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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Marine & Wildlife Policy & Risk Sign in to save

Zero-plastic: AI-assisted sensing for microplastic assessment

Microplastics and Nanoplastics 2026 Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Luís Poersch Bruna de Vargas Guterres, Everson Flores, Everson Flores, Bruna de Vargas Guterres, Everson Flores, Marcelo de Gomensoro Malheiros, Paula Alice Bezerra Barros, Paula Alice Bezerra Barros, Paula Alice Bezerra Barros, Marcelo de Gomensoro Malheiros, Paula Alice Bezerra Barros, Paula Alice Bezerra Barros, Paula Alice Bezerra Barros, Thiago Alves Teixeira, Cristiana Lima Dora, Cristiana Lima Dora, Luís Poersch Luís Poersch Marcelo de Gomensoro Malheiros, Cristiana Lima Dora, Wilson Francisco Britto Wasielesky Junior, Wilson Francisco Britto Wasielesky Junior, Marcelo Rita Pias, Marcelo Rita Pias, Luís Poersch Luís Poersch

Summary

Scientists developed a new device that uses artificial intelligence and microscopy to detect tiny plastic particles (called microplastics) in water. The prototype can spot plastic pieces as small as 3 micrometers - much smaller than the width of a human hair - which could help us better monitor plastic pollution in our water sources. This matters because microplastics are everywhere in our environment and may pose health risks, but until now they've been very difficult to measure accurately.

Polymers

Abstract Microplastics are widespread in aquatic environments, and their quantification remains difficult. This study presents the zero plastic prototype, an open source AI-assisted imaging system designed for microplastic detection. The prototype device uses flow-imaging microscopy to capture particles in the 3–200 $$\mu$$ m range and applies an AI-based segmentation pipeline for image analysis. Laboratory validation was carried out using polystyrene microspheres in the 3–20 $$\mu$$ m range, prepared under reproducible conditions. Comparison with scanning electron microscopy showed agreement for spherical particles larger than 3 $$\mu$$ m. The results define the prototype’s performance under controlled laboratory conditions for polystyrene microspheres and provide a basis for future development toward use in environmental monitoring.

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