Automation and information-measuring equipment

Selective Graphene-Based Biosensors in Environmental Monitoring

Authors

V. G. Petruk
Vinnytsia National Technical University ROR
S. M. Kvaterniuk
Vinnytsia National Technical University ROR
D. R. Latusha
Vinnytsia National Technical University ROR
M. P. Maksymenko
Vinnytsia National Technical University ROR
S. V. Gavadza
Vinnytsia National Technical University ROR
DOI https://doi.org/10.31649/1997-9266-2026-184-1-7-16

Keywords

graphene selective biosensor environmental monitoring heavy metals mathematical modeling

Abstract

The paper provides a comprehensive review of the physicochemical properties of various graphene forms (CVD graphene, GO, rGO, LIG), transduction mechanisms, and surface functionalization strategies, specifically focusing on DNA aptamers. Mathematical modeling methods were employed to analyze the performance of a graphene field-effect transistor (GFET) biosensor. The calculations are based on the Dirac equation for massless fermions, the drift-diffusion model of conductivity, and the Hill-Langmuir isotherm, accounting for the effects of graphene’s quantum capacitance. Analytical characteristics of graphene-based sensors for the detection of heavy metals, pesticides, and pathogens are systematized. The developed GFET mathematical model enabled a quantitative assessment of the Debye screening effect on device sensitivity in liquid media. It was established that to prevent signal loss, the receptor layer (aptamer) length should be 2...3 nm, while the solution ionic strength must not exceed 10 mM. An optimal stability window (pH 6.0...6.2) was identified to prevent aptamer denaturation and metal ion hydrolysis. Modeling results confirmed a potential limit of detection (LOD) within the range of 1.5...2.0 nM, and the calculated selectivity coefficient demonstrates high sensor specificity for lead ions even in the presence of background electrolytes. The findings substantiate the prospects of graphene biosensors as an alternative to conventional analytical methods. It is shown that integrating GFET arrays with Internet of Things (IoT) technologies and artificial intelligence algorithms (neural networks, deep learning) facilitates the development of high-performance systems for continuous, real-time environmental monitoring of water quality and ambient air.

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How to Cite

[1]
“Selective Graphene-Based Biosensors in Environmental Monitoring”, Вісник ВПІ, no. 1, pp. 7–16, Feb. 2026, doi: 10.31649/1997-9266-2026-184-1-7-16.

Author Biographies

V. G. Petruk, Vinnytsia National Technical University

Dr. Sc. (Eng.), Professor, Professor of the Chair of Ecology, Chemistry and Environmental Protection Technologies

S. M. Kvaterniuk, Vinnytsia National Technical University

Dr. Sc. (Eng.), Professor, Professor of the Chair of Ecology, Chemistry and Environmental Protection Technologies

D. R. Latusha, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Ecology, Chemistry and Environmental Protection Technologies

M. P. Maksymenko, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Ecology, Chemistry and Environmental Protection Technologies

S. V. Gavadza, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Ecology, Chemistry and Environmental Protection Technologies

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