Group contribution methods for the prediction of thermophysical and transport properties of ionic liquids

Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures

This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. The study focuses on crucial input parameters, namely the type of cation, the type of anion, the temperature (measured in Kelvin), and the concentration of the ionic liquid (expressed in mol%). This study assesses three influential machine learning algorithms that are based on the Decision Tree methodology. These algorithms include Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB). Furthermore, the study incorporates the use of Glowworm Swarm Optimization (GSO) for hyper-parameter optimization, thereby further elevating the efficacy of the models. The results obtained from the evaluation showcase the exceptional predictive capabilities of the models, with Random Forest (RF) achieving an impressive R2 value of 0.9971, Gradient Boosting (GB) attaining an R2 value of 0.9916, and XGBoost (XGB) yielding an R2 value of 0.9911. In addition to the R2 metric, the study also presents other performance metrics, such as RMSE and MAPE, for each model. This comprehensive assessment of accuracy further solidifies the credibility and effectiveness of the models employed in the estimation of viscosity for ionic liquid solutions.

"Seasoning" antimalarial drugs' action: chloroquine bile salts as novel triple-stage antiplasmodial hits

Malaria is one of the "big three" global infectious diseases, having caused above two hundred million cases and over half a million deaths in 2020. The continuous demand for new treatment options prioritizes the cost-effective development of new chemical entities with multi-stage antiplasmodial activity, for higher efficacy and lower propensity to elicit drug-resistant parasite strains. Following up on our long-term research towards the rescue of classical antimalarial aminoquinolines like chloroquine and primaquine, we have developed new organic salts by acid-base pairing of those drugs with natural bile acids. These antimalarial drug-derived bile salts were screened in vitro against the hepatic, blood and gametocyte stages of Plasmodium parasites, unveiling chloroquine bile salts as unprecedented triple-stage antiplasmodial hits. These findings pave a new pathway for drug rescuing, even beyond anti-malarial and other anti-infective drugs.

Crystal Phase Ionic Liquids for Energy Applications: Heat Capacity Prediction via a Hybrid Group Contribution Approach

In the selection and design of ionic liquids (ILs) for various applications, including heat transfer fluids, thermal energy storage materials, fuel cells, and solvents for chemical processes, heat capacity is a key thermodynamic property. While several attempts have been made to develop predictive models for the estimation of the heat capacity of ILs in their liquid phase, none so far have been reported for the ILs' solid crystal phase. This is particularly important for applications where ILs will be used for thermal energy storage in the solid phase. For the first time, a model has been developed and used for the prediction of crystal phase heat capacity based on extending and modifying a previously developed hybrid group contribution model (GCM) for liquid phase heat capacity. A comprehensive database of over 5000 data points with 71 unique crystal phase ILs, comprising 42 different cations and 23 different anions, was used for parameterization and testing. This hybrid model takes into account the effect of the anion core, cation core, and subgroups within cations and anions, in addition to the derived indirect parameters that reflect the effects of branching and distribution around the core of the IL. According to the results, the developed GCM can reliably predict the crystal phase heat capacity with a mean absolute percentage error of 6.78%. This study aims to fill this current gap in the literature and to enable the design of ILs for thermal energy storage and other solid phase applications.

Probing the heterogeneity of molecular level organization of ionic liquids: a comparative study using neutral Nile red and cationic Nile blue sulfate as fluorescent probes for butyrolactam-based protic ionic liquids

Ionic liquids (ILs) are liquid salts composed of cations and anions, known for their significant local heterogeneity at the molecular level. To understand the microheterogeneity with regard to their local polarity and local viscosity, we have used two structurally similar but chemically distinguishable fluorescent probes: Nile red (NR), a neutral molecule, and Nile blue sulfate (NBS), a charged molecule. A comparative study of the response of the two probes to the molecular level heterogeneity of ILs is expected to provide a better clarity of understanding regarding the charged polar domain and the uncharged hydrophobic domain of ILs. Towards this, we synthesized two butyrolactam-based protic ionic liquids (PILs), i.e., BTF and BTD, with the same ionic headgroup ([BT]+) and different alkyl tails ([RCOO]-), where {R = H, C11H23}. BTF has no significant hydrophobic domain, whereas BTD has a larger hydrophobic domain. Temperature-dependent fluorescence parameters such as fluorescence intensity, lifetime, and anisotropy were measured for both NR and NBS molecules. The use of a pair of structurally similar but ionically different probes enables differential estimation of parameters like the microviscosity of a domain using the fluorescence anisotropy parameter (r). The absorption and emission spectra of both probe molecules are observed to be blue shifted upon going from BTF to BTD. NR showed a significant blue shift in absorption and emission band maxima. Conversely, NBS exhibited a small wavelength shift, possibly influenced by the preferred location of their charged head group domain. Temperature-dependent rotational relaxation time (θ) of NR in BTD is smaller than that of NBS by 60-70%, indicating that stronger charge-charge interactions exist between the polar domain of BTD and NBS. Moreover, it is observed that the local viscosity of the BTF IL around both probes is similar, whereas there is a considerable difference for the BTD IL. These results are an indication that NBS being charged prefers to locate itself in the charged head group region of the IL, whereas NR being neutral tends to reside both in the hydrophobic domain and in the head group but is predominantly located in the hydrophobic domain.

Process Simulation and Optimization on Ionic Liquids

Ionic liquids (ILs) are promising alternative compounds that enable the development of technologies based on their unique properties as solvents or catalysts. These technologies require integrated product and process designs to select ILs with optimal process performances at an industrial scale to promote cost-effective and sustainable technologies. The digital era and multiscale research methodologies have changed the paradigm from experiment-oriented to hybrid experimental-computational developments guided by process engineering. This Review summarizes the relevant contributions (>300 research papers) of process simulations to advance IL-based technology developments by guiding experimental research efforts and enhancing industrial transferability. Robust simulation methodologies, mostly based on predictive COSMO-SAC/RS and UNIFAC models in Aspen Plus software, were applied to analyze key IL applications: physical and chemical CO2 capture, CO2 conversion, gas separation, liquid-liquid extraction, extractive distillation, refrigeration cycles, and biorefinery. The contributions concern the IL selection criteria, operational unit design, equipment sizing, technoeconomic and environmental analyses, and process optimization to promote the competitiveness of the proposed IL-based technologies. Process simulation revealed that multiscale research strategies enable advancement in the technological development of IL applications by focusing research efforts to overcome the limitations and exploit the excellent properties of ILs.

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.

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.

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.

Novel Naproxen Salts with Increased Skin Permeability

The paper presents the synthesis, full identification, and characterization of new salts-L-proline alkyl ester naproxenates [ProOR][NAP], where R was a chain from ethyl to butyl (including isopropyl). All obtained compounds were characterized by Nuclear Magnetic Resonance (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray powder diffractometry (XRD), and in vitro dissolution studies. The specific rotation, phase transition temperatures (melting point), and thermal stability were also determined. In addition, their lipophilicity, permeability, and accumulation in pigskin were determined. Finally, toxicity against mouse L929 fibroblast cells was tested. The obtained naproxen derivatives showed improved solubility and higher absorption of drug molecules by biological membranes. Their lipophilicity was lower and increased with the increase in the alkyl chain of the ester. The derivative with isopropyl ester had the best permeability through pigskin. The use of L-proline isopropyl ester naproxenate increased the permeation of naproxen through the skin almost four-fold. It was also shown that the increase in permeability is not associated with additional risk: all compounds had a similar effect on cell viability as the parent naproxen.

On the Influence of the Menthol Moiety on the Transport Properties of a Homologue Series of Functionalized Bis(trifluoromethylsulfonyl)imide Room-Temperature Ionic Liquids: A Quest for the Structure-Property Relationship

This study explores the transport properties of bis(trifluoromethylsulfonyl)imide-based ionic liquids with a naturally derived (1R,2S,5R)-(-)-menthol moiety in the cationic part. In particular, we investigated the dependence of the dynamic viscosity and electrical conductivity as functions of the alkyl chain length. An important finding of this study is that both properties show nonmonotonic behavior with respect to the alkyl chain length. The nonmonotonic dependency is an obstacle for establishing the relationships between the structure and transport properties of homologues. To overcome this difficulty, we recommend fast property screening using a theoretical model that we developed, which allows for efficient viscosity prediction by means of the group contribution method. As demonstrated in this study, the model allows for reliable predictions of viscosity in the studied series with an overall relative deviation of less than 8%.

Evaluating the hazardous impact of ionic liquids - Challenges and opportunities

Ionic liquids (ILs), being related to the design of new environmentally friendly solvents, are widely considered for applications within the "green chemistry" concept. Due to their unique properties and wide diversity, ILs allow tailoring new separation procedures and producing new materials for advanced applications. However, despite the promising technical performance, environmental concerns highlighted in recent studies focused on the toxicity and biodegradability of ILs and their metabolites have revealed that ILs safety labels are not as benign as previously claimed. This review refers to the fundamentals about the properties and applications of ILs also in the context of their potential environmental effect. Toxicological issues and harmful effects related to the use of ILs are discussed, including the evaluation of their biodegradability and ecological impact on diverse organisms and ecosystems, also with respect to bacteria, fungi, and cell cultures. In addition, this review covers the tools used to assess the toxicity of ILs, including the predictive computational models and the results of studies involving cell membrane models and molecular simulations. Summing up the knowledge available so far, there are still no reliable criteria for unequivocal attribution of toxicity and environmental impact credentials for ILs, which is a challenging research task.

Surfing the Third Wave of Ionic Liquids: A Brief Review on the Role of Surface-Active Ionic Liquids in Drug Development and Delivery

The relevance of ionic liquids (ILs) is now well established in many fields, as their unique properties make them appealing as 1) greener alternatives to organic solvents (first-generation ILs), 2) tunable task-specific materials (second-generation ILs), and 3) multifunctional players in life and pharmaceutical sciences (third-generation ILs). This third wave of ILs encompasses a wide range of compounds, from bioactive molecules with single or even dual therapeutic action, to potential ingredient molecules for drug formulation and transport systems. In this context, the focus of this review is the emergent role of surface-active ionic liquids (SAILs) in drug development and delivery.

Group contribution and atomic contribution models for the prediction of various physical properties of deep eutectic solvents

The urgency of advancing green chemistry from labs and computers into the industries is well-known. The Deep Eutectic Solvents (DESs) are a promising category of novel green solvents which simultaneously have the best advantages of liquids and solids. Furthermore, they can be designed or engineered to have the characteristics desired for a given application. However, since they are rather new, there are no general models available to predict the properties of DESs without requiring other properties as input. This is particularly a setback when screening is required for feasibility studies, since a vast number of DESs are envisioned. For the first time, this study presents five group contribution (GC) and five atomic contribution (AC) models for densities, refractive indices, heat capacities, speeds of sound, and surface tensions of DESs. The models, developed using the most up-to-date databank of various types of DESs, simply decompose the molecular structure into a number of predefined groups or atoms. The resulting AARD% of densities, refractive indices, heat capacities, speeds of sound and surface tensions were, respectively, 1.44, 0.37, 3.26, 1.62, and 7.59% for the GC models, and 2.49, 1.03, 9.93, 4.52 and 7.80% for the AC models. Perhaps, even more importantly for designer solvents, is the predictive capability of the models, which was also shown to be highly reliable. Accordingly, very simple, yet highly accurate models are provided that are global for DESs and needless of any physical property information, making them useful predictive tools for a category of green solvents, which is only starting to show its potentials in green technology.

Thermal Conductivity of Ionic Liquids and IoNanofluids. Can Molecular Theory Help?

Ionic liquids have been suggested as new engineering fluids, specifically in the area of heat transfer, and as alternatives to current biphenyl and diphenyl oxide, alkylated aromatics and dimethyl polysiloxane oils, which degrade above 200 °C, posing some environmental problems. Addition of nanoparticles to produce stable dispersions/gels of ionic liquids has proved to increase the thermal conductivity of the base ionic liquid, potentially contributing to better efficiency of heat transfer fluids. It is the purpose of this paper to analyze the prediction and estimation of the thermal conductivity of ionic liquids and IoNanofluids as a function of temperature, using the molecular theory of Bridgman and estimation methods previously developed for the base fluid. In addition, we consider methods that emphasize the importance of the interfacial area IL-NM in modelling the thermal conductivity enhancement. Results obtained show that it is not currently possible to predict or estimate the thermal conductivity of ionic liquids with an uncertainty commensurate with the best experimental values. The models of Maxwell and Hamilton are not capable of estimating the thermal conductivity enhancement of IoNanofluids, and it is clear that the Murshed, Leong and Yang model is not practical, if no additional information, either using imaging techniques at nanoscale or molecular dynamics simulations, is available.

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.

Hybridizing physical and data-driven prediction methods for physicochemical properties

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach 'distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physical and data-driven baselines and established ensemble methods from the machine learning literature.

Thermal Conductivity of Metastable Ionic Liquid [C2mim][CH3SO3]

Ionic liquids have been suggested as new engineering fluids, namely in the area of heat transfer, as alternatives to current biphenyl and diphenyl oxide, alkylated aromatics and dimethyl polysiloxane oils, which degrade above 200 °C and pose some environmental problems. Recently, we have proposed 1-ethyl-3-methylimidazolium methanesulfonate, [C₂mim][CH₃SO₃], as a new heat transfer fluid, because of its thermophysical and toxicological properties. However, there are some interesting points raised in this work, namely the possibility of the existence of liquid metastability below the melting point (303 K) or second order-disorder transitions (λ-type) before reaching the calorimetric freezing point. This paper analyses in more detail this zone of the phase diagram of the pure fluid, by reporting accurate thermal-conductivity measurements between 278 and 355 K with an estimated uncertainty of 2% at a 95% confidence level. A new value of the melting temperature is also reported, T_melt = 307.8 ± 1 K. Results obtained support liquid metastability behaviour in the solid-phase region and permit the use of this ionic liquid at a heat transfer fluid at temperatures below its melting point. Thermal conductivity models based on Bridgman theory and estimation formulas were also used in this work, failing to predict the experimental data within its uncertainty.

Thermal Conductivities of Choline Chloride-Based Deep Eutectic Solvents and Their Mixtures with Water: Measurement and Estimation

The thermal conductivities of selected deep eutectic solvents (DESs) were determined using the modified transient plane source (MTPS) method over the temperature range from 295 K to 363 K at atmospheric pressure. The results were found to range from 0.198 W·m^-1·K^-1 to 0.250 W·m^-1·K^-1. Various empirical and thermodynamic correlations present in literature, including the group contribution method and mixing correlations, were used to model the thermal conductivities of these DES at different temperatures. The predictions of these correlations were compared and consolidated with the reported experimental values. In addition, the thermal conductivities of DES mixtures with water over a wide range of compositions at 298 K and atmospheric pressure were measured. The standard uncertainty in thermal conductivity was estimated to be less than ± 0.001 W·m^-1·K^-1 and ± 0.05 K in temperature. The results indicated that DES have significant potential for use as heat transfer fluids.

First Report on a Solvent-Free Preparation of Polymer Inclusion Membranes with an Ionic Liquid

A novel and environmentally-friendly procedure for the preparation of polymer inclusion membranes (PIMs) containing an ionic liquid is presented for the first time. Traditionally, PIMs are prepared by a solvent casting method with the use of harmful organic solvents. Here we report a new solvent-free procedure based on a thermal-compression technique which involve the melting of the components of the PIM and the application of a high pressure to the melted specimen to form a flat-sheet film. In our study, we have tested different polymers, such as two cellulose derivatives as well as two thermoplastic polymers, polyurethane (TPU) and poli ε-caprolactone (PCL). The ionic liquid (IL) trioctylmethylammonium chloride (Aliquat 336) has been used to produce PIMs with a fixed composition of 70% polymer–30% IL (w/w). Both TPU and PCL polymers provide successful membranes, which have been thoroughly characterized. PIMs based on the polymer PCL showed a high stability. To test whether the properties of the IL were affected by the preparation conditions, the extraction ability of Aliquat 336 was investigated for both PCL and TPU membranes in terms of Cr(VI) extraction. Satisfactory values (90% extraction) were obtained for both membranes tested, showing this novel procedure as a green alternative for the preparation of PIMs with ILs.

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.