Optimizing the yield and selectivity of high purity nanoparticle clusters

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. 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 clusters fabricated via an electrospray droplet evaporation method followed by purification with differential mobility analysis (DMA) remain poorly understood. We find that the product of the electrospray droplet mean diameter to the third power and nanoparticle concentration governs the yield of individual clusters, while the ratio of the nanoparticle standard deviation to the mean diameter governs the selectivity. The resulting, easily accessible correlations may be used to minimize undesirable clustering, such as protein aggregation in the biopharmaceutical industry, and maximize the yield of a particular type of cluster for nanotechnology and energy applications.