M. Navaid Khan – Monson Lab
"Modeling Self-Assembly of Nanoporous Materials: A Lattice Model Approach"
Zeolites are of great importance in modern technology with applications in catalysis, separations, biosensing and microelectronics. Despite the importance of zeolites, our understanding of how they form, and of the thermodynamics that govern their crystallization remain rather poor. Developing such understanding would assist in the fabrication of new zeolites with tailor-made structures for advanced applications. We aim to address these issues with our molecular modeling work.
We focus our attention on all silica materials, where we have adopted an atomic representation of the silicic acid molecule by representing it as a unit cell on the body-centered cubic lattice. The silicon atom occupies the center of the unit cell and the hydroxyl groups are located at the corners. The condensation reaction is modeled as the sharing of two hydroxyl groups from two different tetrahedral on a single lattice site. This model has proven useful in understanding a variety of processes such as silica polmerization at the iso-electric point, formation of surfactant-templated mesoporous materials, and the thermodynamics of ordered crystalline ground states, particularly zeolite-analogs. Recently, we have extended this model to study silica polymerization under various silica concentrations and pH values, finding gel states and nanoparticle states with surprising trends in nanoparticle size.
We have also applied this model to simulate the formation of crystalline zeolite-analogs using Parallel Tempering Monte Carlo simulations of our lattice model. We have observed a variety of crystalline ground states, including layered and fully connected three dimensional structures, under various conditions in the presence of a model of a structure directing agent (SDA). Our aim is to elucidate the role of SDA in governing the zeolite-analog topology.
Raghuram Thyagarajan – Ford/Maroudas Lab
"Low Dimensional Thermodynamic Models for Self-Assembly of Small Clusters of Colloidal Particles"
Self‐assembly of finite clusters of colloidal particles into crystalline objects is an emerging area of scientific interest that has applications in manufacturing photonic crystals and other meta‐materials. Various assembly methods include the use of thermodynamic variables (e.g. temperature, depletant concentration) or external fields as actuators to tune the level of inter‐particle attraction. Robust methods to produce defect‐free target structures would require reduced‐dimension process models to link the particle‐level dynamics to the actuator states. We employ diffusion mapping on trajectory data sets to identify the underlying low dimensional manifold in the system dynamics. Secondly, we study correlations of low‐dimensional diffusion map coordinates with convenient observables or order parameters. With the appropriate choice of order parameters or coarse‐grained variables, we build free energy landscapes (FELs) and diffusivity landscapes (DLs). These landscapes serve as reduced order models, which can be integrated with real‐time process control algorithms.