Issue 19, 2023

A bimetallic metal–organic framework with high enzyme-mimicking activity for an integrated electrochemical immunoassay of carcinoembryonic antigen

Abstract

Metal–organic frameworks (MOFs) show excellent catalytic activity and have been widely applied in diagnosis of diseases and tumors. However, current assay methods usually involve cumbersome configurations and complicated procedures, which inhibit their practical applications. Hence, a Cu–Ni MOF/carbon printed electrode (CPE)-based integrated electrochemical immunosensor was constructed for highly sensitive and efficient determination of carcinoembryonic antigen (CEA). First, highly conductive carbon ink was screen-printed onto a polyethylene terephthalate substrate to manufacture a CPE. Afterward, an aminated Cu-Ni MOF was prepared by a typical solvothermal strategy and modified on the CPE. Owing to its excellent peroxidase activity, the Cu–Ni MOF can catalytically oxidize hydroquinone using hydrogen peroxide, which greatly amplifies the peak current signal. Then the formation of an immune complex inhibited the catalytic activity of the MOF, thus enabling the quantitative determination of CEA content with a wide linear range of 0.5 pg mL−1–500 ng mL−1 and a low detection limit of 0.16 pg mL−1. Furthermore, the Cu–Ni MOF/CPE-based integrated portable electrochemical immunosensor also showed satisfactory performance in the detection of CEA in clinical serum samples with excellent accuracy, showing great potential for application in point-of-care disease diagnosis.

Graphical abstract: A bimetallic metal–organic framework with high enzyme-mimicking activity for an integrated electrochemical immunoassay of carcinoembryonic antigen

Supplementary files

Article information

Article type
Paper
Submitted
18 Qad 2023
Accepted
11 Leq 2023
First published
11 Leq 2023

Analyst, 2023,148, 4721-4729

A bimetallic metal–organic framework with high enzyme-mimicking activity for an integrated electrochemical immunoassay of carcinoembryonic antigen

Y. Shu, L. Yan, M. Ye, L. Chen, Q. Xu and X. Hu, Analyst, 2023, 148, 4721 DOI: 10.1039/D3AN01221B

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