Synthesis, Modification, and Application of Siloxane Materials for Environmental Sensing

As human populations and resource consumption increase, it is increasingly important to monitor the quality of our environment. While laboratory instruments offer useful information, portable, easy to use sensors would allow environmental analysis to occur on-site, at lower cost, and with minimal operator training. We explore the synthesis, modification, and applications of modified polysiloxane in environmental sensing. Multiple methods of producing modified siloxanes were investigated. Oligomers were formed by using functionalized monomers, producing siloxane materials containing silicon hydride, methyl, and phenyl side chains. Silicon hydride-functionalized oligomers were further modified by hydrosilylation to incorporate methyl ester and naphthyl side chains. Modifications to the siloxane materials were also carried out using post-curing treatments. Methyl ester-functionalized siloxane was incorporated into the surface of a cured poly(dimethylsiloxane) film by siloxane equilibration. The materials containing methyl esters were hydrolyzed to reveal carboxylic acids, which could later be used for covalent protein immobilization. Finally, the siloxane surfaces were modified to incorporate antibodies by covalent, affinity, and adsorption-based attachment. These modifications were characterized by a variety of methods, including contact angle, attenuated total reflectance Fourier transform infrared spectroscopy, dye labels, and 1H nuclear magnetic resonance spectroscopy. The modified siloxane materials were employed in a variety of sensing schemes. Volatile organic compounds were detected using methyl, phenyl, and naphthyl-functionalized materials on a Fabry-Perot interferometer and a refractometer. The Fabry-Perot interferometer was found to detect the analytes upon siloxane extraction by deformation of the Bragg reflectors. The refractometer was used to determine that naphthyl-functionalized siloxanes had elevated refractive indices, rendering these materials more sensitive to some analytes. Antibody-modified siloxanes were used to detect biological analytes through a solid phase microextraction-mediated enzyme linked immunosorbent assay (SPME ELISA). The SPME ELISA was found to have higher analyte sensitivity compared to a conventional ELISA system. The detection scheme was used to detect Escherichia coli at 8500 CFU/mL. These results demonstrate the variety of methods that can be used to modify siloxanes and the wide range of applications of modified siloxanes has been demonstrated through chemical and biological sensing schemes.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.