(205d) Kinetic Monte Carlo Modeling of MIL-53 Metal Organic Framework Crystal Growth | AIChE

(205d) Kinetic Monte Carlo Modeling of MIL-53 Metal Organic Framework Crystal Growth

Authors 

Sampath, J., University of Florida
Vasenkov, S., University of Florida
Ziegler, K., University of Florida
Metal organic frameworks (MOFs) are crystalline, coordination polymers well-known for their applications in separations, catalysis due to their large surface areas and high porosity. These materials are structurally versatile because they are composed of metal cluster and organic ligand pairs, which can be strategically switched to make MOFs with certain functionalities. A subset of MOFs with a “rod-packing” motif (“rod-MOFs) are promising as breakthrough materials for size-selective, passive chemical separation processes due to their fine unidirectional channels. The main challenge for these applications is the optimization of the crystallization process for these MOFs, that is, to make them larger than their conventional micron scales and with negligible intergrowths.

Crystallization through the hydrothermal method is a common process used to synthesize certain classes of metal organic frameworks (MOFs), including MIL-53, which falls under the rod-MOF classification. Though the process is ubiquitous to the formation of a variety of crystals, control of the dynamics of the growth process itself remains a challenge as the closed nature of the batch system typically used for these reactions are reminiscent of a “black box”. A wealth of literature exists on the exploration of crystal growth dynamics through Monte Carlo (MC) and Molecular Dynamics (MD) simulations, as well as powerful models which incorporate both. These models have seldom been applied to investigate the crystal growth kinetics of MOFs, and more specifically, the MOF crystallization phenomena for special cases where the inputs to the system are changing periodically.

An understanding of MOF crystallization phenomena under hydrothermal conditions from a fundamental perspective is emphasized herein through the application of kinetic Monte Carlo algorithms (kMC) based on the solid-on-solid (SOS) model.