Applications of solid phase microextraction with ion and differential mobility spectrometry for the study of jet fuels and organophosphonates

Solid phase microextraction (SPME) with ion and differential mobility spectrometry (IMS and DMS) was investigated for forensics studies. SPME is a rapid extraction technique. IMS and DMS are ambient pressure separation techniques. IMS characterizes ions based on differences in gas-phase mobilities in weak electric fields and DMS in alternating strong and weak electric fields. Development of SPME/IMS and SPME/gas chromatography (GC)/DMS systems are addressed in this dissertation.; SPME with IMS was explored for detection of chemical warfare agents in soil. The analytes used in this study were diisopropyl methylphosphonate (DIMP), diethyl methylphosphonate (DEMP), and dimethyl methylphosphonate (DMMP). A thermal desorption inlet was developed to interface SPME with a hand held ion mobility spectrometer. SPME-IMS offered good repeatability and detection of DIMP, DEMP, and DEMP in soil at concentrations as low as 10 mug/g.; Fuel and volatile organic compounds (VOCs) studies were examined by GC-DMS. A micromachined differential mobility spectrometer with a 10.6 eV photoionization source was used as the GC detector. GC-DMS produces second-order data that is applicable to chemometric analysis. Savitzky-Golay filters were explored as a tool for data smoothing of jet fuel data obtained with GC-DMS. Covariance maps were proposed for data visualization. Improved chromatographic resolution and signal-to-noise ratios were achieved with Savitzky-Golay filters.; Fuels were also examined by GC-DMS. Fuzzy rule-building expert system (FuRES) was used as a pattern recognition method to classify gas chromatograms of fuels. Variations in day-to-day sample collection were evaluated with analysis of variance-principal component analysis (ANOVA-PCA). A classification rate of 95 +/- 0.2% was obtained for the fuels using FuRES. Twelve samples collected one month later were classified correctly with the previously developed FuRES model.; In addition to fuels, GC-DMS was used to characterize benzene, m-xylene, p-xylene, toluene, collectively referred to as BTX, and methyl tert-butyl ether (MTBE). Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were proposed to model and resolve overlapping chromatographic peaks. SIMPLISMA and ALS proved to be suitable tools to model GC-DMS data, modeling all components in a mixture of BTX and MTBE and resolving xylene isomers that co-eluted.