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A comprehensive review of PCF-SPR sensors technology: Plasmonic material, optimization and recent ML modeling approaches
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DOI：10.1016/j.jsamd.2026.101215.png)
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Abstract
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Photonic Crystal Fiber-based Surface Plasmon Resonance (PCF-SPR) sensors have recently come to be considered a powerful and crucial approach to a range of real-time, label-free sensing applications. The more notable features of this tool, such as its high sensitivity and accurate sensing measurement, make it useful for environmental monitoring, biochemical, biological, and medical applications. Recent advancements in PCF-SPR sensor technology have focused on optimizing sensor configuration and structural parameters, besides concentrating on selecting appropriate plasmonic materials and their coating thicknesses. Further advancements rely on integrating Machine Learning (ML) algorithms into PCF-SPR sensor modeling to enhance sensing accuracy, efficiency, and adaptability. This paper, however, provides a detailed, comprehensive review of PCF-SPR sensors in terms of working principle, fabrication methods, structural design optimization, plasmonic material innovation, and sensor modeling techniques. Further, it provides an analysis of recent advances in the integration of ML with PCF-SPR sensors and their impact on improving and developing sensor resolution and sensitivity. Nevertheless, there are still certain limitations and challenges that hinder the ability to make the most of both the particular advantages and outstanding performance of the PCF-SPR sensor. These challenges are represented by structural design and fabrication complexity, the costly aspects of plasmonic material, environmental influences on sensor performance, and experimental validation. To address and overcome these issues, future work should concentrate on cost-effective fabrication, environmental stability, modeling with ML adoption, increasing operational wavelength, and increasing the analyte refractive index (RI) range. This paper highlights the trends, limitations, and future prospects in PCF-SPR sensor development, offering insights into methods for achieving significant robustness, sensitivity, resolution, performance, and practical implementation.
Keywords:
PCF
SPR
Machine learning
ML
Plasmonic material
Confinement loss
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