Advances in PCF-SPR sensors: a comprehensive review of biosensing and environmental applications

Abstract

Surface plasmon resonance (PCF-SPR) sensors based on photonic crystal fibers have become powerful instruments for label-free, real-time biomolecular detection. Their high sensitivity and specificity makes them useful for bioanalytical research, environmental monitoring, and medical diagnostics. Recent developments in PCF-SPR technology have concentrated on enhancing plasmonic materials, optimizing sensor architectures, and integrating machine learning (ML) and artificial intelligence (AI) approaches to improve detection efficiency, accuracy, and adaptability. This review provides a comprehensive analysis of key developments in PCF-SPR sensor design, including the use of numerical simulations, hybrid plasmonic coating materials, and novel sensor configurations for improved resonance conditions. Unlike earlier reviews that mainly focus on structural designs or sensing mechanisms of PCF-SPR sensors, this work uniquely integrates recent advances in AI/ML-assisted optimization and presents comparative analyses of hybrid sensor models. Additionally, it integrates a variety of application areas, such as cancer detection, transformer oil monitoring, household oil sensing, and other cutting-edge domains. Although significant progress has been made in SPR-PCF sensors, challenges such as fabrication complexity, limited detection range, and high material costs persist. Addressing these issues requires further research into cost-effective manufacturing and wider adoption of AI for automated optimization. Future work should prioritize enhancing predictive modeling, broadening detectable analyte ranges, and improving environmental stability. This review highlights the current trends, challenges, and potential future directions in PCF-SPR sensor technology, providing insights into strategies for achieving higher sensitivity, robustness, and practical implementation.