Open Access

Ralph Lehnert

• Context: Solubility prediction based on the general solubility equation (GSE) rests on reliable values for the isobaric heat capacity difference ΔCp,1 of the solid solute. Usually, this value is estimated with either zero or the melting entropy ΔS1(Tm,1) or, in few cases, is extrapolated from data of thermally stable melts of the solute. This causes uncertainties in the prediction. Objective: To improve prediction accuracy a simple regression method is proposed that determines ΔCp,1 from measured solubilities. Materials and methods: Published experimental solubilities in neat organic solvents at 298 K of a model compound (L-(+)-ascorbic acid (LAA)) have been regressed using the GSE together with the Hansen parameter model for the activity coefficient. Results and discussion: Regression yielded ΔCp,1 = 238 J∙mol-1∙K-1 which agrees well with cross-validation results and is consistent with estimates from various group contribution methods. It was found that prediction accuracy improved in the order of increasing ΔCp,1, that is, from 0, via 91 (=ΔS1(Tm,1)) to 238 J∙mol-1∙K-1. It could be shown that mole fraction solubility of LAA can be forecast this way with an accuracy within current inter-laboratory variation. Conclusion: The proposed method shows a general way to improve prediction accuracy of activity coefficient based solubility models by determining ΔCp,1 without resorting to common assumptions. The method is universally applicable and easy to implement.