Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection

• For the first time, The Ariya lab present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems.
• We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation.
• AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively.
• Nano-DIHM provides physicochemical properties of single particles or clusters of nano/microplastics, including size, shape, optical phase, perimeter, surface area, roughness, and edge gradient.
• It distinguishes nano/microplastics from mixtures of organics, inorganics, biological particles, and coated heterogeneous clusters. This technology allows 4D tracking and 3D structural and spatial study of waterborne nano/microplastics.
• Independent transmission electron microscopy, mass spectrometry, and nanoparticle tracking analysis validates nano-DIHM data.
• Complementary modeling demonstrates nano- and microplastics have significantly distinct distribution patterns in water, which affect their transport and fate, rendering nano-DIHM a powerful tool for accurate nano/microplastic life-cycle analysis and hotspot remediation.

Please see the following link for more infromation: Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection | Environmental Science & Technology (acs.org)