Computer-Aided Design and Analysis of Spectrally Aligned Hybrid Plasmonic Nanojunctions for SERS Detection of Nucleobases

Hybrid plasmonic nanojunctions with optimal surface-enhanced Raman scattering (SERS) activity are designed via a computer-aided approach, and fabricated via time-controlled aqueous self-assembly of core@shell gold@silver nanoparticles (Au@Ag NPs) with cucurbit[7]uril (CB7) upon simple mixing. The authors showed that SERS signals can be significantly boosted by the incorporation of a strong plasmonic metal and the spectral alignment between the maximal localized surface plasmon resonance (LSPR) and a laser wavelength used for SERS excitation. In a proof-of-concept application, SERS detection of nucleobases with a 633-nm laser has been demonstrated by positioning them within the nanojunctions via formation of host-guest complexes with CB7, achieving rapid response with a detection limit down to sub-nanomolar concentration and an enhancement factor (EF) up to approximate to 10(9)-10(10), i.e., the minimum required EF for single-molecule detection. Furthermore, machine-learning-driven multiplexing of nucleobases is demonstrated, which shows promise in point-of-care diagnosis of diseases related to oxidative damage of DNA and wastewater-based epidemiology.