ABSTRACT Water contamination by heavy metals such as mercury Hg(II), Pb(II), and Zn(II) poses a serious risk to public health and aquatic ecosystems. Conventional detection methods are often expensive, complex, or poorly suited for rapid and portable analysis. In this study, we designed and numerically optimized a surface plasmon resonance sensor based on a B‐sil/Al/Al 2 O 3 /nanomaterial multilayer platform for detecting individual and binary mixtures of heavy metal ions in aqueous media. The proposed architecture comprises a B‐sil (borosilicate) prism, a 60 nm aluminum layer for plasmon excitation, a 32 nm Al 2 O 3 layer for passivation and optical tuning, and a nanomaterial sensing overlayer. Four nanomaterials were evaluated as sensing layers: graphene oxide (GO), reduced GO, single‐walled carbon nanotubes, and graphene. Optical performance was analyzed using transfer‐matrix modeling under transverse magnetic polarization at 633 nm. Key performance metrics, including angular sensitivity, full width at half maximum (FWHM), figure of merit, and limit of detection (LoD), were extracted and compared. Among the configurations examined, the B‐sil/Al/Al 2 O 3 /GO sensor showed the most balanced performance, with angular sensitivities up to 338(°)/RIU, FWHM values near 2°, and LoD values on the order of 10 −5 RIU. The simulated response also showed clear refractive‐index‐dependent angular shifts for individual ions and binary mixtures such as Hg(II)/Pb(II) and Hg(II)/Zn(II), supported by strong electric‐field localization at the metal–dielectric interface. These results identify the optimized multilayer structure as a promising simulation‐based platform for heavy‐metal sensing and provide a basis for future experimental validation in aqueous systems.
Tene et al. (Sun,) studied this question.
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