High throughput approach to investigating ternary solvents of aqueous non-stoichiometric protic ionic liquids

Automation of liquid crystal phase analysis for SAXS, including the rapid production of novel phase diagrams for SDS-water-PIL systems

Lyotropic liquid crystal phases (LCPs) are widely studied for diverse applications, including protein crystallization and drug delivery. The structure and properties of LCPs vary widely depending on the composition, concentration, temperature, pH, and pressure. High-throughput structural characterization approaches, such as small-angle x-ray scattering (SAXS), are important to cover meaningfully large compositional spaces. However, high-throughput LCP phase analysis for SAXS data is currently lacking, particularly for patterns of multiphase mixtures. In this paper, we develop semi-automated software for high throughput LCP phase identification from SAXS data. We validate the accuracy and time-savings of this software on a total of 668 SAXS patterns for the LCPs of the amphiphile hexadecyltrimethylammonium bromide (CTAB) in 53 acidic or basic ionic liquid derived solvents, within a temperature range of 25-75 °C. The solvents were derived from stoichiometric ethylammonium nitrate (EAN) or ethanolammonium nitrate (EtAN) by adding water to vary the ionicity, and adding precursor ions of ethylamine, ethanolamine, and nitric acid to vary the pH. The thermal stability ranges and lattice parameters for CTAB-based LCPs obtained from the semi-automated analysis showed equivalent accuracy to manual analysis, the results of which were previously published. A time comparison of 40 CTAB systems demonstrated that the automated phase identification procedure was more than 20 times faster than manual analysis. Moreover, the high throughput identification procedure was also applied to 300 unpublished scattering patterns of sodium dodecyl-sulfate in the same EAN and EtAN based solvents in this study, to construct phase diagrams that exhibit phase transitions from micellar, to hexagonal, cubic, and lamellar LCPs. The accuracy and significantly low analysis time of the high throughput identification procedure validates a new, rapid, unrestricted analytical method for the determination of LCPs.

Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Protein aggregation and crystallization with ionic liquids: Insights into the influence of solvent properties

Ionic liquids (ILs) have been used in solvents for proteins in many applications, including biotechnology, pharmaceutics, and medicine due to their tunable physicochemical and biological properties. Protein aggregation is often undesirable, and predominantly occurs during bioprocesses, while the aggregation process can be reversible or irreversible and the aggregates formed can be native/non-native and soluble/insoluble. Recent studies have clearly identified key properties of ILs and IL-water mixtures related to protein performance, suggesting the use of the tailorable properties of ILs to inhibit protein aggregation, to promote protein crystallization, and to control protein aggregation pathways. This review discusses the critical properties of IL and IL-water mixtures and presents the latest understanding of the protein aggregation pathways and the development of IL systems that affect or control the protein aggregation process. Through this feature article, we hope to inspire further advances in understanding and new approaches to controlling protein behavior to optimize bioprocesses.

Influence of Small Quantities of Water on the Physical Properties of Alkylammonium Nitrate Ionic Liquids

This paper presents a comprehensive study of two alkylammonium nitrate ionic liquids. As part of this family of materials, mainly ethylammonium nitrate (EAN) and also propylammonium nitrate (PAN) have attracted a great deal of attention during the last decades due to their potential applications in many fields. Although there have been numerous publications focused on the measurement of their physical properties, a great dispersion can be observed in the results obtained for the same magnitude. One of the critical points to be taken into account in their physical characterization is their water content. Thus, the main objective of this work was to determine the degree of influence of the presence of small quantities of water in EAN and PAN on the measurement of density, viscosity, electrical conductivity, refractive index and surface tension. For this purpose, the first three properties were determined in samples of EAN and PAN with water contents below 30,000 ppm in a wide range of temperatures, between 5 and 95 °C, while the last two were obtained at 25 °C. As a result of this study, it has been concluded that the presence of water is critical in those physical properties that involve mass or charge transport processes, resulting in the finding that the absolute value of the average percentage change in both viscosity and electrical conductivity is above 40%. Meanwhile, refractive index (≤0.3%), density (≤0.5%) and surface tension (≤2%) present much less significant changes.

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.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.

Lyotropic liquid crystal phase behavior of a cationic amphiphile in aqueous and non-stoichiometric protic ionic liquid mixtures

Protic ionic liquids (PILs) are the largest and most tailorable known class of non-aqueous solvents which possess the ability to support amphiphile self-assembly. However, little is known about the effect of solvent additives on this ability. In this study, the lyotropic liquid crystal phase (LLCP) behavior of the cationic surfactant cetyltrimethylammonium bromide (CTAB) was investigated in the model PILs of ethylammonium nitrate (EAN) and ethanolammonium nitrate (EtAN), and derived multi-component solvent systems containing them to determine phase formation and diversity with changing solvent composition. The solvent systems were composed of water, nitric acid and ethylamine (or ethanolamine), with 26 unique compositions for each PIL covering the apparent pH and ionicity ranges of 0-13.5 and 0-11 M, respectively. The LLCPs were studied using cross polarized optical microscopy (CPOM) and small and wide-angle X-ray scattering (SAXS/WAXS). Partial phase diagrams were constructed for CTAB concentrations of 50 wt% and 70 wt% in the temperature range of 25 °C to 75 °C to characterise the effect of surfactant concentration and temperature on the LLCPs in each solvent environment. Normal micellar (L1), hexagonal (H1) and bicontinuous cubic (V1) phases were identified at both surfactant concentrations, and from temperatures as low as 35 °C, with large variations dependent on the solvent composition. The thermal stability and diversity of phases were greater and broader in solvent compositions with excess precursor amines present compared to those in the neat PILs. In acid-rich solvent combinations, the same phase diversity was found, though with reduced onset temperatures of phase formation; however, some structural changes were observed which were attributed to oxidation/decomposition of CTAB in a nitric acid environment. This study showed that the ability of PIL solutions to support amphiphile self-assembly can readily be tuned, and that the ability of PILs to promote amphiphile self-assembly is robust, even with other solvent species present.

Solvation properties of protic ionic liquids and molecular solvents

Ionic liquids (ILs) are highly tailorable solvents with many potential applications. Knowledge about their solvation properties is highly beneficial in the utilization of ILs for specific tasks, though for many ILs this is currently unknown.

Solvation properties of protic ionic liquid–molecular solvent mixtures

In this study, we have investigated the solvation properties of binary mixtures of PILs with molecular solvents. The selected binary solvent systems are the PILs ethylammonium nitrate (EAN) and propylammonium nitrate (PAN) combined with either water, methanol, acetonitrile or DMSO.