Deep eutectic solvents (DES) are promising solvents in biocatalysis, because they are designable, renewable, biodegradable, and cheap, and thus are an alternative to organic solvents for enzyme catalyzed reactions that involve hydrophobic substrates. The thermophysical properties of aqueous DES mixtures such as density, viscosity, substrate solubility, or thermodynamic activity of water can be designed by varying the components, their mole fractions, and the temperature. In addition, the substrates and products have an effect on the thermophysical properties of the reaction medium. Thus, a very large design space has to be explored in order to design optimal reaction systems. Molecular dynamics simulations offer a holistic atomic-resolution approach to DES modelling, because they predict thermodynamic and transport properties and provide a detailed molecular understanding, which is essential to guide the design of DESs. This molecular insight is important, because the physical effects which result in deviations from ideal mixing behavior are still not well understood.
The goal of the proposed project is to derive rules on how to design multi-component deep eutectic substrate-solvent systems, which at a given temperature have a sufficiently low viscosity. Therefore, we will predict for a large number of aqueous mixtures their viscosity at different water content and systematically investigate the transition from pure DES to dilute aqueous mixtures. Thermophysical data from the simulations will be integrated and compared to published experimental data of DESs by using the data exchange format ThermoML.
This research project is funded by DFG in the framework of the Stuttgart Cluster of Excellence SimTech (EXC2075).
Publications
- Gültig, M., Range, J.P., Schmitz, B., Pleiss, J.: Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data. 67, 3340–3350 (2022). https://doi.org/10.1021/acs.jced.2c00391.
- Gygli, G., Pleiss, J.: Simulation Foundry: automated and F.A.I.R. molecular modelling. J Chem Inf Model. 60, 1922–1927 (2020). https://doi.org/10.1021/acs.jcim.0c00018.
- Gygli, G., Xu, X., Pleiss, J.: Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Sci Rep. 10, 21395–21395 (2020). https://doi.org/10.1038/s41598-020-78101-y.
- Xu, X., Range, J., Gygli, G., Pleiss, J.: Analysis of thermophysical properties of deep eutectic solvents by data integration. J Chem Eng Data. 65, 1172–1179 (2020). https://doi.org/10.1021/acs.jced.9b00555.
- Baz, J., Held, C., Pleiss, J., Hansen, N.: Thermophysical properties of glyceline-water mixtures investigated by molecular modelling. Phys Chem Chem Phys. 21, 6467–6476 (2019). https://doi.org/10.1039/C9CP00036D.
- Ferrario, V., Hansen, N., Pleiss, J.: Interpretation of cytochrome P450 monooxygenase kinetics by modeling of thermodynamic activity. J Inorg Biochem. 183, 172–178 (2018). https://doi.org/10.1016/j.jinorgbio.2018.02.016.
Project Members
