Issue 47, 2022

pH-Responsive epitope-imprinted magnetic nanoparticles for selective separation and extraction of chlorogenic acid and caffeic acid in traditional Chinese medicines

Abstract

Chlorogenic acid and caffeic acid often coexist in traditional Chinese medicines (TCMs) and play roles as antioxidation, antiviral, antitumor and anti-inflammatory agents. Due to their low content and the presence of structural analogues, they cannot be effectively separated by conventional extraction methods. Molecularly imprinted polymers, as synthesized receptors with antibody-like binding properties, have significant advantages in separating structural analogues. However, the harsh imprinting conditions easily induced the degradation of chlorogenic acid. Therefore, caffeic acid was used as an epitope template to replace chlorogenic acid for imprinting. Boronic acid-functionalized magnetic nanoparticles (MNPs) were selected as substrates, which could not only facilitate the immobilization and removal of the templates by pH regulation, but also achieve rapid separation under an external magnetic field. Tetraethyl orthosilicate was selected as an imprinting monomer which allowed for precise control of the thickness of the imprinting layer by adjusting the imprinting time. The prepared epitope-imprinted MNPs showed excellent specificity, in combination with high performance liquid chromatography, have been successfully applied to the selective separation and detection of chlorogenic acid and caffeic acid in TCMs.

Graphical abstract: pH-Responsive epitope-imprinted magnetic nanoparticles for selective separation and extraction of chlorogenic acid and caffeic acid in traditional Chinese medicines

Supplementary files

Article information

Article type
Paper
Submitted
14 Mph 2022
Accepted
08 Pun 2022
First published
08 Pun 2022

Anal. Methods, 2022,14, 4931-4937

pH-Responsive epitope-imprinted magnetic nanoparticles for selective separation and extraction of chlorogenic acid and caffeic acid in traditional Chinese medicines

R. Xing, T. Xue, P. Ye, L. Yang, R. Wang, X. Chen and S. Hu, Anal. Methods, 2022, 14, 4931 DOI: 10.1039/D2AY01667B

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