Selective Graphene-Based Biosensors in Environmental Monitoring

Authors

  • V. G. Petruk Vinnytsia National Technical University
  • S. M. Kvaterniuk Vinnytsia National Technical University
  • D. R. Latusha Vinnytsia National Technical University
  • M. P. Maksymenko Vinnytsia National Technical University
  • S. V. Gavadza Vinnytsia National Technical University

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.

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

References

A. M. Teli, S. M. Mane, S. A. Beknalkar, R. K. Mishra, W. Jeon, and J. C. Shin, “Graphene-Based Gas Sensors: State-of-the-Art Developments for Gas Sensing Applications,” Micromachines, vol. 16, no. 8, p. 916, Aug. 2025, https://doi.org/10.3390/mi16080916 .

M. A. Zafar, D. Waligo, O. K. Varghese, and M. V. Jacob, “Advances in graphene-based electrochemical biosensors for on-site pesticide detection,” Front. Carbon, vol. 2, pp. 1325970, Nov. 2023, https://doi.org/10.3389/frcrb.2023.1325970 .

Y.-T. Wang, et al., “A comprehensive review of graphene-based biosensors: Fabrication, applications, characterization and future perspectives — A review,” APL Bioengineering, vol. 9, no. 3, pp. 031504, Sep. 2025, https://doi.org/10.1063/5.0266596 .

C.-W. Huang, C. Lin, M. K. Nguyen, A. Hussain, X.-T. Bui, and H. H. Ngo, “A review of biosensors for environmental monitoring: principle, application, and corresponding achievement of sustainable development goals,” Bioengineered, vol. 14, no. 1, pp. 58-80, Dec. 2023, https://doi.org/10.1080/21655979.2022.2095089 .

O. Moldovan, B. Iñiguez, M. J. Deen, and L. F. Marsal, “Graphene electronic sensors – review of recent developments and future challenges,” IET Circuits, Devices & Syst, vol. 9, no. 6, pp. 446-453, Nov. 2015, https://doi.org/10.1049/iet-cds.2015.0259 .

N. Alzate-Carvajal, and A. Luican-Mayer, “Functionalized Graphene Surfaces for Selective Gas Sensing,” ACS Omega, vol. 5, no. 34, pp. 21320-21329, Sep. 2020, https://doi.org/10.1021/acsomega.0c02861 .

R. Pereira, et al., “Cost-Effective Fabrication of Laser-Induced Graphene Electrochemical Cell for NADH Detection,” ACS Omega, vol. 10, no. 41, pp. 48100-48110, Oct. 2025, https://doi.org/10.1021/acsomega.5c04282 .

H. Kitadai, M. Yuan, Y. Ma, and X. Ling, “Graphene-Based Environmental Sensors: Electrical and Optical Devices,” Molecules, vol. 26, no. 8, p. 2165, Apr. 2021, https://doi.org/10.3390/molecules26082165 .

M. Saqib, et al., “Electrochemical Detection of Heavy Metals Using Graphene-Based Sensors: Advances, Meta-Analysis, Toxicity, and Sustainable Development Challenges,” Biosensors, vol. 15, no. 8, p. 505, Aug. 2025, https://doi.org/10.3390/bios15080505 .

M. Akbari, M. J. Shahbazzadeh, L. La Spada, and A. Khajehzadeh, “The Graphene Field Effect Transistor Modeling Based on an Optimized Ambipolar Virtual Source Model for DNA Detection,” Applied Sciences, vol. 11, no. 17, p. 8114, Aug. 2021, https://doi.org/10.3390/app11178114 .

J. P. Ramoso, M. Rasekh, and W. Balachandran, “Graphene-Based Biosensors: Enabling the Next Generation of Diagnostic Technologies — A Review,” Biosensors, vol. 15, no. 9, p. 586, Sep. 2025, https://doi.org/10.3390/bios15090586 .

G. Song, H. Han, and Z. Ma, “Anti-Fouling Strategies of Electrochemical Sensors for Tumor Markers,” Sensors, vol. 23, no. 11, p. 5202, May 2023, https://doi.org/10.3390/s23115202 .

V. G. Petruk, et al., “Analysis of the Promising Thin Film Materials for Graphene — Based Solar Panels in Decarbonization and Circular Economy Processes,” Visnyk of Vinnytsia Politechnical Institute, vol. 182, no. 5, pp. 17-24, 2025, https://doi.org/10.31649/1997-9266-2025-182-5-17-24 .

S. Wei, Y. Dou, S. Song, and T. Li, “Functionalized-Graphene Field Effect Transistor-Based Biosensor for Ultrasensitive and Label-Free Detection of β-Galactosidase Produced by Escherichia coli,” Biosensors, vol. 13, no. 10, p. 925, Oct. 2023, https://doi.org/10.3390/bios13100925 .

D. M. Goodwin, M. Carta, M. M. Ali, D. Gillard, and O. J. Guy, “Enhanced Nitrogen Dioxide Detection Using Resistive Graphene-Based Electronic Sensors Modified with Polymers of Intrinsic Microporosity,” ACS Sens., vol. 10, no. 2, pp. 1378-1386, Feb. 2025, https://doi.org/10.1021/acssensors.4c03291 .

M. Khan, K. Indykiewicz, P. Tam, and A. Yurgens, “High Mobility Graphene on EVA/PET,” Nanomaterials, vol. 12, no. 3, p. 331, Jan. 2022, https://doi.org/10.3390/nano12030331 .

K. Aran, B. Goldsmith, and M. Moarefian, “Applications of Graphene Field Effect Biosensors for Biological Sensing,” in Trends in Biosensing Research: Advances, Challenges and Applications, F. Lisdat and N. Plumeré, Eds., Cham: Springer International Publishing, 2024, pp. 37-70. https://doi.org/10.1007/10_2024_252 .

J. Li, P. H. Q. Pham, W. Zhou, T. D. Pham, and P. J. Burke, “Carbon-Nanotube–Electrolyte Interface: Quantum and Electric Double Layer Capacitance,” ACS Nano, vol. 12, no. 10, pp. 9763-9774, Oct. 2018, https://doi.org/10.1021/acsnano.8b01427 .

Y. Dong, A. Lee, D. K. Ban, K. Wang, and P. Bandaru, “Femtomolar Level-Specific Detection of Lead Ions in Aqueous Environments, Using Aptamer-Derivatized Graphene Field-Effect Transistors,” ACS Appl. Nano Mater., vol. 6, no. 3, pp. 2228-2235, Feb. 2023, https://doi.org/10.1021/acsanm.2c05542 .

S. Mukherjee, et al., “A Graphene and Aptamer Based Liquid Gated FET-Like Electrochemical Biosensor to Detect Adenosine Triphosphate,” IEEE Trans. on Nanobioscience, vol. 14, no. 8, pp. 967-972, Dec. 2015, https://doi.org/10.1109/TNB.2015.2501364 .

X. Wang, Z. Hao, T. R. Olsen, W. Zhang, and Q. Lin, “Measurements of aptamer–protein binding kinetics using graphene field-effect transistors,” Nanoscale, vol. 11, no. 26, pp. 12573-12581, 2019, https://doi.org/10.1039/C9NR02797A .

R. J. S. Banicod, N. Tabassum, D.-M. Jo, A. Javaid, Y.-M. Kim, and F. Khan, “Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens,” Biosensors, vol. 15, no. 10, p. 690, Oct. 2025, https://doi.org/10.3390/bios15100690 .

M. A. Hussain, “Integrating environmental sensors and IoT for real-time detection of heavy metal pollutants in aquatic ecosystems,” IJAM, vol. 38, no. 5, pp. 1428-1445, Nov. 2025, https://doi.org/10.12732/ijam.v38i5.1339 .

J. Parthasarathy, S. S. Kumar, and J. Sundararajan, “AI-driven graphene-based electrochemical sensors for predictive detection of heavy metals in wastewater,” Microchemical Journal, vol. 221, p. 116839, Feb. 2026, https://doi.org/10.1016/j.microc.2026.116839 .

B. G. Chansi, M. Mir Wani, and T. Basu, “AI-Assisted Biosensors for Environmental Pollutant Monitoring,” in Biosensors for Environmental Analysis and Monitoring, Kiran, Ed., Cham: Springer Nature Switzerland, 2026, pp. 113-151. https://doi.org/10.1007/978-3-032-09430-8_5 .

Downloads

Abstract views: 164

Published

2026-02-26

How to Cite

[1]
V. G. . Petruk, S. M. Kvaterniuk, D. R. Latusha, M. P. Maksymenko, and S. V. Gavadza, “Selective Graphene-Based Biosensors in Environmental Monitoring”, Вісник ВПІ, no. 1, pp. 7–16, Feb. 2026.

Issue

No. 1 (2026)

Section

Automation and information-measuring equipment

Metrics

Downloads

Download data is not yet available.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Most read articles by the same author(s)

1 2 3 > >>