Experimental and Computational Studies of Hierarchically Organized Nanomaterials

This thesis investigates the controlled self-assembly of complex hierarchical materials across multiple length scales, focusing on their design, properties, and applications. A significant emphasis is placed on the development of novel, reversible chiral superstructures based on metal-amino acid building blocks. These materials exhibit tunable physicochemical and chiroptical properties, making them promising candidates for applications in chiroptical devices, biomimetic materials, and chemical separation techniques. Their reversible nature, facilitated by dynamic nanoscale interactions, enables structural interconversion in response to external stimuli, providing a unique advantage over previously reported chiral materials. Beyond chiral self-assembly, this work explores the role of perfluorinated copolymer assemblies in energy storage, environmental remediation, and biomedical imaging. Solid polymer electrolytes based on these assemblies enhance the performance of sodium metal batteries, while fluorophilic interactions in perfluoropolyether-based sorbents enable efficient removal of perfluoroalkyl substances. Additionally, computational modeling reveals how polymer segment mobility influences fluorine-based MRI contrast agents, optimizing their imaging performance. Finally, molecular dynamics simulations are employed to study key self-assembly processes, including hydroxyapatite mineralization in bones and light-driven self-assembly of nanoscale building blocks. By integrating experimental and computational approaches, this thesis advances the fundamental understanding of self-assembly mechanisms and offers new strategies for designing functional materials with broad applications in sensing, separation, biomedicine, and sustainable technologies.