Electrical Conductivity Of Ionic Liquids Research Articles

Eco-friendly synthesis of cluster-like Dy2Ce2O7 nanoparticles using orange juice and their application in electrochemical determination of isoniazid

In this study, cluster-like (CL) Dy2Ce2O7 nanoparticles were synthesized via an easy and rapid sonochemical pathway. X-ray diffraction (XRD), and field emission scanning electron microscopy (FE-SEM) were used for the characterization of nanoparticles. The construction of an electrochemical sensor was performed to determine isoniazid (IZN) based on ionic liquid (IL) and the cluster-like nanostructure of Dy2Ce2O7 modified carbon paste electrode (CL-Dy2Ce2O7/IL/CPE). Using the fabricated sensor, cyclic voltammetry (CV) along with differential pulse voltammetry (DPV) have been utilized to examine the catalytic activity of the modified electrode toward oxidation of IZN in 0.1 M phosphate buffer solution (PBS) with pH of 7.0. The large specific surface area and electrocatalytic properties of CL-Dy2Ce2O7 as well as the good electrical conductivity of ionic liquid greatly enhanced the current signal of the IZN at the CL-Dy2Ce2O7/IL/CPE. The process at the surface of the electrode was under the diffusion control mechanism and the diffusion coefficient for IZN was 1.08 × 10−5 cm2/s. Additionally, with a low detection limit of 9.0 nM, the peak current of IZN rose linearly with concentration in the range of 0.02–340.0 μM. Our findings demonstrate that the sensitive and selective detection of IZN in pharmaceutical and biological samples can be achieved using the CL-Dy2Ce2O7/IL/CPE.

Prediction of Electrical Conductivity of Ionic Liquids: From COSMO-RS Derived QSPR Evaluation to Boosting Machine Learning

Prediction of Electrical Conductivity of Ionic Liquids: From COSMO-RS Derived QSPR Evaluation to Boosting Machine Learning

The Impact of Chemical Modifications on The Ionic Conductivity of Ionic Liquid Encapsulated in Carbon Nanotubes, Potential Application in Aluminum Ion Battery

The transport and thermodynamic properties of 1-ethyl-3methyl imidazolium hexafluorophosphate ([EMIM][PF6]) ionic liquid encapsulated in carbon nanotubes or graphite represent a significant challenge in the development of Aluminum-ion batteries. To achieve high energy density in Aluminum-ion batteries, it is explored the use of ionic liquid solvents as electrolytes to enhance the ionic conductivity of the ionic liquid when encapsulated in carbon nanotubes as electrodes. The doping effects of sulfur and boron atoms in single-walled carbon nanotubes (SWCNTs) have been investigated to determine their impact on the ionic transfer number of the ionic liquid encapsulated in SWCNTs. The Green-Kubo formalism has been employed to calculate the ionic transfer number and electrical conductivity of the ionic liquid when encapsulated in SWCNTs doped with sulfur and boron atoms. The results obtained using the Green-Kubo formalism indicate that sulfur doping in SWCNTs, particularly in those with a larger radius, serves as a selective porous electrode material, significantly enhancing the electrical conductivity of the confined ionic liquid. Furthermore, the radial distribution function between the carbon alkyl groups in the ionic liquid and the carbon wall of the carbon nanotube, which is doped with sulfur and boron atoms, demonstrates that the doping effect does not alter the radial distribution function. The Green-Kubo result obtained for the electrical conductivity of the ([EMIM][PF6]) ionic liquid demonstrates excellent agreement with the experimental data.

QSPR Models Based on Multiple Linear Regression and Back-Propagation Artificial Neural Network for Predicting Electrical Conductivity of Ionic Liquids

QSPR Models Based on Multiple Linear Regression and Back-Propagation Artificial Neural Network for Predicting Electrical Conductivity of Ionic Liquids

Machine learning coupled with group contribution for predicting the electrical conductivity of ionic liquids with experimental accuracy

Accurate prediction of the electrical conductivity of ionic liquids (ILs) is crucial for their efficient design and application in various fields. In this study, new group contribution (GC) method is proposed for the prediction of the electrical conductivity of pure ILs as a function of temperature (T) and pressure (p). This group contribution scheme enables the comprehensive description of diverse ionic liquids and the differentiation among isomers. Four machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machines (SVM) and Deep Neural Networks (DNN) are developed and compared with a conventional approach and other methods recently proposed in the literature. The proposed models are developed using the largest collection of data reported to date, which includes a total of 7598 data points for 520 ionic liquids of various chemical families. This database underwent a rigorous and thorough review, evaluation, and reduction process to ensure its reliability. The GC scheme was adopted from our previous work. The results indicate that the DNN model outperforms other approaches, closely followed by the SVM model. The predictive accuracy of both models falls within the experimental uncertainty range, validating their reliability. Additionally, the influence of cationic and anionic families on model performance is explored, providing valuable insights into IL behavior.

Electrical Conductivity of Ionic Liquids 1-Hexyl-3-Methylimidazolium Chloride (HMIM) and 1-Methyl-3-Octylimidazolium Chloride (OMIM) in Mixtures with Selected Alkoxy Alcohols over a Wide Temperature Range

Ionic liquids have been the subject of intense research because of their unique electrochemical properties and potential applications in various fields. In this article, we analyze the electrical conductivity of two selected ionic liquids, 1-hexyl-3-methylimidazolium chloride (HMIM) and 1-eethyl-3-octylimidazolium chloride (OMIM), in various alkoxy alcohols such as 2-methoxyethanol, 2-ethoxyethanol, 2-propoxyethanol and 2-butoxyethanol. Our research focuses on attempting to analyze the impact of the molecular structure of both the ionic liquids and alkoxy alcohols on their electrical conductivity properties. The results of our study can be highly beneficial in the design of advanced electrochemical materials and their various applications.

Electrical Conductivity and Association of 1-butyl-3-methylpyridinium bis{(trifluoromethyl)sulfonyl}amide in Some Polar Solvents

The influence of alternating current frequency in the determination of the electrical conductivity of ionic liquids’ (ILs) dilute solutions in polar solvents has been considered. The frequency ranges in which the influence of polarization processes on electrodes occur and ionic relaxation occurs in the bulk of the solution have been excluded from the results of the electrical conductivity measurements. The association constants for Ka ILs in polar solvents published in literature were analyzed. A discrepancy between the values of Ka was noted, which is associated with the use of different calculation equations for electrical conductivity and the insufficiently correct consideration of the frequency dependance of the measured resistance. Based on the measured values of the electrical conductivity of dilute solutions of 1-butyl-3-methylpyridinium bis{(trifluoromethyl)sulfonyl}amide ([Bmpy][NTf2]) in acetonitrile (AN), dimethyl sulfoxide (DMSO) and dimethylformamide (DMF) in the 20--65 °C temperature range, the thermodynamic characteristics of the [Bmpy][NTf2] association were determined. The effect of temperature on the molar electrical conductivity of [Bmpy][NTf2] at infinite dilution λ0 and the association constant Ka have been considered. The Walden product (λ0η), where η is the viscosity of the solvent, was also analysed. It was shown that in AN, DMSO, and DMF, λ0η changes in different ways with increasing temperature; however, the value of λ0η/(εT) corrected for permittivity ε and absolute temperature T does not depend on the temperature and nature of the solvent. As the temperature rises, the electrical conductivity of the dilute solutions of [Bmpy][NTf2] increases in direct proportion to the ratio of the permittivity to dipole dielectric relaxation time of the solvent

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations.

Machine learning assisted Structure-based models for predicting electrical conductivity of ionic liquids

Ionic liquids (ILs) have attracted a great deal of attention as vital compounds that are widely used in various industries, such as the chemical and petroleum industries. Therefore, determining the properties of ionic liquids, such as electrical conductivity (EC), is an important issue. In this study, four different machine learning approaches including K-nearest neighbours (KNN), extreme gradient boosting (XGB), adaptive boosting-decision tree (Ada-DT), and adaptive boosting-support vector regression (Ada-SVR) were established to predict EC of ILs based on two categories namely: (I) smart models based on ILs chemical structure and temperature, and (II) smart models based on ILs thermodynamic properties and temperature. For this purpose, 2625 laboratory data points corresponding to 225 ILs have been used. The results showed that based on Model (I), KNN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.1976 and 0.9935, respectively, is the best model in predicting EC. Also, based on Model (II), the XGB model with RMSE and R2 values of 0.1447 and 0.9965, respectively, is the most powerful and accurate model for estimating electrical conductivity. Sensitivity analysis revealed that temperature and molecular weight have a direct and inverse effect on increasing the electrical conductivity of ILs, respectively. The investigation of the effects of different chemical bonds (in the chemical structure of ionic liquids) on ILs EC showed that –CH2- and > N- substructures have the most negative and positive effect on EC, respectively. Also, the proposed smart paradigms were capable of accurately predicting the effect cation alkyl chain length on electrical conductivity. Based on the leverage technique assessment more than 95% of total data points are in a valid region which indicates that intelligent models are reliable. Consequently, the introduction of intelligent models based on the chemical structure of ILs, the much broader data bank, and the higher accuracy of the proposed models are among the innovations and advantages of this study compared to the previous researches. Findings of this study highlights that chemical structure-intelligence based approaches provide robust, reliable and accurate way to predict EC of ILs.

Bio-based, self-adhesive, and self-healing ionogel with excellent mechanical properties for flexible strain sensor.

Bio-based ionogels with versatile properties are highly desired for practical applications. Herein, we designed a novel self-healing, anti-freezing, and self-adhesive ionogel with excellent sensor capability. The ionogel was obtained by cross-linking amino groups (chitosan) and aldehyde groups (dextran oxide) to form Schiff-base bonds in the ionic liquids (EMIMOAc) with TA. Ionogels inherited the superior electrical conductivity of ionic liquids (IG2, 1.1 mS cm−1). Due to the dynamic reaction of Schiff-base bonds, the obtained IG2 possessed self-healing properties (self-healing efficiency = 89%). The presence of TA also provided the ionogel with excellent self-adhesive properties (IG2/TA, adhesive strength to hogskin = 8.05 kPa). Owing to the low freezing point and low vapor pressure of ionic liquids, ionogels were endowed with anti-freeze properties and resistance to solvent volatility. Moreover, the ionogel can act as a strain sensor, and exhibited excellent sensitivity and sensing performance. Our work provided a green and effective method in preparation of the high performance ionogel sensor, which could accommodate future practical industrial applications.

A group contribution-based prediction method for the electrical conductivity of ionic liquids

A group contribution method is developed for estimating the electrical conductivity of ionic liquids (ILs). Based on 1578 collected experimental data covering 57 ILs in a wide range of temperature (248.05–468.15 K) and electrical conductivity (0.0017–9.1670 S m−1), the parameters of each group (cation, anion, substituent) in the method are optimized. A deterministic algorithm-based on multivariate linear regression is used with 1121 data points covering 57 ILs used as training set and the reaming 457 data points covering 20 ILs are used for validation. The prediction results (from test set) expressed as an average absolute relative deviation (AARD %) of 6.8% between the experimental and predicted electrical conductivities of ILs illustrate the good predictive capability of this group contribution method, with a maximum relative deviation (RD %) of 26.6%. This group contribution-based method can be easily extended to new IL groups that are not involved in this study once the experimental data of ILs involving these new groups become available.

Electrochemical study of anatase TiO2 nanotube array electrode in electrolyte based on 1,3-diethylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquid

The density, viscosity and electrical conductivity of ionic liquid 1,3-diethylimidazolium bis(trifluoromethylsulfonyl)imide, (C2C2imTFSI), and 0.5-M solution of LiTFSI in C2C2imTFSI were determined at different temperatures. The LiTFSI/C2C2imTFSI system was tested as a possible electrolyte for lithium-ion batteries by using anatase TiO2 nanotube array electrode as anode material for the first time. The electrochemical testing has shown not only the improvement of lithium-ion insertion/deinsertion properties by increasing temperature but also the existence of a decomposition of the electrolyte, detecting the change of colour. The decomposition of electrolyte leads to the formation of a film on the surface of the electrode which improves Coulombic efficiency during cycling.

Accurate prediction of electrical conductivity of ionic liquids + propylene carbonate binary mixtures

Ionic liquids (ILs) have remarkable features which make them applicable in numerous processes as environmentally friendly and promising chemicals. However, high viscosity and moderate conductivity of pure ILs have limited their application. Propylene carbonate (PC) is one of the most important and widely used compounds which is utilized in combination with pure ILs to overcome these challenges. Electrical conductivity is one of the most significant physical properties which affect the transport capabilities of ILs. Therefore, looking for a simple and accurate model to predict the electrical conductivity of ILs-PC binary mixtures is crucial. In this communication, a new model based on the least square support vector machine (LSSVM) was developed for accurate estimation of electrical conductivities of different ILs in PC solution under various operating conditions. Coupled simulated annealing (CSA) algorithm was used for optimizing the LSSVM model. Moreover, the presented model was successfully compared with other well-known models found in the literature. The results demonstrated that the predicted values obtained by the proposed LSSVM were in excellent agreement with experimental electrical conductivity values with R2 of 0.998 and 0.983 for training and testing data, respectively. Additionally, it was observed that the results of the proposed model exhibit higher reliability and accuracy in comparison to the other correlations. Finally, a sensitivity analysis was performed on influencing factors and results showed that IL molecular weight, temperature, and IL concentration has respectively the highest effect on electrical conductivity.

Nonlinear electrical conductivity of ionic liquid modified WS2/EPDM field grading material

In this work, a small amount of ionic liquids (ILs, 1-vinyl-3-ethylimidazolium tetrafluoroborate) was used to modify tungsten disulfide (WS2), and then, the modified WS2 was mixed with ethylene propylene diene monomer (EPDM) to obtain IL-WS2/EPDM composites with different IL contents. The content of WS2 in composites was determined from the result of our previous work. The nonlinear electrical conductivity of composites at 25 °C, 50 °C, and 80 °C was measured and analyzed subsequently. A small amount of ILs has an influence on the nonlinear property of composites, and the inflection point of the ternary composite can be controlled by changing IL contents. The direct current (DC) breakdown strength and thermal conductivity of composites were also measured and researched. Addition of ILs would decrease the breakdown strength of IL-WS2/EPDM composites.

Measurement and Correlation of the Electrical Conductivity of the Ionic Liquid [BMIM][TFSI] in Binary Organic Solvents

In this paper, the electrical conductivities of ionic liquid 1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ([BMIM][TFSI]) in mixed organic solvents of propylene carbonate (PC) + γ-butyrolactone (GBL) and ethylene carbonate (EC) + dimethyl carbonate (DMC) were measured. The effects of mixed organic solvents with different ratios (1:0, 2:1, 1:1, 1:2, 0:1) on the electrical conductivity of ionic liquid were investigated. The organic solvents EC and DMC have a synergistic effect for the electrical conductivity of [BMIM][TFSI]–EC/DMC, and the optimal ratio of EC to DMC is 1:1; however, the organic solvents have no synergistic effect in [BMIM][TFSI]–PC/GBL. The concentration dependence of the electrical conductivity of the solution can be well fitted by the empirical Casteel–Amis equation. The effects of temperature on electrical conductivity can be described by the Vogel–Tamman–Fulcher (VTF) equation and the Arrhenius equation. The IL concentration dependence of the activation energy Ea and t...

Temperature Dependence on Density, Viscosity, and Electrical Conductivity of Ionic Liquid 1-Ethyl-3-Methylimidazolium Fluoride

Ionic liquids are considered environmentally friendly media for various industrial applications. Basic data on physicochemical properties are significant for a new material, in terms of developing its potential applications. In this work, 1-ethyl-3-methylimidazolium fluoride ([EMIm]F) ionic liquid was synthesized via an anion metathesis process. Physical properties including the density, viscosity, electrical conductivity, and thermal stability of the product were measured. The results show that the density of [EMIm]F decreases linearly with temperature increases, while dynamic viscosity decreases rapidly below 320 K and the temperature dependence of electrical conductivity is in accordance with the VFT (Vogel–Fulcher–Tammann) equation. The temperature dependence of the density, conductivity, and viscosity of [EMIm]F can be expressed via the following equations: ρ = 1.516 − 1.22 × 10−3 T, σm = 4417.1exp[−953.17/(T − 166.65)] and η = 2.07 × 10−7exp(−5.39 × 104/T), respectively. [EMIm]F exhibited no clear melting point. However, its glass transition point and decomposition temperature are −71.3 °C and 135 °C, respectively.

The development of the UNIFAC-CONDUCT model as a novel approach for the estimation of the conductivity of pure ionic liquids

The correlation and prediction of physical properties of ionic liquids (ILs) is important in the development and design of novel structures for a variety of applications. In particular, for the application as electrolytic media for electrochemical applications, the electrical conductivity of ILs is an important property. Herein, we define a novel model called the UNIFAC-CONDUCT for the estimation of IL conductivity which mimics the principles of the UNIFAC-VISCO model. 784 data points for 38 ILs were used to establish this group contribution model, covering a wide range of temperature (248.15–468.15 K) and a broad range of electrical conductivity (0.002–14.54 S·m−1). Three sets of UNIFAC-CONDUCT parameters were determined to calculate the conductivity of pure ILs by three methodologies wherein the parameters of ion VFT equations, A, B and To, and binary interaction parameters, αmn, are selectively either fixed (based on previously reported values) and/or optimized by Marquardt technique. Correlations by the three methods are compared and analyzed, and further correlated with the previously predicted viscosities using the Nernst-Stokes relationship to support the quality of both the UNIFAC-VISCO model, previously proposed by our group, and the UNIFAC-CONDUCT model developed in this paper. Finally, the quality of the proposed method was further assessed through the prediction of the conductivity of one binary system containing two different ILs as the function of the temperature and composition with an accuracy close to 4.1%.

Structure‐based model for prediction of electrical conductivity of pure ionic liquids

A structure‐based method was proposed to estimate the electrical conductivity of ionic liquids covering wide ranges of temperature (238.15–484.1 K) and electrical conductivity (0.0001524–19.3 S/m) based on experimental data collect from literature from 1998 to 2015. The influences of temperature and ion structure on electrical conductivity were also discussed. The mean absolute percentage error between the calculated and literature data was 6.02%, with 6.12% for the training set (1978 data points, 177 ILs) and 5.10% for the test set (217 data points, 11 ILs). © 2016 American Institute of Chemical Engineers AIChE J, 62: 3751–3762, 2016

Measurement and correlation of electrical conductivity of ionic liquid [EMIM][DCA] in propylene carbonate and γ-butyrolactone

Electrical conductivities of ionic liquid (IL) 1-ethyl-3-methylimidazolium dicyanamide ([EMIM][DCA]) in propylene carbonate (PC) and γ-butyrolactone (GBL) were measured and correlated. The effects of temperature and IL concentration on the electrical conductivity were investigated comprehensively. The electrical conductivity of mixture increases with increasing temperature. When the mole fraction of IL in mixture increases from 0 to 1, electrical conductivity increases to a maximum value, and then decreases with IL mole fraction. Besides, the maximum point moves to high IL concentration as temperature increasing. The electrical conductivity data were correlated by Vogel-Tamman-Fulcher (VTF) and Arrhenius equation. VTF equation describes the temperature dependence of electrical conductivity with high precision, but it cannot be used to describe the effects of IL concentration. Arrhenius equation is able to describe the temperature and IL concentration dependence of electrical conductivity, but the accuracy is modest. In Arrhenius equation, the relationship between parameters (activation energy Ea and pre-exponential factor A) and the mole fraction of IL were obtained. Taking advantages of both Arrhenius equation and VTF equation, we proposed an improved four-parameter equation (quasi-Arrhenius equation) to correlate the electrical conductivity, in which the dependence of temperature and IL concentration were included, and the correlation results agreed well with experimental data.

Use of Aromatic Ionic Liquids in the Reduction of Surface Phenomena of Crude Oil–Water System and their Synergism with Brine

Enhanced oil recovery is governed primarily by the role of interfacial tension between crude oil and water. Interfacial tension (IFT) of the crude oil–water system is one of the vital factors in the analysis of the capillary forces affecting trapped oil within the reservoir rocks. High salinity and temperature of the reservoirs tend to make researchers search for new surfactants to lower the interfacial tension in crude oil–water systems. The current study hopes to create a move toward solving the above problem through the use of aromatic ionic liquids (ILs) based on imidazolium as the cation and various anions such as [Cl]−, [Br]−, [BF4]−, [H2PO4]−, [HSO4]−, and [PF6]− in different concentrations. This work involves the study of the effect of concentration, temperature, time, and brine on the fate of surface tension (SFT) of water and interfacial tension of crude oil–water systems. The present study also addresses the trend in the electrical conductivity of ILs in water along with the effect of temperatu...