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Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects

Biosensors 2025 11 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 68 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Mohamed Abdel‐Hamid, Mohamed Abdel‐Hamid, Mi‐Ran Ki, Hyo Jik Yoon, Hyo Jik Yoon, Hyo Jik Yoon, Hyo Jik Yoon, Hyo Jik Yoon, Mi‐Ran Ki, Seung Pil Pack Seung Pil Pack

Summary

This review covers advances in tiny sensor devices called microfluidic sensors that can detect trace amounts of pollutants including microplastics in water and environmental samples. Better detection tools matter for human health because they enable faster, more accurate monitoring of microplastic contamination in drinking water and food sources.

The widespread and persistent occurrence of micropollutants-such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)-has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies.

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