title: A cataluminescence-based vapor-sensitive sensor array for discriminating flammable liquid vapors.
abstract: This paper describes a cataluminescence-based (CTL-based) vapor-sensitive sensor array containing 10 kinds of catalytic nanoparticles for rapid detection and discrimination of 10 flammable liquid (FL) vapors. The catalytic nanoparticles are directly deposited on heating filaments with the formation of the sensing elements. When the vapor samples are imported to the sensor array with carrier gas, the CTL intensity varies with the nanoparticles. The fingerprints of 10 FL vapors are discriminated according to the distinct CTL response patterns through a linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) in SPSS (version 16.0). The canonical patterns are clearly clustered into 10 different groups with a classification accuracy of 100%. The sensor array also applies to several real-world samples. Two kinds of simulated actual vapors, originating from the combustion of carpet in the presence and absence of gasoline, can be effectively distinguished. The developed CTL-based vapor-sensitive sensor array offers a new strategy for the rapid detection of FL vapors owing to its stability, reversibility, portability and low costs.
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, PR China.
authors.forename: Bowei
authors.initials: B
authors.lastname: Liu
authors.affiliation: Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Department of Chemistry, Tsinghua University, Beijing 100084, PR China.
authors.forename: Hao
authors.initials: H
authors.lastname: Kong
authors.affiliation: School of Life Science, Beijing Institute of Technology, Beijing 100081, PR China. Electronic address: bitluo@bit.edu.cn.
authors.forename: Aiqin
authors.initials: A
authors.lastname: Luo
keywords: Cataluminescence (CTL); Flammable liquid (FL); Nanoparticle; Pattern recognition; Sensor array; Vapor