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Identification of microplastics with cantilever enhanced photoacoustic spectroscopy in infrared region

Journal of environmental chemical engineering 2025 2 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Sudatta Das, Rodrigo D Fernandes, Juho Karhu, Blaž Hrovat, Arto Koistinen, Roberto Martins, Carlos Marques, Juha Toivonen, Erkki Ikonen

Summary

This study demonstrated cantilever-enhanced photoacoustic spectroscopy as a new method for identifying microplastics, finding that the technique provided sensitive, polymer-specific detection of MP particles without requiring sample preparation, offering a potential field-deployable monitoring tool.

Polymers

Microplastics have become ubiquitous in the environment, posing significant threats to ecosystems and organisms across the globe. Accurate risk assessment of these pollutants hinges on the availability of comprehensive qualitative and quantitative data. This paper demonstrates the potential of photoacoustic spectroscopy (PAS) coupled with an external cavity quantum cascade laser (EC-QCL) for identifying various plastic polymers within the mid-infrared (mid-IR) range. To simulate the presence of microplastics in aqueous environments, the particles were prepared under controlled conditions through cryogenic milling of commercial polymer beads, followed by dispersion in ultrapure water. Finally, these simulated samples underwent filtration using gold-coated polycarbonate filters. The microplastics deposited on these filters were successfully identified by analyzing the photoacoustic (PA) spectra of individual particles or small clusters. The resulting absorption spectra were compared with both attenuated total reflectance (ATR) library spectra and transmission (TRX) spectra. While the PA spectra closely matched the transmission spectra, they exhibited shifts in absorption peak positions compared to ATR spectra. This study serves as a proof of concept, highlighting the capability of photoacoustic spectroscopy in identifying microplastic particles in environmental samples and paving the way for more accurate and efficient microplastic detection methodologies.

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