3D Plasmonic Gold Nanopocket Structure for SERS Machine Learning-Based Microplastic Detection

Abstract

Microplastics (MPs) are present not only in the environment but also in drinking water, food, and consumer products. These MPs being toxic, carcinogenic, endocrine disrupting, and genetic risk creators cause several diseases. Despite various approaches, the development of onsite applicable, facile, and quick MP detection methods is still challenging. Here, 3D-plasmonic gold nanopocket (3D-PGNP) nanoarchitecture is formed on a paper substrate for simultaneous MP filtration and detection. The paper-based 3D-PGNP is integrated with a syringe filter device, and then, MP-containing solutions are injected through the syringe. Subsequent detection of the MPs using the surface-enhanced Raman scattering (SERS) successfully identifies the MPs without pretreatment. The interface and volumetric hotspot generation of 3D-PGNP around the captured MPs significantly improves the sensitivity, which is confirmed by finite-difference time-domain simulation. Then, the SERS mapping images obtained from a portable Raman spectrometer are transformed into digital signals via machine learning (ML) technique to identify and quantify the MP distribution. The developed SERS-ML-based MP detection method is applied for mixture MPs and for real matrix samples, demonstrating that the method provides improved accuracy. This system is expected to be used for various MPs detection and for environmentally hazardous substances, such as bacteria, viruses, and fungi.