Issue 12, 2023

Electrostatic assemblies of molecularly imprinted polymers on the surface of electrospun nanofiber membranes for the point-of-care detection of thiodiglycol, a sulfur mustard poisoning metabolic marker

Abstract

In this study, molecularly imprinted polymers (MIPs) were assembled on the surface of ethylene imine polymer (PEI)/poly(vinyl alcohol) (PVA) electrospun nanofiber membranes for the point-of-care testing (POCT) of thiodiglycol (TDG), a sulfur mustard poisoning metabolic marker, using concentrated gold nanoparticles (AuNPs) as the signal reporting units. The MIPs/PEI/PVA nanofiber membranes could capture TDG specifically through the recognition interaction between MIPs and TDG. Then, AuNPs were adsorbed onto the MIPs/PEI/PVA nanofiber membranes through the Au–S interaction between TDG and AuNPs to produce a visible red color. In order to improve the sensitivity, the silver-enhanced solutions were used to deepen the color of the nanofiber membranes and the software Image J was used to read the gray value as the signal response for subsequent analysis. There was a good linear relationship between the color change of the MIPs/PEI/PVA nanofiber membranes and the TDG concentration from 0.1 ng mL−1 to 1.0 μg mL−1, and the limit of detection was 38 pg mL−1. This method was applied for the selective detection of TDG in urine, showing great potential for the clinical diagnosis of mustard gas poisoning.

Graphical abstract: Electrostatic assemblies of molecularly imprinted polymers on the surface of electrospun nanofiber membranes for the point-of-care detection of thiodiglycol, a sulfur mustard poisoning metabolic marker

Supplementary files

Article information

Article type
Paper
Submitted
06 ก.พ. 2566
Accepted
01 มี.ค. 2566
First published
02 มี.ค. 2566

Anal. Methods, 2023,15, 1500-1505

Electrostatic assemblies of molecularly imprinted polymers on the surface of electrospun nanofiber membranes for the point-of-care detection of thiodiglycol, a sulfur mustard poisoning metabolic marker

Y. J. Luo, Q. C. Ye, T. J. Xie, L. L. Tian, Y. Yan, Z. Lei, D. M. Wang, C. Z. Huang, Y. F. Li and S. J. Zhen, Anal. Methods, 2023, 15, 1500 DOI: 10.1039/D3AY00185G

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