November 22, 2024
Reza Azin

Reza Azin

Academic Rank: Professor
Address: -
Degree: Ph.D in -
Phone: -
Faculty: Faculty of Petroleum, Gas and Petrochemical Engineering

Research

Title A New Thermodynamic Approach for Protein Partitioning in Reverse Micellar Solution
Type Article
Keywords
Journal Physical Chemistry Research
DOI
Researchers Shahriar Osfouri (First researcher) , Reza Azin (Third researcher)

Abstract

Reverse micellar systems are nano-fluids with unique properties that make them attractive in high selectivity separation processes, especially for biological compounds. Understanding the phase behavior and thermodynamic properties of these nano-systems is the first step in process design. Separation of components by these nano-systems is performed upon contact of aqueous and reverse micellar phases. Due to the complexities of the molecular interactions of components, phase behavior studies of these solutions are different from regular liquid-liquid systems, and few thermodynamic models have been developed to describe distribution of extract between phases. In this study, a thermodynamic model with ?-? approach and use of equations of states is developed for the first time to describe the protein phase equilibria in reverse micellar systems. The developed model assumes that some reverse micelles act as active surfaces which can adsorb protein molecules. In addition, the non-ideal behavior of micellar solution was modeled by three equation of states, i.e. van der Waals, Peng-Robinson, and Soave-Redlich-Kwong. Results showed that Soave-Redlich-Kwong equation of state shows the best match with experimental data of bovine serum albumin extraction from aqueous solution using reverse micellar solution of cetyltrimethylammonium bromide, a cationic surfactant. In addition, results indicate that the proposed thermodynamic model can describe the changes in electrostatic forces and increase in active surfaces on equilibrium protein extraction. Moreover, the standard deviation shows an excellent match between experimental data and model predictions.