4.2 Article

The onset of surface-enhanced Raman scattering for single-particle detection of submicroplastics

Journal

JOURNAL OF ENVIRONMENTAL SCIENCES
Volume 121, Issue -, Pages 58-64

Publisher

SCIENCE PRESS
DOI: 10.1016/j.jes.2021.08.044

Keywords

Microplastics; Nanoplastics; Gold nanourchins; SERS; Food contamination

Funding

  1. Hong Kong Polytechnic University, Hong Kong SAR, China [P0001274]

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This study presents the application of surface-enhanced Raman scattering (SERS) for detecting submicro- and nanoplastics. The results show that the Raman scattering effect of polystyrene (PS) can be induced by a few gold nanourchins (AuNU), and the excitation wavelength of 785 nm is appropriate for the aggregation of AuNU. These findings are important for the development of SERS-based tools for detecting plastic particles smaller than 1 μm in food and environmental samples.
Microplastics represent an emerging environmental problem worldwide, raising ecological and food safety concerns. Compared to microplastics, there is growing evidence of an even higher abundance of submicro-and nanoplastics in the environment, but a reliable monitoring method for detecting these smaller-sized plastics is lacking. Herein we presented the application of surface-enhanced Raman scattering (SERS) for this purpose. Particles of polystyrene (PS; 600 nm) were used as the probe analyte. Gold nanourchins (AuNU; 50 nm), i.e. urchin-shaped nanoparticles with irregular spikes around the core, were used as the SERS-active substrate. The effectiveness of SERS on PS was evaluated at a single-particle level with different numbers of AuNU in order to determine the minimum conditions required for the onset of the SERS effect. Our findings suggest that SERS of a single particle of PS can be induced by as few as 1-5 particles of AuNU, and that the use of excitation wavelength at 785 nm is appropriate to meet the red-shifted surface plasmon resonance of AuNU upon aggregation. These specifications provide additional information for the development of SERS-based tools for detecting plastic particles < 1 mu m in food and environmental samples. (c) 2022 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

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