Surface Plasmon Resonance of Gold Nano‐Sea‐Urchins Controlled by Machine‐Learning‐Based Regulation in Seed‐Mediated Growth | Zendy

Wu Chia-Chen | Zendy; Pan Fei | Zendy; Su Yen-Hsun | Zendy

Open Access

Surface 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.

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