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

被引：4

作者：

Hua, Hongling ^{[1
,2
]}

Xie, Xiaohui ^{[1
,2
]}

Sun, Jinjin ^{[1
,2
]}

Qin, Ge ^{[1
,2
]}

Tang, Caiyan ^{[1
,2
]}

Zhang, Zhen ^{[1
,2
]}

Ding, Zhaoqiang ^{[1
,2
]}

Yue, Weiwei ^{[1
,2
]}

机构：

[1] Shandong Normal Univ, Sch Phys & Elect, Shandong Prov Key Lab Med Phys & Image Proc Techn, Jinan 250014, Shandong, Peoples R China

[2] Shandong Normal Univ, Inst Mat & Clean Energy, Jinan 250014, Shandong, Peoples R China

来源：

ADVANCES IN CONDENSED MATTER PHYSICS | 2018年 / 2018卷

基金：

中国国家自然科学基金;

关键词：

VAPOR-DEPOSITION; NICKEL FOAM; NANOPARTICLES; FLUORESCENCE; DISCRIMINATION; FABRICATION;

D O I：

10.1155/2018/2361571

中图分类号：

O469 [凝聚态物理学];

学科分类号：

070205 ;

摘要：

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.

引用

页数：8

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