Optics and photonics enable important technological solutions for critical areas such as health, communications, energy, and manufacturing. Novel nanofabrication techniques, on the other hand, have enabled the realization of ever shirking devices. On-chip photonic micro-resonators, the fabrication of which was made possible in the recent decade thanks to the progress in nanofabrication, provide a sensitive and scalable transduction mechanism that can be used for biochemical sensing applications. The recognition and quantification of biological molecules is of great interest for a wide range of applications from environmental monitoring and hazard detection to early diagnosis of diseases such as cancer and heart failure. A sensitive and scalable biosensor platform based on an optimized array of silicon nitride microring resonators is proposed for multiplexed, rapid, and label-free detection of biomolecules. The miniature dimension of the proposed sensor allows for the realization of handheld detection devices for limited-resource and point-of-care applications. To realize these sensors, the design, fabrication, stabilization, and integration challenges are addressed. Especially, the focus is placed on solving a major problem in using resonancebased integrated photonic sensors (i.e., the insufficiency of wavelength scan accuracy in typical tunable lasers available) by using an interferometric referencing technique for accurate resonance tracking. This technique can improve the limit of detection of the proposed sensor by more than one order of magnitude. The method does not require any temperature control or cooling, and the biosensor platform does not require narrow linewidths necessary for the biosensors based on ultrahigh quality factor resonators, thus enabling low-cost and reliable integration on the biosensor platform.

abstract: According to sources of the Centers for Disease Control and Prevention, approximately 1.7 million traumatic brain injury (TBI) cases occur annually in the United States. TBI results in 50 thousand deaths, nearly 300 thousand hospitalizations and 2.2 million emergency room visits causing a $76 billion economic burden in direct and indirect costs. Furthermore, it is estimated that over 5 million TBI survivors in the US are struggling with long-term disabilities. And yet, a point-of-care TBI diagnostic has not replaced the non-quantitative cognitive and physiological methods used today. Presently, pupil dilation and the Glasgow Coma Scale (GCS) are clinically used to diagnose TBI. However, GSC presents difficulties in detecting subtle patient changes, oftentimes leaving mild TBI undiagnosed. Given the long-term deficits associated with TBIs, a quantitative method that enables capturing of subtle and changing TBI pathologies is of great interest to the field. The goal of this research is to work towards a test strip and meter point-of-care technology (similar to the glucose meter) that will quantify several TBI biomarkers in a drop of whole blood simultaneously. It is generally understood that measuring only one blood biomarker may not accurately diagnose TBI, thus this work lays the foundation to develop a multi-analyte approach to detect four promising TBI biomarkers: glial fibrillary acidic protein (GFAP), neuron specific enolase (NSE), S-100β protein, and tumor necrosis factor-α (TNF-α). To achieve this, each biomarker was individually assessed and modeled using sensitive and label-free electrochemical impedance techniques first in purified, then in blood solutions using standard electrochemical electrodes. Next, the biomarkers were individually characterized using novel mesoporous carbon electrode materials to facilitate detection in blood solutions and compared to the commercial standard Nafion coating. Finally, the feasibility of measuring these biomarkers in the same sample simultaneously was explored in purified and blood solutions. This work shows that a handheld TBI blood diagnostic is feasible if the electronics can be miniaturized and large quantity production of these sensors can be achieved. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2017