Designing aqueous deep eutectic mixtures by data-integrated simulation

Postdoc Project

Project Description

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. Moreover, they have been successfully used as solvents in chemical reactions such as alkylation or in electrochemical energy storage. Typically, an ammonium salt such as choline chloride is mixed with a hydrogen bond donor such as urea or glycerol, resulting in a liquid at room temperature. However, the ammonium salt can also be replaced by other components, and DESs consisting of glucose:fructose:sucrose or glucose:fructose:water have been characterized. The thermophysical properties of aqueous DES mixtures such as viscosity, substrate solubility, or thermodynamic activity of water can be designed by varying the components, their mole fraction, 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 is available. Established thermodynamic models such as group contribution methods, equations of state, or activity coefficient models can only cover fractions of the design space. In contrast, molecular dynamics (MD) 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 resulting in deviations from ideal mixing behaviour and in the reduction of the melting temperature are still not well understood. To reduce the computational expense involved in screening the design space a combination of MD simulations with machine learning based on fingerprints generated by MD will be used. This approach was tested previously for the prediction of activity coefficients. Here, it will be extended to predict novel substrate-solvent systems with desired properties, such as specific substrate concentration, low viscosity, and controlled thermodynamic activity of water.

The goal of the project is to derive rules on how to design multi-component deep eutectic substrate-solvent systems, which at a given temperature are liquid and have a sufficiently low viscosity. Therefore, we will predict for a large number of aqueous mixtures their melting point and 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. The parameter study will be performed using our Simulation Foundry as a workflow platform which enables scalable, interoperable, and re-usable simulations for parameter studies.

Project Information

Project Name Designing aqueous deep eutectic mixtures by data-integrated simulation
Project Duration September 2022 - August 2024
Project Leader Jürgen Pleiss
Niels Hansen
Project Members Marcelle Spera, postdoctoral researcher
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