Development and Application of Two-Dimensional Micro Gas Chromatography

The increasing need for rapid and on-site analysis of environmental, pharmaceutical and petrochemical samples has driven the development of micro gas chromatographs (µGC) for the last few decades. To improve the µGC’s ability to separate and analyze complex samples, we implemented multi-channel multi-dimensional µGC technology. This dissertation describes the design, assembly, characterization, operation and chromatogram reconstruction of a 1D & 2D µGC system as well as their applications on water contamination analysis and exhaled breath analysis. The 1D µGC system has sub-parts-per-billion level sensitivity and can analyze a water sample in 15 minutes. This system was coupled with purge-and-trap sampling and provided sensitive and rapid field analysis of water contamination. Quantification results agreed well with those obtained by an analytical lab using standard analytical methods and benchtop instruments. This system offers a lab-on-a-chip solution for sensitive and rapid water analysis with EPA compliant sample collection methods. The 2D µGC system has high peak capacity (>200 for 1-by-2 channel and >430 for 1-by-4 channel) and high sensitivity (sub-ppb). The 1-by-2 channel 2D µGC was used to analyze the exhaled breath of acute respiratory distress syndrome (ARDS) patients. ARDS is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity, and is notably difficult to diagnosis. Breath samples were drawn from mechanical ventilators in the ICU and analyzed by the fully automated 2D µGC within about 30 minutes. A machine learning algorithm was developed to analyze the metabolic information within breath chromatograms and achieved an overall diagnostic accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value when compared to adjudications performed by physicians based on the Berlin criteria. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing medical practices and improving patient outcomes.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153488/1/sharonml_1.pd