Publication Date
2025
Document Type
Dissertation
Committee Members
Ivan Medvedev, Ph.D. (Advisor); Amit Sharma, Ph.D. (Committee Member); Brent Foy, Ph.D. (Committee Member); Sarah Tebbens, Ph.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Exhaled human breath contains a wealth of volatile molecular species which bear an imprint of compounds dissolved in blood. Assessing trace amounts of these species in exhaled breath requires a highly sensitive and selective method. Terahertz (THz) rotational spectroscopy satisfies these needs by detecting numerous and narrow, molecule specific spectral features with feature intensity dependent on the quantity of absorbing molecules, molecular structure, and experimental parameters. Expanding the applicability of THz sensing to biological gases requires systems to measure this spectral data, reliable analysis of the spectra, and demonstration of the utility of biological data extracted from the spectra. To facilitate robust spectral analysis especially in variable, complex media like breath, a model of absorption line shape was developed to simulate instrumental line shapes from first principles. Fitting the modelled line shape to experimental data was validated to confirm the accuracy of derived gas parameters. Enabled by the line shape model, a novel THz spectroscopic chemical sensor was developed to perform general gas analysis and breath sampling. This sensor facilitated semi-autonomous sensing of gas samples while achieving detection limits near a few parts per billion for many species and for some strongly absorbing species at near parts per trillion levels. As a demonstration of breath sensing with the novel tabletop sensor, the sensor was challenged to analyze breath samples of many participants in two parallel projects. In the first project, the sensor was applied to the identification of recent inhaler use via detection of the exogenous inhaler propellant. This propellant, HFA-134a (1,1,1,2-tetrafluoroethane), was characterized for THz detection in the sensor. Breath from ten participants before and after use of an HFA-134a containing inhaler was analyzed. The propellant was consistently detected in breath shortly after inhaler use and, in many cases, up to 30 minutes after. In the second project, the sensor was tasked with analysis of exhaled breath from human participants undergoing a period of sleep deprivation. Analyzed breath from two cohorts undergoing a ~36-hour period was correlated to reaction time metrics. A support vector classifier built with this data was able to classify potential declines in performance with nearly 70% accuracy.
Page Count
185
Department or Program
Department of Mathematics and Statistics
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0001-5144-6297
