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Escherichia coli bacteria detection by using graphene-based biosensor

Akbari, Elnaz and Buntat, Zolkafle and Afroozeh, Abdolkarim and Zeinalinezhad, Alireza and Nikoukar, Ali (2015) Escherichia coli bacteria detection by using graphene-based biosensor. IET Nanobiotechnology, 9 (5). pp. 273-279. ISSN 1751-8741

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Official URL: https://www.ncbi.nlm.nih.gov/pubmed/26435280

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 0-105 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.

Item Type:Article
Uncontrolled Keywords:microorganisms, drugs, neural nets, support vector machines
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:55056
Deposited By: Fazli Masari
Deposited On:09 Aug 2016 04:07
Last Modified:01 Aug 2017 04:35

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