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Microplastic Identification Methods for Microfluidic Applications: Towards Rapid Detection in Aquatic Environments
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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.
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Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
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This review explores how microfluidic devices combined with artificial intelligence can detect water pollutants including microplastics and nanoplastics in real-time, outside the laboratory. Traditional water testing requires large lab equipment, but these portable chip-based systems can identify contaminants quickly and accurately using machine learning. This technology could improve monitoring of microplastic contamination in drinking water and other water sources.
Optical Methods for Identification and Classification of Microplastics as Birefringent Material
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Microplastics are turning up everywhere in our water, but testing for them usually requires slow, expensive lab equipment. This review rounds up new light-based detection methods—including one using AI that can scan up to 10,000 particles per minute with over 90% accuracy—that could make testing faster, cheaper, and portable enough for real-world use. While these tools aren't perfect yet (they still struggle to tell microplastics apart from natural debris), they represent a promising step toward being able to routinely check our water for contamination that could affect human health.
A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Microplastic Detection and Analysis
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Scientists are increasingly using AI to detect microplastics faster than traditional lab methods like microscopes and chemical testing, according to a review of 113 studies. While AI shows promise, sometimes spotting these tiny plastic particles with over 90% accuracy, the field still lacks standardized testing methods, making it hard to compare results across studies or trust the technology is ready for widespread use. This matters because better, faster microplastic detection could eventually help us understand how much of this pollutant we're exposed to in our food, water, and air.
In Situ Micro/Nanoplastic Sensing Technologies: Optical, Electrochemical and Biosensor Approaches
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Tiny plastic particles are everywhere in our environment, and this review rounds up the latest tools scientists are building to detect them faster and more accurately—from light-based sensors to electronic and biology-based detectors. The big takeaway is that portable electrochemical devices (think small, sensor-based gadgets) currently show the most promise for real-world testing of water, food, or other samples on the spot. This matters because better detection tools are the first step toward understanding how much plastic we're actually exposed to and what it might mean for our health.
Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects
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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.
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