Biomaterial-integrated metasurfaces for biomarker detection

Today, healthcare system is in the midst of a crucial transition from centralized care to self-monitoring approaches, aiming to reach more individuals; to reduce the workload in hospitals; and to minimize healthcare costs. In this regard, biosensor platforms play a mainstay role in disease diagnostics as an alternative or complementary to current interventions. However, integrating existing biosensing systems into the point-of-care (POC) settings is mostly hampered by the need for bulky instrumentation, lengthy assay procedures, and high-cost platforms. In this thesis, we aim to develop a new biosensing strategy that integrates biomaterials with metasurfaces for this manner. To achieve this aim, optical disks were recycled to obtain their inherent nanograting surfaces as the plastic substrates. Accordingly, two different types of metasurfaces were fabricated (i) by coating the plastic substrates with titanium (Ti), silver (Ag) and gold (Au), and (ii) the latter one was coated with Ag only. Poly-L-Lysine (PLL) was then integrated onto these surfaces to construct a biopolymeric adlayer. To enhance sensing characteristics, biomaterial (PLL)-integrated metasurfaces were decorated with gold nanoparticles (AuNPs), and also, nanoislands (NI) were formed on these surfaces through a chemical reduction reaction of chloroauric acid and hydroxylamine hydrochloride. To benchmark analytical performance of sensors, bulk sensitivity analysis was performed with 1%-70% glycerol solutions. In addition, exosomes were employed as model biomarkers in this study since they are released from kidneys to renal space dynamically, and they carry critical information on disease conditions, holding pivotal impact to be employed for early diagnosis of kidney diseases. With the aforementioned sensing scheme, minute concentrations of exosomes were detected over a wide-sensing area decorated with either anti-CD81 antibodies or anti-CD63 antibodies. In sum, we anticipate that this platform would resolve overwhelming challenges in cost- and complex fabrication-related challenges, and consequently, it would offer an affordable and facile-to-use diagnostic platform for this realm.