Fundamental studies of Rocket propellant fuel using GC × GC - TOFMS instrumentation with chemometric data analysis

Abstract

University of Washington Abstract Fundamental studies of Rocket propellant fuel using GC &times GC &mdash TOFMS instrumentation with chemometric data analysis. Benjamin Kehimkar Chair of the Supervisory Committee: Professor Robert E. Synovec Department of Chemistry For most of the research presented herein, samples of complex composition, specifically rocket propellant (RP) fuels, were analyzed. The performance of a fuel is directly linked to composition; understanding the connection between composition and physical properties will lead to better control of said properties and overall performance of the fuels. Samples of RP fuels were analyzed using comprehensive two dimensional gas chromatograph coupled to a time of flight mass spectrometer (GC &times GC &mdash TOFMS) using a reverse column configuration; a long primary column (polar) followed by a short (non polar) secondary column. This instrumentation setup was used to obtain multidimensional chromatograms and achieve great temporal separation of many compounds (including various isomers) listed herein as alkanes, cyclocalkanes and aromatics which can be found in RP fuels. GC &times GC &mdash TOFMS chromatograms are extremely information rich, making the manual process of interpretation unwieldy; therefore various algorithms are used in the analysis, many of which are known as chemometrics. Chemometrics are a specific class of analysis tools that use linear algebra to analyze data to achieve various goals. There are numerous chemometric techniques; some even have variations, therefore choosing the appropriate technique for a given data set is important. Principal component analysis (PCA) was used to analyze the sources of compositional variance between samples. PCA is extremely sensitive to variance between samples and is used to glean the relevant information and summarize the variance as succinct data matrices (scores and loadings). Partial least squares (PLS) is used to draw connections between two separate sets of measurements that are somehow related to each other; such as the chemical information found in the chromatograms and the measured physical properties such as density or kinematic viscosity. Often the intent of using PLS is to approximate values of interest using other related measurements for various reasons (cost, practicality, convenience). Of course to have significant confidence in the PLS models, they must undergo validation, to ensure these predicted values are representative/reasonable. Finally, parallel factor analysis (PARAFAC) is used to deconvolute (mathematically resolve) signals associated with different analytes that were poorly separated in the chromatogram. Successful deonvolution of analytes allows for better identification of analytes when searching for a match in a mass spectral library. Analysis of data, i.e. the interpretation of data, obtained from an instrument is an inseparable part of research. An algorithm of analysis and detection of a specific list of compounds that may potentially be found in a sample, presented herein, is temporal mass spectral ratio analysis method (TMSRA) which was developed for analyzing peaks in GC&ndashMS chromatograms and identifies pure mass channels (<italic>m<italic>/<italic>z<italic>); this was achieved via analysis of the ratios between <italic>m<italic>/<italic>z<italic>. The application of these analysis tools and interpretation of their respective results are discussed.