Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

Wei Lu, Xiao Dong, Lili Qiu*, Zequn Yan, Zihui Meng, Min Xue, Xuan He, Xueyong Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design, the sensor array can illustrate the influence of the nitryl quantity and generate a separate response region of nitroaromatics for pattern recognition with 95.25% of variance explained in the measurements by the first three principal components (PCs). The statistical analysis endowed the cross-reactive array with better classification and identification ability and this novel detection platform provided a wider applied range among other harmful chemicals in a simple sensor array with customized functionality.

Original languageEnglish
Pages (from-to)130-137
Number of pages8
JournalJournal of Hazardous Materials
Volume326
DOIs
Publication statusPublished - 2017

Keywords

  • Molecular imprinting
  • Nitroaromatics
  • Pattern recognition
  • Photonic crystal (PhC)
  • Principal component analysis (PCA)

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