The properties of core-shell plasmonic nanoparticles, including scattering, extinction, absorption, and thickness, are heavily influenced by their fabrication materials. Our work involves an AI-driven approach to streamline the selection of these nanoparticles. Using neural networks, the study aims to predict the best core-shell combinations based on specific desired parameters. This method provides an efficient tool for optimizing material selection in diverse applications.
Collaborators:
Dr. Lafifa Jamal
Professor
Department of Robotics and Mechatronics Engineering
Faculty of Engineering and Technology
Email: [email protected]