UC Riverside

High-performing sensors have played a pivotal role in expanding our understanding of biological systems, disease diagnosis, environmental monitoring, and national security. The technical capability they provide has enabled us to obtain in-depth information and insights towards improving human health. One area of sensing that exemplifies this progress is the development of label free sensors which allow direct analysis of molecular interactions. Among these methods surface plasmon resonance (SPR) has emerged as a powerful, real-time detection technique for studies of biological interactions, drug discovery, and other important aspects that lead to new disease diagnosis. Through the implementation of new materials and methods SPR and other label-free sensors have expanded the range of analytes tested. This Dissertation aims to establish improvements in materials and methodologies through technology advancement for solving current sensor limitations. The work focuses on enhancing sensing signal while limiting the impact of nonspecific interactions on label-free methods, providing expanded molecular identity information, and overcoming challenges encountered when detecting small molecules. Chapters 2, 3, and 4 demonstrate advancements in unique biomimetic surfaces to enable the exploration of new biological systems as well as block nonspecific interactions. Chapter 2 focuses on a tethered membrane system to promote incorporation of relevant constituents into lipid bilayers without compromising membrane mobility property and drug delivery interactions. Chapter 3 employs a charged membrane to suppress nonspecific interactions and explores the working mechanism. Chapter 4 expands the capabilities of label-free sensing systems through development of curved membrane platforms that mitigate the decay limits through modeling of lipid distribution in vesicles. Chapter 5 exploits the plasmonic properties of SPR chips to enhance signals in matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) , which is further facilitated with development of machine learning models to identify bacterial species. In Chapter 6, the limitation of small molecule analysis with SPR is tackled by taking advantage of pressure effects to provide specific gas sensing. Each of these Chapters provides novel advancements in sensing capabilities by addressing performance-impairing limitations in label-free sensors. Research goals are achieved both from improvements to SPR systems and incorporation of other methodologies to augment SPR results.