Activity coefficients of binary methanol alcohol mixtures from cluster weighting

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Molecular Modeling

Background:

• Hydrogen bonding in alcohols is fundamental to their physical and chemical properties.
• Understanding alcohol mixtures is vital for applications like solvent extraction and metal separation.
• Previous studies often lack detailed molecular-level insights into complex alcohol systems.

Purpose of the Study:

• To investigate the hydrogen bond network in small alcohols and their mixtures.
• To explore the impact of molecular size and branching on hydrogen bonding.
• To provide a computational framework for predicting thermodynamic properties of alcohol systems.

Main Methods:

• Utilized the extended tight binding method GFN2-xTB for cluster evaluation and geometry optimization.
• Employed a genetic algorithm to generate thousands of alcohol clusters.
• Applied the binary quantum cluster equilibrium (bQCE) approach with the peacemaker 2.8 code for thermodynamic property calculations.

Main Results:

• Evaluated and discussed interaction energies and geometries for neat alcohol systems.
• Calculated thermodynamic properties including vaporization enthalpies and activity coefficients.
• Analyzed combined distribution functions of hydrogen bond distances and angles for neat and mixed clusters.

Conclusions:

• The study provides a detailed molecular-level understanding of hydrogen bonding in small alcohols and mixtures.
• Computational methods, including GFN2-xTB and bQCE, are effective for studying complex alcohol systems.
• The findings have implications for optimizing processes in solvent and metal extraction.