Dynamic ultrafiltration model for proteins: a colloidal interaction approach.

Area of Science:

• Biophysics
• Chemical Engineering
• Materials Science

Background:

• Ultrafiltration is crucial for protein separation and purification.
• Accurate prediction of permeation rates is essential for optimizing membrane processes.
• Existing models often lack detailed descriptions of protein-protein interactions at the membrane interface.

Purpose of the Study:

• To develop a rigorous dynamic mathematical model for predicting protein ultrafiltration rates.
• To incorporate sophisticated descriptions of protein-protein interactions controlling permeation.
• To enable quantitative, a priori predictions of filtration rates based on system parameters.

Main Methods:

• Developed a dynamic mathematical model based on protein-protein interactions near the membrane surface.
• Accounted for electrostatic interactions using a Wigner-Seitz cell approach and nonlinear Poisson-Boltzmann equation.
• Calculated London-van der Waals forces using approximated screened, retarded Lifshitz-Hamaker constants.
• Incorporated configurational entropy effects and electroviscous effects.

Main Results:

• The model quantitatively predicts protein filtration rates.
• Predictions are a function of zeta potential, pH, ionic strength, applied pressure, protein size, and membrane resistance.
• The model shows excellent agreement with experimental data for bovine serum albumin (BSA) filtration.

Conclusions:

• The developed model provides a robust framework for predicting protein ultrafiltration.
• The model's accuracy is demonstrated through validation with experimental BSA filtration data.
• This a priori model, with no adjustable parameters, advances the understanding and optimization of membrane-based protein separation processes.