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The Development and Prospect of Microplastic Detection and Analysis Technology
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This review paper takes stock of the tools scientists use to detect microplastics—tiny plastic particles that have been found in our water, food, and even our bodies—and finds that no single method is good enough yet to reliably catch the smallest, most concerning particles (nanoplastics). This matters because better detection technology, especially tools combining AI with chemical analysis, is a necessary first step before we can accurately understand how much plastic we're actually exposed to and what it might mean for our health.
During recent years, nano-detection technology and machine learning-assisted identification methods have developed rapidly, providing new ideas for the precision, standardization and high-throughput quantification of microplastis detection. This article systematically reviews from early detection methods such as sieving and optical microscopy, to mainstream chemical identification techniques such as Fourier Transform infrared spectroscopy (FTIR), Raman spectroscopy, pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), and then to atomic force microscopy-infrared (AFM-IR), tip enhanced Raman spectroscopy (TERS), and machine learning. The evolution process and technical advantages and disadvantages of cutting-edge methods such as deep learning-assisted automatic recognition are analyzed, with a focus on core challenges such as matrix interference in complex environments, bottlenecks in nanoplastic detection, and the lack of method standardization. Research shows that a single technology is difficult to meet the comprehensive demands of high- throughput, nanoscale, and on-site rapid detection, while the integration of multimodal technologies and artificial intelligence-driven intelligent analysis will become the breakthrough direction. It is necessary to accelerate the establishment of a globally shared spectral database, unified operation norms, and the research and development of low-cost portable devices, in order to achieve efficient, accurate and comparable monitoring of microplastics in environmental samples, and provide solid technical support for the prevention.
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Recent progress and technological advancements for detection of micro/nano-plastics in the environment
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This review surveys the latest analytical tools for detecting micro- and nanoplastics across environmental samples, covering imaging, spectroscopy, electrochemical sensors, and artificial intelligence. It highlights how the very small size and chemical complexity of nanoplastics makes detection especially challenging, and discusses how AI integration is improving accuracy and throughput. Advancing detection methods is foundational to understanding the true scale of microplastic contamination and its risks to ecosystems and human health.
Challenges and Advances in Analytical Techniques to Detect Micro- and Nanoplastics
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This research review summarizes the current methods scientists use to detect and study microplastics and nanoplastics - tiny plastic particles that can get into our environment, food, and bodies. The authors explain that identifying these extremely small plastic pieces is very challenging and requires advanced laboratory techniques to understand what types of plastics they are and how much is present. Better detection methods are important because we need to understand how much plastic pollution we're exposed to and its potential effects on human health.
Separation and Detection of Microplastics in Human Exposure Pathways: Challenges, Analytical Techniques, and Emerging Solutions
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This review examines the challenges of detecting and measuring microplastics in human tissues and body fluids, where they have been confirmed to exist. Current methods struggle with very small particles (nanoplastics) and lack standardized procedures, making it hard to accurately assess how much plastic is in our bodies. The authors highlight promising new technologies like microfluidic systems and AI-assisted analysis that could improve our ability to measure human exposure.
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.
Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods
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This review examines current methods for detecting and measuring microplastics in water, soil, and biological samples, including microscopy and spectroscopy techniques. The authors highlight how artificial intelligence could make detection faster and more accurate. Standardized testing methods and better health risk assessments are needed to understand and manage the dangers microplastics pose to human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.