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Hierarchical Assembly

Hierarchical Assembly

Seeks to understand the impact of material building block structure and environment or interface architecture on the formation of functional materials, and thus emphasizes topics related to the dynamics of assembly processes and the response and reorganization of these systems to local and global perturbations. 

Hierarchical assemblies offer the potential to tailor material response in ways not possible with homogenous materials, as multiscale heterogeneous systems exhibit unique properties not inherent in the individual building blocks from which they are comprised. The constituent parts are able to move and transform in response to changes in the environment and applied stimuli. This leads to desirable functional properties that underpin applications in energy storage, transduction, sensing and computation. Taking full advantage of this class of materials demands a deep understanding of the underlying interactions that drive formation and coupled motion. Therefore, the Hierarchical Assembly theme at the CNMS seeks to deterministically create responsive assemblies of hierarchical soft and hybrid materials by developing a mechanistic understanding of molecular, ionic, and electronic motion as functions of building block composition, chemical environment, and interface architecture. We will cultivate capabilities that provide new insights into how the forces that influence coupled motion and structure across molecular, nano- and mesoscales, ultimately drive macroscopic function.

Seizing this opportunity requires new approaches to create and characterize dynamic multiscale systems. Modulation of assembly kinetics will be accomplished using novel techniques for tuning material building blocks and shaping interfaces. Correlations between their formation or reorganization and the onset or change of function will be revealed using advanced multimodal characterization tools that operate in situ. Such tools are essential for visualizing where these buildings blocks are and how they move in response to local stimuli, providing critical information for the calibration of simulations. Simulation and modeling, in turn, will help direct synthesis, guide experiment, and interpret data. Together, these capabilities will provide CNMS users with a tightly woven work flow and data analytics framework to understand and direct the assembly and response of complex hierarchical assemblies away from equilibrium.

Ultimately, we need to be able to ‘catch’ or drive materials into desired states over extended length scales. To achieve this goal, research will follow specific aims that allow correlating changes in material architecture and function from the earliest stages of material formation and the onset of function, through the evolution of hierarchical systems over time. The aims are;

Aim 1 - Decode Dynamics in Material Assembly to understand how to harness and shape kinetic pathways that influence hierarchical structure and function. This can only by accomplished by pushing the limits of in situ characterization, modeling/simulation, and visualization techniques that measure the coordinated motion and structure formation exhibited by ensembles of molecular, polymeric, and hybrid organic/inorganic material building blocks.

Aim 2- Employ Responsive Higher Order Systems to inform an understanding of how subtle changes in composition and structure impact response. This will require unraveling the links between hierarchical architecture and dynamic transitions in bulk and thin film soft and hybrid materials. Work will advance hybrid material and polymer synthesis while leveraging multimodal characterization, coupled with theory/simulation/data analysis. .

Aim 3 - Advances in the Creation and Utilization of Evolving Materials will be achieved by developing an understanding of how ‘fluid,’ loosely coupled, macromaterials (i.e., lipid and polymer bilayers) reorganize and shift their steady state structure as they are driven repeatedly away from equilibrium to advance new classes of materials that exhibit ‘learning’ behavior.