(152g) Selective Nanomanufacturing of Particle-Specific Oligomeric Clusters

Benjamin Robinson, ¹ Rajesekhar Anumolu,¹ Leonard F. Pease III^1,2*

1. Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112

2. Departments of Internal Medicine and Pharmaceutics & Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112

ABSTRACT

Here we demonstrate the ability to selectively fabricate nanoparticle clusters composed of two or more diverse types of particles.  Quantitatively predicting and controlling nanoparticle aggregation within drying droplets remains challenging despite recent advances in the fabrication of nanoclusters from nanoelectronic and other materials.  Here, we report our modeling effort to statistically predict the cluster distribution for two or more distinct particle types and compare these distributions to those obtained experimentally using electrospray differential mobility analysis (ES-DMA).  In this technique, droplets encapsulate two or more random particles and then dry to form the nanoclusters.  To predict the formation of oligomeric nanoparticle clusters formed from monomers in solution, we use previously developed theory that predicts the probability of cluster formation for one type of particle and then multiply it by the mixing probability for two or more distinct particles.  The predicted probabilities are in good agreement with measured cluster distribution from ES-DMA measurements.  These results now predict the conditions necessary to generate nanoparticle clusters from any combination of two or more particles using droplet drying techniques.  The resulting, easily accessible correlations may be used to minimize undesirable clustering along with maximizing yield of particle specific nanophotonic heteroclusters and others. Complex nanoclusters such as coupled quantum dots are important to nanoenergy, nanophotonics, and nanomedicine applications as well as fundamental studies of heterocluster properties.