Study on a TCM evaluation method based on an MIP-modified MOF sensor with highly selective electrocatalytic activity—an Artemisia annua L. perspective

Abstract

Quantitative analysis of artemisinin (ART) in Artemisia annua decoction samples is crucial for the quality assessment of Artemisia annua samples; however, no comprehensive solution currently exists for its rapid and sensitive quantification. This gap necessitates a novel method that accommodates the complex composition of traditional Chinese medicine samples. In this study, we developed an electrochemical sensor suitable for determining the ART content level in Artemisia annua. By introducing MIP-MOF composites, the sensor was endowed with selectivity based on spatial and electronic structures specific to particular molecules. This sensor, which mimics the in vivo pharmacological activation process of ART, could swiftly and selectively measure ART concentrations, thereby providing a reflection of the efficacy of the samples. The sensors’ limit of detection and limit of quantification were determined to be 1.738 × 10−13 M and 4.764 × 10−9 M, respectively. Methodology validation confirmed the great selectivity and accuracy of the sensor. Tests conducted across various Artemisia annua decoction samples, including those from online and offline sources, as well as deteriorated samples, yielded results consistent with the expected ART content levels, demonstrating the sensor's potential for application in Artemisia annua sample quality assessment.

Graphical abstract: Study on a TCM evaluation method based on an MIP-modified MOF sensor with highly selective electrocatalytic activity—an Artemisia annua L. perspective

Supplementary files

Article information

Article type
Paper
Submitted
10 Feb 2025
Accepted
24 Mar 2025
First published
10 Apr 2025

Anal. Methods, 2025, Advance Article

Study on a TCM evaluation method based on an MIP-modified MOF sensor with highly selective electrocatalytic activity—an Artemisia annua L. perspective

J. Zhang, Q. Chen, X. Gao, Z. Suo, D. Wu, Y. Zhou, Y. Zeng, Y. Li, Y. Che and Q. Chen, Anal. Methods, 2025, Advance Article , DOI: 10.1039/D5AY00226E

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