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Nagoya University Develops Cost-Effective Microplastic Monitoring Using Porous Materials and Machine Learning

Nagoya University Develops Cost-Effective Microplastic Monitoring Using Porous Materials and Machine Learning
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Inexpensive Microplastic Monitoring Through Porous Materials And Machine Learning

Inexpensive microplastic monitoring through porous materials and machine learning

Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates.

Porous materials and machine learning provide inexpensive microplastic monitoring method

Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates. Details of the method, developed by researchers at Nagoya University with collaborators at the National Institute for Materials Sciences in Japan and others, are published in the journal Nature Communications.

Affordable Microplastic Tracking via Porous Materials and AI

Affordable Microplastic Tracking via Porous Materials and AI
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