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How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
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This review examines how artificial intelligence methods including machine learning and deep learning are being integrated with nanophotonic sensor platforms to enhance environmental monitoring capabilities, with applications including microplastic and contaminant detection in portable, real-time systems.
The escalating threats of environmental pollution underscore the urgent need for advanced monitoring technologies that are portable, label-free, and capable of real-time operation. Nanophotonics has emerged as a transformative approach by manipulating light-matter interactions at the nanoscale, enabling enhanced sensitivity and miniaturization for environmental sensing applications. Recent studies demonstrate that integrating Artificial Intelligence (AI) algorithms with nanophotonic platforms significantly enhances both analytical performance and structural design, improving signal interpretation, classification accuracy, and optimization efficiency. This review systematically examines the integration of AI-driven methods in detecting and characterizing organic pollutants, microplastics, and heavy metals. Furthermore, Chang 2 developments such as inverse design and software-defined nanophotonics demonstrate the potential of AI to accelerate photonic device development, enabling adaptive and reconfigurable sensing architectures. Collectively, the convergence of nanophotonics and AI establishes a foundation for intelligent and data-driven environmental sensors characterized by high sensitivity, rapid response, and autonomous analytical capability.
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