Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/32764
Title: Utilizing the High Sensitivity toward MMP-2 of an Electrochemically Reduced Graphene Oxide Aptasensor
Authors: Jarić, Stefan 
Schobesberger, Silvia
Bobrinetskiy, Ivan 
Knežević, Nikola 
Keywords: ERGO; aptasensor; electrochemical reduction; MMP2; EIS
Issue Date: May-2024
Conference: The 4th International Electronic Conference on Biosensors, Basel, online, 20–22 May 2024
Abstract: A fully electrochemical approach for fabrication and detection is employed to develop a simple, low-cost, and highly sensitive biosensor for matrix metalloproteinase 2. Graphene oxide (GO) was deposited using the simplest technique, i.e., drop casting over the working electrodes commercially available in the form of an interdigitated electrode array. The GO monolayer uniform film was reduced using an electrochemical approach to obtain an electrochemically reduced graphene oxide (ERGO) ultra-thin film. The ERGO's conductivity and electrochemical activity were controlled by reduction process optimization, where the number of cyclic voltammetry cycles was determined to obtain a highly conductive ERGO film. In our approach, GO was reduced in a 1x PBS solution with a voltage range of -0.4 to -1.2 V and a scanning rate of 50 mV s-1. We observed that 20 cycles of CV scanning produce stable and highly conductive ERGO. Furthermore, the biosensor was constructed using specific anti-MMP2 aptamers, which are covalently attached to the ERGO surface by pyrene-based chemistry. Initially, we tested the sensitivity in a buffer medium, finding a limit of detection of 3.32 pg mL-1 using electrochemical impedance spectroscopy (EIS), which is much lower than that of previously reported graphene-based devices of similar technology. Moreover, the MMP-2 biosensor showed a high specificity toward different similar proteins. The wide range of active MMP-2 concentrations (10 pg mL-1 to 100 ng mL-1) opens the potential for the development of point-of-care devices for the early prediction of different diseases, with an emphasis on cancer. This work was supported by projects funded by the European Union’s Horizon 2020 research and innovation programme NANOFACTS under grant agreement no. 952259 ( https://doi.org/10.3030/952259).
URI: https://open.uns.ac.rs/handle/123456789/32764
Appears in Collections:IBS Publikacije/Publications

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