(684g) Optimizing the Yield and Selectivity of High Purity Nanoparticle Clusters

Clustering nanoparticles presents a powerful paradigm with which to access properties not otherwise available using individual molecules, individual nanoparticles or bulk materials. However, the governing parameters that precisely tune the yield and selectivity of quantum dot (QD) clusters fabricated via an electrospray droplet evaporation method followed by purification with differential mobility analysis (DMA) remain poorly understood. Here we investigate the parameters that govern the yield and selectivity of small clusters composed of nanoparticles using a Monte Carlo simulation that accounts for spatial and dimensional distributions in droplet and nanoparticle density and size. The resulting, easily accessible correlations may be used to maximize the yield of a particular type of cluster for nanotechnology and energy applications.