Mapping the phase diagram of alkyl ligands on nanoparticle surfaces with molecular simulations and field theoretic models
- Author(s): Satish, Pratima
- Advisor(s): Geissler, Phillip L
- et al.
Some of the most important and interesting phenomena in physical chemistry, such as heterogeneous catalysis, semi-conduction, and self-assembly depend crucially upon the surface properties of the material under consideration. This is particularly relevant for nanoscopic objects, whose surface-to-volume ratio is much higher than macroscopic materials. Thus, it is often necessary to carefully engineer nanoparticle surfaces so as to prevent them rom coalescing or reacting with their environment. This is achieved by using passivating ligands that stabilize nanoparticle surfaces and consequently, modify the chemical, optical, and electrical properties of nanocrystals and modulate inter-nanoparticle interactions. As a result, gaining an understanding of ligand behavior is essential to synthesizing new nanomaterials with useful technological applications; particularly because probing ligand structure is experimentally difficult.
We approach this problem by performing atomistic computer simulations of alkyl ligands on a semiconducting nanocrystal facet to elucidate their phase behavior at different temperatures and solvent conditions. These simulations provide a detailed description of the structure of the ligand molecules, specifically providing insight into the order-disorder transition they undergo as the temperature is varied. This phase transition changes the arrangement of the surface ligands, affecting how a nanoparticle interacts with solvent and other nanoscale objects in its environment. We proceed to map the observed statistics of ligand orientation onto a coarse-grained field theoretic model of the ordering transition, which is parametrized by physical properties obtained from simulation data. By extracting the underlying physics of the transition and removing irrelevant atomistic details, this coarse-grained model considerably reduces computational costs, while still describing the collective behavior of ligand molecules on a nanoparticle surface. This new understanding can be leveraged to describe ligand ordering when multiple nanoparticle surfaces are close to each other and its effect on the phase behavior of ligand passivated nanocrystals.