article · Scientific Reports
A dual-core photonic crystal fibre sensor utilising surface plasmon resonance is designed for detecting substances with a low refractive index. The structure incorporates an external silver coating for real-time monitoring of refractive index variations, protected against oxidation by a thin layer of titanium dioxide. Five core structural parameters, including pitch, air hole diameter, and silver thickness, are optimised using a Taguchi orthogonal array. The resulting design achieves a spectral sensitivity of 10,000 nm/RIU and an amplitude sensitivity of 235,882 RIU-1. Machine learning models, specifically multi-layer perceptron networks trained with particle swarm optimisation, predict confinement loss across various geometric configurations. A genetic algorithm further optimises the structure to maximise this loss. This integrated computational framework provides accurate performance forecasting and yields a sensitive optical sensor suitable for detecting low refractive index analytes.
Designing high-performance optical sensors usually requires slow, resource-intensive trial-and-error simulation. Combining statistical design methods with machine learning and genetic algorithms accelerates the identification of optimal sensor dimensions. This approach enables the rapid development of exceptionally sensitive fibre-optic devices capable of stable, real-time chemical monitoring without degradation from oxidation.
The sensor design is intended for low refractive index analyte detection, with specific relevance to pharmaceutical inspection. Potential users include quality control laboratories and pharmaceutical manufacturers requiring real-time optical screening. The work is at an early design and computational stage, as the findings rely on statistical modelling, neural network prediction, and algorithmic optimisation rather than physical fabrication or experimental field testing.
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Designing Photonic Crystal Fibers incorporating the Surface Plasmon Resonance Phenomenon (PCF-SPR) has led to numerous interesting applications. This investigation presents an exceptionally responsive surface plasmon resonance sensor, seamlessly integrated into a dual-core photonic crystal fiber, specifically designed for low refractive index (RI) detection. The integration of a plasmonic material, namely silver (Ag), externally deposited on the fiber structure, facilitates real-time monitoring of variations in the refractive index of the surrounding medium. To ensure long-term functionality and prevent oxidation, a thin layer of titanium dioxide (TiO<sub>2</sub>) covers the silver coating. To optimize the sensor, five key design parameters, including pitch, air hole diameter, and silver thickness, are fine-tuned using the Taguchi L<sub>8</sub>(2<sup>5</sup>) orthogonal array. The optimal results obtained present spectral and amplitude sensitivities that reach remarkable values of 10,000 nm/RIU and 235,882 RIU-1, respectively. In addition, Artificial Neural Network (ANN) optimization techniques, specifically Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), are used to predict a critical optical property of the sensor confinement loss (α<sub>loss</sub>). These predictions are derived from the same input structure parameters that are present in the full L<sub>32</sub>(2<sup>5</sup>) design experiment. A genetic algorithm (GA) is then applied for optimization with the goal of maximizing the confinement loss. Our results highlight the effectiveness of training PSO artificial neural networks and demonstrate their ability to quickly and accurately predict results for unknown geometric dimensions, demonstrating their significant potential in this innovative context. The proposed sensor design can be used for various applications including pharmaceutical inspection and detection of low refractive index analytes.
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DOI: 10.1038/s41598-024-55817-9
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