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Field-enhancement calculations in plasmonic tagged nanoplastics improved by machine learning
Summary
Researchers combined rigorous electromagnetic modeling of plasmonic nanoparticle-tagged nanoplastics with neural networks to rapidly predict surface-enhanced Raman scattering field enhancement across a wide range of plastic types and metal nanoparticle compositions, offering a faster design tool for sensitive nanoplastic detection.
Great scientific interest has recently been directed towards the development of sensitive methods for the detection, monitoring, and characterization of nano-plastics dispersed in the environment. A promising approach to increase sensitivity is based on the use of SERS (Surface-Enhanced Raman Scattering) producing strong, easily recognized signals that allow sensitive and quantitative detection of nano-plastics thanks to the induced electromagnetic field enhancement. Computational approaches capable of simulating such enhanced optical response appear crucial for the development of SERS detection methods. Here, we model a system of plasmonic tagged nanoplastic particles using a rigorous electromagnetic description combined with neural networks to speed up the calculations and explore the field-enhancement response in a wider range of plastics and metal nano-particle size and compositions. Our results show that this combined approach may serve as a rapid tool to explore the parameter space and optimize SERS detection.