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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 Micro- and Nanoplastics in Unstained Tissue with Optical Photothermal Infrared Spectroscopy

2024 2 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 Wimberger, Robert Zimmerleiter, Wolfgang Wadsak, Mike Hettich, Lukas Kenner, Markus Brandstetter

Summary

Researchers demonstrated that optical photothermal infrared spectroscopy can detect unlabeled micro- and nanoplastics as small as 250 nanometers in mammalian tissue samples without staining or labeling. The technique significantly outperformed traditional FTIR spectroscopy in spatial resolution and signal quality when imaging particles in complex biological matrices. The study also introduced a semi-automated machine learning analysis to speed up detection, offering a powerful new tool for studying nanoplastic accumulation in tissues.

Body Systems
Models
Study Type In vivo

ABSTRACT In this study, we investigate the efficacy of Optical Photothermal Infrared (O-PTIR) spectroscopy, also known as mid-infrared photothermal (MIP) microscopy, for the detection of micro- and nano plastics (MNPs) down to diameters of 250 nm in mammalian tissues. Experiments with both in vitro 3D cell cultures derived from HTC116 colorectal cancer cell line and in vivo mouse tissue models were conducted to evaluate the spatial resolution limits and quality of spectra that formed the basis for label-free and non- destructive identification of MNPs. Our findings demonstrate the superior resolution of O-PTIR in imaging individual particles of 250 nm in mouse kidney tissues, surpassing the capabilities of traditional FTIR spectroscopy, which was applied as a reference technique. Furthermore, we introduce a semi-automated 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 produces high-quality, artefact-free spectral images in a contact-less manner and significantly outperforms FTIR in terms of spatial resolution and signal-to-noise ratio in complex biological matrices.

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