ANFIS modeling for bacteria detection based on GNR biosensor
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...
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| Format: | Article |
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John Wiley and Sons Ltd
2016
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| _version_ | 1796861939617366016 |
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| author | Akbari, E. Buntat, Z. Shahraki, E. Zeinalinezhad, A. Nilashi, M. |
| author_facet | Akbari, E. Buntat, Z. Shahraki, E. Zeinalinezhad, A. Nilashi, M. |
| author_sort | Akbari, E. |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T20:04:01Z |
| format | Article |
| id | utm.eprints-72472 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:04:01Z |
| publishDate | 2016 |
| publisher | John Wiley and Sons Ltd |
| record_format | dspace |
| spelling | utm.eprints-724722017-11-26T03:37:03Z http://eprints.utm.my/72472/ ANFIS modeling for bacteria detection based on GNR biosensor Akbari, E. Buntat, Z. Shahraki, E. Zeinalinezhad, A. Nilashi, M. TK Electrical engineering. Electronics Nuclear engineering 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. John Wiley and Sons Ltd 2016 Article PeerReviewed 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 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937458242&doi=10.1002%2fjctb.4761&partnerID=40&md5=18d3eb15c0860564dae9ac4d661bef8b |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Akbari, E. Buntat, Z. Shahraki, E. Zeinalinezhad, A. Nilashi, M. ANFIS modeling for bacteria detection based on GNR biosensor |
| title | ANFIS modeling for bacteria detection based on GNR biosensor |
| title_full | ANFIS modeling for bacteria detection based on GNR biosensor |
| title_fullStr | ANFIS modeling for bacteria detection based on GNR biosensor |
| title_full_unstemmed | ANFIS modeling for bacteria detection based on GNR biosensor |
| title_short | ANFIS modeling for bacteria detection based on GNR biosensor |
| title_sort | anfis modeling for bacteria detection based on gnr biosensor |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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