Coupling metal organic frameworks nanozyme with carbon nanotubes on the gradient porous hollow fiber membrane for nonenzymatic electrochemical H 2 O 2 detection

In this paper, we present a gradient porous hollow fiber structure integrated the signal transduction within a microspace, serving as a platform for cellular metabolism monitoring. We developed a nonenzymatic electrochemical electrode by coupling carbon nanotubes (CNT) and metal organic frameworks (...

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Veröffentlicht in:Analytica chimica acta 2024-03, Vol.1293, p.342285
Hauptverfasser: Ma, Shuyan, Xiao, Shenghao, Hong, Yinhui, Bao, Yuheng, Xu, Zhikang, Chen, Dajing, Huang, Xiaojun
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Sprache:eng
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Zusammenfassung:In this paper, we present a gradient porous hollow fiber structure integrated the signal transduction within a microspace, serving as a platform for cellular metabolism monitoring. We developed a nonenzymatic electrochemical electrode by coupling carbon nanotubes (CNT) and metal organic frameworks (MOF) nanozyme on three-dimensional (3D) gradient porous hollow fiber membrane (GPF) for in-situ detection of cell released hydrogen peroxide (H O ). The GPF was used as a substrate for cell culture as well as the supporting matrix of the working electrode. The ultrasonically coupled CNT@MOF composite was immobilized on the outer surface of the GPF by means of pressure filtration. Notably, the MOF, acting as a peroxidase mimic, exhibits superior stability compared to traditional horseradish peroxidase. The incorporation of CNT not only provided sufficient specific surface area to improve the uniform distribution of MOF nanozyme, but also formed 3D conductive network. This network efficiently facilitates the electrons transfer during the catalytic process of the MOF, addressing the inherent poor conductivity of MOFs. The GPF-CNT@MOF nonenzymatic bioelectrode demonstrated excellent electrocatalytic performance including rapid response, satisfactory sensing selectivity, and attractive stability, which enabled the development of a robust in-situ cellular metabolic monitoring platform.
ISSN:1873-4324