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From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification
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This review surveys the state of micro- and nanoplastic detection methods, concluding that integrating AI-driven spectral analysis, hyperspectral imaging, and thermogravimetric approaches with conventional techniques is essential to close the accuracy and throughput gaps needed for reliable environmental monitoring and risk assessment.
monitoring, and the establishment of standardized analytical protocols. Emerging strategies that integrate AI-driven spectral analysis, hyperspectral imaging, and thermogravimetric analysis show promise for improving the accuracy and throughput of MP detection. Overall, this review highlights the importance of integrating conventional analytical methods with advanced computational tools and developing high-throughput, environmentally sustainable detection strategies to improve our understanding of the environmental fate of MPs and NPs as well as support future risk assessment and policy development.
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From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification
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Researchers reviewed the landscape of analytical methods for detecting microplastics and nanoplastics — including spectroscopy, mass spectrometry, thermal analysis, hyperspectral imaging, and AI-assisted tools — finding that AI-driven spectral analysis and thermogravimetric approaches show the most promise for improving throughput and extending detection to nanoscale particles.
From Microplastics to Nanoplastics: Critical Advances and Persistent Challenges in Detection and Quantification
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Researchers critically review the full range of analytical techniques for detecting and quantifying microplastics — from spectroscopy and mass spectrometry to AI-assisted hyperspectral imaging — identifying nanoplastic detection, real-time monitoring, and the absence of standardized protocols as the field's most persistent challenges.
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.
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.
Recent Trends in Microplastic Detection based on Machine Learning and Artificial Intelligence
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This chapter reviews recent trends in using machine learning and artificial intelligence for microplastic detection, addressing limitations of traditional microscopic and spectroscopic methods. The authors highlight how hyperspectral imaging combined with ML algorithms can classify and quantify microplastic samples more effectively, with improved recognition speed and cost-efficiency. The study suggests that AI-based approaches have significant potential for advancing large-scale microplastic monitoring.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.