Authors: C-J Zhong, S. Lu, L. Wang, X. Shi, M. Schadt, W. Hao and J. Luo
Affilation: State University of New York at Binghamton, United States
Pages: 427 - 430
Keywords: nanoparticles, sensing arrays, pattern recognition
Nanostructured sensing arrays combined with pattern-recognition analysis are expected to provide new opportunities for enhancing the design of sensor materials in terms of sensitivity and selectivity. We report findings of an investigation of nanostructured sensing arrays for the detection of volatile organic compounds (VOCs) and the data analysis based on pattern recognition using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The nanostructured array elements consist of thin film assemblies of alkanethiolate-monolayer-capped gold nanoparticles which were formed by molecularly-mediated assembly using mediators or linkers of different chain lengths and functional groups. Each array element displayed linear responses to the vapor concentration. The observed high specificity to VOCs constitutes an unprecedented example resulting from the unique combination of hydrogen-bonding donor/acceptor and hydrophobicity in the interparticle structure. A set of ANNs along with PCAs was used for the analysis of a series of vapor responses. The PCA technique was used to cluster data and feature extraction. A hierarchical BP neural networks system was employed as the pattern classifier, which was shown to enhance the correct pattern recognition rate. Our findings are significant because the nanostructure sequesters chemical or biological molecules into the interior or surface with porous access or specific binding properties, which alter or modify the electrical or optical properties of the nanoscale materials.