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Direct On-Analyte Fabrication of Au Nanoparticles for Substrate-Free SERS Detection of Micro and Nanoplastics

ACS Omega 2025 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 43 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Ayoub Laghrissi, Horst‐Günter Rubahn, Jacek Fiutowski

Summary

Scientists developed a novel technique where gold nanoparticles are grown directly onto the surface of polystyrene plastic beads, enabling highly sensitive detection of the beads using surface-enhanced Raman spectroscopy (SERS) without needing a separate detection substrate. This proof-of-concept approach allows individual plastic particles to be optically fingerprinted even at extremely low concentrations. Advancing detection sensitivity is critical as researchers try to track nanoplastics — the smallest and most health-relevant plastic particles — in environmental and biological samples.

Polymers

Detection of micro- and nanoplastic particles at extremely low concentrations in complex matrices is a critical goal in environmental science and regulatory frameworks. Surface-enhanced Raman spectroscopy (SERS) offers unique advantages for detecting molecular species in such mixtures, relying solely on their characteristic fingerprints. However, its application for plastic particles has been constrained due to weak analyte-substrate interactions. Here, we demonstrate a fabrication proof of concept where gold nanoparticles are synthesized directly onto polystyrene (PS) beads, creating hybrid bead structures specifically designed as substrate-free digital SERS (dSERS). This approach allows direct optical detection of the bead fingerprint, highlighting the interplay between bead fabrication and detection. Based on these findings, we anticipate that substrate-free dSERS will emerge as the method of choice for reliable and ultrasensitive detection of a wide range of analytes, laying the groundwork for a "facial recognition-like" approach to plastic identification.

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