http://hdl.handle.net/11375/30318

Paper-based microfluidic devices have been a promising device platform for point-of-care diagnostic applications. It is known for its cost-effective and simple nature. Various analytes, such as metabolites, electrolytes, and pathogens, have been quantified in various applications. One of the key advantages of paper-based devices is the inherent property of capillary flow, which allows for the transport of fluids without the need for external pumping. However, since their discovery, applications of paper-based microfluidics have become increasingly complicated. Using expensive materials or complex surface modification to push the device's performance to its limits. Many have lost sight of what made paper-based microfluidic devices attractive in the first place. Despite these advances and recently reported devices having extremely low sensitivity, none have been commercialized, like the first testing strips. This thesis presents a novel material that can mimic the properties of paper. The proposed material can be easily printed onto carbon electrodes to form a printed microfluidic device. The fabrication process is cheap and scalable for mass production. Unlike existing devices, the proposed material allows for a completely new area of design and optimization. This technology successfully demonstrated that it could similarly transport fluids to paper and the electrochemical detection of glucose. The results show that the printed microfluidic devices could detect glucose with high sensitivity and low detection limits, making them potentially useful for medical diagnostics. The material was also integrated onto commercially available screen-printed electrodes and shown to improve performance. The composition of the material is flexible and capable of being tuned for specific needs. For example, semi-conductive microparticles can be integrated for an improvement in electrochemical performance. The results show that the printed microfluidic devices offer a cost-effective, easy-to-produce and reliable alternative to current methods, with potential application in point-of-care testing.

http://hdl.handle.net/11375/30318