Sandwich-structured GaIn(Zn)P/ZnSeS@ZnS quantum dots-Ag@Fe3O4@SiO2 magnetoplasmonic nanosensor with simulation-driven design for influenza A(H1N1) virus biosensing

Developing next-generation ultrasensitive bioanalytical sensing systems requires multifunctional nanoarchitectures that integrate engineered photophysics with highly selective biorecognition interfaces. We report on a multifunctional, simulation-guided design of a fluorescence nanosensor for ultrasensitive detection of Influenza A (H1N1) virus in human saliva, integrating heavy-metal-free GaIn(Zn)P/ZnSeS@ZnS quantum dots (QDs) with magnetoplasmonic molecularly imprinted silica shell (Ag@Fe₃O₄@SiO₂-MIBs) interface. The QDs, engineered with a compositionally graded ZnSeS inner shell and ZnS outer shell, exhibit strong red emission (λ_emi = 652 nm) and high photoluminescence quantum yield (QY = 78 ± 1.4 %) in aqueous media following ligand exchange with thioglycolic acid (TGA). Self-consistent field (SCF) simulations revealed that TGA capping significantly stabilised the QDs surface and induced distinct magnetic properties, confirming favourable surface energetics for biosensing applications. The TGA-GaIn(Zn)P/ZnSeS@ZnS QDs were conjugated to H1N1-specific DNA aptamers and incorporated with graphene oxide (GO), forming a Förster resonance energy transfer (FRET)-based nanoprobe that switches from an "off" to "on" state upon viral recognition. Target-induced aptamer folding disrupted the QD-GO interaction, thereby restoring the QDs fluorescence. To amplify the fluorescence signal and enable selective enrichment, virus-imprinted Ag@Fe₃O₄@SiO₂-MIBs were employed. Finite-difference time-domain (FDTD) simulations demonstrated strong plasmon-exciton coupling between QDs and the Ag core, yielding approximately an 18-fold local field enhancement at a 5 nm spacing. The combined effect of molecular imprinting, magnetic separation, and plasmonic amplification enabled a detection limit of 0.15 pg/mL with high specificity against non-target viruses. This study presented a computationally guided design of hybrid nanomaterials for next-generation, point-of-care viral diagnostics with enhanced optical and molecular recognition performance.