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Published on: February 1, 2022
Implantable Graphene Fiber Sensor Functionalized with Enzyme-Mimicking Fe-Porphyrin for In Vivo Simultaneous
Shanting Li1, Kaiyuan Yao2, Min Hu1
1Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science & Technology, Wuhan430074, China.
Abstract:
Nitric oxide (NO) and hydrogen peroxide (H2O2) are critical in redox homeostasis, cell signaling, and tumor progression, yet the simultaneous detection of both remains challenging due to their low concentrations and high reactivity. Herein, we developed an implantable, fully flexible electrochemical biosensor based on a Fe-porphyrin metal-organic framework (i.e., PCN-224(Fe)) for real-time monitoring of NO and H2O2 in complex biological environments. This platform employs PCN-224(Fe) with enzyme-mimicking activity as the catalytic material, combined with nitrogen and boron codoped graphene fiber as the freestanding and flexible microelectrode substrate. The resultant electrochemical sensor demonstrated outstanding performance, with detection limits of 3.0 nM for NO and 0.5 μM for H2O2, and sensitivities of 2.99 mA cm-2 mM-1 and 1.04 mA cm-2 mM-1, respectively. This performance surpasses most previously reported electrochemical sensors. The sensor also shows excellent selectivity and reproducibility, facilitating continuous monitoring of NO and H2O2 signals in the tumor microenvironment. The proposed fiber-based sensor enables in situ, real-time, continuous monitoring of NO and H2O2 in cancer cells, tissues, and living organisms, thereby effectively discriminating between cancerous and normal samples based on significantly elevated biomarker levels in malignant tissues. When implanted directly into living tissues, the sensing device captures dynamic biomarker fluctuations in vivo with high fidelity, offering a distinct advantage over traditional in vitro assays, which often introduce signal loss and concentration artifacts during sample processing. As a result, this approach provides a more accurate reflection of actual in vivo conditions and holds great promise for assessing tumor progression as well as monitoring therapeutic responses.
