Capman, Nyssa2024-06-052024-06-052024-03https://hdl.handle.net/11299/263712University of Minnesota Ph.D. dissertation. March 2024. Major: Mechanical Engineering. Advisors: Steven Koester, Christopher Hogan. 1 computer file (PDF); xxv, 211 pages.The use of graphene in gas sensors has been increasing in recent years, as graphene has many attractive properties including high carrier mobility, excellent conductivity, and high surface-area-to-volume ratio. Both individual graphene sensors and “electronic nose” (e-nose) sensor arrays have been applied to detecting many gaseous chemicals involved in indoor and outdoor air pollution, food quality, and disease detection in breath. Volatile organic compounds (VOCs) are one important category of chemicals in all of these applications. While graphene sensors have been shown to be effective at detecting and discriminating between VOCs, limitations still exist. This dissertation will describe solutions to two of these problems: Improving selectivity through functionalization and detecting target analytes in the presence of a background interferant.A graphene-based e-nose comprised of 108 sensors functionalized with 36 different chemical receptors was applied to sensing 5 VOCs at 4 concentrations each. The 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) were chosen based on their importance as indicators of diseases such as lung cancer, since disease diagnosis in exhaled breath is one possible application of these arrays. The VOC discrimination ability of the sensor arrays was found to be near-perfect (98%) when using a Bootstrap Aggregated Random Forest classifier. Even with the addition of 1-octene, a compound highly similar to octane and therefore likely to cause high numbers of misclassifications, the sensors still achieved high classification accuracy (89%). The behavior of individual, unfunctionalized graphene varactors was also examined in the presence of VOCs mixed with oxygen. Response signal patterns unique to each VOC + oxygen mixture were revealed. As these patterns developed over the entire gas exposure period, a Long Short-Term Memory (LSTM) network was chosen to classify the gas mixtures as this algorithm utilizes the entire time series. Even in the presence of varying levels of oxygen, three VOCs (ethanol, methanol, and methyl ethyl ketone) at 5 concentrations each could be classified with 100% accuracy, and the VOC concentration could be resolved within approximately 100-200 ppm. This discrimination success was also possible despite the sensors exhibiting varied drift patterns typical of graphene sensors.enGas sensorGrapheneMachine learningSensing gas mixturesSurface functionalizationsVolatile organic compoundsThesis or Dissertation