamide anion, which were characterized in detail in terms of their purity (Ion Chromatography) and thermal properties (Differential Scanning Calorimetry), as well as stability. The obtained substances were used to develop curing systems with ethylene glycol, which were successfully tested for their application with bisphenol A diglycidyl ether molecule. In addition, the curing process and its relationship to the structure of the ionic liquid are characterized in detail. The research showed that hexamethylenetetramine-based new ionic liquids can be successfully designed using well-known and simple synthetic methods-the Delepine reaction. Moreover, attention was paid to their stability, related limitations, and the application of hexamethylenetetramine-based ionic liquids in epoxy-curing systems.

Tunable Multi-Phase System for Highly Chemo-Selective Oxidation of Hydroxymethyl-Furfural

Three different multiphase systems (MP 1-3) comprised of two immiscible liquids, with or without an ionic liquid (IL: methyltrioctyl ammonium chloride), were investigated for the oxidation of 5-hydroxymethyl-furfural (HMF) over 5 % Ru/C as a catalyst and air (8 bar) as an oxidant. These conditions proved versatile for an excellent control of the reaction selectivity to 4 distinct products derived from full or partial oxidation of the carbonyl and alcohol functions of HMF, and each one achieved in 87-96 % isolated yield at complete conversion. MP1 based on water and isooctane, yielded 2,5-furandicarboxylic acid (FDCA, 91 % yield). In MP2, obtained by adding the IL to MP1, the oxidation proceeded towards the formation of 5-formyl-2-furancarboxylic acid (FFCA, 87-89 % yield). MP2 also proved successful in the design of a one pot-two step oxidation/reduction sequence to prepare 5-hydroxymethyl-2-furancarboxylic acid (HMFCA, 85 % yield). In MP3, the use of an acetonitrile/cyclooctane biphase yielded 2,5-diformylfuran (DFF, 96 % yield). All the multiphase systems MP 1-3 allowed a perfect segregation of the catalyst in a single phase (either the hydrocarbon or the IL) distinct from the one containing HMF and its oxidation products. This was crucial not only for the catalyst/product separation but also for the recycle of Ru/C that was possible under all the tested conditions. Accordingly, MP-reaction were run in a semicontinuous mode without removing the catalyst from the reactor nor resorting to conventional separation and activation techniques. Negligible Ru leaching, less than 0.96 ppb, was measured in all cases.

Design Optimization of Three-Layered Metamaterial Acoustic Absorbers Based on PVC Reused Membrane and Metal Washers

Waste management represents a critical issue that industrialized countries must necessarily deal with. Sustainable architecture involves the reuse of materials with the aim of significantly reducing the amount of waste produced. In this study, a new layered membrane metamaterial was developed based on three layers of a reused PVC membrane and reused metal washers attached. The membranes were fixed to a rigid support, leaving a cavity between the stacked layers. The samples were used to measure the sound absorption coefficient with an impedance tube. Different configurations were analyzed, changing the number of masses attached to each layer and the geometry of their position. These measurements were subsequently used to train a model based on artificial neural networks for the prediction of the sound absorption coefficient. This model was then used to identify the metamaterial configuration that returns the best absorption performance. The designed metamaterial behaves like an acoustic absorber even at low frequencies.

Effect of the cation structure on the properties of homobaric imidazolium ionic liquids

In this work we investigate the structure-property relationships in a series of alkylimidazolium ionic liquids with almost identical molecular weight. Using a combination of theoretical calculations and experimental measurements, we have shown that re-arranging the alkyl side chain or adding functional groups results in quite distinct features in the resultant ILs. The synthesised ILs, although structurally very similar, cover a wide spectrum of properties ranging from highly fluid, glass forming liquids to high melting point crystalline salts. Theoretical ab initio calculations provide insight on minimum energy orientations for the cations, which then are compared to experimental X-ray crystallography measurements to extract information on hydrogen bonding and to verify our understanding of the studied structures. Molecular dynamics simulations of the simplest (core) ionic liquids are used in order to help us interpret our experimental results and understand better why methylation of C² position of the imidazolium ring results in ILs with such different properties compared to their non-methylated analogues.

Generation and characterization of nanobubbles in ionic liquid for a green extraction of polyphenols from Carya cathayensis Sarg

Nanobubbles (NBs) generated-nanojets membrane poration have gained enormous attention. In this study, NBs were fabricated as a novel green approach to assist ionic liquid (IL) [C₄C₁im][BF₄] extraction of polyphenols from Carya cathayensis Sarg. husk. NBs were successfully generated with mean size of 85.47 ± 5 nm, zeta potential of +39 ± 2.24 mV, and concentration of 21.15 ± 0.75 × 10⁸ particles/mL (stable for over 48 h in IL solution). Compared to common solutions extract, IL-NBs extract showed significantly higher (p < 0.05) antioxidant activity and polyphenols yields with a total polyphenol, total flavonoid, and total tannins contents of 85.67 ± 2.05 mg GAE/g DW, 42.44 ± 1.17 mg CE/g DW, and 8.2 ± 0.05 mg TAE/g DW, respectively. The SEM results confirmed that NBs' nanojets caused morphological destruction of the husk powder. Overall, IL-NBs solution showed better extraction efficiency of polyphenols than other solutions, giving insight into a new "green" nanotechnology-based extraction method.

Tunning CO2 Separation Performance of Ionic Liquids through Asymmetric Anions

This work aims to explore the gas permeation performance of two newly-designed ionic liquids, [C₂mim][CF₃BF₃] and [C₂mim][CF₃SO₂C(CN)₂], in supported ionic liquid membranes (SILM) configuration, as another effort to provide an overall insight on the gas permeation performance of functionalized-ionic liquids with the [C₂mim]⁺ cation. [C₂mim][CF₃BF₃] and [C₂mim][CF₃SO₂C(CN)₂] single gas separation performance towards CO₂, N₂, and CH₄ at T = 293 K and T = 308 K were measured using the time-lag method. Assessing the CO₂ permeation results, [C₂mim][CF₃BF₃] showed an undermined value of 710 Barrer at 293.15 K and 1 bar of feed pressure when compared to [C₂mim][BF₄], whereas for the [C₂mim][CF₃SO₂C(CN)₂] IL an unexpected CO₂ permeability of 1095 Barrer was attained at the same experimental conditions, overcoming the results for the remaining ILs used for comparison. The prepared membranes exhibited diverse permselectivities, varying from 16.9 to 22.2 for CO₂/CH₄ and 37.0 to 44.4 for CO₂/N₂ gas pairs. The thermophysical properties of the [C₂mim][CF₃BF₃] and [C₂mim][CF₃SO₂C(CN)₂] ILs were also determined in the range of T = 293.15 K up to T = 353.15 K at atmospheric pressure and compared with those for other ILs with the same cation and anion's with similar chemical moieties.

Environment-sensitive emission of anionic hydrogen-bonded urea-derivative-acetate-ion complexes and their aggregation-induced emission enhancement

Anions often quench fluorescence (FL). However, strong ionic hydrogen bonding between fluorescent dyes and anion molecules has the potential to control the electronic state of FL dyes, creating new functions via non-covalent interactions. Here, we propose an approach, utilising ionic hydrogen bonding between urea groups and anions, to control the electronic states of fluorophores and develop an aggregation-induced emission enhancement (AIEE) system. The AIEE ionic hydrogen-bonded complex (IHBC) formed between 1,8-diphenylnaphthalene (p-2Urea), with aryl urea groups at the para-positions on the peri-phenyl rings, and acetate ions exhibits high environmental sensitivities in solution phases, and the FL quantum yield (QY) in ion-pair assemblies of the IHBC and tetrabutylammonium cations is more than five times higher than that of the IHBC in solution. Our versatile and simple approach for the design of AIEE dye facilitates the future development of environment-sensitive probes and solid-state emitting materials.

The Solubility of Gases in Ionic Liquids: A Chemoinformatic Predictive and Interpretable Approach

This work comprises the study of solubilities of gases in ionic liquids (ILs) using a chemoinformatic approach. It is based on the codification, of the atomic inter-component interactions, cation/gas and anion/gas, which are used to obtain a pattern of activation in a Kohonen Neural Network (MOLMAP descriptors). A robust predictive model has been obtained with the Random Forest algorithm and used the maximum proximity as a confidence measure of a given chemical system compared to the training set. The encoding method has been validated with molecular dynamics. This encoding approach is a valuable estimator of attractive/repulsive interactions of a generical chemical system IL+gas. This method has been used as a fast/visual form of identification of the reasons behind the differences observed between the solubility of CO₂ and O₂ in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM PF₆ ) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.

Design of concentrated colloidal dispersions of iron oxide nanoparticles in ionic liquids: Structure and thermal stability from 25 to 200 °C

HYPOTHESIS

Some of the most promising fields of application of ionic liquid-based colloids imply elevated temperatures. Their careful design and analysis is therefore essential. We assume that tuning the structure of the nanoparticle-ionic liquid interface through its composition can ensure colloidal stability for a wide temperature range, from room temperature up to 200 °C.

EXPERIMENTS

The system under study consists of iron oxide nanoparticles (NPs) dispersed in ethylmethylimidazolium bistriflimide (EMIM TFSI). The key parameters of the solid-liquid interface, tuned at room temperature, are the surface charge density and the nature of the counterions. The thermal stability of these nanoparticle dispersions is then analysed on the short and long term up to 200 °C. A multiscale analysis is performed combining dynamic light scattering (DLS), small angle X-ray/neutron scattering (SAXS/SANS) and thermogravimetric analysis (TGA).

FINDINGS

Following the proposed approach with a careful choice of the species at the solid-liquid interface, ionic liquid-based colloidal dispersions of iron oxide NPs in EMIM TFSI stable over years at room temperature can be obtained, also stable at least over days up to 200 °C and NPs concentrations up to 12 vol% (≈30 wt%) thanks to few near-surface ionic layers.

Advances and Challenges for the Electrochemical Reduction of CO2 to CO: From Fundamentals to Industrialization

The electrochemical carbon dioxide reduction reaction (CO₂ RR) provides an attractive approach to convert renewable electricity into fuels and feedstocks in the form of chemical bonds. Among the different CO₂ RR pathways, the conversion of CO₂ into CO is considered one of the most promising candidate reactions because of its high technological and economic feasibility. Integrating catalyst and electrolyte design with an understanding of the catalytic mechanism will yield scientific insights and promote this technology towards industrial implementation. Herein, we give an overview of recent advances and challenges for the selective conversion of CO₂ into CO. Multidimensional catalyst and electrolyte engineering for the CO₂ RR are also summarized. Furthermore, recent studies on the large-scale production of CO are highlighted to facilitate industrialization of the electrochemical reduction of CO₂ . To conclude, the remaining technological challenges and future directions for the industrial application of the CO₂ RR to generate CO are highlighted.

Ionic Liquid Electrolytes for Electrochemical Energy Storage Devices

For decades, improvements in electrolytes and electrodes have driven the development of electrochemical energy storage devices. Generally, electrodes and electrolytes should not be developed separately due to the importance of the interaction at their interface. The energy storage ability and safety of energy storage devices are in fact determined by the arrangement of ions and electrons between the electrode and the electrolyte. In this paper, the physicochemical and electrochemical properties of lithium-ion batteries and supercapacitors using ionic liquids (ILs) as an electrolyte are reviewed. Additionally, the energy storage device ILs developed over the last decade are introduced.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Thermodiffusion anisotropy under a magnetic field in ionic liquid-based ferrofluids

Ferrofluids based on maghemite nanoparticles (NPs), typically 10 nm in diameter, are dispersed in an ionic liquid (1-ethyl 3-methylimidazolium bistriflimide - EMIM-TFSI). The average interparticle interaction is found to be repulsive by small angle scattering of X-rays and of neutrons, with a second virial coefficient A2 = 7.3. A moderately concentrated sample at Φ = 5.95 vol% is probed by forced Rayleigh scattering under an applied magnetic field (up to H = 100 kA m-1) from room temperature up to T = 460 K. Irrespective of the values of H and T, the NPs in this study are always found to migrate towards the cold region. The in-field anisotropy of the mass diffusion coefficient Dm and that of the (always positive) Soret coefficient ST are well described by the presented model in the whole range of H and T. The main origin of anisotropy is the spatial inhomogeneities of concentration in the ferrofluid along the direction of the applied field. Since this effect originates from the magnetic dipolar interparticle interaction, the anisotropy of thermodiffusion progressively vanishes when temperature and thermal motion increase.

Ionic liquids for regulating biocatalytic process: Achievements and perspectives

Biocatalysis has found enormous applications in sorts of fields as an alternative to chemical catalysis. In the pursue of green and sustainable chemistry, ionic liquids (ILs) have been considered as promising reaction media for biocatalysis, owing to their unique characteristics, such as nonvolatility, inflammability and tunable properties as regards polarity and water miscibility behavior, compared to organic solvents. In recent years, great developments have been achieved in respects to biocatalysis in ILs, especially for preparing various chemicals. This review tends to give illustrative examples with a focus on representative chemicals production by biocatalyst in ILs and elucidate the possible mechanism in such systems. It also discusses how to regulate the catalytic efficiency from several aspects and finally provides an outlook on the opportunities to broaden biocatalysis in ILs.

Interfacial and Aggregation Behaviour of Sodium Dodecyl Sulphate Induced by Ionic Liquids

Aggregation studies of anionic surfactant sodium dodecyl sulphate (SDS) was investigated in aqueous 1-butyl-3-methylimidazolium chloride [bmim]Cl and N-butyl-N-methyl pyrrolidinium tetrafluoroborate [bmp]BF₄ ionic liquid (IL) solutions respectively. Systems were studied by surface tension, conductance, UV-VIS absorption/emission spectroscopy and dynamic light scattering. Critical micelle concentration (CMC) values gradually decreased with increasing IL concentration which indicates synergistic interaction between ILs and SDS. Gibbs free energy change results demonstrated spontaneous micellization induced by ILs; however the effect of ILs were not similar to the corresponding regular salts (NaCl and NaBF₄). Aggregation number (n) of micelles, determined by fluorescence quenching method, indicate that the 'n' values increase with increasing ILs concentration, induced by the oppositely charged IL cation. Size of the micelles, determined by dynamic light scattering studies, increased with increasing ILs concentration, which were due to the formation of larger aggregates; the aggregates are considered to be comprised of the anionic surfactant with a substantial proportion of ILs cation as the bound counter ions. Such studies are considered to shed further light in the fundamentals of IL induced micellization as well as in different practical applications.

Prediction of ionic conductivity of imidazolium-based ionic liquids at different temperatures using multiple linear regression and support vector machine algorithms

The conductivity of various imidazolium-based ILs has been predicted via QSPR approach using MLR and SVM regression coupled with stepwise model-building. This will aid the screening of suitable ILs with desired conductivity for specific applications.

Viscosity models for ionic liquids and their mixtures

Review of principles and limitations of viscosity models for ionic liquids and their mixtures focusing on the use of inappropriate mixing rules for molten salts.