Doctor of Philosophy (PhD)



Document Type



Volatile organic compounds (VOCs) are prevalent in everyday life, ranging from household chemicals, naturally occurring scents from common plants and animals, to industrial-scale chemicals. Many of these VOCs are known to cause adverse health and environmental effects and require regulation to prevent pollution. Detecting VOCs plays a critical role in food quality control, environmental quality control, medical diagnostics, and explosives detection. Thus, development of adequate sensing devices for detection and discrimination of VOCs is of great importance. In recent years, use of quartz crystal microbalance (QCM) based sensor arrays for analyses of VOCs has attracted significant interest. Detection of VOCs using QCM-based sensors is dependent upon coating materials; hence, development of suitable coating materials is also of great importance. Over the years, QCM-based sensors have provided great promise for detecting VOCs; however, they have not provided this same potential for discrimination between different VOCs. Thus, this dissertation is focused on development of reusable QCM-based sensor arrays for detection and discrimination of VOCs using ionic liquids (ILs) and a group of uniform materials based on organic salts (GUMBOS) as coating materials. GUMBOS and ILs are similar classes of ionic materials, where GUMBOS represent solid phase organic salts with melting points between 25°C and 250°C, while ILs are organic salts with melting points below 100°C and are typically liquid at room temperature.

Within this dissertation the synthesis and characterization of novel ILs and GUMBOS are discussed. Moreover, composite materials using IL-polymer blends are also presented. Vapor sensing properties of all ILs, GUMBOS, and composites were evaluated for use as coating materials in sensor arrays for detection and discrimination towards a wide range of VOCs. Two different sensor array schemes, multisensor array (MSA) and virtual sensor array (VSA), are described and examined throughout this dissertation. Finally, statistical techniques, such as principal component analysis (PCA) and discriminant analysis (DA), were used to develop predictive models to quantify the accuracy of MSAs and VSAs.

The first reports of a QCM-based MSA to discriminate VOCs by classes, and a QCM-based VSA for discrimination of closely related chlorinated VOCs are presented within this dissertation. Overall, these studies demonstrate capabilities of QCM-based vapor sensor arrays with ionic coating materials for accurate discrimination and detection of VOCs.



Committee Chair

Warner, Isiah

Available for download on Tuesday, October 20, 2020