Melting Point Of Ionic Liquids Research Articles

Planting the Seeds of a Decision Tree for Ionic Liquids: Steric and Electronic Impacts on Melting Points of Triarylphosponium Ionic Liquids.

While machine learning and artificial intelligence offer promising avenues in the computer-aided design of materials, the complexity of these computational techniques remains a barrier for scientists outside of the specific fields of study. Leveraging decision tree models, inspired by empirical methodologies, offers a pragmatic solution to the knowledge barrier presented by artificial intelligence (AI). Herein, we present a model allowing for the qualitative prediction of melting points of ionic liquids derived from the crystallographic analysis of a series of phosphonium-based ionic liquids. By carefully tailoring the steric and electronic properties of the cations within these salts, trends in the melting points are observed, pointing toward the critical importance of π interactions to forming the solid state. Quantification of the percentage of these π interactions using modern quantum crystallographic approaches reveals a linear trend in the relationship of C-Hπ and π-π stacking interactions with melting points. These structure-property relationships are further examined by using computational studies, helping to demonstrate the inverse relationship of dipole moments and melting points for ionic liquids. The results provide valuable insights into the features and relationships that are consistent with achieving low Tm values in phosphonium salts, which were not apparent in earlier studies. The data gathered are presented in a simple decision tree format, allowing for visualization of the data and providing guidance toward developing yet unreported compounds.

Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks.

Ionic liquids (ILs) have wide and promising applications in fields such as chemical engineering, energy, and the environment. However, the melting points (MPs) of ILs are one of the most crucial properties affecting their applications. The MPs of ILs are affected by various factors, and tuning these in a laboratory is time-consuming and costly. Therefore, an accurate and efficient method is required to predict the desired MPs in the design of novel targeted ILs. In this study, three descriptor-based machine learning (DBML) models and eight graph neural network (GNN) models were proposed to predict the MPs of ILs. Fingerprints and molecular graphs were used to represent molecules for the DBML and GNNs, respectively. The GNN models demonstrated performance superior to that of the DBML models. Among all of the examined models, the graph convolutional model exhibited the best performance with high accuracy (root-mean-squared error = 37.06, mean absolute error = 28.79, and correlation coefficient = 0.76). Benefiting from molecular graph representation, we built a GNN-based interpretable model to reveal the atomistic contribution to the MPs of ILs using a data-driven procedure. According to our interpretable model, amino groups, S+, N+, and P+ would increase the MPs of ILs, while the negatively charged halogen atoms, S-, and N- would decrease the MPs of ILs. The results of this study provide new insight into the rapid screening and synthesis of targeted ILs with appropriate MPs.

Smart emulsion system driven by light‐triggered ionic liquid molecules and its application in eco‐friendly water‐saving dyeing

AbstractThe smart emulsification and demulsification system with the light response is a useful tool in various industries, including green chemistry, catalytic reaction, pharmaceuticals, and environmental remediation. Herein, an ionic liquid crystal compound with a light triggered switch based on the azobenzene group [(4‐{3‐methyl‐1‐[3‐(8‐octyloxyoctyl)oxy‐4‐oxobutanoyl]imidazo‐lium‐1‐yl}octyl)oxy] ‐N‐(4‐methylphenyl)benzene‐1,2‐diazene bromide (MOIAzo), was designed and synthesized, which could cause reversible transition between emulsification and demulsification through the light trigger. The ionic liquid has an efficient photoinduced liquefaction process, which dramatically lowers the melting point of ionic liquids from 79 to 9.2 oC. This significantly broadens the liquid state temperature of the ionic liquid crystal. The ionic liquid crystal MOIAzo exhibits both photoinduced and thermally induced nematic liquid crystal properties. The smart emulsion system was effectively employed in an eco‐friendly water‐saving dyeing process of cationic dyes for cationic dyeable polyester (CDP) fabrics, which used only half the amount of water compared with the conventional water bath dyeing method. After dyeing, the oil and water phases can be efficiently separated through the light irradiation, and the oil phase can be reused for the subsequent dyeing process. This novel smart emulsion dyeing method greatly reduces the water consumption and wastewater discharge. MOIAzo as a light‐triggered ionic liquid molecule opens up new dimensions in green chemistry.

Estimation of conformational entropy for ionic liquids from NMR spectroscopy

Abstract Conformational entropy (Sconf) plays a key role in the low melting points of ionic liquids. In this study, a nuclear magnetic resonance (NMR)-based approach was developed to experimentally estimate Sconf of 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide. J-coupling constants from the 1D 1H NMR and 2D spectra of the butyl group in the cation were analyzed using the Karplus equation optimized by density functional theory calculations. The obtained conformational population and entropy values coincided reasonably well with previously reported results, indicating the usefulness of the NMR approach.

Role of conformational entropy in low melting point of ionic liquids: a molecular dynamics simulation study

Abstract Ionic liquids (ILs) are liquid salts which have very low melting points among the salts. To understand the contribution of conformational entropy (Sconf) to the low melting points of ILs, molecular dynamics (MD) simulations of 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([Cnmim][NTf2] where n = 2, 4, 6, 8, and 10) were performed in liquid and gas states. The increase in Sconf with increasing alkyl chain length corresponded to an increase in fusion entropy (ΔfusS), suggesting that the other entropic contributions such as kinetic and configurational entropies depend negligibly on the alkyl chain length. Comparing the same cation, Sconf in the liquid state with a long alkyl chain was slightly smaller than that in the gas state because the trans conformers of the cation were favored in the liquid state. The trans conformers of the cations in the liquid state were stabilized by the van der Waals and Coulomb interactions. Meanwhile, populations of the trans conformer for the anion in the gas and liquid state were almost the same.

Machine Learning Models for Melting Point Prediction of Ionic Liquids: CatBoost Approach

Using ionic liquids as phase changing materials is of particular interest in the context of heat storage. As a consequence, predicting accurately the melting point of ionic liquids is of capital importance as it is one of the most important thermophysical properties in this context. In this work we consider a data set composed of 2249 different ionic liquids, with a majority of imidazole or ammonium cation-based molecules. We present a free and easy-to-use melting point predictive algorithm built on the CatBoost algorithm, making strong use of molecular descriptors. Based on LASSO, we select the most relevant descriptors for the task at hand and compare the model with previous ones.

Beware of proper validation of models for ionic Liquids!

The melting point (MP) of an ionic liquid (IL) is one of the key physical properties as it determines the lower limit of the IL working temperature range. In this work, we analysed the recently published studies to predict MP of ILs. While we were able to reproduce the statistical parameters reported by the authors, we found that the performance of the models with new test set data was much lower than the reported statistical values. The discrepancy was due to the validation protocol (random split of the initial set into training/test subsets) that did not allow correct estimation of how contributions of individual ions affect the model performance. Using a more rigorous validation protocol we reached good agreement between the training and test set statistical parameters. We strongly suggest using this protocol for proper validation of models for other properties of ILs to avoid reporting overoptimistic statistical parameters. We also showed that the Transformer Convolutional Neural Network, which was based on the representation of molecules as text (SMILES), proposed a model with significantly higher prediction accuracy as compared to those developed using descriptors that were used in the previous studies. The RMSE of this model is 44 °C and the model is applicable to any type of ILs. The data and developed models are publicly available online at http://ochem.eu/article/135195.

Predicting melting point of ionic liquids using QSPR approach: Literature review and new models

Quantitative structure–property relationships (QSPRs) for predicting melting point temperature (Tm) of ionic liquids (ILs) are reviewed and the new models are proposed by using the experimental data extracted from the literature for 953 salts. The models include both regression of Tm data and classification of the ILs with respect to their state of matter (liquid/solid) at T=300 K. A variety of machine learning algorithms is applied, including: partial least squares regression, stepwise multiple linear regression, and a number of common classifiers (k-nearest neighbors, naive Bayes, linear discriminant analysis, support vector machines). An effect of the molecular descriptors set as well as the computational level used for the ions’ geometry optimization is analyzed and followed in the final model selection protocol, which comprises all the standard steps of good practice of QSRP modeling, e.g. cross-validation, external validation, and the applicability domain analysis. Furthermore, as a key novelty, the robustness of the models is checked for different combining rules, defined as the averaging functions for obtaining the descriptors of ILs from those given for individual ions. The finally selected and recommended models are discussed in detail in terms of various statistics, as well as addressed to other methods reported in the literature. An effect of the chemical family of both cation and anion on the modeling performance is highlighted. Additionally, the predictions of both Tm and state of matter of more than 35,000 virtual cation–anion combinations are given in order to present the range of potential applications of the new methods in computer-aided molecular design of new ILs displaying demanded phase behavior.

Anomalous Melting Point of Multicharge Ionic Liquids: Structural, Electrostatic, and Orbital Properties of [Ln(NO3)6]3- (Ln = Ce, Pr) Anions.

Salts composed of multicharged cations/anions usually exhibit a large lattice energy and strong Coulomb force, which results in high melting points. However, an increasing number of highly charged ionic liquids exceed expectations based on conventional experience; even their melting points are much lower than those found for simple ionic liquids composed of monovalent ions. To further study this phenomenon, we studied a group of stable ionic liquids containing tricharged [Ce(NO3)6]3- and [Pr(NO3)6]3- anions. The structures for [C6mim]3[Ce(NO3)6] and [C6mim]3[Pr(NO3)6] were determined by single-crystal X-ray diffraction with triclinic and P1̅ space groups. The electrostatic potential density per unit ion surface and volume was proposed and calculated. Additionally, theoretical analysis based on Hirshfeld surface and charge decomposition was carried out to explore the intermolecular interaction and electronic structure of the lanthanide anions. The electrostatic and orbital properties were found to be more useful for understanding the melting points of highly charged salts compared with the sole use of lattice energy. The electrostatic potential density per unit ion surface and volume showed a linear relationship with the melting point of ionic liquids composed of monovalent to trivalent ions. These structure-melting point relationships will be beneficial for expounding new low-melting-point ionic liquids with a wide liquidus range.

The effect of descriptor choice in machine learning models for ionic liquid melting point prediction.

The characterization of an ionic liquid's properties based on structural information is a longstanding goal of computational chemistry, which has received much focus from ab initio and molecular dynamics calculations. This work examines kernel ridge regression models built from an experimental dataset of 2212 ionic liquid melting points consisting of diverse ion types. Structural descriptors, which have been shown to predict quantum mechanical properties of small neutral molecules within chemical accuracy, benefit from the addition of first-principles data related to the target property (molecular orbital energy, charge density profile, and interaction energy based on the geometry of a single ion pair) when predicting the melting point of ionic liquids. Out of the two chosen structural descriptors, ECFP4 circular fingerprints and the Coulomb matrix, the addition of molecular orbital energies and all quantum mechanical data to each descriptor, respectively, increases the accuracy of surrogate models for melting point prediction compared to using the structural descriptors alone. The best model, based on ECFP4 and molecular orbital energies, predicts ionic liquid melting points with an average mean absolute error of 29 K and, unlike group contribution methods, which have achieved similar results, is applicable to any type of ionic liquid.

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.

A novel method for predicting melting point of ionic liquids

For industrial applications, it is important to know phase transition properties such as melting points of ionic liquids (ILs). A novel correlation is introduced to predict melting points of important classes of ILs including imidazolium-, pyridinium-, pyrrolidinium-, ammonium-, phosphonium-, and piperidinium-based ILs and different type of anions with specific cation/anion moieties. It is based only on the number of some of atoms in cationic and anionic structures as well as two correcting functions for the presence of some specific cation/anion moieties. The numbers of carbon, hydrogen and nitrogen atoms are used for cation. Meanwhile, the numbers of hydrogen, nitrogen, bromine, chlorine and aluminum atoms are applied for anion. The measured data of 195 different types of ILs were used to derive the new correlations. The calculated coefficient of determination (R2), root mean square error (RMS), mean absolute error (MAE), mean absolute percent errors (MAPEs) and maximum of errors of the new model are 0.857, 26.1 K, 20.1 K, 6.5 and 91.2 K, respectively. Meanwhile, the calculated RMS, MAE, MAPE and maximum of errors for one of the best available group contribution (GC) methods where it can be applied only for 159 ILs are 53.6 K, 35.9 K, 11.6 and 253.6 K, respectively. For further 48 ILs with complex structure of cations and different anions, the values of RMS, MAE, MAPE and maximum of errors for the new model is also much lower than GC method. These results confirm that the reliability of the new model is good as well as it can be applied for a wide range of imidazolium-, pyridinium-, pyrrolidinium-, ammonium-, phosphonium-, and piperidinium-based ILs with different type of anions.

A density functional theory based approach for predicting melting points of ionic liquids.

Accurate prediction of melting points of ILs is important both from the fundamental point of view and from the practical perspective for screening ILs with low melting points and broadening their utilization in a wider temperature range. In this work, we present an ab initio approach to calculate melting points of ILs with known crystal structures and illustrate its application for a series of 11 ILs containing imidazolium/pyrrolidinium cations and halide/polyatomic fluoro-containing anions. The melting point is determined as a temperature at which the Gibbs free energy of fusion is zero. The Gibbs free energy of fusion can be expressed through the use of the Born-Fajans-Haber cycle via the lattice free energy of forming a solid IL from gaseous phase ions and the sum of the solvation free energies of ions comprising IL. Dispersion-corrected density functional theory (DFT) involving (semi)local (PBE-D3) and hybrid exchange-correlation (HSE06-D3) functionals is applied to estimate the lattice enthalpy, entropy, and free energy. The ions solvation free energies are calculated with the SMD-generic-IL solvation model at the M06-2X/6-31+G(d) level of theory under standard conditions. The melting points of ILs computed with the HSE06-D3 functional are in good agreement with the experimental data, with a mean absolute error of 30.5 K and a mean relative error of 8.5%. The model is capable of accurately reproducing the trends in melting points upon variation of alkyl substituents in organic cations and replacement one anion by another. The results verify that the lattice energies of ILs containing polyatomic fluoro-containing anions can be approximated reasonably well using the volume-based thermodynamic approach. However, there is no correlation of the computed lattice energies with molecular volume for ILs containing halide anions. Moreover, entropies of solid ILs follow two different linear relationships with molecular volume for halides and polyatomic fluoro-containing anions. Continuous progress in predicting crystal structures of organic salts with halide anions will be a key factor for successful prediction of melting points with no prior knowledge of the crystal structure.

Surface Activity and Aggregation Behavior of Siloxane-Based Ionic Liquids in Aqueous Solution.

Six novel siloxane-based surface-active ionic liquids (SAILs)--siloxane ammonium carboxylate [Si(n)N(2)-CA(1), (n = 3, 4)]--were designed and synthesized. Their melting points, surface activities, and self-aggregation behavior in aqueous solution were studied. The results showed that because of the bulky hydrophobic siloxane chains at the end of the tail, all six siloxane-based SAILs are room-temperature ionic liquids (RT-SAILs). The introduction of the siloxane group can reduce the melting point of ionic liquids to below room temperature and can promote the micellization and aggregation behavior more efficiently. These siloxane-based SAILs can greatly reduce the surface tension of water, as shown by the critical aggregation concentration (γCAC) values of 20 mN·m(-1); all six siloxane RT-SAILs can form a vesicle spontaneously in aqueous solution, indicating potential uses as model systems for biomembranes and vehicles for drug delivery.

Predicting the melting points of ionic liquids by the Quantitative Structure Property Relationship method using a topological index

A Quantitative Structure Property Relationship (QSPR) model was developed to predict the melting points of ionic liquids (ILs) with diverse classes of cations and anions. The QSPR model was based on the general topological index (TI) proposed in our previous work. The TI was successfully used for the prediction of the decomposition temperature of ILs and the toxicity of ILs in acetylcholine esterase and Leukemia Rat Cell Line. ILs are a class of molten salts which are composed entirely of cations and anions, therefore the descriptors for ILs are generally calculated from cations and anions separately and the interaction between them is neglected. In this study, besides the two sets of TIs generated from cations and anions, a third TI was used to depict the interaction of anions and cations. The QSPR model is on the base of eight kinds of ILs, which are imidazolium, benzimidazolium, pyridinium, pyrrolidinium, ammonium, sulfonium, triazolium and guanidinium. The regression coefficient (R2) and the overall average absolute deviation (AAD) are 0.778 and 7.20%, respectively.

Ionic liquids: Prediction of melting point by molecular-based model

The aim of is this study is to develop molecular-based model for prediction of melting points of diverse classes of ionic liquids. For this purpose, exhaustive literature survey was conducted in order to collect comprehensive database of the melting points of ionic liquids. The melting points of 808 diverse ionic liquids belongs to Sulfonium, Ammonium, Pyridinium, 1,3-Dialkyl imidazolium, Tri-alkyl imidazolium, Phosphonium, Pyrrolidinium, Double imidazolium, 1-Alkyl imidazolium, Piperidinium, Pyrroline, Oxazolidinium, Amino acids, Guanidinium, Morpholinium, Isoquinolinium and Tetra-alkyl imidazolium have been collected from 131 various references. Quantitative Structure–Property Relationship (QSPR) approach was applied in order to develop a reliable model for the prediction of the melting points of ionic liquids. As far as the authors concern, the investigated data has the broadest range of anion and cation structures among the previous studies which enhances its generality to predict melting point of unknown ionic liquids. The final QSPR model derived by Genetic Function Approximation (GFA) contains 12 descriptors and quantified by the following statistical parameters: R2train=0.658, R2test=0.721 and Average Absolute Relative Deviation (AARD)=7.3%. The Applicability Domain (AD) of the model is also investigated. It turns out that the model's domain is defined broadly enough to contain all the trained and test sets which verified the reliability of the model. Finally, the proposed model has been scrutinized by several validation techniques and its robustness and reliability is investigated. The results of the validation techniques indicate the present model is robust, stable and reliable one.

A quantum mechanical study of alkylimidazolium halide ionic liquids

Thirty imidazolium (IM) halide compounds were studied using quantum chemical calculations. Geometry optimization and interaction energy calculations were performed using the B3LYP/6-311++G(d,p) method for ions composed of one alkylimidazolium cation and two or three halogen anions. The obtained structures were consistent with experimental results. In addition, a linear correlation between melting points and interaction energies was obtained for the compounds studied, and this relationship was consistent with that obtained for amino acid cation based ionic liquids. Our Letter demonstrates the potential for quantum mechanical calculations to predict the melting points of ionic liquids.

A group contribution method to predict the melting point of ionic liquids

The melting points of several families of ionic liquids were predicted with a simple group contribution method. A set of 200 ionic liquids was used to obtain the contributions for anion and cation groups. The optimum parameters of the method were obtained using a genetic algorithm-based on multivariate linear regression. Then, the melting temperatures of another 200 ionic liquids were predicted, and the results were compared with experimental data available in the literature. The study shows that the proposed group contribution method represents an excellent alternative for the estimation of the melting point of diverse ionic liquids from the knowledge of their molecular structure, with an average deviation of 7%.

The Influence of Hydrogen‐Bond Defects on the Properties of Ionic Liquids

Ionic liquids (ILs) are salts with uncommonly low melting points that are formed by a combination of specific cations and anions; they display distinctive properties and can be used in a variety of applications. The working temperature range of an ionic liquid is set by the melting point and the boiling or decomposition point. In particular, the melting point (Tm) varies substantially between different ILs for reasons presently not fully understood, but which we explore herein. We show that the melting points of imidazolium ionic liquids can be decreased by about 100 K if an extended ionic and hydrogen-bond network is disrupted by localized interactions, which can also be hydrogen bonds. Evidence for the presence of ion–ion interactions through hydrogen bonds was reported by Dymek et al., Avent et al., and Elaiwi et al. some time ago. It is reasonable to assume that the interesting features of the melting points must be related to the formation of structures in the solid and the liquid phases of the ILs. Extended hydrogen-bond networks in the liquid phase were reported with possible implications for both the structure and solvent properties of the ILs. Dupont et al. described pure imidazolium ILs as hydrogenbonded polymeric supramolecules. Antonietti et al. suggested that these supramolecular solvent structures represent an interesting molecular basis of molecular recognition and self-organization processes. However, in all of these examples it is suggested that hydrogen bonds strengthen the structure of ILs leading to properties similar to those of molecular liquids. This idea is also the basis of most of the structure– property relations discussed in the literature including quantitative structure–property relationships (QSPR) methods to correlate the melting points of ILs based on “molecular descriptors” derived from quantum chemical calculations. Such empirical correlations suffer from the fact that large experimental data sets are required and that the statistical methods used are rather complex. In addition, no interpretation of these fundamental physical properties at the molecular level is provided. Krossing et al. have developed a simple predictive framework to calculate the melting point of a given ionic liquid based on lattice and solvation free energies. They showed that ILs are liquid under standard ambient conditions because the liquid state is thermodynamically favorable, owing to the large size and conformational flexibility of the ions involved. This leads to small lattice enthalpies and large entropy changes that favor the liquid state. For such studies substituted imidazolium, pyrrolidinium, pyridinium, and ammonium cations have been used along with fluorometalate, triflate, and bis(trifluoromethylsulfonyl)imide anions. Unfortunately, Krossing s results do not correlate with experimentally obtained melting points for protic ionic liquids (PILs) reported byMarkusson et al. The reason for the large deviations of the predicted from the experimental melting points is probably related to the general trend of increasing Tm with the increasing size of the anions. We do not intend to present another framework for predicting ionic liquid properties here. Instead we want to demonstrate that in addition to the large size and conformational flexibility of the ions, local defects such as directional hydrogen bonds can significantly decrease the melting points of ionic liquids. For eight imidazolium-based ionic liquids we show that these defects can increase their working temperature range by up to 100 K and thus expand the spectrum of potential applications. This was suggested previously by Fumino et al. based on spectroscopic measurements and DFT calculations on IL aggregates. They assumed that local and directional types of interactions present defects in the Coulomb system which may lower the melting points, viscosities, and enthalpies of vaporization. In contrast, based on quantum chemical calculations, Hunt claimed that an increase in the melting points and viscosities upon methylation at C(2) stem from reduced entropy. Noack et al. showed very recently that neither the “defect hypothesis” of Fumino et al. nor the “entropy hypothesis” of Hunt alone can explain the changes in the physicochemical properties. However, in all these studies the data base was not sufficiently large and other effects such as volume changes could not be excluded for the ILs under investigation. [*] Dipl.-Chem. C. Roth, Dr. K. Fumino, Dr. D. Paschek, Prof. Dr. R. Ludwig Universit t Rostock, Institut f r Chemie Abteilung f r Physikalische Chemie Dr. Lorenz Weg 1, 18059 Rostock (Germany) Fax: (+49)381-498-6517 E-mail: ralf.ludwig@uni-rostock.de

QSAR correlation of the melting points for imidazolium bromides and imidazolium chlorides ionic liquids

The melting points of ionic liquids (ILs) of imidazolium bromides and imidazolium chlorides have been investigated by means of quantitative structure–activity relationship (QSAR) approach in order to develop prediction models for predicting the melting points of ionic liquid salts. The cationic structures of these ILs were optimized by means of Hyperchem software and MOPAC program. QSAR module of Materials Studio software and Genetic Algorithm (GA) programs were employed to calculate and select the structure descriptors of ILs, then prediction models correlating the selected structure descriptors and melting points of ionic liquids were set up by using the multiple linear regressions (MLR) method and the back-propagation artificial neural network (BP ANN) method, separately. Finally, the obtained QSAR models, including MLR model and BP ANN model, were validated by external test sets. In this work, three data sets, which were 30 imidazolium bromides, 20 imidazolium chlorides and the merging of above two data sets, respectively, were used to investigate the QSAR correlation of the melting points of ILs. The results demonstrated that the prediction mean absolute errors (MAEs) of MLR models for test sets of those three data sets were in the order of 20.52 K, 13.59 K and 21.95 K, and the prediction MAEs of BP ANN models were 8.77 K, 4.98 K and 9.31 K, respectively. It indicated that the predictions of two models for all melting points of ILs were reliable, and the prediction precision of BP ANN model was higher than that of MLR model.