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Detection of blue nanoplastics using resonance Raman spectroscopy coupled with plasmonic nanostructured substrates
Summary
Researchers developed a method for detecting blue-colored nanoplastics using resonance Raman spectroscopy combined with plasmonic nanostructured substrates as surface-enhanced Raman scattering (SERS) substrates. The approach addressed the challenge of detecting sub-micron particles in complex environmental matrices where physical and chemical aging alter nanoplastic signatures.
The growing presence of nanoplastics (NPs) in aquatic and terrestrial environments has raised global concern, prompting researchers and policymakers to develop advanced analytical methods for their detection and monitoring. Among these, surface-enhanced Raman scattering (SERS) spectroscopy has emerged as a promising technique due to its high sensitivity. However, detecting NPs remains challenging because of their small size (below 1 μm), heterogeneous composition, and the physical and chemical changes NPs undergo through environmental aging and contamination. Blue-colored fragments and fibers rank among the most commonly reported microplastic pollutants in aquatic environments. Therefore, their high prevalence motivated our investigation into the detection of their nanoscale counterparts. In this study, we demonstrate a feasible approach for detecting secondary blue nanoplastics at single-particle level by combining SERS with resonance Raman (RR) spectroscopy. SEM-EDX analysis was employed to characterize the blue nanoplastics and the SERS substrates prepared. These particles often contain copper phthalocyanine (CuPc), a persistent pigment commonly used in plastic manufacturing. We have observed that CuPc exhibits a strong RR signal under appropriate laser excitation, allowing it to serve as a marker for tracking blue nanoplastics. Our approach integrates the distinctive RR signal of CuPc with customized nanostructured SERS substrates, fabricated via colloidal and soft lithography, to enhance detection sensitivity. This work highlights the potential of RR-assisted SERS as a powerful tool for advancing secondary nanoplastic detection in environmental research.
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