Akbari, E. and Buntat, Z. and Shahraki, E. and Zeinalinezhad, A. and Nilashi, M. (2016) ANFIS modeling for bacteria detection based on GNR biosensor. Journal of Chemical Technology and Biotechnology, 91 (6). pp. 1728-1736. ISSN 0268-2575
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Abstract
BACKGROUND: Graphene is an allotrope of carbon with two-dimensional (2D) monolayer honeycombs. A larger detection area and higher sensitivity can be provided by a graphene based nanosenor because of its two-dimensional structure. In addition, owing to its special characteristics including electrical, optical and physical properties, graphene is a known more suitable candidate than other materials for use in sensor applications. RESULT: In this research, a set of novel models employing field effect transistor (FET) structures using graphene has been proposed and the current-voltage (I-V) characteristics of graphene have been employed to model the sensing mechanism. An adaptive neuro fuzzy inference system (ANFIS) algorithm has been used to provide another model for the current-voltage (I-V) characteristic. CONCLUSION: It has been observed that the graphene device experiences a large increase in conductance when exposed to Escherichia coli bacteria at 0-104 cfu mL-1 concentrations. Accordingly, the proposed model exhibits satisfactory agreement with the experimental data and this biosensor can detect E. coli bacteria providing high levels of sensitivity.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Adaptive optics, Bacteria, Biosensors, Carbon, Escherichia coli, Field effect transistors, Fuzzy inference, Fuzzy systems, Graphene, Inference engines, Adaptive neuro-fuzzy inference system, ANFIS, E. coli, Escherichia coli bacteria, Graphene nanoribbon (GNR), IV characteristics, Two Dimensional (2 D), Two-dimensional structures, Nanoribbons, carbon nanotube, graphene, magnetic nanoparticle, multi walled nanotube, nanotube, quantum dot, silica nanoparticle, adaptive neuro fuzzy inference system, algorithm, Article, bacterium detection, biosensor, electric current, electric potential, Escherichia coli, field effect transistor, fuzzy system, nonhuman |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electrical Engineering |
| ID Code: | 72472 |
| Deposited By: | Narimah Nawil |
| Deposited On: | 26 Nov 2017 03:37 |
| Last Modified: | 26 Nov 2017 03:37 |
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