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Online Size-exclusion and Ion-exchange Chromatography on a SAXS Beamline
Published on: January 5, 2017
Micellar gradients in size-exclusion simulated moving bed chromatography
D A Horneman1, M Ottens, J T F Keurentjes
1Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
Journal of Chromatography. A
|March 1, 2006
Summary
Gradient elution in surfactant-aided size-exclusion chromatography (SASEC) is achievable using a micellar gradient on simulated moving bed (SMB) chromatography. This method enhances protein separation by modifying selectivity with non-ionic micelles.
Area of Science:
- Chromatography
- Separation Science
- Analytical Chemistry
Background:
- Size-exclusion chromatography (SEC) separates molecules based on size.
- Selectivity in SEC can be enhanced by modifying the mobile phase.
- Non-ionic micelles can alter chromatographic selectivity.
Purpose of the Study:
- To develop and demonstrate a method for gradient elution in surfactant-aided size-exclusion chromatography (SASEC) using simulated moving bed (SMB) chromatography.
- To investigate the application of micellar gradients in SMB chromatography for improved separation.
- To optimize protein separation using a non-ionic surfactant gradient.
Main Methods:
- Development of a method for precise positioning of a micellar gradient within an SMB system.
- Utilizing non-ionic surfactant C12E23 as the gradient-forming solute in the mobile phase.
- Application of the developed method to a practical protein separation challenge.
Main Results:
- Successful implementation of gradient elution in SASEC using SMB chromatography.
- Demonstration of effective micellar gradient positioning over the SMB system.
- Achieved a relevant chromatographic protein separation, showcasing the method's utility.
Conclusions:
- Gradient elution in SASEC is feasible and effective on an SMB platform.
- The developed method allows for controlled micellar gradients, enhancing chromatographic performance.
- SASEC with micellar gradients offers a powerful approach for complex separation problems, particularly for proteins.
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