Enhancing Solubility Predictions of Plastic Additives in Depolymerization Solvents Using Quantum Chemical Conformer Analysis and COSMO-RS

Abstract The success of recycling plastics by means of depolymerization requires the selective purification of regenerated monomers from complex mixtures. This can be assisted by accurate prediction of the phase behavior of large, flexible, and multifunctional additive molecules, which remains a challenge for current thermodynamic models. In this study, the solubility of 11 representative additives and three PET monomers was measured in two common depolymerization solvents, water and ethylene glycol, covering a wide range from practically insoluble (<1 ppm) to 41.6 wt %. To benchmark predictive capabilities, a quantum-chemistry workflow was investigated, integrating RDKit conformer generation, Gaussian DFT optimizations, and COSMO-RS thermodynamic modeling. Systematic conformer sampling proved critical for bulky, high-molecular-weight additives such as antioxidants. A comparison of three Pople-type basis sets revealed a trade-off between computational cost and accuracy. The 6–311++G(d,p) basis set provided the best balance (RMSE = 1.31 log units), while 3–21+G(d) reduced computational cost at lower accuracy, and 6–311++G(3df,3pd) yielded only minor improvements for select cases at significantly higher cost. Prediction accuracy was highest for more soluble compounds but decreased with increasing molecular weight, conformational flexibility, and Gibbs free energy of fusion. To improve solid–liquid equilibrium predictions, melting points and fusion enthalpies were measured using DSC and compared with QSPR estimates. This combined computational-experimental approach supports the development of energy-efficient purification strategies in chemical recycling of condensation polymers such as PET.