Unexpected Energy Applications of Ionic Liquids

Ionic liquids and their various analogues are without doubt the scientific sensation of the last few decades, paving the way to a more sustainable society. Their versatile suite of properties, originating from an almost inconceivably large number of possible cation and anion combinations, allows tuning of the structure to serve a desired purpose. Ionic liquids hence offer a myriad of useful applications from solvents to catalysts, through to lubricants, gas absorbers, and azeotrope breakers. The purpose of this review is to explore the more unexpected of these applications, particularly in the energy space. It guides the reader through the application of ionic liquids and their analogues as i) phase change materials for thermal energy storage, ii) organic ionic plastic crystals, which have been studied as battery electrolytes and in gas separation, iii) key components in the nitrogen reduction reaction for sustainable ammonia generation, iv) as electrolytes in aluminum-ion batteries, and v) in other emerging technologies. It is concluded that there is tremendous scope for further optimizing and tuning of the ionic liquid in its task, subject to sustainability imperatives in line with current global priorities, assisted by artificial intelligence.

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.

Insights into the estimation of surface tensions of mixtures based on designable green materials using an ensemble learning scheme

Precise estimation of the physical properties of both ionic liquids (ILs) and their mixtures is crucial for engineers to successfully design new industrial processes. Among these properties, surface tension is especially important. It's not only necessary to have knowledge of the properties of pure ILs, but also of their mixtures to ensure optimal utilization in a variety of applications. In this regard, this study aimed to evaluate the effectiveness of Stochastic Gradient Boosting (SGB) tree in modeling surface tensions of binary mixtures of various ionic liquids (ILs) using a comprehensive dataset. The dataset comprised 4010 experimental data points from 48 different ILs and 20 non-IL components, covering a surface tension range of 0.0157-0.0727 N m-1 across a temperature range of 278.15-348.15 K. The study found that the estimated values were in good agreement with the reported experimental data, as evidenced by a high correlation coefficient (R) and a low Mean Relative Absolute Error of greater than 0.999 and less than 0.004, respectively. In addition, the results of the used SGB model were compared to the results of SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN, three based ANNs, PSO-ANN, GA-ANN, ICA-ANN, TLBO-ANN, ANFIS, ANFIS-ACO, ANFIS-DE, ANFIS-GA, ANFIS-PSO, and MGGP models. In terms of the accuracy, the SGB model is better and provides significantly lower deviations compared to the other techniques. Also, an evaluation was conducted to determine the importance of each variable in predicting surface tension, which revealed that the most influential factor was the mole fraction of IL. In the end, William's plot was utilized to investigate the model's applicability range. As the majority of data points, i.e. 98.5% of the whole dataset, were well within the safety margin, it was concluded that the proposed model had a high applicability domain and its predictions were valid and reliable.

Atom surface fragment contribution method for predicting the toxicity of ionic liquids

In this study, a novel method-atom surface fragment contribution (ASFC)-was proposed for assessing the properties of compounds. We developed a predictive model using the ASFC method based on the sigma surface areas (S_σ-surface) of fragments/groups for estimating the toxicity of ILs. A toxicity dataset of 140 ILs towards leukemia rat cell line (ICP-81) was gathered and employed to train and validate models. The S_σ-surface values of atoms in each group were firstly calculated from the COSMO profiles of cations and anions for ILs. Then the S_σ-surface values of 26 groups were obtained and used as input descriptors for modelling. The R² and MSE of the built ASFC model were 0.924 and 0.071, respectively. Results indicate that the ASFC model developed by the new approach possesses great accuracy and reliability. In total, the ASFC method has extensive potential for the application of estimating diverse properties of ILs and other compounds due to its remarkable advantages.

The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors

Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (T_m). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values T_m of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as R_Validation², Q_Validation², and IIC_Validation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of T_m are also extracted.

The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.

Progress in the catalytic glycolysis of polyethylene terephthalate

This paper briefly reviews the development history of polyethylene terephthalate (PET) and the recycling of PET. As one of the most promising way to degrade PET into oligomers and monomers that can be used for the production of high-quality PET, catalytic glycolysis is highlighted in this review. The developments on metal salt, metal oxide and ionic solvent catalysts for glycolysis of PET are systematically summarized, besides, the proposed catalytic mechanisms of ionic liquids (ILs) and deep eutectic solvents (DESs) are presented. The metallic catalysts show high catalytic performance but causing serious environmental pollution and high waste treatment costs, thereby it is proposed that metal-free catalysts, especially ILs and DESs can be the "greener" alternatives to address the PET waste problem. Additionally, the studies related to the glycolysis kinetics are discussed in this review, showing the results that PET glycolysis process consists of heterogeneous and homogeneous depolymerization, and different models should be used to investigate different depolymerization stages in order to obtain a more realistic picture.

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.

Predicting Melting Points of Biofriendly Choline-Based Ionic Liquids with Molecular Dynamics

In this work, we introduce a simulation-based method for predicting the melting point of ionic liquids without prior knowledge of their crystal structure. We run molecular dynamics simulations of biofriendly, choline cation-based ionic liquids and apply the method to predict their melting point. The root-mean-square error of the predicted values is below 24 K. We advocate that such precision is sufficient for designing ionic liquids with relatively low melting points. The workflow for simulations is available for everyone and can be adopted for any species from the wide chemical space of ionic liquids.

Melting Temperature Estimation of Imidazole Ionic Liquids with Clustering Methods

Ionic liquids (ILs) are ionic compounds with low melting points that can be designed to be used in an extensive set of commercial and industrial applications. However, the design of ILs is limited by the quantity and quality of the available data in the literature; therefore, the estimation of physicochemical properties of ILs by computational methods is a promising way of solving this problem, since it provides approximations of the real values, resulting in savings in both time and money. We studied two data sets of 281 and 134 liquids based on the molecule imidazole that were analyzed with QSPR techniques. This paper presents a software architecture that uses clustering techniques to improve the robustness of estimation models of the melting point of ILs. These results indicate an error of 6.25% in the previously unmodeled data set and an error of 4.43% in the second data set. We have an improvement with the second data set of 1.81% over the last results previously found.

Database and new models based on a group contribution method to predict the refractive index of ionic liquids

Refractive index is one of the important physical properties, which is widely used in separation and purification. In this study, the refractive index data of ILs were collected to establish a comprehensive database, which included about 2138 pieces of data from 1996 to 2014. The Group Contribution-Artificial Neural Network (GC-ANN) model and Group Contribution (GC) method were employed to predict the refractive index of ILs at different temperatures from 283.15 K to 368.15 K. Average absolute relative deviations (AARD) of the GC-ANN model and the GC method were 0.179% and 0.628%, respectively. The results showed that a GC-ANN model provided an effective way to estimate the refractive index of ILs, whereas the GC method was simple and extensive. In summary, both of the models were accurate and efficient approaches for estimating refractive indices of ILs.

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Abstract: In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd²⁺ in the presence of Cu²⁺ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu²⁺ concentration on the stripping response to Cd²⁺ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd²⁺ in the presence of Cu²⁺ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd²⁺ and the stripping peak currents of Cu²⁺ and Cd²⁺. The factors affecting the SWASV detection of Cd²⁺ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd²⁺ in soil samples with satisfactory results.

Ionic Liquid Crystals: Versatile Materials

This Review covers the recent developments (2005-2015) in the design, synthesis, characterization, and application of thermotropic ionic liquid crystals. It was designed to give a comprehensive overview of the "state-of-the-art" in the field. The discussion is focused on low molar mass and dendrimeric thermotropic ionic mesogens, as well as selected metal-containing compounds (metallomesogens), but some references to polymeric and/or lyotropic ionic liquid crystals and particularly to ionic liquids will also be provided. Although zwitterionic and mesoionic mesogens are also treated to some extent, emphasis will be directed toward liquid-crystalline materials consisting of organic cations and organic/inorganic anions that are not covalently bound but interact via electrostatic and other noncovalent interactions.

New models for predicting thermophysical properties of ionic liquid mixtures

A series of semi-empirical models and artificial neural network models were developed to predict thermophysical properties of ionic liquid mixtures.

A quantitative prediction of the viscosity of ionic liquids using Sσ-profilemolecular descriptors

A QSPR study of ILs using MLR and SVM algorithms based on COSMO-RS molecular descriptors (S_σ-profile).

IN SILICO PREDICTION OF MELTING POINTS OF IONIC LIQUIDS BY USING MULTILAYER PERCEPTRON NEURAL NETWORKS

Quantitative structure–property relationship (QSPR) was used to predict melting points of 62 ionic liquids (ILs), which include ammonium, pyrrolidiniu, imidazolium, pyridiniu, piperidiniu, phosphonium ionic liquid salts. The structures of ionic liquids were optimized by Hyperchem software and MOPAC program, and stepwise multiple linear regression method was applied to select the relevant structural descriptors. The predicting models correlating selected descriptors and melting points were set up using multiple linear regressions (MLR) and multilayer perceptron neural network (MLP NN), separately. The obtained linear and nonlinear QSPR models were validated by internal and external test sets. According to the obtained results, the correlation coefficients between predicted and experimental melting points for training, test and validation sets were; 0.91, 0.86 and 0.79 for MLR model. These values for MLP NN model were; 0.97, 0.96 and 0.85, respectively. The results of this study revealed the high applicability of QSPR approach to melting point prediction of ILs.

A COSMO-RS based guide to analyze/quantify the polarity of ionic liquids and their mixtures with organic cosolvents

A COSMO-RS descriptor (S(sigma-profile)) has been used in quantitative structure-property relationship (QSPR) studies by a neural network (NN) for the prediction of empirical solvent polarity E(T)(N) scale of neat ionic liquids (ILs) and their mixtures with organic solvents. S(sigma-profile) is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge of chemical structures on the polarity (sigma) scale. Firstly, a radial basis neural network exact fit (RBNN) is successfully optimized for the prediction of E(T)(N), the solvatochromic parameter of a wide variety of neat organic solvents and ILs, including imidazolium, pyridinium, ammonium, phosphonium and pyrrolidinium families, solely using the S(sigma-profile) of individual molecules and ions. Subsequently, a quantitative structure-activity map (QSAM), a new concept recently developed, is proposed as a valuable tool for the molecular understanding of IL polarity, by relating the E(T)(N) polarity parameter to the electronic structure of cations and anions given by quantum-chemical COSMO-RS calculations. Finally, based on the additive character of the S(sigma-profile) descriptor, we propose to simulate the mixture of IL-organic solvents by the estimation of the S(sigma-profile)(Mixture) descriptor, defined as the weighted mean of the S(sigma-profile) values of the components. Then, the E(T)(N) parameters for binary solvent mixtures, including ILs, are accurately predicted using the S(sigma-profile)(Mixture) values from the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the E(T)(N) polarity of a wide variety of pure ILs as well as their mixtures with organic solvents, which exhibit significant positive and negative deviations from ideality.