Blood glucose monitoring is essential for effective diabetes management, as well as for early screening and detection of glycemic abnormalities. However, conventional techniques suffer from limitations, which includes invasiveness, enzyme instability, and delayed physiological response. This research introduces a Kretschmann-based multilayer surface plasmon resonance (SPR) glucose detector operates through label-free refractive index modulation rather than enzymatic electrochemical detection. The device has a BK-7 prism substrate, a silver (Ag) plasmonic layer, and dielectric layers of bismuth germanate (Bi₁₂GeO₂₀) and bismuth germanium oxide (Bi₄Ge 3 O₁₂), and it functions at 633 nm. In order to minimize reflectance and enhance electric field confinement at the sensor interface, the transfer matrix approach was used to optimize the thickness of the layers. With 50 nm, an effective optical thickness of 0.3 nm, and 2 nm layers of Ag, Bi₄Ge 3 O₁₂, and Bi₁₂GeO₂₀, respectively, the optimized design achieves a theoretical minimum reflectance of 0.007%. The sensitivity, quality factor, and figure of merit of the sensor are a peak local differential sensitivity of 250°/RIU, 14.415 and 47.17 RIU −1 , respectively. The resonance angle exhibits a strong linear dependence on refractive index (R 2 = 0.9977), described by θ (°) = 177.04 RI − 162.08, corresponding to a global sensitivity of 177.04°/RIU. This enables accurate glucose quantification over physiological refractive index ranges (1.335–1.347 RIU), relevant to diabetes screening and non-invasive glucose detection applications. Electric field analysis shows enhancement up to 15.5 × 10 4 V/m at 74.7°, indicating strong evanescent wave interaction with the sensing medium. Comparative analysis with existing SPR sensors shows competitive performance while maintaining structural simplicity. The proposed multilayer SPR configuration provides a high-sensitivity refractive-index sensing platform suitable for non-invasive glucose screening, early detection, and monitoring in diabetes management. This work represents a numerical proof-of-concept, and experimental validation under physiologically relevant conditions will be required to establish clinical applicability.
Sathiya et al. (Sun,) studied this question.
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