Hierarchically Programmable Materials with Propagating Stimulus Responsive Elements and Metamaterial Ultrastructuring

This project aims to develop new adaptive materials that can be 3D-structured using high-resolution additive manufacturing techniques. These soft macromolecular materials can then be used to realize functional materials systems capable of behaviors such as learning, memory and oblivion and signal propagation. Besides the molecular design of materials, the project also deals with their structural processing, scale-bridging analysis and assembly control.
Functional material formats comprise mechanical surface topographies with substructured elements. These react to mechanical forces via switchable structural states. The material systems combine structural elements with positive, negative and zero Poisson's ratio with mechanical elements that encode bi-stable mechanical states. These are used to direct mechanical impulses to defined, mechanically switchable geometries within the material transforming them into locked states. These persist as stable but can be “erased” by external stimuli (oblivion).

These approaches require the design and construction of novel ultra-elastic, resilient as well as designable and stimulus (e.g. temperature) responsive structural (protein-based) macromolecular materials and their structural processing. Cutting-edge high-resolution additive manufacturing techniques guarantee the ultrastructural processing in 3D. In addition, an in-depth physicochemical analysis of the structure-function relationships is required. Moreover, a simulation of physical material responses will lead to an understanding of structure-property relationships. The researchers are also aiming to control environmental stimuli via μ-fluidic elements.

Prof. Dr. Bastian Rapp
Dr. Stefan Schiller

Principal Investigators
Prof. Dr. Thorsten Hugel
Prof. Dr. Lars Pastewka
Prof. Dr. Bastian Rapp

Responsible Investigators
Dr. Bizan Balzer
Dr. Thomas Pfohl
Dr. Stefan Schiller

Postdoctoral Researcher
Dr. Matthias Huber

Doctoral Researcher
Qingchuan Song