Sensor Testing System for Environmental and Process Applications
Mark M. Disko1, Andrew E. Riley1, Cornelius H. Brons1, Charles Rebick1, Manuel A. Francisco1, Boris Mizaikoff2 and Christina Young2, (1)Corporate Strategic Research, ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ 08801, (2)School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, MS&E Bldg., Atlanta, GA 30332-0

Sensing science at ExxonMobil is enabled by a recently assembled Sensor Testing System (SeTS) that incorporates a NeSSITM based multi-analyzer sampling system. The system was designed for flexible gas mixing (currently four gases plus air), rapid sampling to GC, mass spec, spectroscopic, and discrete MEMS or metal oxide based gas sensors. We will report initial experiments with SeTS) around a training set composed of benzene, toluene, and xylene gases varied in a concentration range of 1 to 100 ppm resulting in a 100+ member training set. These gases represent an initial set for estimating environmental exposures including hydrocarbons in ambient air, water, and process gas streams. Initial experiments combine conventional GC measurements, mid-infrared FT-IR spectroscopy with waveguide-enhanced sensitivity, and self-assembled monolayer (SAM) coated microcantilevers. A rapid search for selective SAMs is enabled by the (SeTS gas mixing and sampling system. Chemometrics-based analysis of multivariate FT-IR data shows substantial promise for accurate predictions at ppm constituent levels and below. We see significant promise for this technology for rapid testing of microanalytics (GC, MS, FT-IR), polymer or metal oxide gas sensors, and associated pre-concentration systems. In future, the flexible NeSSITM-based design enables the integration of microreactors along with the testing system. Furthermore, rapid transfer from lab testing to plant environments is facilitated by the inherent robustness of NeSSITM-based sampling systems, and small device footprints

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Microtechnology and Process Analytics

The Preliminary Program for 2007 Spring National Meeting