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Development of solution-gated graphene transistor model for biosensors

Hediyeh Karimi12, Rubiyah Yusof12*, Rasoul Rahmani1, Hoda Hosseinpour3 and Mohammad T Ahmadi45

Author Affiliations

1 Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur 54100, Malaysia

2 Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur 54100, Malaysia

3 Department of Biotechnology Industry, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia

4 Computational Nanoelectronic Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Jalan Semarak, Johor 81310, Malaysia

5 Nanotechnology Research Center Nanoelectronic Group, Physics Department, Urmia University, Urmia 57147, Iran

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Nanoscale Research Letters 2014, 9:71  doi:10.1186/1556-276X-9-71

Published: 11 February 2014


The distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (Ids and Vgmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.

Graphene; DNA hybridization; Optimization; Solution-gated field effect transistor; Single-nucleotide polymorphism; Particle swarm optimization