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This study aims to enhance the sensitivity and accuracy of SPR fiber sensors through AI integration and simulation.

AI-augmented surface Plasmon Resonance (SPR) fiber sensor simulation in COMSOL v6.2 for ultra-sensitive biosensing applications

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Randomized trial demonstrates enhanced biosensing accuracy with AI-augmented SPR fiber sensors, suggesting broader applications.

This study aims to enhance the sensitivity and accuracy of SPR fiber sensors through AI integration and simulation.
Conducted simulations in COMSOL Multiphysics to evaluate electromagnetic responses at various wavelengths and angles of incidence.
Implemented machine learning techniques, including neural regression and support vector machines, for data analysis and peak detection.
Investigated the effects of refractive index variations on resonance excitation under TM polarization.
Achieved a sensitivity of approximately 1000 nm/RIU for the proposed biosensor design, outperforming conventional sensors (500-800 nm/RIU).
Demonstrated that neural regression improved peak detection in noisy SPR spectra, enhancing analytical precision.
Validated the design's feasibility for label-free detection of biological samples, indicating potential in healthcare diagnostics and environmental monitoring.

Oraibi et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial demonstrates enhanced biosensing accuracy with AI-augmented SPR fiber sensors, suggesting broader applications.

This study aims to enhance the sensitivity and accuracy of SPR fiber sensors through AI integration and simulation.
Conducted simulations in COMSOL Multiphysics to evaluate electromagnetic responses at various wavelengths and angles of incidence.
Implemented machine learning techniques, including neural regression and support vector machines, for data analysis and peak detection.
Investigated the effects of refractive index variations on resonance excitation under TM polarization.
Achieved a sensitivity of approximately 1000 nm/RIU for the proposed biosensor design, outperforming conventional sensors (500-800 nm/RIU).
Demonstrated that neural regression improved peak detection in noisy SPR spectra, enhancing analytical precision.
Validated the design's feasibility for label-free detection of biological samples, indicating potential in healthcare diagnostics and environmental monitoring.

Oraibi et al. (2026) studied this question.

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