Conference article, meeting abstract
Optimization of self-assembled plasmonic nanoparticle cluster lattices on a substrate
Publication Details
Authors: | Tsegay Korsa, M.; Charconnet, M.; Petersen, S.; Seifert, A.; Adam, J. |
Publication year: | 2021 |
Pages range : | TBD |
Book title: | European Materials Research Society 2021 Fall Meeting: Symposium R: Nanomaterials- electronics & -photonics - Online, Waswaw, Poland : Duration: 20. Sept 2021 - 23. Sept 2021 |
URN / URL: |
Abstract
Due to their unique ability to con ne light to the nanoscale, plasmonic nanoparticles are the topic of investigation in photovoltaics, nano-sensors, drug delivery, and nano-optics. Furthermore, the optical response of these materials can be tuned and optimized for different applications by controlling the shape, size, and configuration of nanoparticle assemblies. In this regard, the modelling of nanoparticles plays a significant role in obtaining optimized nanostructures. To predict the optical response and to support experimental results of regular self-assembled plasmonic nanoparticle supper-lattices, we modelled different combinations and geometric parameters of nanoparticle clusters using the finite element method (FEM). The FEM simulations show a strong increase in the degrees of freedom with increasing particles per cluster, which enormously increases computational time, making optimization routines impossible for a large cluster of nanoparticles. Nevertheless, the optical response of particle clusters shows that the plasmonic modes arise from the single-particle mode, coupling modes, and the cluster lattice etc. Here, we present a statistical approach to predicting the plasmonic response of a sample. Scanning electron microscope (SEM) image processing of self-assembled nanoparticles gives the statistical information of cluster morphologies and distribution on the substrate. Based on the image processing input, we optimize linear combinations of FEM models to predict the whole sample's optical response. We support and verify our results by the experimental extinction curves of self-assembled nanoparticle samples. Our results indicate the potential of predicting the plasmonic effect of the large-scale particle cluster arrangements from the response of single, varying clusters.
Due to their unique ability to con ne light to the nanoscale, plasmonic nanoparticles are the topic of investigation in photovoltaics, nano-sensors, drug delivery, and nano-optics. Furthermore, the optical response of these materials can be tuned and optimized for different applications by controlling the shape, size, and configuration of nanoparticle assemblies. In this regard, the modelling of nanoparticles plays a significant role in obtaining optimized nanostructures. To predict the optical response and to support experimental results of regular self-assembled plasmonic nanoparticle supper-lattices, we modelled different combinations and geometric parameters of nanoparticle clusters using the finite element method (FEM). The FEM simulations show a strong increase in the degrees of freedom with increasing particles per cluster, which enormously increases computational time, making optimization routines impossible for a large cluster of nanoparticles. Nevertheless, the optical response of particle clusters shows that the plasmonic modes arise from the single-particle mode, coupling modes, and the cluster lattice etc. Here, we present a statistical approach to predicting the plasmonic response of a sample. Scanning electron microscope (SEM) image processing of self-assembled nanoparticles gives the statistical information of cluster morphologies and distribution on the substrate. Based on the image processing input, we optimize linear combinations of FEM models to predict the whole sample's optical response. We support and verify our results by the experimental extinction curves of self-assembled nanoparticle samples. Our results indicate the potential of predicting the plasmonic effect of the large-scale particle cluster arrangements from the response of single, varying clusters.
