Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 2. Viscosity

Enhanced furfural extraction using neoteric hydrophobic solvents for sustainable biomass recovery and bioenergy applications

The recovery of furfural from hemicellulosic biowastes is important for developing sustainable and renewable energy alternatives to fossil fuels. However, current methods are inefficient and environmentally questionable. To address this issue, this study introduces neoteric hydrophobic solvents, specifically deep eutectic solvents (DESs) and ionic liquids (ILs). Of the 32 solvents tested, thymol:decanoic acid 1:1 (Thy:DecA) DES and trihexyltetradecyl phosphonium bis(trifluoro methylsulfonyl) imide [P14,6,6,6][NTf2] IL were the most effective, with extraction efficiencies of 94.1% and 97.1%, respectively. These solvents outperformed the reference solvent toluene, with an efficiency of 81.2%, while also showing favorable characteristics in multiple investigated criterions. For the first time, excellent performance stability was demonstrated under various operational conditions and reusability over multiple extraction and regeneration cycles. Furthermore, to provide insights into the molecular mechanisms of extraction, computational quantum chemistry modeling was employed, which showed a strong agreement with the experimental results. The development of these new neoteric solvents for furfural recovery from biowaste offers a highly effective, sustainable, and eco-friendly alternative to traditional solvents, representing a significant breakthrough in the field of renewable bioenergy production and sustainable materials recovery.

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.

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure-property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure-property models are discussed.

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.

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.

Ether functionalisation, ion conformation and the optimisation of macroscopic properties in ionic liquids

Ionic liquids are an attractive material class due to their wide liquid range, intrinsic ionic conductivity, and high chemical as well as electrochemical stability. However, the widespread use of ionic liquids is hindered by significantly higher viscosities compared to conventional molecular solvents. In this work, we show how the transport properties of ionic liquids can be altered significantly, even for isostructural ions that have the same backbone. To this end, structure-property relationships have been determined for a set of 16 systematically varied representative ionic liquids. Variations in molecular structure include ammonium vs. phosphonium, ether vs. alkyl side chains, and rigid vs. flexible anions. Ab initio calculations are used to relate molecular structures to the thermal, structural and transport properties of the ionic liquids. We find that the differences in properties of ether and alkyl functionalised ionic liquids are primarily dependent on minimum energy geometries, with the conformational flexibility of ether side chains appearing to be of secondary importance. We also show unprecedented correlations between anion conformational flexibility and transport properties. Critically, increasing fluidity upon consecutive introduction of ether side chains and phosphonium centres into the cation is found to be dependent on whether the anion is flexible or rigid. We demonstrate that targeted design of functional groups based on structure-property relationships can yield ionic liquids of exceptionally high fluidity.