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A Novel Plasmonic MIM Sensor Using Integrated 1 × 2 Demultiplexer for Individual Lab-on-Chip Detection of Human Blood Group and Diabetes Level in the Visible to Near-Infrared Region
92
Citations
38
References
2024
Year
Photonic SensorOptical MaterialsEngineeringHuman Blood GroupOptoelectronic DevicesBiomedical EngineeringIntegrated CircuitsSensor TechnologyBiosensing SystemsOptical PropertiesBioimagingNanophotonicsPlanar Waveguide SensorPlasmonic MaterialElectrical EngineeringLoc SensorBiomedical AnalysisBiophotonicsDiabetes LevelRefractive IndexOptical SensorsPlasmonicsFinite Element MethodBiomedical SensorsSensorsBiomedical DiagnosticsApplied PhysicsSensor DesignNanofabricationIndividual Lab-on-chip Detection
In this article, we present an innovative plasmonic lab-on-chip (LoC) sensor featuring three ports, utilizing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times2$ </tex-math></inline-formula> demultiplexer to detect two separate samples without cross-contamination. The performance of the sensor is numerically investigated using the finite element method (FEM), yielding a maximum sensitivity of 865.9 nm per refractive index unit (RIU) and a figure of merit (FOM) of 58.4 in the 700–920 nm range. The proposed metal–insulator–metal (MIM) structure, incorporating a diamond-shaped silicon nanodot array with a perimeter of less than 100 nm, serves as a sensing surface, detecting changes in the refractive index (RI) of the surrounding medium. Our suggested sensor demonstrates an extraordinary transmission coefficient (TC) of around −18-dB, it surpasses classical optics predictions, and its nanoscale light confinement enhances interaction with analytes. Our LoC sensor, based on the MIM structure, achieves high accuracy and precision in measuring small RI changes, making it portable and suitable for on-site applications, and settings with limited resources. This sensor streamlines medical processes by rapidly detecting blood groups and diabetes levels with minimal sample volume, potentially improving patient care efficiency.
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