A smart sensor system for the detection of toxic organophosphorus and toxic organosulfur vapors at trace concentrations has been designed, fabricated, and tested against a wide variety of vapor challenges. The key features of the system are an array of four surface acoustic wave (SAW) vapor sensors, temperature control of the vapor sensors, the use of pattern recognition to analyze the sensor data, and an automated sampling system including thermally desorbed preconcentrator tubes (PCTs).All the electronics necessary to control and operate the various subsystems and to collect and process the data are included in the system. Organophosphorus analytes were detected at concentrations as low as 0.01 mg/m* 123 456in 2 min, and the organosulfur analyte was detected at 0.5 mg/m3 in 2 min. Pattern recognition algorithms correctly classified these analytes at these concentrations and discriminated them for a variety of other organic vapors.

Surface acowrtk wave (SAW) devices coated with selecthrely sorbent materials are being investigated as monitors for personnel safety where detectlon of hazardous vapors Is requlred at wb-part-per-mlliion concentrations in ambient alr that can contain Interferences at much higher concentratbns. A set of ten SAW devices coated with polymer materials designed to interact wkh different vapor propertles has been used to detect hazardous vapors by producing unique fingerprhrts to represent a glven vapor. The coatlngs were exposed to nlne lndlvldual vapors and two-component mlxtures of the vapors representlng dlfferent chemical classes and concentratlons, and the resunlng data matrix was studied by uslng pattern recognkhm methods. Four of the coatings were vapors were used as a prediction set t o x c l a a c a t i o n capacky of a llnear dlscrlmlnant developed In that study. Ail of the vapors were correctly identlfled, except water. Principal components analysls and clustering methods were a p pHed to the responses of the coatlngs to ail the vapors, inciudlng mixtures. The Individual vapors cluster into speclflc regions In space, and the mlxtures lie In the areas between the clusters. Supervlsed learning techniques were used to reduce to eight the number of sensors necessary to identify the hazardous vapors in the presence of mixtures.common to a prevkus study, and their r to the slngle

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A chemical derivatization scheme has been developed for the sensitive and selective determination of hydrazine, monomethylhydrazine (MMH) and 1,l-dimethylhydrazine (UDMH) by fluorescence spectrometry. Incorporation of hydrazine into an aromatic framework by derivatization with o-phthalaldehyde (OPA), naphthalene-2,3dicarbaldehyde (NDA), or anthracene-2,3dicarbaldehyde (ADA) creates an efficient fluorophore the emission wavelength of which is red-shifted from the original reagent. The fluorescence emission for each of the different derivatizing reagents (OPA, NDA, and ADA), is minimal and nearly within the noise of the background. The hydrazine derivatives, on the other hand, are intensely fluorescent and characterized by a broad fluorescence emission centred at 376 nm for OPA, 500 nm for NDA, and 549 nm for ADA. For the NDA hydrazine derivative, a linear concentration dependence is observed from 50 ng 1-1 to 500 yg 1-1 of hydrazine (correlation coefficient, r > 0.999). The response time necessary to give 90% of a fullscale response is <2 min. The response of the ADA reagent is similar; however, its response time is faster ( < O S min), and its detection limit is higher (150 ng 1-1). By careful control of the pH and the aromatic dicarbaldehyde chosen, it is possible to differentiate quantitatively between the hydrazine, MMH, and UDMH levels present in mixed samples. The detection limits for MMH and UDMH using the NDA reagent are 120 ng 1-1 and 40 yg 1-1, respectively.

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The U.S. Naval Research Laboratory has been engaged in a research program to develop sensor-based technologies to perform rapid automated fuel-quality surveillance. This approach is based on the development of quantitative models from the partial least-squares (PLS) regression of near-infrared (NIR) spectroscopic measurements of a representative calibration set of petroleum-derived fuels. As fuels from nonpetroleum sources become available it will be necessary to extend these chemometric models to accommodate Fischer-Tropsch (FT) synthetic fuels and biofuels. This extension is complicated by the fact that these new fuels will be initially introduced as blending components with petroleum-derived fuels. Chemometric modeling methodologies have been developed to identify and estimate the content of FT and biofuel present; then this information is used to estimate the bulk properties of the blends. With this approach, biodiesel content can be predicted, with respect to absolute error, to within 1.7% of its true value 95% of the time with a lower limit of detection of 1.5% using a single PLS model. The diesel fuel PLS property prediction models are applicable to diesel fuels blended with biodiesel fuel once that particular biodiesel fuel is incorporated in said models. The FT content in blends with petroleum fuels can be predicted, with respect to absolute error, to within 6.9% of its true value 95% of the time with a lower limit of detection of 15% using a series of paired PLS models for identification and quantification. In the presence of FT fuel, the PLS property models can be used after applying a correction factor that is derived from the identity and concentration of the FT fuel present.