4.5 Article

Investigating peak dispersion in free-flow counterflow gradient focusing

Journal

ELECTROPHORESIS
Volume 43, Issue 5-6, Pages 776-784

Publisher

WILEY
DOI: 10.1002/elps.202100203

Keywords

Counterflow gradient focusing; Free-flow electrophoresis; Peak dispersion; Protein purification

Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC)
  2. Ontario Centres of Excellence
  3. Ontario Research Fund

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Free-flow electrophoresis (FFE) is a useful tool for separation and collection of charged solutes in biological applications, and a free-flow counterflow gradient focusing (FF-CGF) mechanism has been proposed to improve resolution and versatility. A theoretical model was developed to study factors influencing solute dispersion, revealing asymmetric solute distribution over a wide range of parameters. FF-CGF shows potential for high-resolution separations and high-throughput protein purification, providing guidance for optimizing experimental conditions and a strong framework for further development.
Free-flow electrophoresis (FFE) enables the continuous separation and collection of charged solutes, and as a result, it has drawn interest as both a preparative and an analytical tool for biological applications. Recently, a free-flow counterflow gradient focusing (FF-CGF) mechanism has been proposed with the goal of improving the resolution and versatility of FFE. To realize this potential, the factors that influence solute dispersion deserve further attention, including the gradient strength and the parabolic profile of the counterflow. Therefore, the goal of this work is to develop a theoretical model to study the interplay between these factors and molecular diffusion. Overall, an asymmetric solute distribution emerges for a wide range of parameters, and this behavior can be characterized with an exponentially modified Gaussian function. Results show that FF-CGF can achieve high-resolution separations, with the potential for high-throughput protein purification. Moreover, this work provides a practical guide for optimizing experimental conditions, as well as a strong framework for understanding and developing FF-CGF further.

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