Competition between CO₂-philicity and Mixing Entropy Leads to CO₂ Solubility Maximum in Polyether Polyols
In carbon dioxide-blown polymer foams, the solubility of carbon dioxide (CO₂) in the polymer profoundly shapes the structure and, consequently, the physical properties of the foam. One such foam is polyurethane-commonly used for thermal insulation, acoustic insulation, and cushioning which increasingly relies on CO₂ to replace environmentally harmful blowing agents. Polyurethane is produced through the reaction of isocyanate and polyol, of which the polyol has the higher capacity for dissolving CO₂. While previous studies have suggested the importance of the effect of hydroxyl end groups on CO₂ solubility in short polyols (<1000 g/mol), their effect in polyols with higher molecular weight (≥1000 g/mol) and higher functionality (>2 hydroxyls per chain)-as are commonly used in polyurethane foams-has not been reported. Here, we show that the solubility of CO₂ in polyether polyols decreases with molecular weight above 1000 g/mol and decreases with functionality using measurements performed by gravimetry-axisymmetric drop-shape analysis. The nonmonotonic effect of molecular weight on CO₂ solubility results from the competition between effects that reduce CO₂ solubility (lower mixing entropy) and effects that increase CO₂ solubility (lower ratio of hydroxyl end groups to ether backbone groups). To generalize our measurements, we modeled the CO₂ solubility using a perturbed chain-statistical associating fluid theory (PC-SAFT) model, which we validated by showing that a density functional theory model based on the PC-SAFT free energy accurately predicted the interfacial tension.
© 2022 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). The authors express their gratitude to Prof. Richard C. Flagan of Caltech for helpful discussions while planning, performing, and writing up this work and to Dr. Sriteja Mantha of Caltech for help with the group contribution method. The authors would also like to thank Dr. Maria Rosaria Di Caprio for help training A.S.Y. to use G-ADSA at the University of Naples. A.S.Y. acknowledges support by the Dow University Partnership Initiative and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301. The authors declare no competing financial interest.
Published - ie2c02396.pdf
Supplemental Material - ie2c02396_si_001.pdf