4.7 Article

Reducing diffusion limitations in Ion exchange grafted membranes using high surface area nonwovens

Journal

JOURNAL OF MEMBRANE SCIENCE
Volume 514, Issue -, Pages 53-64

Publisher

ELSEVIER
DOI: 10.1016/j.memsci.2016.02.046

Keywords

UV grafting; Islands-in-the-sea nonwoven; Diffusion limited; Ion exchange protein capture; Shrinking core

Funding

  1. North Carolina Biotechnology Center (NCBC)
  2. Kenan Institute of Engineering, Technology and Science

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Polybutylene terephthalate (PBT) nonwovens can be readily grafted with glycidyl methacrylate (GMA) via UV induced radical polymerization to create uniform and conformal polymer brush networks around each fiber that can be chemically modified to function as anion or cation exchangers. Protein binding capacities achieved by these grafted materials are many times larger than monolayer coverage around the fibers, but require very long residence times to reach equilibrium due to diffusional limitations within the grafted layers. The rates of adsorption of proteins by ion exchange were measured in an islands-in-the-sea (I/S) PBT nonwoven with average fiber diameter of approximately 1 mu m and in a commercially available PBT nonwoven with average fiber diameter of approximately 3 mu m. Both nonwovens were grafted successfully with poly(glycidyl methacrylate) (PGMA) and they showed almost identical ion exchange equilibrium protein binding capacities at similar weight % grafting. However, the grafted nonwoven membrane exhibited a substantially higher amount of protein binding at short times and it was able to reach equilibrium in a fraction of the time required by the grafted commercial nonwoven with larger fiber diameters. The faster rate of protein adsorption observed with the PBT nonwoven is the result of the thinner PGMA graft layer thicknesses around the fibers compared to those in the commercial PBT with the same weight % grafting. The data for the rate of adsorption of protein through the functionalized PGMA grafted layers was analyzed using a shrinking core model. (C) 2016 Elsevier B.V. All rights reserved.

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