| Summary: | This project aims to design and simulate surface plasmon resonance (SPR) sensors for refractive index-based classification of olive oil variants. The work demonstrates how fiber-based SPR structures can be optimized to distinguish oils of different origins through their optical signatures. The method relies on finitedifference time-domain (FDTD) simulations, where materials, layer thicknesses, boundary conditions, mesh settings, and readout monitors were systematically configured to reproduce resonance spectra. The study design focuses on comparing transmission and reflection configurations under identical conditions, with results analyzed in terms of sensitivity, resonance wavelength shift, full width at half depth (FWHM), detection accuracy, quality factor (QF), and a contrast-weighted figure of merit (FOM). The main outcomes show that the reflection readout achieves a sensitivity of 2566.34 nm·RIU⁻¹ and an FOM of 114.35, outperforming the transmission readout (1997.02 nm·RIU⁻¹ sensitivity, FOM 71.74). Although sensitivity is slightly reduced, the introduction of a 1 nm aluminum oxide (Al₂O₃) layer in the silver-based sensor structure results in deeper resonance dips (0.4483 contrast vs. 0.4014 in the baseline silver) and improved spectral contrast, further enhancing the FOM (114.41). Key performance metrics include: for the gold-based sensor, FWHM is approximately 14.39 nm with a contrast of 0.0712; for the silverbased sensor, FWHM is 37.47 nm with a contrast of 0.4014; and for the optimized silver-alumina composite sensor, FWHM is 38.99 nm with a contrast of 0.4483 and FOM 114.41. These findings provide a foundation for further optimization of fiberbased SPR sensors and highlight their potential application in food quality monitoring and related sensing domains.
|
|---|
