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

Abstract 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 ≈10⁹–10¹⁰, 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.