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Updated: May 2, 2026

Multifunctional Hybrid Fe2O3-Au Nanoparticles for Efficient Plasmonic Heating

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Machine learning-driven optimization of monolithic gold plasmonic sensors: Achieving ultrahigh sensitivity with

Sonia Akter¹, Hasan Abdullah^1,2

¹Bangladesh Army International University of Science and Technology (BAIUST), Cumilla, Bangladesh.

|March 13, 2026

Summary

This study combines machine learning with photonic crystal fiber sensors for advanced biosensing. Optimized gold-coated sensors and ML algorithms achieve high sensitivity and accuracy in detecting biological analytes.

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Last Updated: May 2, 2026

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Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

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Published on: March 7, 2018

Area of Science:

Nanophotonics
Biosensing
Machine Learning

Background:

Surface plasmon resonance (SPR) biosensing offers high sensitivity but faces challenges in performance optimization and data interpretation.
Integrating advanced computational frameworks with novel sensor designs is crucial for enhancing biosensing capabilities.

Purpose of the Study:

To develop a highly sensitive and accurate SPR biosensor by co-designing a gold-coated photonic crystal fiber (PCF-SPR) sensor with a machine learning (ML) computational framework.
To evaluate the performance of different ML regression models for predicting the optical responses of the PCF-SPR sensor.

Main Methods:

An asymmetric circular photonic crystal fiber (PCF) with a 50 nm gold coating was designed to maximize evanescent field-analyte interaction.
COMSOL Multiphysics simulations generated synthetic data for refractive index variations (1.33-1.38), capturing key optical parameters.
Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Random Forest Regression (RFR) models were employed and compared for predictive accuracy.

Main Results:

The PCF-SPR sensor achieved a record wavelength sensitivity of 31,846.46 nm/RIU with minimal variation (0.02%) across the biological refractive index range.
A resolution of [Formula: see text] RIU was demonstrated, indicating high precision in analyte detection.
Multiple Linear Regression (MLR) outperformed nonlinear models, showing superior accuracy in predicting confinement loss (MAE = 3.97, RMSE = 5.03) and sensitivity (MAE = 40.18, RMSE = 50.54).

Conclusions:

The synergistic integration of optimized gold-microstructures and interpretable machine learning provides a robust pipeline for high-sensitivity, noise-resilient biosensing.
This approach surpasses previous ML-enhanced plasmonic sensors in key performance metrics while simplifying sensor fabrication.
The developed framework establishes a new benchmark for advanced biosensing applications.