Investigation of the CO2-solubility in deep eutectic solvents using COSMO-RS and molecular dynamics methods

COSMO-RS-based assessment of thermodynamic tools in predicting the polar and non-polar solvents efficiency in vegetable oil extraction

CONTEXT

One of the prevalent methods for evaluating separation performance is to predict the interactions of solvent and solute molecules. The infinite dilution activity coefficient, Gibbs free energy, and sigma profiles provided insights into the solubilization of a solute and revealed the intensity of the solution's molecular interactions. The effective thermodynamic tools (infinite dilution activity coefficient, Gibbs free energy) were evaluated for predicting the efficiency of 18 polar and non-polar organic solvents in rubber seed oil (RSO) extraction. An infinite dilution activity coefficient was computed to evaluate the solubility of the rubber seed oil model compound (linoleic acid) in the organic solvents. Gibbs free energy was evaluated to show the energy change associated with the molecules mixing process and forecast the miscibility of linoleic acid molecules in the solvents. Moreover, the study examined the sigma profiles and sigma surfaces of organic solvents and linoleic acid to acquire a deeper insight into their similarities and how they interact molecularly. According to the computational prediction and experimental verification, the thermodynamic properties of Gibbs free energy and activity coefficient proved to be highly effective tools for screening polar and moderately polar solvents, predicting the molecular interactions with solute. Whereas the sigma profile and sigma surface were found to be the most efficient tools for evaluating the efficacy of non-polar solvents. Solvents with moderate polarity, such as tetrahydrofuran and diethyl ether, as well as non-polar solvents like pentane, heptane, and n-hexane, proved to be effective and favorable for oil extraction, resulting in the highest oil yields of approximately 27.0%. Overall, the COSMO-RS method demonstrates its utility in estimating the solubility of RSO in organic solvents, enabling early identification of the most effective solvent.

METHODS

The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions.

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Carbon dioxide solubility in choline chloride-based deep eutectic solvents under diverse conditions

CONTEXT

Global warming is a severe problem experiencing the climate crisis due to rising CO₂ emissions. Deep eutectic solvents (DESs) have recently attracted a lot of attention as potential absorbents to mitigate carbon dioxide CO₂ emissions because of their large CO₂ capacities and stability under diverse conditions. Designing a potent DES requires knowledge of molecular-level understanding including structure, dynamics, and interfacial properties in DESs. In this study, we investigate the CO₂ sorption and diffusion in different DESs at different temperatures and pressure using molecular dynamics (MD) simulations. Our results demonstrate that CO₂ molecules preferentially concentrate at the CO₂-DES interface, and the diffusion of CO₂ in bulk DESs increases with increasing pressure and temperature. The solubility of CO₂ in the three DESs increases as ChCL-ethylene glycol < ChCL-urea < ChCL-glycerol at high pressure (58.6 bar).

METHODS

The initial configuration for MD simulations included DES and CO₂ and produced the solvation box using PACKMOL software. The geometries are optimized in the Gaussian 09 software at the theoretical level of B3LYP/6-311 + G*. The partial atomic charges were fitted to an electrostatic surface potential using the CHELPG method. MD simulations were carried out by using the NAMD version 2.13 software. VMD software was used to take the snapshots. TRAVIS software is used to determine spatial distribution functions.

Experimental investigation of novel ternary amine-based deep eutectic solvents for CO2 capture

This study investigates the effect of using water as a low-viscosity component in ternary amine-based deep eutectic solvents (DESs) on the physicochemical properties, thermal stability, and CO2 absorption capacity of the resulting DESs. It should be emphasized that water is a component of the ternary DES. The effect of water content in the DES, type of hydrogen bond acceptors (HBAs), hydrogen bond donors (HBDs), and HBA:HBD ratio on the above parameters was investigated. Moreover, the effect of temperature and pressure on the CO2 absorption capacity of DESs was predicted using the predictive model COSMO-RS. This model was also used to predict the CO2 solubility in the DESs and the results were compared with the experimental values. The results showed that the addition of small amounts of water, e.g., 5 and 10 wt% during preparation, can significantly decrease the viscosity of the resulting DESs, up to 25% at room temperature, while maintaining the high CO2 absorption capacity and high thermal stability. The ternary DESs based on MEA exhibited a high CO2 absorption capacity of 0.155-0.170 g CO2 / g DES. The ternary DESs were found to be thermally stable with a decomposition temperature of 125°C, which promotes the use of such solvents in post-combustion capture processes. Finally, COSMO-RS proved to be a suitable tool for qualitative prediction of CO2 solubility in DESs and demonstration of trends related to the effects of temperature, pressure, molar ratio, water content, HBD and HBA on CO2 solubility.

New molecular structure based models for estimation of the CO2 solubility in different choline chloride-based deep eutectic solvents (DESs)

In this study, CO₂ solubility in different choline chloride-based deep eutectic solvents (DESs) has been investigated using the Quantitative Structure-Property Relationship (QSPR). In this regard, the effect of different structures of the hydrogen bond donor (HBD) in choline chloride (ChCl) based deep eutectic solvents (DESs) has been studied in different temperatures and different molar ratios of ChCl as hydrogen bond acceptor (HBA) to HBD. 12 different datasets with 390 data on the CO₂ solubility were chosen from the literature for the model development. Eight predictive models, which contain the pressure and one structural descriptor, have been developed at the fixed temperature (i.e. 293, 303, 313, or 323 K), and the constant molar ratio of ChCl to HBD equal to 1:3 or 1:4. Moreover, two models were also introduced, which considered the effects of pressure, temperature, and HBD structures, simultaneously in the molar ratios equal to 1:3 or 1:4. Two additional datasets were used only for the further external validation of these two models at new temperatures, pressures, and HBD structures. It was identified that CO₂ solubility depends on the "EEig02d" descriptor of HBD. "EEig02d" is a molecular descriptor derived from the edge adjacency matrix of a molecule that is weighted by dipole moments. This descriptor is also related to the molar volume of the structure. The statistical evaluation of the proposed models for the unfixed and fixed temperature datasets confirmed the validity of the developed models.

Structural and dynamic properties of eutectic mixtures based on menthol and fatty acids derived from coconut oil: a MD simulation study

The structural and dynamical properties of the binary mixture of Menthol (MEN) and Fatty acids (FAs) were investigated using molecular dynamics simulations. To this end, the relationship between the structural and dynamical properties of the eutectic mixtures of MEN and FAs with different molar percentages of FAs are studied. Structural properties of the eutectic mixtures were characterized by calculating the combined distribution functions (CDFs), radial distribution functions (RDFs), angular distribution functions (ADFs), hydrogen bonding networks, and spatial distribution functions (SDF). Additionally, our Results indicated robust interactions between menthol and Caprylic acid molecules Finally, the transport properties of the mixtures were investigated using the mean square displacement (MSD) of the centers of mass of the species, self-diffusion coefficients and vector reorientation dynamics (VRD) of bonds. Overall, our simulation results indicated that intermolecular interactions have a significant effect on the dynamic properties of species.

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Carbon Capture from Biogas by Deep Eutectic Solvents: A COSMO Study to Evaluate the Effect of Impurities on Solubility and Selectivity

Deep eutectic solvents (DES) are compounds of a hydrogen bond donor (HBD) and a hydrogen bond acceptor (HBA) that contain a depressed melting point compared to their individual constituents. DES have been studied for their use as carbon capture media and biogas upgrading. However, contaminants’ presence in biogas might affect the carbon capture by DES. In this study, conductor-like screening model for real solvents (COSMO-RS) was used to determine the effect of temperature, pressure, and selective contaminants on five DES’ namely, choline chloride-urea, choline chloride-ethylene glycol, tetra butyl ammonium chloride-ethylene glycol, tetra butyl ammonium bromide-decanoic acid, and tetra octyl ammonium chloride-decanoic acid. Impurities studied in this paper are hydrogen sulfide, ammonia, water, nitrogen, octamethyltrisiloxane, and decamethylcyclopentasiloxane. At infinite dilution, CO2 solubility dependence upon temperature in each DES was examined by means of Henry’s Law constants. Next, the systems were modeled from infinite dilution to equilibrium using the modified Raoults’ Law, where CO2 solubility dependence upon pressure was examined. Finally, solubility of CO2 and CH4 in the various DES were explored with the presence of varying mole percent of selective contaminants. Among the parameters studied, it was found that the HBD of the solvent is the most determinant factor for the effectiveness of CO2 solubility. Other factors affecting the solubility are alkyl chain length of the HBA, the associated halogen, and the resulting polarity of the DES. It was also found that choline chloride-urea is the most selective to CO2, but has the lowest CO2 solubility, and is the most polar among other solvents. On the other hand, tetraoctylammonium chloride-decanoic acid is the least selective, has the highest maximum CO2 solubility, is the least polar, and is the least affected by its environment.