A Multiscale Approach to Modeling Plasmonic Nanorod Biosensors

Abstract

Due to their strongly enhanced optical near-fields, plasmonic nanostructures are promising candidates as ultrasensitive label-free sensors of single molecule binding kinetics. However, the interpretation of nanoplasmonic sensing data is complicated by the spatial inhomogeneity of the near-field response and the stochastic nature of molecule-nanoparticle interactions, which makes it difficult to accurately count the number of adsorbed molecules per nanosensor. We combined electromagnetic calculations with stochastic diffusion-reaction simulations in order to investigate how these two sources of noise influence the uncertainty in measured molecular association and dissociation rate constants and concentration for the most common type of plasmonic nanosensor, the nanorod. Using this multiscale in silico tool, we show how to minimize the measurement uncertainty, and we identify the optimum nanorod aspect ratio for quantitative sensing.