Modeling Colloidal Photonic Nanostructures - from Quantum Simulations to Optimizing Self-Assembled Plasmonic Nanoparticle Superlattices

Designing new plasmonic nanomaterials with unique physical and chemical characteristics and outstanding optical properties remains a critical task for advanced energy conversion and sensing concepts. Optimizing the material properties to improve their functionality and performance in plasmonic applications is a subsequent challenge to be tackled. This presentation will overview recent advances in the computational design of potential future plasmonic materials, such as transition metals and transparent conducting oxides, and their application in colloidal plasmonic structures. The extraction of complex dispersion characteristics from density functional theory (DFT) calculations allows the integration into subsequent electromagnetic modeling steps, ranging from isolated particles of various shapes and materials to self-assembled plasmonic nanoparticle superlattices, exhibiting collective plasmonic crystal responses. <br/> <br/>The talk will furthermore propose a path to minimize the gap between combined quantum- and electromagnetic simulations and experimental characterization of self- or chemically assembled nanostructures, employing a combination of statistical image analysis (based on SEM images of self-assembled clusters), finite-element modeling, and material- and structure optimization. Based on image statistics, we introduce and superimpose various morphological irregularities in our model alongside optimization of particle morphology, potential shell thickness, and particle materials. Our results, corroborated by electromagnetic simulations and complemented by optical characterization and SERS measurements, provide insight into the near-field enhancement of nanospheres, nanorods, and nanotriangles, arranged in sub-wavelength superlattices of macroscopic dimensions.