Assessment of carbon dioxide solubility in ionic liquid/toluene/water systems by extended PR and PC-SAFT EOSs: Carbon capture implication

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Determination of standard molar volume of 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide on titanium dioxide surface

The fluids near the solid substrate display different properties compared to the bulk fluids owing to the asymmetric interaction between the fluid and substrate; however, to the best of our knowledge, no work has been conducted to determine the interfacial properties of fluids experimentally. In this work, we combined a pycnometer with experimental measurements and data processing to determine the standard thermodynamic properties of interfacial fluids for the first time. In the study, 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Hmim][NTf2]) and titanium dioxide (P25) were chosen as the probes to prove the concept. It was found that, with the combination of the Gay-Lussac pycnometer and the colligative law, together with selecting a suitable solvent, it is possible and reliable to determine the standard molar volume of the immobilized [Hmim][NTf2]. Compared to the bulk phase, the molar volumes of [Hmim][NTf2] on the P25 surface reduce by 20.8%-23.7% at temperatures from 293.15 to 323.15 K, and the reduction degrees decrease with increasing temperatures. The newly determined standard thermodynamic data was used to obtain the model parameters of hybrid electrolyte perturbed-chain statistical associating fluid theory density functional theory (ePC-SAFT-DFT), and further predictions of the density of interfacial ionic liquids with different film thicknesses were proved to be reliable in comparison with the experiment results.

Predicting the Thermodynamics of Ionic Liquids: What to Expect from PC-SAFT and COSMO-RS?

Two popular thermodynamic modeling frameworks, namely, the PC-SAFT equation of state and the COSMO-RS model, are benchmarked for their performance in predicting the thermodynamic properties of pure ionic liquids (ILs) and the solubility of CO₂ in ILs. The ultimate goal is to provide an illustration of what to expect from these frameworks when applied to ILs in a purely predictive way with established parametrization approaches, since the literature generally lacks their mutual comparisons. Two different modeling approaches with respect to the description of the molecular structure of ILs are tested within both models: a cation-anion pair as (i) a single electroneutral supermolecule and (ii) a pair of separately modeled counterions (ion-based approach). In general, we illustrate that special attention should be paid when estimating unknown thermodynamic data of ILs even with these two progressive thermodynamic frameworks. For both PC-SAFT and COSMO-RS, the supermolecule approach generally yields better results for the vapor pressure and the vaporization enthalpy of pure ILs, while the ion-based approach is found to be more suitable for the solubility of CO₂. In spite of some shortcomings, COSMO-RS with the supermolecule approach shows the best overall predictive capabilities for the studied properties. The ion-based strategy within both models has significant limitations in the case of the vaporization properties of ILs. In COSMO-RS, these limitations can, to a certain extent, be surpassed by additional quantum mechanical calculations of the ion pairing in the gas phase, while the ion-based PC-SAFT approach still needs a sophisticated improvement to be developed. As an initiating point, we explore one possible and simple route considering a high degree of cross associations between the counterions in the gas phase.

Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m²/g) and pore volume (cm³/g) of the MOFs and the output was CO₂ uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO₂ adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO₂ adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.

Thermodynamical Origin of Nonmonotonic Inserting Behavior of Imidazole Ionic Liquids into the Lipid Bilayer

The GPU-accelerated molecular dynamics simulations are performed to explore the dynamical inserting process of ionic liquids (ILs) into the lipid bilayer. We found that the free ions and clusters coexist in the system, but only the cation can insert into the lipid bilayer. In specific, after a microsecond-scale simulation (up to 1.16 μs), the inserting rate increases first and then decreases nonmonotonic as side chain of cation (n_chain) elongates, peaking at n_chain = 10. However, the inserting free energy decreases with n_chain, indicating the inserting process is easier for the larger n_chain. Such contrary originates from the formation of cluster, where the cluster dissociating energy shows that only cluster for n_chain ≤ 10 can dissociate spontaneously. Hence, the inserting rate is determined by the balance between n_chain and cluster stability. These quantitative competition mechanisms shed light to the rational design of the biocompatible ILs toward their applications in the biochemical-related fields.

Thermodynamic Assessment of the Partitioning of Acetone between Supercritical CO2 and Polystyrene Using the Polar PC-SAFT Equation of State

Supercritical carbon dioxide (scCO₂) has gained considerable attention in the process industry due to its favorable economic, environmental, and technical characteristics. Polymer processing is one of the key industrial applications where scCO₂ plays an important role. In order to be able to efficiently design the polymer processing equipment, understanding the phase behavior and partition of solutes between scCO₂ and polymers is necessary. This paper investigates the partitioning of acetone - a conventional polar cosolvent - between scCO₂ and polystyrene - a glassy polymer. We highlight the importance of taking into account the polar interactions between acetone molecules and their role in the polymer phase behavior. The system is modeled under a wide range of temperatures and pressures (278.15-518.2 K and 1.0-20.0 MPa) using the polar version of the perturbed chain statistical associating fluid theory (polar PC-SAFT) equation of state. The results show that at relatively low pressure, the system exhibits a vapor-liquid-liquid (VLL) three-phase region bounded by two two-phase regions (VL and LL). At high pressure, VLL and VL regions disappear and only the LL region remains. The temperature effect is more interesting, showing a transition of upper critical solution temperature behavior to lower critical solution temperature behavior at 10 MPa and 398.15 K. It is found that neglecting the polar term can lead to significant changes in the description of the polymeric-system phase behavior especially at lower temperatures. No such differences are observed at higher temperatures (above 500 K) where the effect of polar interaction is considerably weaker.

Ionic Liquids/Deep Eutectic Solvents-Based Hybrid Solvents for CO2 Capture

The CO2 solubilities (including CO2 Henry’s constants) and viscosities in ionic liquids (ILs)/deep eutectic solvents (DESs)-based hybrid solvents were comprehensively collected and summarized. The literature survey results of CO2 solubility illustrated that the addition of hybrid solvents to ILs/DESs can significantly enhance the CO2 solubility, and some of the ILs-based hybrid solvents are super to DESs-based hybrid solvents. The best hybrid solvents of IL–H2O, IL–organic, IL–amine, DES–H2O, and DES–organic are [DMAPAH][Formate] (2.5:1) + H2O (20 wt %) (4.61 mol/kg, 298 K, 0.1 MPa), [P4444][Pro] + PEG400 (70 wt %) (1.61 mol/kg, 333.15 K, 1.68 MPa), [DMAPAH][Formate] (2.0:1) + MEA (30 wt %) (6.24 mol/kg, 298 K, 0.1 MPa), [TEMA][Cl]-GLY-H2O 1:2:0.11 (0.66 mol/kg, 298 K, 1.74 MPa), and [Ch][Cl]-MEA 1:2 + DBN 1:1 (5.11 mol/kg, 298 K, 0.1 MPa), respectively. All of these best candidates show higher CO2 solubility than their used pure ILs or DESs, evidencing that IL/DES-based hybrid solvents are remarkable for CO2 capture. For the summarized viscosity results, the presence of hybrid solvents in ILs and DESs can decrease their viscosities. The lowest viscosities acquired in this work for IL–H2O, IL–amine, DES–H2O, and DES–organic hybrid solvents are [DEA][Bu] + H2O (98.78 mol%) (0.59 mPa·s, 343.15 K), [BMIM][BF4] + DETA (94.9 mol%) (2.68 mPa·s, 333.15 K), [L-Arg]-GLY 1:6 + H2O (60 wt %) (2.7 mPa·s, 353.15 K), and [MTPP][Br]-LEV-Ac 1:3:0.03 (16.16 mPa·s, 333.15 K) at 0.1 MPa, respectively.