Fano resonances in plasmonic aluminum nanoparticle clusters for precise gas detection: Ultra-sensitivity to the minor environmental refractive index perturbations
Source abstract
Abstract
Abstract In this study, a compositional molecular cluster of aluminum (Al) nanoparticles (NPs) has been proposed and examined numerically to design efficient and applicable nanoscale plasmonic sensors. Considering the scattering spectral profile of Al/Al 2 O 3 NPs in a decamer orientation that are deposited on a SiO 2 substrate with the determined geometrical dimensions, we detected pronounced Fano resonance dip in the UV spectrum. The position and narrowness of the Fano minima in the declared wavelength region have been evaluated for various structural sizes with different oxide layer thicknesses to enhance its quality. Finding the appropriate nanocluster dimensions, we exposed the structure to the presence of various gases with extremely minor differences in refractive indices. The position of Fano minima in the UV spectrum and its depth yields an ability to extremely precise localized surface plasmon resonance (LSPR) sensing in the mentioned spectrum for the subtle environmental variations. Measuring the accuracy of the compositional Al-based decamer, we quantified the corresponding figure of merit (FoM) as 15.24, while the refractive index of the surrounding medium was a variant factor. Ultimately, we proved that the examined structure has a strong potential to exploit in designing precise, CMOS-compatible, and efficient plasmonic subwavelength sensors that are able to detect extremely small perturbations of the ambient. Finite-difference time-domain (FDTD) method is utilized as a numerical method to extract the optical properties of the examined plasmonic subwavelength cluster.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Plasmonic and Surface Plasmon Research · Gold and Silver Nanoparticles Synthesis and Applications · Metamaterials and Metasurfaces Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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