Carbon nanotube-based chemical sensing

Abstract

In this thesis, we introduce approaches to carbon nanotube-based sensing for applications in environmental monitoring, disease diagnostics, and food analysis: In Chapter 1, we introduce carbon nanotube-based sensing. We describe parameters that give rise to the sensing capabilities of CNT-based sensors and discuss important performance parameters of carbon nanotube sensors. In Chapter 2, we demonstrate voltage-activated sensing of carbon monoxide using a sensor comprising iron porphyrin and functionalized single walled carbon nanotubes (F-SWCNTs). Modulation of the gate voltage offers a predicted extra dimension for sensing. Specifically, the sensors show significant increase in sensitivity toward CO when negative gate voltage is applied. In Chapter 3, we describe the design of a sensor for the highly selective detection of acrylates using conditions for the aerobic oxidative Heck reaction. The sensors mirror the catalytic processes and selectively respond to electron deficient alkenes by adapting a catalytic reaction system to modulate the doping levels in carbon nanotubes. In Chapter 4, we introduce sensor arrays consisting of imidazolium-based ILs with different substituents and counterions to provide selective responses for known biomarkers of infectious diseases of the lungs. In Chapter 5, we discuss a sensor array comprised of platform 20 functionalized SWCNT sensing channels for the classification of cheese, liquor, and edible oil samples based on their odor. We classify unknown food samples using a k-nearest neighbors model and a random forest model trained on extracted features. This protocol allows us to accurately differentiate between five cheese and five liquor samples (91% and 78% respectively) and only slightly lower (73%) accuracy for five edible oils.