Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects
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| Опубликовано в:: | Biosensors vol. 15, no. 8 (2025), p. 474-508 |
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| Главный автор: | |
| Другие авторы: | , , |
| Опубликовано: |
MDPI AG
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| Online-ссылка: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Abdelhamid Mohamed A. A. |u Faculty of Education and Arts, Sohar University, Sohar 311, Oman; mabdelhamid@su.edu.om | |
| 245 | 1 | |a Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Pesticides | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Biodegradation | ||
| 653 | |a Technological change | ||
| 653 | |a Nanoparticles | ||
| 653 | |a Biosensors | ||
| 653 | |a Contaminants | ||
| 653 | |a Nanomaterials | ||
| 653 | |a Chromatography | ||
| 653 | |a Lab-on-a-chip | ||
| 653 | |a Machine learning | ||
| 653 | |a Graphene | ||
| 653 | |a Pollution detection | ||
| 653 | |a Innovations | ||
| 653 | |a Signal processing | ||
| 653 | |a Pollution monitoring | ||
| 653 | |a Nanotechnology | ||
| 653 | |a Data analysis | ||
| 653 | |a Micropollutants | ||
| 653 | |a Lasers | ||
| 653 | |a Multiplexing | ||
| 653 | |a Aptamers | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Heavy metals | ||
| 653 | |a Anomalies | ||
| 653 | |a Public health | ||
| 653 | |a Real time | ||
| 653 | |a Pollution | ||
| 653 | |a Biocompatibility | ||
| 653 | |a Perfluoroalkyl & polyfluoroalkyl substances | ||
| 653 | |a Laboratories | ||
| 653 | |a Ecosystems | ||
| 653 | |a Imprinted polymers | ||
| 653 | |a Polymers | ||
| 653 | |a Reagents | ||
| 653 | |a Microfluidics | ||
| 653 | |a Fabrication | ||
| 653 | |a Smartphones | ||
| 653 | |a Costs | ||
| 653 | |a Microplastics | ||
| 653 | |a Sensors | ||
| 653 | |a Consumer products | ||
| 653 | |a 3-D printers | ||
| 653 | |a Ablation | ||
| 653 | |a Perfluorochemicals | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Mi-Ran, Ki |u Department of Biotechnology and Bioinformatics, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea; allheart@korea.ac.kr | |
| 700 | 1 | |a Yoon, Hyo Jik |u Institute of Natural Science, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea; hyojik88@korea.ac.kr | |
| 700 | 1 | |a Pack, Seung Pil |u Department of Biotechnology and Bioinformatics, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea; allheart@korea.ac.kr | |
| 773 | 0 | |t Biosensors |g vol. 15, no. 8 (2025), p. 474-508 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243985486/abstract/embedded/09EF48XIB41FVQI7?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243985486/fulltextwithgraphics/embedded/09EF48XIB41FVQI7?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243985486/fulltextPDF/embedded/09EF48XIB41FVQI7?source=fedsrch |