Estimation of the heat capacities of deep eutectic solvents

Increasing the Dissolution Rate of Polystyrene Waste in Solvent-Based Recycling

Solvent-based recycling of plastic waste is a promising approach for cleaning polymer chains without breaking them. However, the time required to actually dissolve the polymer in a lab environment can take hours. Different factors play a role in polymer dissolution, including temperature, turbulence, and solvent properties. This work provides insights into bottlenecks and opportunities to increase the dissolution rate of polystyrene in solvents. The paper starts with a broad solvent screening in which the dissolution times are compared. Based on the experimental results, a multiple regression model is constructed, which shows that within several solvent properties, the viscosity of the solvent is the major contributor to the dissolution time, followed by the hydrogen, polar, and dispersion bonding (solubility) parameters. These results also indicate that cyclohexene, 2-pentanone, ethylbenzene, and methyl ethyl ketone are solvents that allow fast dissolution. Next, the dissolution kinetics of polystyrene in cyclohexene in a lab-scale reactor and a baffled reactor are investigated. The effects of temperature, particle size, impeller speed, and impeller type were studied. The results show that increased turbulence in a baffled reactor can decrease the dissolution time from 40 to 7 min compared to a lab-scale reactor, indicating the importance of a proper reactor design. The application of a first-order kinetic model confirms that dissolution in a baffled reactor is at least 5-fold faster than that in a lab-scale reactor. Finally, the dissolution kinetics of a real waste sample reveal that, in optimized conditions, full dissolution occurs after 5 min.

Deep Eutectic Solvents: Properties and Applications in CO2 Separation

Nowadays, many researchers are focused on finding a solution to the problem of global warming. Carbon dioxide is considered to be responsible for the "greenhouse" effect. The largest global emission of industrial CO₂ comes from fossil fuel combustion, which makes power plants the perfect point source targets for immediate CO₂ emission reductions. A state-of-the-art method for capturing carbon dioxide is chemical absorption using an aqueous solution of alkanolamines, most frequently a 30% wt. solution of monoethanolamine (MEA). Unfortunately, the usage of alkanolamines has a number of drawbacks, such as the corrosive nature of the reaction environment, the loss of the solvent due to its volatility, and a high energy demand at the regeneration step. These problems have driven the search for alternatives to that method, and deep eutectic solvents (DESs) might be a very good substitute. Many types of DESs have thus far been investigated for efficient CO₂ capture, and various hydrogen bond donors and acceptors have been used. Deep eutectic solvents that are capable of absorbing carbon dioxide physically and chemically have been reported. Strategies for further CO₂ absorption improvement, such as the addition of water, other co-solvents, or metal salts, have been proposed. Within this review, the physical properties of DESs are presented, and their effects on CO₂ absorption capacity are discussed in conjunction with the types of HBAs and HBDs and their molar ratios. The practical issues of using DESs for CO₂ separation are also described.

Critical Properties of Ternary Deep Eutectic Solvents Using Group Contribution with Extended Lee-Kesler Mixing Rules

One of the most commonly used molecular inputs for ionic liquids and deep eutectic solvents (DESs) in the literature are the critical properties and acentric factors, which can be easily determined using the modified Lydersen-Joback-Reid (LJR) method with Lee-Kesler mixing rules. However, the method used in the literature is generally applicable only to binary mixtures of DESs. Nevertheless, ternary DESs are considered to be more interesting and may provide further tailorability for developing task-specific DESs for particular applications. Therefore, in this work, a new framework for estimating the critical properties and the acentric factor of ternary DESs based on their molecular structures is presented by adjusting the framework reported in the literature with an extended version of the Lee-Kesler mixing rules. The presented framework was applied to a data set consisting of 87 ternary DESs with 334 distinct compositions. For validation, the estimated critical properties and acentric factors were used to predict the densities of the ternary DESs. The results showed excellent agreement between the experimental and calculated data, with an average absolute relative deviation (AARD) of 5.203% for ternary DESs and 5.712% for 260 binary DESs (573 compositions). The developed methodology was incorporated into a user-friendly Excel worksheet for computing the critical properties and acentric factors of any ternary or binary DES, which is provided in the Supporting Information. This work promotes the creation of robust, accessible, and user-friendly models capable of predicting the properties of new ternary DESs based on critical properties, thus saving time and resources.

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R² = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

The Role of Hydrogen Bond Donor on the Extraction of Phenolic Compounds from Natural Matrices Using Deep Eutectic Systems

Recently, deep eutectic systems (DESs) as extraction techniques for bioactive compounds have surfaced as a greener alternative to common organic solvents. In order to study the effect of these systems on the extraction of phenolic compounds from different natural sources, a comprehensive review of the state of the art was carried out. In a first approach, the addition of water to these systems and its effect on DES physicochemical properties such as polarity, viscosity, and acidity was investigated. This review studied the effect of the hydrogen bond donor (HBD) on the nature of the extracted phenolics. The effects of the nature of the HBD, namely carbon chain length as well as the number of hydroxyl, methyl, and carbonyl groups, have shown to play a critical role in the extraction of different phenolic compounds. This review highlights the differences between DES systems and systematizes the results published in the literature, so that a more comprehensive evaluation of the systems can be carried out before any experimental trial.

A Global Model for the Estimation of Speeds of Sound in Deep Eutectic Solvents

Deep eutectic solvents (DESs) are newly introduced green solvents that have attracted much attention regarding fundamentals and applications. Of the problems along the way of replacing a common solvent by a DES, is the lack of information on the thermophysical properties of DESs. This is even more accentuated by considering the dramatically growing number of DESs, being made by the combination of vast numbers of the constituting substances, and at their various molar ratios. The speed of sound is among the properties that can be used to estimate other important thermodynamic properties. In this work, a global and accurate model is proposed and used to estimate the speed of sound in 39 different DESs. This is the first general speed of sound model for DESs. The model does not require any thermodynamic properties other than the critical properties of the DESs, which are themselves calculated by group contribution methods, and in doing so, make the proposed method entirely independent of any experimental data as input. The results indicated that the average absolute relative deviation percentages (AARD%) of this model for 420 experimental data is only 5.4%. Accordingly, based on the achieved results, the proposed model can be used to predict the speeds of sound of DESs.