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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 Nanoplastics Sign in to save

Detection of Unlabeled Polystyrene Micro- and Nanoplastics in Mammalian Tissue by Optical Photothermal Infrared Spectroscopy

Analytical Chemistry 2025 6 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Kristina Duswald, Verena Pichler, Verena Kopatz, Tanja Limberger, Verena Karl, David Hennerbichler, Robert Zimmerleiter, Wolfgang Wadsak, Mike Hettich, Elisabeth S. Gruber, Lukas Kenner, Markus Brandstetter

Summary

Researchers demonstrated that a new imaging technique called O-PTIR spectroscopy can detect unlabeled plastic particles as small as 200 nanometers inside mammalian tissues without damaging the samples. Combined with machine learning for faster analysis, this method significantly outperforms traditional infrared spectroscopy for finding nanoplastics in biological tissue. Better detection tools like this are essential for understanding how much plastic actually accumulates in human organs.

Polymers
Body Systems
Models
Study Type In vivo

In this study, we investigate the efficacy of optical photothermal infrared (O-PTIR) spectroscopy, also known as mid-infrared photothermal (MIP) microscopy, for label-free and nondestructive detection of micro- and nanoplastics (MNPs) down to diameters of 200 nm in mammalian tissues. Experiments with both in vitro three-dimensional cell cultures derived from HTC116 colorectal cancer cell line and in vivo mouse tissue models were conducted. Spherical polystyrene particles served as reliable model systems for evaluating spatial resolution limits and quality of spectra. Our findings demonstrate the superior resolution of O-PTIR in imaging individual particles of 200 nm in mouse kidney tissues, surpassing the capabilities of traditional Fourier transform infrared (FTIR) spectroscopy. Furthermore, we apply a semiautomated image analysis that incorporates machine learning algorithms to accelerate the detection process, thus improving throughput and minimizing the potential for human error. The results confirm that O-PTIR is able to provide high-quality, artifact-free spectral images in a contact-less manner and significantly outperforms traditional infrared spectroscopy in terms of spatial resolution and signal-to-noise ratio in complex biological matrices.

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