Autonomous photonic biosensor

Early diagnosis and targeted disease treatment are essential elements of healthcare provision. Current systems often revolve around symptomatic diagnosis and laboratory testing when needed. These systems often result in semi-blind treatment or long wait times. Point-of-care biosensors that can quantify specific biomarkers have an important role to play in this context as they bridge the gap of quick and specific testing in healthcare provision. Photonic biosensors have been demonstrated as a laboratory diagnostic tool with well-established advantages, achieving low limits of detections for protein biomarkers. Several issues remain before they can be translated into commonly used point-of-care instruments. Ideally, integrated photonic biosensors should be inexpensive, sensitive and easy to use. However, many existing photonic biosensors require complex readout equipment, such as external spectrometers, or precise and bulky optical coupling. Presented in this thesis is the development of a low-cost photonic biosensing instrument. The modality used is that of a chirped one-dimensional guided mode resonance sensor. When used in conjunction with a monochromatic source, detection can be accomplished simply by a camera in a compact microscope-style configuration. A study into the performance of low-cost components was conducted to understand the sources of noise. With the understanding gained from this study the instrument was able to achieve a bulk refractive index sensitivity of 3.1 ±0.6 ×10-5 Refractive Index Units. This limit of detection is comparable to other laboratory based modalities and so the device was tested with C-Reactive Protein and Immunoglobulin G assays as example proteins. The best performance achieved was detection of 1 ng/mL for C Reactive Protein, well below the clinically relevant range. The limits of detection achieved using low-cost components indicates that photonic biosensors are well suited to clinical situations and have the potential to play a significant role in the development of diagnostics in the future for healthcare.