Plastic waste is currently produced at an alarmingly high rate, nearing 400 Mt per year. The accumulation of plastics in the environment is growing rapidly, yet our knowledge of their persistence is very limited. Efficient and affordable dissolution and chemical upcycling of plastic wastes are also a significant hurdle in the conversion of plastic polymers to value-added chemicals, and finding a suitable solvent is also a major concern. Ionic liquids (ILs) have recently demonstrated their ability to dissolve and convert polyethylene terephthalate (PET) into valuable products. However, identifying an optimal IL from the large number of anion and cation combinations possible is quite challenging. To address this issue, the COSMO-RS (COnductor-like Screening MOdel for Real Solvents) model has emerged as a reliable computational tool that can screen numerous ILs based on the different thermodynamic properties that are needed for polymer dissolution. The current study demonstrates the dissolution behavior of plastic wastes in ILs using the COSMO-RS model. In this study, 99 cations and 95 anions were chosen and combined to form 9405 ILs, which were evaluated by predicting logarithmic activity coefficient (ln(γ)) and excess enthalpies (H^E) of typical plastic wastes (PET, polystyrene, polypropylene, and polyvinylchloride). Based on the COSMO-RS predicted thermodynamic properties (ln(γ) and H^E), anions such as acetate, formate, glycinate, and N-methylcarbamate in combination with the cations like superbase, ammonium, and pyrrolidinium are predicted to be suitable solvents for plastic dissolution. The predicted ln(γ) and H^E results are further validated with the experimental results and the predicted thermodynamic properties and experimental results are in good alignment. An excess enthalpy calculation demonstrated that strong hydrogen bond interactions between the polymer and the IL are vital factors for efficient dissolution to occur, with the anion and the cation of the IL having a similar effect on the process.

Carbon dioxide (CO₂) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO₂ in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO₂ solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO₂ solubility (ln(x CO₂ )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO₂ solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO₂ capture and utilization.