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Issue 18, 2019
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Controlled synthesis of PEGylated surface protein-imprinted nanoparticles

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Abstract

High recognition selectivity has been the main object in developing protein-imprinted materials. Here, we demonstrate a novel strategy for the controlled synthesis of PEGylated surface protein-imprinted nanoparticles with reduced nonspecific binding, which is based on sequential two steps of surface-initiated reversible addition–fragmentation chain transfer aqueous precipitation polymerization (SI-RAFT APP). Click chemistry was employed to construct hydrophilic nanocores with both high-density RAFT chain transfer agents and template-capturing groups. Through the first-step SI-RAFT APP, protein-imprinted nanoshells were formed over the nanocores using lysozyme as a model template. By the second-step SI-RAFT APP, nonlinear PEG chains were grafted from the core–shell imprinted nanoparticles before the removal of the template. Both the thickness of the imprinted nanoshells and the length of the grafted chains could be readily controlled by the polymerization time. Thus the obtained PEGylated core–shell particles exhibited greatly improved template binding selectivity compared with the non-PEGylated controls, typically with the imprinting factor increasing from 2.1 to 9.1. Meanwhile, the PEGylation process did not impair but significantly enhance the protein binding capacity. The generality of the established approach was preliminarily proved by imprinting another template protein, bovine hemoglobin. This work represents the first example for the controlled synthesis and post-imprinting functionalization of surface protein-imprinted nanoparticles via SI-RAFT polymerization.

Graphical abstract: Controlled synthesis of PEGylated surface protein-imprinted nanoparticles

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Supplementary files

Article information


Submitted
02 Jul 2019
Accepted
31 Jul 2019
First published
31 Jul 2019

Analyst, 2019,144, 5439-5448
Article type
Paper

Controlled synthesis of PEGylated surface protein-imprinted nanoparticles

X. Yang, Y. Sun, Y. Xiang, F. Qiu and G. Fu, Analyst, 2019, 144, 5439
DOI: 10.1039/C9AN01221D

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