Nanoparticle-structured sensing array materials and pattern recognition for VOC detection

Nanostructured sensing arrays combined with pattern-recognition analysis provide new opportunities for enhancing the design of sensor materials in terms of sensitivity and selectivity. In this work, we report findings of an investigation of nanostructured sensing arrays for the detection of volatile organic compounds (VOCs) and nitro-aromatic compounds (NACs) and the data analysis based on pattern recognition using principle 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 NACs 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 network system was employed as the pattern classifier, which was shown to enhance the correct pattern-recognition rate. A satisfactory identification performance of the system has been demonstrated for a set of vapor responses. The results have also provided us important insights into the delineation of the design criteria for constructing nanostructured sensing arrays.