RESUMO
A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.
RESUMO
Linear sensor arrays made from small molecule/carbon black composite chemiresistors placed in a low-headspace volume chamber, with vapor delivered at low flow rates, allowed for the extraction of new chemical information that significantly increased the ability of the sensor arrays to identify vapor mixture components and to quantify their concentrations. Each sensor sorbed vapors from the gas stream and, thereby, as in gas chromatography, separated species having high vapor pressures from species having low vapor pressures. Instead of producing only equilibrium-based sensor responses that were representative of the thermodynamic equilibrium partitioning of analyte between each sensor and the initial vapor phase, the sensor responses varied depending on the position of the sensor in the chamber and the time since the beginning of the analyte exposure. The concomitant spatiotemporal (ST) sensor array response therefore provided information that was a function of time, as well as of the position of the sensor in the chamber. The responses to pure analytes and to multicomponent analyte mixtures comprised of hexane, decane, ethyl acetate, chlorobenzene, ethanol, and/or butanol were recorded along each of the sensor arrays. Use of a non-negative least-squares (NNLS) method for analysis of the ST data enabled the correct identification and quantification of the composition of two-, three-, four-, and five-component mixtures from arrays using only four chemically different sorbent films. In contrast, when traditional time- and position-independent sensor response information was used, these same mixtures could not be identified or quantified robustly. The work has also demonstrated that, for ST data, NNLS yielded significantly better results than analyses using extended disjoint principal components modeling. The ability to correctly identify and quantify constituent components of vapor mixtures through the use of such ST information significantly expands the capabilities of such broadly cross-reactive arrays of sensors.
