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<p>We present NanoSensorLab, an open-access MATLAB-based simulation toolbox grounded in Mie scattering theory for the advanced modeling and optimization of plasmonic nanoparticle-based sensors. The toolbox comprises two integrated modules: Nanoscattering, which computes scattering (Q<sub>sca</sub>), absorption (Q<sub>abs</sub>), and extinction (Q<sub>ext</sub>) efficiencies alongside key optical metrics such as peak wavelength, line width, and quality factor (QF); and Nanosensor, which evaluates sensor performance parameters including sensitivity (S) and figure-of-merit (FOM). NanoSensorLab supports multilayered spherical geometries, nanoshells, nanomatryoshkas, and gain-assisted configurations, and incorporates a material library that includes noble metals, transition metal nitrides, and graphene with user-defined optical properties. The toolbox enables rapid design of high-performance LSPR-based sensors and facilitates parametric studies via an intuitive graphical interface. Simulations demonstrate that graphene-integrated TiN-based nanomatryoshkas can achieve sensitivities up to 925.28&#xa0;nm/RIU, representing a 125.52% enhancement over conventional designs. Benchmarking against COMSOL Multiphysics validates the numerical accuracy of the implementation. NanoSensorLab offers a streamlined, customizable platform for designing plasmonic biosensors targeting biomedical diagnostics such as glucose monitoring and cancer cell detection. The toolbox is freely available at: <a href="https://github.com/aloksinghphy/Toolbox">https://github.com/aloksinghphy/Toolbox</a>.</p>

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An Open-Access Framework for Optimizing Plasmonic Nanoparticle-Based Sensors in Biomedical Applications

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Alok Singh,
  • Manmohan Singh Shishodia

摘要

We present NanoSensorLab, an open-access MATLAB-based simulation toolbox grounded in Mie scattering theory for the advanced modeling and optimization of plasmonic nanoparticle-based sensors. The toolbox comprises two integrated modules: Nanoscattering, which computes scattering (Qsca), absorption (Qabs), and extinction (Qext) efficiencies alongside key optical metrics such as peak wavelength, line width, and quality factor (QF); and Nanosensor, which evaluates sensor performance parameters including sensitivity (S) and figure-of-merit (FOM). NanoSensorLab supports multilayered spherical geometries, nanoshells, nanomatryoshkas, and gain-assisted configurations, and incorporates a material library that includes noble metals, transition metal nitrides, and graphene with user-defined optical properties. The toolbox enables rapid design of high-performance LSPR-based sensors and facilitates parametric studies via an intuitive graphical interface. Simulations demonstrate that graphene-integrated TiN-based nanomatryoshkas can achieve sensitivities up to 925.28 nm/RIU, representing a 125.52% enhancement over conventional designs. Benchmarking against COMSOL Multiphysics validates the numerical accuracy of the implementation. NanoSensorLab offers a streamlined, customizable platform for designing plasmonic biosensors targeting biomedical diagnostics such as glucose monitoring and cancer cell detection. The toolbox is freely available at: https://github.com/aloksinghphy/Toolbox.