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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 Environmental Sources Nanoplastics Sign in to save

Fast detection and 3D imaging of nanoplastics and microplastics by stimulated Raman scattering microscopy

Cell Reports Physical Science 2023 23 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Jianpeng Ao, Guanjun Xu, Han Wu, Lifang Xie, Juan Liu, Kedong Gong, Xuejun Ruan, Jin Han, Kejian Li, Wei Wang, Tianxiang Chen, Minbiao Ji, Liwu Zhang

Summary

Researchers developed a fast imaging technique using stimulated Raman scattering microscopy to detect and create 3D maps of nanoplastics and microplastics at the single-particle level. The method can identify plastic particles as small as 100 nanometers and distinguish between different polymer types without the need for dyes or labels. This technology could help scientists more accurately track tiny plastic particles in environmental and biological samples.

Polymers
Body Systems

Nanoplastic pollution represents an increasing concern for the scientific community. However, the identification of tiny nano-/microplastics (smaller than 5 μm) is still a huge challenge. Herein, we report rapid detecting and 3D imaging of nano-/microplastics at the single-particle level via a strategy based on stimulated Raman scattering microscopy. We demonstrate detection of nanoplastics as small as 100 nm and discriminate between different types of nanoplastics using high wavenumber scanning with low wavenumber verification. Nano-/microplastics, such as polyethylene and polypropylene, are detected in atmospheric and human lung tissue samples. This work provides a 3D, single-particle-level nano-/microplastics detection method, holding great potential to facilitate the characterization of nano-/microplastics in the environment and human tissues. By demonstrating the real-world utility of our methodology, we hope to foster wider adoption and understanding of this critical tool.

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