Affiliations 

  • 1 Faculty of Electrical Engineering, Institute of High Voltage & High Current, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
  • 2 Faculty of Electrical Engineering, Institute of High Voltage & High Current, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia. zolkafle@fke.utm.my
  • 3 The Department General of Fars Province Education, Iran
  • 4 Chemistry Department, Anar Branch, Islamic Azad University, Anar, Iran
  • 5 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
IET Nanobiotechnol, 2015 Oct;9(5):273-9.
PMID: 26435280 DOI: 10.1049/iet-nbt.2015.0010

Abstract

Graphene is an allotrope of carbon with two-dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by graphene-based nanosenor because of its 2D structure. In addition, owing to its special characteristics, including electrical, optical and physical properties, graphene is known as a more suitable candidate compared to other materials used in the sensor application. A novel model employing a field-effect transistor structure using graphene is proposed and the current-voltage (I-V) characteristics of graphene are employed to model the sensing mechanism. This biosensor can detect Escherichia coli (E. coli) bacteria, providing high levels of sensitivity. It is observed that the graphene device experiences a drastic increase in conductance when exposed to E. coli bacteria at 0-10(5) cfu/ml concentration. The simple, fast response and high sensitivity of this nanoelectronic biosensor make it a suitable device in screening and functional studies of antibacterial drugs and an ideal high-throughput platform which can detect any pathogenic bacteria. Artificial neural network and support vector regression algorithms have also been used to provide other models for the I-V characteristic. A satisfactory agreement has been presented by comparison between the proposed models with the experimental data.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.