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Sensitivity Performance of Optical Waveguide Sensors with Different Shapes of Cladding Layer for Microplastics Detection

Journal of Physics Conference Series 2023 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Ahmad Izzat Mohd Hanafi, Nur Najahatul Huda Saris, Sevia Mahdaliza Idrus, Amir Syahir, A Hamzah

Summary

Researchers simulated three optical waveguide sensor designs for detecting microplastics in water and found that C-shaped fiber geometry achieved the highest sensitivity, about 2.8 times greater than D-shaped or rectangular designs. Greater exposure of the sensor core to the surrounding water increased the light-analyte interaction responsible for detection.

Abstract With microplastics pollution becoming a global concern, there comes a need for sensors to attain an optimal level of sensitivity to detect microplastics in water. This work investigated the effects of cladding layer shapes on the sensitivity performance of an optical waveguide sensor for microplastics detection in water. In this research, three different cladding shapes—C-shaped fiber, D-shaped fiber, and rectangular waveguide with circular core—were simulated by using Wave Optics Module-COMSOL Multiphysics® software. The results indicated that the C-shaped fiber exhibited significantly higher sensitivity, with a sensitivity value of 1.070x10 −3 compared to the D-shaped fiber and rectangular waveguide with 3.845x10 −4 and 3.842x10 −4 , respectively. The sensitivities of the D-shaped fiber and rectangular waveguide were relatively similar and did not exhibit any significant difference. The higher sensitivity of the C-shaped fiber is attributed to its larger exposed core area to the analyte, resulting in higher interaction of the evanescent wave with the analyte. However, fabricating the C-shaped fiber is more challenging compared to the other two shapes. This research highlights the significance of cladding shapes in optical waveguide sensor sensitivities and provides design optimization insights for microplastics detection in water.

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