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(127k) Multiscale Modeling of Hierarchical Nanomaterials Via Molecular and Colloidal Self-Assembly

Zhao, M. - Presenter, University of Chicago
Research interests:

Hierarchical self-assembly provides a route for the bottom-up fabrication of nanomaterials with tailored structure and function that cannot readily be produced by top-down processing. Multi-scale computational modeling is a powerful tool to establish fundamental understanding and control of hierarchical assembly processes across different length/time scales.

  1. Molecular-level structures: As a postdoc working with Prof. Andrew Ferguson at the University of Chicago, my current research interest is to explore hierarchical self-assembly of peptoids using all-atom and coarse-grained molecular dynamics simulations to guide the rational design of peptoid-based nanomaterials. Peptoids are synthetic peptidomic polymers with defined sequence control, high biocompatibility, and high stability to thermal denaturation and enzymatic degradation, with various applications as antimicrobials, drugs, and catalysts. Using all-atom molecular simulations, we identified a previously unknown hierarchical self-assembly pathway for the formation of crystalline peptoid sheets and made testable predictions for the stable structures as a function of solvent conditions that were subsequently validated by our experimental collaborators. We also developed the first MARTINI-compatible coarse-grained peptoid forcefield from bottom-up parameterization from all-atom molecular dynamic simulations. This model accurately reproduces aggregation behavior and free energy landscapes with 25-fold speedups comparing to atomistic simulations to enable access to vastly longer length and time scales for assembly and expose, for the first time, the long-time, many-body aggregation mechanisms. Taken together, my work in this area has established new understanding and control of peptoid assembly using molecular modeling and simulation conducted in close concert with experimental collaborators.
  2. Mesoscopic behaviors: The structure and stability of self-assembled nanomaterials is strongly influenced by the solvent conditions in which assembly occurs: macroscopic solvent behaviors such as evaporation, contact line dynamics, and interfacial phenomena, can have a huge impact on the thermodynamics, kinetics, and morphology of assembly. My PhD research under the direction of Prof. Xin Yong at the SUNY Binghamton concerned the mesoscale modeling of particle assembly in evaporating droplets by integrating Brownian dynamics simulations of the colloidal particle dynamics with lattice Boltzmann simulations of the multiphase fluid dynamics. We studied particle assembly for both colloidal particles in the liquid bulk and at the liquid-vapor interface of static and evaporating droplets and the coupling to contact line dynamics. In this work, we established liquid-vapor interfaces as a promising platform to fabricate qualitatively different depositions from bulk colloids. Further, we identified substrate patterning could be used to manipulate depositions under capillary interaction with complex curved interfaces. These mesoscale simulations elevated understanding of particle assembly within evaporating droplets, with applications in many engineering applications, such as printing, surface coating, and material fabrications.

My PhD and postdoctoral training experiences give me many opportunities to explore functional materials across scales. In the future, I am extremely excited to establish my research on multiscale modeling of functional materials linking molecular structures to mesoscale/macroscale behaviors. All-atom and coarse-grained molecular simulations provide access to the atom-level self-assembly structures and thermodynamic driving forces and can be interrogated to construct coarse-grained models of particle interactions by bottom-up parameterization. Mesoscale/microscale simulations with modified particle-particle and particle-fluid interactions inspired by molecular simulations hold enormous potential to improve the predictive performance of functional biomolecules such as peptoids and peptides assembly in solvents. I will devote my research to establish the understanding of biomolecular assembly in solvents across scales. The outcomes of my research will help to manipulate hierarchical self-assembly processes and benefit engineering applications related to hierarchical nanomaterials.

Teaching interests:

My background and training make me well prepared to teach undergraduate and graduate courses in computational modeling, fluid dynamics, thermodynamics, and transport phenomena. I have served as an assistant lecturer for a graduate course ME 641 concerning mesoscale modeling of complex fluids. I also have extensive experience training and mentoring undergraduate researchers and have published 2 papers with undergraduate students that I have trained. As a mentor, I understand the importance of clear communication, alignment of expectations, and a defined program of mentorship and training to build self-efficacy and independence. As evidence of my commitment and passion for mentorship, I recently completed the mentorship training at the University of Chicago.