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Microplastic Identification Methods for Microfluidic Applications: Towards Rapid Detection in Aquatic Environments
Summary
Scientists are developing tiny, portable lab-on-a-chip devices that could detect microplastics in water in real time, replacing today's slow, manual lab testing. This review paper summarizes existing research on these mini-detection tools, which combine smart particle-sorting techniques with light-based sensors and AI to spot plastic particles automatically. Faster, more widespread microplastic monitoring matters because it could help us better understand how much of this pollution ends up in our water—and potentially in our bodies—so we can track risks and respond more quickly.
The escalating accumulation of microplastics (MPs) in marine ecosystems presents a critical environmental crisis. However, current monitoring efforts rely heavily on labor-intensive, contamination-prone, and time-consuming laboratory analyses. While these conventional off-chip methods provide high accuracy, they inherently lack the throughput and autonomy required for continuous, real-time oceanic surveillance. To bridge this technological gap, microfluidic technologies (Lab-on-a-Chip) provide a viable route towards miniaturized, reagent-free in situ detection with reduced sample volumes and continuous operation capability. This review examines the transition from benchtop to field-deployable platforms and organizes the available microfluidic approaches for MP analysis into a structured overview. We examine on-chip sample manipulation and complementary separation techniques, such as acoustophoresis, dielectrophoresis, and optical tweezers, which are essential for isolating target particles from complex environmental matrices and overcoming intrinsic microfluidic challenges. Following sample preparation, we provide a comprehensive evaluation of state-of-the-art optical and spectroscopic identification methods optimized for continuous flow detection. Finally, we address current analytical limitations and discuss how the integration of machine learning with dynamic spectral libraries could enable autonomous, field-deployed monitoring networks for long-term MP surveillance.