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<p>The aim of this theoretical study is to propose a surface plasmon resonance (SPR) sensor to detect the glucose levels in urine samples for improved diabetes management. The Kretschmann configuration is being modified by adding gadolinium fluoride (GdF₃) acting as a dielectric and MoS₂ (molybdenum disulfide) as a two-dimensional (2D) material layer between the plasmonic silver metal layer and the sensing medium, enabling label-free quantitative glucose detection. The performance of the proposed sensor is reliant on the layer thicknesses. To achieve the minimum reflectance (R<sub>min</sub>) and high sensitivity required for non-invasive diagnostics, the thickness of the layers has been optimized. The transfer matrix method (TMM) has been utilized for reflectance computation of the proposed multilayer SPR structure. At a fixed wavelength of 633&#xa0;nm, the sensor performance is evaluated employing the angular interrogation technique by observing the SPR resonance angle. The key performance parameters, such as sensitivity (S), figure of merit (FoM), detection accuracy (DA), and full width at half maximum (FWHM), have been computed using MATLAB-simulated SPR curves. For a 10&#xa0;g/dL glucose concentration, maximum sensitivity and FoM values of 216.66&#xa0;deg/RIU and 32.71 RIU⁻<sup>1</sup> are attained. At a wider refractive index (RI) range of 1.33 to 1.40, corresponding to physiologically relevant conditions for diabetes monitoring, the performance has been evaluated for the proposed sensor chip configuration. Within this RI range, the highest sensitivity value obtained is 300&#xa0;deg/RIU with a FoM of 53.0973 RIU⁻<sup>1</sup>. These results demonstrate that the proposed multilayer structure provides a highly sensitive and reliable platform for glucose detection in diabetes, supporting the advancement of non-invasive diagnostic technologies aligned with Sustainable Development Goal 3 (Good Health and Well-Being).</p>

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Transfer matrix modelling of a GdF3/MoS2 enhanced SPR sensor for label-free quantitative urine glucose detection in diabetes management: a simulation-based study

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • U. Arun Kumar,
  • Abdulrahman Saad Alqahtani,
  • Azath Mubarakali,
  • P. Parthasarathy,
  • Arun Uniyal

摘要

The aim of this theoretical study is to propose a surface plasmon resonance (SPR) sensor to detect the glucose levels in urine samples for improved diabetes management. The Kretschmann configuration is being modified by adding gadolinium fluoride (GdF₃) acting as a dielectric and MoS₂ (molybdenum disulfide) as a two-dimensional (2D) material layer between the plasmonic silver metal layer and the sensing medium, enabling label-free quantitative glucose detection. The performance of the proposed sensor is reliant on the layer thicknesses. To achieve the minimum reflectance (Rmin) and high sensitivity required for non-invasive diagnostics, the thickness of the layers has been optimized. The transfer matrix method (TMM) has been utilized for reflectance computation of the proposed multilayer SPR structure. At a fixed wavelength of 633 nm, the sensor performance is evaluated employing the angular interrogation technique by observing the SPR resonance angle. The key performance parameters, such as sensitivity (S), figure of merit (FoM), detection accuracy (DA), and full width at half maximum (FWHM), have been computed using MATLAB-simulated SPR curves. For a 10 g/dL glucose concentration, maximum sensitivity and FoM values of 216.66 deg/RIU and 32.71 RIU⁻1 are attained. At a wider refractive index (RI) range of 1.33 to 1.40, corresponding to physiologically relevant conditions for diabetes monitoring, the performance has been evaluated for the proposed sensor chip configuration. Within this RI range, the highest sensitivity value obtained is 300 deg/RIU with a FoM of 53.0973 RIU⁻1. These results demonstrate that the proposed multilayer structure provides a highly sensitive and reliable platform for glucose detection in diabetes, supporting the advancement of non-invasive diagnostic technologies aligned with Sustainable Development Goal 3 (Good Health and Well-Being).