A sensor system has been constructed that is capable of detecting and discriminating between various explosives
presented in ocean water with detection limits at the 10-100 parts per trillion level. The sensor discriminates between
different compounds using a biologically-inspired fluorescent polymer sensor array, which responds with a unique
fluorescence quenching pattern during exposure to different analytes. The sensor array was made from commercially
available fluorescent polymers coated onto glass beads, and was demonstrated to discriminate between different
electron-withdrawing analytes delivered in salt water solutions, including the explosives 2,4,6-trinitrotoluene (TNT) and
tetryl, the explosive hydrolysis products 2-amino-4,6-dinitrotoluene and 4-amino-2,6-dinitrotoluene, as well as other
explosive-related compounds and explosive simulants. Sensitivities of 10-100 parts per trillion were achieved by
employing a preconcentrator (PC) upstream of the sensor inlet. The PC consists of the porous polymer Tenax, which
captures explosives from contaminated water as it passes through the PC. As the concentration of explosives in water
decreased, longer loading times were required to concentrate a detectable amount of explosives within the PC.
Explosives accumulated within the PC were released to the sensor array by heating the PC to 190 C. This approach
yielded preconcentration factors of up to 100-1000x, however this increased sensitivity towards lower concentrations of
explosives was achieved at the expense of proportionally longer sampling times. Strategies for decreasing this sampling
time are discussed.
Changes in the fluorescence of semiconductor nanocrystals were explored as a potential sensing mechanism for the
detection of chemicals associated with landmines, IEDs and HME materials. A series of quantum dots (QDs) with
fluorescence emissions spanning the visible spectrum was investigated using the Stern-Volmer relationship,
specifically measuring the effect of quencher concentration on QD fluorescence intensity and photo-excited lifetime.
The series of QDs was investigated with respect to their ability to donate excited-state electrons to an electronwithdrawing
explosive related compound (ERC). Electron transfer was monitored by observing the steady-state
fluorescence signal and the excited-state lifetimes of the QDs in the presence of ERC1. Increased sensitivities of
QDs towards ERC1 were observed as the size and emission wavelength of the QDs decreased. As the QDs size
decreased, the Stern-Volmer quenching constants increased. The larger QD exhibited the lowest Ksv and is thought
to be quenched by a purely static quenching mechanism. As QD size decreased, an additional collisional quenching
mechanism was introduced, denoted by a non-linearity in the quenching-vs-concentration Stern-Volmer plot.
Increases in quenching efficiency were due to increased excited-state lifetimes, and the introduction of a collisional
quenching mechanism. The quenching constant for the smallest QD was approximately an order of magnitude
higher than those of similarly evaluated commercially available fluorescent polymers, suggesting that QDs could be
exploited to develop sensitive detectors for electron-withdrawing compounds such as nitroaromatics.
This paper details the use of a genetic algorithm (GA) as a method to preselect spectral feature variables for
chemometric algorithms, using spectroscopic data gathered on explosive threat targets. The GA was applied to laserinduced
breakdown spectroscopy (LIBS) and ultraviolet Raman spectroscopy (UVRS) data, in which the spectra
consisted of approximately 10000 and 1000 distinct spectral values, respectively. The GA-selected variables were
examined using two chemometric techniques: multi-class linear discriminant analysis (LDA) and support vector
machines (SVM), and the performance from LDA and SVM was fed back to the GA through a fitness function
evaluation. In each case, an optimal selection of features was achieved within 20 generations of the GA, with few
improvements thereafter. The GA selected chemically significant signatures, such as oxygen and hydron peaks from
LIBS spectra and characteristic Raman shifts for AN, TNT, and PETN. Successes documented herein suggest that this
GA approach could be useful in analyzing spectroscopic data in complex environments, where the discriminating
features of desired targets are not yet fully understood.
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 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 to various degrees. Similar to gas chromatography,
species having high vapor pressures were separated from species having low vapor pressures. Instead of producing
typical sensor responses representative of thermodynamic equilibrium between each sensor and an unchanging vapor
phase, sensor responses varied depending on the position of the sensor in the chamber and the time from the beginning
of the analyte exposure. This spatiotemporal (ST) array response 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 multi-component 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 2-, 3-, 4- and 5-component mixtures from arrays using only 4
chemically different sorbent films and sensor training on pure vapors only. In contrast, when traditional time- and
position-independent sensor response information was used, significant errors in mixture identification were observed.
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.