This CAREER award supports theoretical and computational research and education on fundamental questions in nanoparticle self-assembly. Nanoparticles are tiny pieces of matter, consisting of only a few hundred to thousands of atoms, and can have extraordinary properties. When large numbers of nanoparticles are arranged in ordered patterns, called "superlattices", they can act as highly efficient solar cells, can function as new types of sensors in medicine and technology, and can be used as filters to purify water and air. Because of their small size arranging nanoparticles into superlattices cannot be performed particle by particle. Instead, researchers utilize "self-assembly", a process inspired by nature; self-assembly builds bio-molecular structures like biological cell. In self-assembly, nanoparticles are subjected to conditions that favor automatic formation of ordered patterns. Ideally, nanoparticles arrange themselves into the targeted superlattice due to mutually attractive forces. However, the forces between these tiny particles are not well understood and self-assembly often results in disordered structures or in patterns that are different from the desired one.
The most important forces between nanoparticles during self-assembly do not come from the particles themselves, but from so-called ligands, short chains of atoms that form a soft protective layer on the surface of nanoparticles. During self-assembly, ligands on nearby nanoparticles interact with each other in complicated ways that are difficult to probe in experiments. In this project, the PI and his team will develop new theoretical models and computer simulation methods that will increase understanding of ligand interactions. Computer simulations will be used to reveal how the length and number of ligands, as well as their interactions with the nanoparticles, determine what superlattice will form during self-assembly. To this end, new computational methods will be developed that allow the simulation of the self-assembly of large numbers nanoparticles and their ligands. The results of this research will provide a reference for future experiments and will help pave the way for more targeted self-assembly of nanoparticle superlattices; it will contribute to broad efforts to engineer improved devices based on nanoparticles. Educational activities of this award focus on improving student success in an undergraduate Physical Chemistry class at the University of Utah. Research has shown that many students struggle with the abstract concepts and advanced mathematics of Physical Chemistry courses. The PI will address these challenges by developing a set of computer simulation exercises that are designed to illustrate key concepts of molecular fluctuations and help student build accurate mental models and reliable intuition for the behavior of atoms and molecules.
This CAREER award supports theoretical and computational research and education that uses molecular models and computer simulations to predict the self-assembly of nanocrystals and to improve student success in an undergraduate physical chemistry course at the University of Utah. Research activities will focus on predicting ordered superstructures that form during the self-assembly of nanocrystals. Gaining precise experimental control over the spatial organization of nanocrystals is paramount for tuning the properties of nanomaterials. Interactions between nanocrystals during self-assembly are determined by organic ligands that cover the surface of nanocrystals. To reveal the complex role of ligands in the self-assembly of superlattices, the research team will develop computationally efficient molecular models of nanocrystals and ligands. With these models, the research team will determine the thermodynamics and kinetics of superlattice formation, using molecular dynamics simulation, free energy calculations, and Monte Carlo sampling. The specific aims of the project are: 1. Develop coarse-grained computational models of alkyl ligands that capture effects of different solvent environments through many-body interactions. 2. Compute phase diagrams for nanocrystal superlattices as a function of nanocrystal shape, ligand length and coverage, and solvent quality and content. Determine kinetic accessibility of superlattices by molecular dynamics simulations of self-assembly and superlattice transformations. 3. Sample equilibrium distributions of ligands on nanocrystal surfaces, as a function of surface binding energies and solvent conditions. Determine effects of ligand rearrangements on self-assembly outcomes. This project is aimed to answer fundamental questions concerning the structure and spatial distribution of ligands and their interactions during self-assembly and reveal the relative importance of experimental parameters including nanocrystal size and shape, ligand parameters, and self-assembly methods. Educational activities supported under this award include introducing sophisticated molecular computer simulations, performed and analyzed by undergraduate students, as a main teaching tool in the physical chemistry course CHEM 3070 at the University of Utah. A major challenge for students in physical chemistry is to connect unfamiliar molecular fluctuations with abstract concepts like entropy. To address this problem, the PI will design several comprehensive simulation activities that elucidate microscopic fluctuations and illustrate key concepts of physical chemistry. In these activities, students will perform, analyze, and visualize sophisticated computer experiments. Discussion of concepts in class will be based on student-generated simulation results. Furthermore, students will use computer simulations as research and discovery tools, and these activities will be tightly integrated with research activities on nanocrystal self-assembly.
The Division of Materials Research and the Division of Chemistry contribute funds to this award.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
