Polythiophene Transistors as Gas Sensors for Electronic Nose Applications
Electronic noses have been studied for decades, but are still relatively uncommon in commercial use because fabricating a gas sensor array, the heart of the electronic nose, is costly and difficult. Commonly, the most difficult part of fabricating a sensor array is integrating the individual sensor elements into one platform, an expensive proposition using current gas sensors like metal oxide devices. Thin film transistors (TFTs) made from polythiophene, an organic semiconductor, stand to be attractive candidates for sensor arrays because they can be easily arrayed with deposition methods like inkjet printing and they have a rich chemistry that can exploited to tune their sensor behavior. The thesis of this work is that functionalized polythiophene TFTs are compelling, and perhaps superior, candidates in sensor arrays for electronic nose applications.
First, polythiophene TFTs are demonstrated to be viable gas sensors. Basic electrical and sensing behavior is introduced and initial metrics and difficulties are addressed. Physical characterizations of polythiophene based gas sensors are carried out using Grazing Incidence X-ray Diffraction (GIXD), X-ray Reflectivity (XRR), and Quartz Crystal Microbalance (QCM) techniques. Novel in situ XRR and QCM measurements have shown, for the first time, the presence of a physical interaction between the gaseous analyte and the sensor film. These physical changes are corroborated and compared with the electrical response. Strategies for engineering better gas sensors are proposed and demonstrated. Using elegant and robust motifs, simple but powerful sensor arrays are fabricated that demonstrate discrimination between analytes even in the presence of mixtures. These arrays are capable of discriminating analytes based on the size and arrangement of their molecules, an important but previously unexplored avenue. Discrimination based on the analyte's functional groups is also presented. Based on these findings, a mechanistic model is also proposed which is consistent with experimental observations and highlights more pathways for engineering better sensor arrays.