Abstract

A five node sensor array, consisting of three films of gold nanoparticles functionalized with p-terphenylthiol, dodecanethiol and mercapto-(triethylene glycol) methyl ether, and films of poly(3-hexylthiophene) and polypyrrole, was integrated into a portable, microprocessor-based system. The system was evaluated for the detection of chloroform, diisopropyl methylphosphonate (DIMP), ethanol, hexane, methanol, and toluene vapors. Direct comparison of the five sensor films with respect to sensitivity, response time and recovery time was made by measurement of the resistance changes upon simultaneous exposure to each analyte. In general, the sensor films responded, with greatest sensitivity, to organic analyte molecules with similar chemical functionality (e.g., polarity). For example, the dodecanethiol-functionalized gold nanoparticle film sensor excelled at detecting hexane, while the mercapto-(triethylene glycol) methyl ether-functionalized nanoparticle film exhibited superb detection of ethanol and chloroform. Although the poly(3-hexylthiophene) film was very sensitive to polar analytes, including DIMP, in many cases it suffered from relatively long recovery times. Following training of the sensor system, successful differentiation and detection of the analytes were realized using a relatively simple algorithm based on “minimization of the squares of differences” method. The ability of the system to optimally differentiate these analytes is considered within the context of principal component analysis, and the effects of long-term sensor drift are discussed.

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