Control and Prediction of Plasmonic Gold Nanoparticle Assembly

Abstract

Optically-active materials can be identified by their changeable optical signatures, allowing for noncontact insights into the nanoscale behavior of the material and predicting its functional use. This work discusses the optical characterization and plasmonic response of various metal nanoparticles, including those bio-templated. First, we explore the interaction between protein fiber and gold nanoparticles, emphasizing the effects of ionic strength and particle aspect ratio on the assembly process. Automated image analysis further sheds light on the unique behaviors of Au nanoparticles under varying assembly conditions. Secondly, we examine the structure-property relationship of plasmonic nanoparticle clusters, especially nanospheres. Advanced imaging techniques reveal detailed structural information, leading to a proposed non-invasive assembly approach under hyperspectral microscopy. We developed advanced machine learning tools to fast, accurate predict the plasmonic response. Finally, we investigate the orientation of anisotropic plasmonic nanoparticles using polarized hyperspectral scattering, focusing on gold particles of differing aspect ratios. This investigation provides an in-depth understanding of the structure-property relationship for both nanospheres and nanorods. The culmination of this research highlights the potential of advanced machine learning and characterization techniques in predicting and controlling plasmonic gold nanoparticle assembly, offering invaluable insights into their optical properties.