This thesis reports on the preparation, development and use of novel cross-reactive optical microsensors for high-speed vapor detection. Microsensor arrays were employed to detect low-level explosives and explosives-like vapors and discriminate between simple and complex odors. Porous silica microspheres with an incorporated environmentally-sensitive fluorescent dye are employed in high-density sensor arrays to monitor fluorescence changes during nitroaromatic compound (NAC) vapor exposure. We show that single-element arrays permit the detection of low-level nitroaromatic compound vapors because of sensor-to-sensor reproducibility and signal averaging.; As discussed in Chapter 4, a fiber optic based sensor array was employed to determine the presence or absence of NAC vapors in variable backgrounds of humidity and volatile organic compound (VOC) vapors.; Chapter 5 discusses the first portable optical nose designed for on-site fieldwork. A field deployable instrument was developed to detect low-level 2,4-DNT vapors. The system was characterized and demonstrated the ability to detect 120 ppb 2,4-DNT vapor in blind (unknown) humidified samples during a supervised field test.; In Chapter 6, a fiber optic bead-based sensor array platform employed to discriminate between six different odors and air carrier gas is discussed. We demonstrated, for the first time, that our microbead-based artificial nose system was capable of being trained on multiple odors (both simple and complex) for discrimination purposes.; In Chapter 7, automatic sensor identification of sensor classes within a high-density randomized array is demonstrated without knowing sensor locations a priori.; In the last chapter of this thesis, we discuss high-density sensor arrays incorporating hundreds to thousands of replicates for each sensor type. For at least two multiplexed randomized arrays, the new extraction process was nearly equivalent to extracting the individual sensor-odor response profiles. This chapter also reports that response data, which were collectively extracted from different arrays prepared with the same sensor types, show reproducibility from array-to-array even when different data extraction methods are explored. (Abstract shortened by UMI.)
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