Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without su...

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Main Authors: Hongling Hua, Xiaohui Xie, Jinjin Sun, Ge Qin, Caiyan Tang, Zhen Zhang, Zhaoqiang Ding, Weiwei Yue
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Advances in Condensed Matter Physics
Online Access:http://dx.doi.org/10.1155/2018/2361571
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spelling doaj-4901cbdd017c4453b15d239913fdce972020-11-24T22:54:24ZengHindawi LimitedAdvances in Condensed Matter Physics1687-81081687-81242018-01-01201810.1155/2018/23615712361571Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural NetworkHongling Hua0Xiaohui Xie1Jinjin Sun2Ge Qin3Caiyan Tang4Zhen Zhang5Zhaoqiang Ding6Weiwei Yue7Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaA kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.http://dx.doi.org/10.1155/2018/2361571
collection DOAJ
language English
format Article
sources DOAJ
author Hongling Hua
Xiaohui Xie
Jinjin Sun
Ge Qin
Caiyan Tang
Zhen Zhang
Zhaoqiang Ding
Weiwei Yue
spellingShingle Hongling Hua
Xiaohui Xie
Jinjin Sun
Ge Qin
Caiyan Tang
Zhen Zhang
Zhaoqiang Ding
Weiwei Yue
Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
Advances in Condensed Matter Physics
author_facet Hongling Hua
Xiaohui Xie
Jinjin Sun
Ge Qin
Caiyan Tang
Zhen Zhang
Zhaoqiang Ding
Weiwei Yue
author_sort Hongling Hua
title Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
title_short Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
title_full Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
title_fullStr Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
title_full_unstemmed Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network
title_sort graphene foam chemical sensor system based on principal component analysis and backpropagation neural network
publisher Hindawi Limited
series Advances in Condensed Matter Physics
issn 1687-8108
1687-8124
publishDate 2018-01-01
description A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.
url http://dx.doi.org/10.1155/2018/2361571
work_keys_str_mv AT honglinghua graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT xiaohuixie graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT jinjinsun graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT geqin graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT caiyantang graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT zhenzhang graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT zhaoqiangding graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
AT weiweiyue graphenefoamchemicalsensorsystembasedonprincipalcomponentanalysisandbackpropagationneuralnetwork
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