Issue 12, 2023

Fe–N hollow mesoporous carbon spheres with high oxidase-like activity for sensitive detection of alkaline phosphatase

Abstract

Due to the vital role of alkaline phosphatase (ALP) in clinical diagnoses and biomedical research, a sensitive and selective detection method for ALP activity is of considerable importance. Herein, a facile and sensitive colorimetric assay for the detection of ALP activity was developed based on Fe–N hollow mesoporous carbon spheres (Fe–N HMCS). Fe–N HMCS were synthesized by a practical one-pot method with aminophenol/formaldehyde (APF) resin as the carbon/nitrogen precursor, silica as the template and iron phthalocyanine (FePC) as the iron source. Thanks to the highly dispersed Fe–N active sites, Fe–N HMCS exhibited exceptional oxidase-like activity. In the presence of dissolved oxygen, Fe–N HMCS were able to effectively convert colorless 3,3′,5,5′-tetramethylbenzidine (TMB) into oxidized TMB (oxTMB) with blue color, while the reducing agent of ascorbic acid (AA) inhibited the color reaction. Based on this fact, an indirect and sensitive colorimetric sensing method was developed to detect alkaline phosphatase (ALP) with the assistance of the substrate L-ascorbate 2-phosphate (AAP). This ALP biosensor exhibited a linear range of 1–30 U L−1 and a limit of detection (LOD) of 0.42 U L−1 in standard solutions. In addition, this method was applied to detect ALP activity in human serum with satisfactory results. This work offers a positive reference for the reasonable excavation of transition metal–N carbon compounds in ALP-extended sensing applications.

Graphical abstract: Fe–N hollow mesoporous carbon spheres with high oxidase-like activity for sensitive detection of alkaline phosphatase

Supplementary files

Article information

Article type
Paper
Submitted
28 Mar 2023
Accepted
06 May 2023
First published
09 May 2023

Analyst, 2023,148, 2825-2833

Fe–N hollow mesoporous carbon spheres with high oxidase-like activity for sensitive detection of alkaline phosphatase

Y. Chen, L. Zhao, B. Zhang, Y. Guan, C. Yao and X. Xu, Analyst, 2023, 148, 2825 DOI: 10.1039/D3AN00475A

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