Molecular Methods for Separation Processes

Dr.-Ing. S. Müller

Novel separation processes require non-volatile, stable and cheap solvents with high selectivities and capacities. To meet these objectives, our group develops innovative separation processes for the separation of complex mixtures. With a focus on predictive modeling, modern molecular methods are applied and extended for the investigation and design of these separation processes.

The predictive model for electrolyte systems COSMO-RS-ES

The COSMO-RS-ES model has shown good capabilities in the prediction of phase equilibria in electrolyte systems with conventional salts, but its application was limited to aqueous mixtures and low to moderate concentrations. In the course of this project the model has been combined with considerations for long-range electrostatics that account for concentration dependent properties and underscreening at low relative permittivity values. Thereby predictions of phase equilibria in highly concentrated non-aqueous electrolyte systems containing conventional salts have been considerably improved. At the present developmental stage, the revision and extension of the model has included ionic liquid systems. This newly extended COSMO-RS-ES model is therefore capable of consistently handling electrolyte systems with salts or ionic liquids, aqueous or non-aqueous from infinite dilution to the fused salt state. Future prospects will focus on the modelling of mixed salt/ionic liquid systems.


Predictive equation of state for complex mixtures containing electrolytes

The use of predictive methods to calculate thermophysical properties of electrolyte solutions, although may be less expensive than obtaining experimental data, presents modelling challenges due to the existence of charged species. These methods are useful to improve the operating conditions of industrial processes and to strengthen the knowledge of biophysical systems’ behaviour.

COSMO-RS is an effective predictive model, which uses quantum chemistry calculations and a statistical thermodynamics approach to describe interactions between the compounds of a non-ideal liquid mixture. Although this method requires no prior knowledge of the properties of the system, it is limited to low-pressure applications and does not properly describe ionic mixtures.

In an effort to model compressible liquids, the COSMO-SAC-Phi equation of state combines lattice-fluid theory with COSMO-RS, at similar computational costs. Furthermore, refinements to interpret the contribution of charged ions have been proposed in COSMO-RS-ES. This method describes close-range and long-range interactions with COSMO-RS theory and Pitzer-Debye-Hückel model, respectively.

The aim of this project is the development of an open-source predictive equation of state for complex mixtures containing electrolytes. To achieve this purpose, an expansion to accommodate the electrolyte behaviours is proposed for the COSMO-SAC-Phi equation of state.

Solvent Impact on Enzyme Behavior through Molecular Dynamics Simulations

We investigate the behavior of horse liver alcohol dehydrogenase (HLADH) in different organic solvents and deep eutectic solvents (DESs) using molecular dynamics (MD) simulations. Transferring the biocatalysis of alcohol dehydrogenase from aqueous to non-conventional reaction media can help to overcome limitation of aqueous solutions such as (1) limited solubility of reagents, (2) induction of side reactions or (3) limited enzymatic stability. To shed light on the molecular interactions of HLADH with different solvent mixtures we opted to perform MD simulations at atomistic resolution in addition to the experimental analysis. Our results identify the solvation effects of DESs (e.g., see Fig. 1), individual DES components, and organic solvents on the specific activity and stability of HLADH. The simulations also identify suitable organic solvents for ADH catalysis at an organic-aqueous phase boundary. Additionally, our focus on the active center of HLADH and its structural changes in different environments helps to quantify enzyme-substrate interactions. Our findings deepen the understanding of enzyme-solvent interactions in non-conventional reaction media and provide insights to guide solvent engineering.