A New Thermodynamic Approach for Protein Partitioning in Reverse Micellar Solution

نویسندگان

  • Reza Azin Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
  • Shahriar Osfouri Department of Oil, Gas and Petrochemical Engineering, Persian Gulf University, Iran
  • Tuba Tayebi Department of chemical Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
چکیده مقاله:

Reverse micellar systems are nanofluids 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 nanosystems is the first step in process design. Separation of components by these nanosystems is performed upon contact of aqueous and reverse micellar phases. Due to the complexities of components molecular interactions, 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 φ-φ approach and use of equations of states (EOS) 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. Also, the non-ideal behavior of micellar solution was modeled by three EOS, i.e. vdW, PR, and SRK. Results showed that SRK EOS shows the best match with experimental data of Bovine Serum Albomin (BSA) extraction from aqueous solution using reverse micellar solution of CTAB, 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. Also, the standard deviation shows an excellent match between experimental data and model predictions.

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عنوان ژورنال

دوره 6  شماره 1

صفحات  105- 113

تاریخ انتشار 2018-03-01

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