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Advances in Condensed Matter Physics
Volume 2018, Article ID 2361571, 8 pages
https://doi.org/10.1155/2018/2361571
Research Article

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

1Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China
2Institute of Materials and Clean Energy, Shandong Normal University, Jinan 250014, China

Correspondence should be addressed to Weiwei Yue; nc.ude.unds@ieweuy

Received 8 December 2017; Accepted 31 January 2018; Published 4 March 2018

Academic Editor: Jiandi Zhang

Copyright © 2018 Hongling Hua et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.