Multivariate design and data analysis for plasmonic sensing

Abstract

Plasmonic sensing using surface plasmon resonances remains an area with unclear detection limits. The introduction of metallic nanostructures as nanoantennas can improve plasmonic sensing through localized surface plasmon resonances (LSPR), complex lattice effects, and simultaneous propagating plasmons. The combination of plasmonic effects through near or far-field coupling lead to more complex phenomena called plasmonic hybridization. Multivariate analysis methods significantly improve the sensing performance in terms of figures of merit by exploiting multiple features of the resonance curves. Likewise, multivariate design approaches provide optimized plasmonic substrates through electromagnetic simulations when the same curve features are used as in multivariate data analysis to achieve optimized performance metrics. Here, we demonstrate with one-dimensional gold nanogratings that multivariate design concepts lead to optimized performance in plasmonic sensing, which is also confirmed experimentally.