SO2 In Ionic Liquids Research Articles

Fabrication and application of a 3D-rGO electrochemical sensor for SO2 detection in ionic liquids.

Sulfur dioxide (SO2) is a common atmospheric pollutant. Currently, most of the detection methods are based on chemical reactions and optical absorption principles. However, these methods have some limitations in their detection range and accuracy, especially in complex environments. In this work, sulfur dioxide was absorbed by an ionic liquid, and a new electrochemical sensor based on 3D-rGO/CB was developed for electrochemical detection. Specifically, carbon black (CB) nanoparticles were incorporated with graphene oxide (GO) sheets using spray drying technology to form a highly porous and interconnected 3D-GO/CB microsphere structure. Then, the 3D-rGO/CB/GCE electrochemical sensor was fabricated by electrochemical reduction of the composite material onto a glassy carbon electrode (GCE) surface and used to detect sulfur dioxide in ionic liquids. The results demonstrated that the sensor had excellent conductivity and mass transfer preferable performance catalytic activity for SO2 in ionic liquids, and a linear detection range of 100-3500 ppm. Besides, the detection limit was 52.3 ppm (S/N = 3). Moreover, it had high selectivity, stability, and repeatability. This work significantly contributed to the development of advanced electrochemical sensors with improved performance for detecting SO2 in ionic liquids and has a potential application prospect in the field of electrochemical gas detection.

Comparison of LSSVM model results with artificial neural network model for determination of the solubility of SO2 in ionic liquids

Abstract In this study, the least square support vector machine (LSSVM) as a robust approach along with genetic algorithm (GA) was utilized for prediction of SO2 solubility in ionic liquids (ILs). The proposed model used the pressure, temperature, critical temperature, boiling temperature, critical pressure, critical compressibility factor, and acentric factor as input parameters. To develop the highly accurate model, the 232 data points were randomly split into training and testing sets. This model provides an average absolute relative deviation (AARD) of 4.6%, illustrating good accuracy and validity. In addition, an artificial neural network (ANN) was developed for prediction of SO2 solubility in ionic liquids. A thorough comparison between the proposed LSSVM and ANN models was conducted through statistical and graphical tools demonstrating the superiority of the former over the latter to predict the solubility of SO2 in ILs. Afterward, a sensitivity analysis was performed on the LSSVM model, elucidating that the pressure and temperature have the greatest effects on the SO2 solubility. Furthermore, the Leverage approach identified that only 2.8% of data points are outside the applicability domain of the proposed LSSVM model, which highlights the model is statistically acceptable and can be used as a predictive approach.

Predictive Thermodynamic Models for Ionic Liquid–SO2 Systems

Two types of predictive thermodynamic models, i.e., the UNIFAC model and GCLF EOS (group contribution lattice fluid equation of state), were applied to predict the solubility of SO2 in ionic liquids (ILs). The group interaction parameters were estimated by correlating the solubility data of SO2 in ILs exhaustively collected from the articles published until July 2015. It was verified that both the UNIFAC model and GCLF EOS could be used for predicting the solubility of SO2 in ILs, but the UNIFAC model gives results that are better than those of GCLF EOS. The UNIFAC model then was used to identify the structure–property relation for SO2 solubility and selectivity of CO2 to SO2, whereas GCLF EOS was used to investigate the volume expansivity of ILs upon the addition of SO2. The results showed that volume expansivity is independent of the different combinations of cations and anions and exhibits a linear relationship with SO2 solubility in ILs.

Solubilities of CO2, CH4, C2H6, and SO2 in ionic liquids and Selexol from Monte Carlo simulations

Monte Carlo simulations are used to calculate the solubility of natural gas components in ionic liquids (ILs) and Selexol, which is a mixture of poly(ethylene glycol) dimethyl ethers. The solubility of the pure gases carbon dioxide (CO2), methane (CH4), ethane (C2H6), and sulfur dioxide (SO2) in the ILs 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Cnmim][Tf2N], n = 4, 6), 1-ethyl-3-methylimidazolium diethylphosphate ([emim][dep]), and Selexol (CH3O[CH2CH2O]nCH3, n=4, 6) have been computed at 313.15K and several pressures. The gas solubility trend observed in the experiments and simulations is: SO2>CO2>C2H6>CH4. Overall, the Monte Carlo simulation results are in quantitative agreement with existing experimental data. Molecular simulation is an excellent tool to predict gas solubilities in solvents and may be used as a screening tool to navigate through the large number of theoretically possible ILs.

Testing solubility data of H2S and SO2 in ionic liquids for sulfur-removal processes

Solubility data of mixtures containing hydrogen sulfide in ionic liquids and sulfur dioxide in ionic liquids at several temperatures and pressures have been tested for thermodynamic consistency. Accurate and consistent data of these mixtures are needed for the design of separation processes for removing sulfur compounds from industrial gases, processes in which ionic liquids present tremendous potential as solvents and extracting agents. The study considers data of twelve binary hydrogen sulfide+ionic liquids mixtures and three binary sulfur dioxide+ionic liquid mixtures. The modified Peng–Robinson equation of state proposed by Kwak and Mansoori is used for correlating the solubility data and a flexible thermodynamic consistency method is applied to analyze 80 isothermal P-x data sets including 630 data points available in the open literature. Modeling is found acceptable in all cases and the thermodynamic consistency method gives a clear conclusion about consistency or inconsistency of each set of data.

Absorption of SO2 by thermal-stable functional ionic liquids with lactate anion

The capture of SO2 by ionic liquids (ILs) has drawn much attention due to the unique properties of ILs. Among the ILs, guanidinium- and alkanolaminium-based ILs have been widely studied by many groups experimentally or theoretically. However, the thermal stability of the ILs is low, and the degradation of the ILs may occur during the desorption process of SO2 at high temperatures. In this work, 1-butyl-3-methylimidazolium lactate ([Bmim]L) with high thermal stability was synthesized and was used to absorb SO2. It has been found that the IL can absorb large amounts of SO2 and the solubility of SO2 in the IL decreases with the increase of temperature; the absorption of SO2 by the IL is reversible, and the IL can be reused for the absorption of SO2 without obvious loss of absorption capacity. The comparison of [Bmim]L with other ILs based on lactate anion suggests that the cations of ILs have a significant influence on the absorption and desorption behaviors of SO2 while the length of the alkyl chains has no obvious influence on the solubility of SO2 in imidazolium-based ILs with lactate anion. The imidazolium-based ILs with lactate anion are promising absorbents for the removal of SO2 from the flue gas.