4.7 Review

Strategies to remove templates from molecularly imprinted polymer (MIP) for biosensors

期刊

TRAC-TRENDS IN ANALYTICAL CHEMISTRY
卷 170, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2023.117437

关键词

Molecularly imprinted polymers; Template removal; Biosensors; Electrochemical sensors; Optical sensors

向作者/读者索取更多资源

This review focuses on the removal strategies of templates from molecularly imprinted polymers (MIPs) used in diagnostic biosensors. Chemical-based and electrochemical-based template extraction approaches are summarized and evaluated, providing guidance for researchers in the fields of analytical chemistry, diagnostics, and materials science for the design of MIP-based sensors.
Molecularly imprinted polymers (MIP) have been of significant interest, especially for usage as recognition elements in biosensors. The sensitivity of a biosensor is largely governed by the interactions between the analyte and the recognition element. This represents a challenge for molecularly imprinted polymers as the interaction with the analyte relies on the successful creation of a template imprint. A crucial part of the process is the removal strategy of the template from the surrounding polymeric network. Since the inception of the field, there have been many strategies for this removal process, and these are the topic of this review. The focus is on template removal strategies from MIPs utilized for diagnostic biosensors. The literature survey of various strategies can be summarized into chemical-based and electrochemical-based template extraction. Generally, for chemical extraction solvents and acids are employed, whereas for electrochemical extraction, cyclic voltammetry is commonly used. These approaches are summarized and evaluated in the current review to provide guidance to researchers working in the areas of analytical chemistry, diagnostics, and materials science to better design MIP-based sensors.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据