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Stochastic modeling of affinity adsorption.

J Hubble1

  • 1Department of Chemical Engineering, University of Bath, Claverton Down, Bath, BA2 7AY, U.K. j.hubble@bath.ac.uk

Biotechnology Progress
|June 2, 2001
PubMed
Summary

A new stochastic model predicts affinity adsorption kinetics and equilibrium, considering surface effects. High ligand density on resins may limit binding by the "off" constant more than mass transfer.

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Area of Science:

  • Surface Chemistry
  • Adsorption Science
  • Biophysical Chemistry

Background:

  • Understanding adsorption kinetics and equilibrium is crucial for affinity-based separation processes.
  • Existing models often simplify surface interactions, neglecting proximity and packing effects.

Purpose of the Study:

  • To develop a stochastic model incorporating surface proximity and packing effects for affinity adsorption.
  • To predict adsorption kinetics and equilibrium under various conditions.

Main Methods:

  • Development of a novel stochastic model for affinity adsorption.
  • Simulation of adsorption processes considering surface density constraints.
  • Analysis of equilibrium relationships (Freundlich-type and Langmuir-type).

Main Results:

  • The model successfully incorporates surface proximity and packing effects into adsorption predictions.
  • Equilibrium predictions can yield either Freundlich- or Langmuir-type relationships based on chosen conditions.
  • Increased "off" constant significantly impacts the time to reach equilibrium under surface density constraints.

Conclusions:

  • The stochastic model provides a more comprehensive understanding of affinity adsorption.
  • Binding kinetics in high-density ligand systems are potentially limited by the dissociation rate ("off" constant) rather than mass transfer.
  • This has implications for designing efficient affinity resins and separation processes.

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