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There has recently been an extensive amount of work in the field of nanoplasmonics, where plasmonic phenomena occurring on a variety of nanostructrues present an alternative approach to surface plasmon resonance (SPR) biosensing. The architectures of these nanostructures are often complex, where localized regions of high plasmonic activity (sensitive to RI changes) are interdispersed among regions of little to no plasmonic activity (with no RI sensitivity). To date, the bulk of work on nanoplasmonic sensors has focused on the optimization of a nanostructure in terms of its optical characteristics, in terms of either its sensitivity to RI changes or likewise, in its figure of merit (FOM).
In contrast, there has been very little discussion on the role of analyte transport in nanoplasmonic sensing. It is known that the selective immobilization of bioreceptors only to the sensitive regions can lead to significant increases in sensing performance: a result that is solely tied to increases in analyte transport. Hence, when selectively functionalized and operated under diffusion-limited conditions, the rate of analyte transport becomes strongly dependent on the nanoplasmonic architecture. Using numerical results, we have previously shown that this rate is dependent on two factors (i) the fill fraction of the sensitive regions, and (ii) the difference in size between individual localized sensitive regions and the overall size of the sensing surface.
Despite the lack of discussion, the role of analyte transport remains paramount for the design of a nanoplasmonic structure. For example, changes to a nanostructure that result in an increase in RI sensitivity might induce a decrease in the rate of analyte capture, resulting in a sensor with an overall reduction in performance.
In this talk we will demonstrate the ties between optical performance and the rate of analyte transport. We will present experimental data taken from a variety of plasmonic nanostructures, including variation of both their base photonic element (nanorods, nanodisks, and wires) as well as their packing density. We will show that, despite the large differences in optical characteristics, the characteristics of analyte transport follow relatively simple scaling trends. We will show that these experimental data have very good match to analytical predictions. In addition, we will discuss how the kinetics between analyte and bioreceptor will affect the optimal design of a nanostructure: sparse arrays of photonic elements have dominant performance for systems having strong kinetics, whereas for systems with poor kinetics, dense arrays of photonic elements have better performance.
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