(245g) Improved Fouling-Resistant Membranes Using High Throughput Synthesis and Screening: Status Report | AIChE

(245g) Improved Fouling-Resistant Membranes Using High Throughput Synthesis and Screening: Status Report

Authors 

Zhou, M. - Presenter, Rensselaer Polytechnic Institute
Yun, S. H. - Presenter, Rensselaer Polytechnic Institute
Gu, M. - Presenter, Rensselaer Polytechnic Institute
Kilduff, C. - Presenter, Rensselaer Polytechnic Institute
Belfort, G. - Presenter, Rensselaer Polytechnic Institute


As an alternate to a hypothesis-driven approach to develop anti-fouling membranes, which has been widely used, we offer a new combinatorial high throughput photo graft-induced polymerization (HT-PGP) method to synthesize and screen for surfaces with desired characteristics. Specifically, we are interested in identifying vinyl monomers that resist protein or natural organic matter adsorption and that can be grafted onto poly(ether sulfone) (PES) without initiating agents. In this regard, we have confirmed previously discovered monomers (poly(ethylene glycol) (PEG) and zwitterionic (Zwit) and discovered new monomers (including several amines) that exhibit protein-resistance. We have also shown that molecular weight and small chemical variations within a class (i.e. PEG or Zwit) can substantially change the efficacy of such monomers. Grafting conditions are also very important to the grafting efficiency. Here, we validate the HT-PGP's reproducibility and scalability and then demonstrate it's ability to discover new surfaces for 5 different feed solutions (natural organic matter (humic acid), hen egg-white lysozyme, supernatant from Chinese Hamster Ovary (CHO) cells in phosphate buffered saline (PBS) solution as a model cell suspension, and immunoglobulin G (IgG) precipitated in the absence and presence of bovine serum albumin (BSA) in high salt solution as a model precipitation process). In addition, we show, for the first time, a HT selectivity versus capacity plot for five classes of grafted monomers. Also, using quantitative structure-property relationship (QSPR) modeling, we are able to predict the lysozyme fouling parameters from a training data set using 51 molecular descriptors (R2= 0.77).