Open AccessSurface Plasmon Resonance of Gold Nano‐Sea‐Urchins Controlled by Machine‐Learning‐Based Regulation in Seed‐Mediated Growth
Author(s) -
Wu Chia-Chen,
Pan Fei,
Su Yen-Hsun
Publication year - 2021
Publication title -
advanced photonics research
Language(s) - English
Resource type - Journals
ISSN - 2699-9293
DOI - 10.1002/adpr.202170031
Subject(s) - surface plasmon resonance , artificial neural network , surface plasmon , plasmon , artificial intelligence , gold coast , biological system , computer science , surface (topology) , nanoparticle , projection (relational algebra) , machine learning , materials science , nanotechnology , algorithm , biology , mathematics , optoelectronics , geography , geometry , archaeology
Machine‐Learning Predicted Surface Plasmon Resonance In article number 2100052 , Chia‐Chen Wu, Fei Pan, and Yen‐Hsun Su employ genetic‐algorithm‐based artificial neural networks (GANNs) to coordinate the synthesis parameters with surface plasmon. A well‐trained GANN model through machine learning and empirical database yields a precise projection of the surface plasmon wavelength of gold sea‐urchin‐like nanoparticles (GSNPs) via seed‐mediated growth. The optimal fabrication parameters of GSNPs can thus be efficiently specified to meet a desired standard.