Chemical imaging by mass spectrometry is a powerful approach by which to map spatial distributions of molecules to better understand their function in the system of interest. Over the last thirty years, MSI has evolved into a very powerful analytical tool for the investigation of chemically-complex samples including biological tissues, catalytic surfaces and thin layer chromatography plates, among many others. The work in this dissertation aimed to characterize existing MSI methods, while also developing novel instrumentation able to overcome the challenges found in a variety of applications. Different sample preparation and ionization techniques were evaluated to maximize detection of lipid species in brain tissues subjected to traumatic injury to better understand the biological processes involved. Next, differential mobility separation was coupled to an ambient MSI system that resulted in increased signal-to-noise ratios and image contrast. Third, bulky catalytic granite surfaces were imaged to determine specific mineral reactivity and demonstrate the ability of desorption electrospray ionization to image such samples. Fourth, a novel technique was developed names Robotic Plasma Probe Ionization (RoPPI), which uses a vision system-guided robotic arm to probe irregular surfaces for three dimensional surface imaging. Finally, a software program was developed to automatically screen MSI datasets acquired from thin layer chromatography separations for spot-like shapes corresponding to mixture components; this program was named DetectTLC. This research resulted in instrumentation advances for MSI that have enabled increased chemical diversity, enhanced sensitivity and image contrast, imaging of bulky or irregularly-shaped surfaces, and multivariate tools to facilitate data interpretation.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54892 |
| Date | 27 May 2016 |
| Creators | Stryffeler, Rachel Bennett |
| Contributors | Fernandez, Facundo M. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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