Decoding Macroion Self-Assembly and Solvation: A Multi-Scale Computational Approach to Self-Recognition

About This Grant

With the support of the Macromolecular, Supramolecular and Nanochemistry Program of the Division of Chemistry, Professor Mesfin Tsige of the University of Akron will carry out research using theory and computer simulation to better understand how large, charged molecules called macroions interact and self-organize in liquid environments. These molecules are important in nature and in materials science, and they sometimes show unusual behaviors such as recognizing similar molecules or forming stable, hollow, shell-like structures resembling blackberries. These behaviors are not well explained by existing scientific models. The research could lead to new ways of designing materials for drug delivery, water purification, and other technologies. The project will also support education and workforce development by involving high school and undergraduate students in hands-on computational research. The research team will also collaborate with local industry to strengthen regional connections between academic research and real-world applications. This project will address a fundamental gap in understanding the physical principles that govern macroion self-assembly by employing a comprehensive suite of multiscale computational methods. All-atom molecular dynamics simulations will be used to calculate solvation shell energies, providing insight into the role of solvent quality and ion interactions. Potential of mean force (PMF) simulations will explore how macroions selectively associate, with a focus on the influence of charge distribution and counterion effects. For systems involving chiral macroions, density functional theory (DFT) will be applied to capture electronic structure contributions to assembly behavior. The final phase of the project will develop highly coarse-grained models, using insights gained from atomistic simulations and structural features identified through data-driven analysis, to enable efficient simulation of large-scale aggregation phenomena, including hollow, blackberry-type structure formation. These efforts are expected to yield a predictive framework for macroion behavior that connects molecular-level interactions to mesoscale self-assembly processes relevant to both synthetic and biological systems. 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.