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Monitoring Protein Adsorption with Solid-state Nanopores
Published on: December 2, 2011
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Electro-osmotic flow in nanoconfinement: Solid-state and protein nanopores
Minglun Li1, Murugappan Muthukumar1
1Department of Polymer Science and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, USA.
The Journal of Chemical Physics
|February 27, 2024
Summary
Electro-osmotic flow (EOF) in nanopores influences molecule speed. This study reveals EOF
Area of Science:
- Physics
- Biophysics
- Physical Chemistry
Background:
- Electro-osmotic flow (EOF) drives fluid motion in nanopores under electric fields.
- Its precise role in single-molecule electrophoresis through nanopores remains unclear due to complex hydrodynamic and electrostatic interactions.
- Understanding EOF is crucial for controlling analyte translocation velocity.
Purpose of the Study:
- To investigate the fundamental role of electro-osmotic flow in analyte translocation through various nanopores.
- To analyze how pore geometry, surface charge, and applied electric fields influence EOF and translocation dynamics.
- To elucidate the relationship between pore characteristics and EOF behavior in single-molecule analysis.
Main Methods:
- Utilized the Poisson-Nernst-Planck and Navier-Stokes (PNP-NS) coupled equations.
- Modeled cylindrical solid-state nanopores and three protein nanopores (α-hemolysin, MspA, CsgG).
- Simulated fluid velocity profiles as a function of pore properties and electric field strength.
Main Results:
- Apparent charges of protein nanopores differ from their net charge; inner surface charge correlates with apparent charge.
- Fluid velocity exhibits non-monotonic dependence on applied voltage.
- Each protein nanopore displays unique EOF and velocity-voltage relationships, not solely determined by net charge.
- Point mutations can significantly alter EOF direction and magnitude.
Conclusions:
- EOF is a critical factor in macromolecule transport through nanopores, with complex dependencies on pore characteristics.
- Nanopore design, including charge distribution and geometry, can be tuned to control EOF and analyte translocation speed.
- This computational analysis provides insights for designing advanced nanopore systems for molecular analysis and separation.
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