May 8 - 13, 2022
Honolulu, Hawaii
May 23 - 25, 2022 (Virtual)
2022 MRS Spring Meeting

Symposium SF13—From Actuators and Energy Harvesting Storage Systems to Living Machines

Technological innovations and requirements of modern applications have driven research towards active materials, capable to perform functions in specific system environments. The realization of directed movement has increased the attention paid towards polymeric materials, such as liquid crystalline elastomers or shape-memory hydrogels. Fascinating advances in materials science, including multifunctional soft materials, energy harvesting and actuation-schemes open up innovative paths to design and operate appliances and robots. Autonomous systems like soft robots, which could be realized by integrating multiple functions including energy generation and harvesting (e.g., catalysis, motion, photovoltaic, osmosis), energy storage (batteries, mechanical storage, thermal energy), sensory functions, and the capability of motion could be imagined. Nature has extensively served as a great source of inspiration for humans to design and develop innovative technologies. Plant-inspired robotic systems consider how plants perform and adapt their growth as well as how they vary biomechanical properties (stiffness and rigidity) to anchor, attach, and climb. This symposium will focus on all kinds of advances in designing and constructing living machines as a new generation of robots, and in increasing the efficiency, autonomy and lifespan of the systems.

Topics will include:

  • Energy harvesting and storage in multifunctional systems
  • Soft robotics and electrically conductive soft or stretchable materials for force sensing, actuating, and electronics
  • Application-driven design of multifunctional materials with capabilities of intelligent systems
  • Life-like technologies inspired by the scientific investigation of biological systems
  • Bionic principles for multifunctionality, bio-inspired design, and biomorphous materials design e.g. biologically derived adhesives
  • Self-sensing and self-healing materials
  • Stimuli-responsive polymer-based systems that respond to e.g. pH, temperature, (bio)molecules, light, electrical, strain
  • Liquid-crystalline elastomers, shape-memory polymers, adaptive polymers
  • Ferroelectric, magnetostrictive, and magnetoelectric materials
  • Characterization methods for functions and structures
  • Integrated Multi-material Fabrication 3D/4D Printing
  • Virtual material design, computational design, multiscale modelling and simulation

Invited Speakers:

  • Sung-Hoon Ahn (Seoul National University, Republic of Korea)
  • Marc Behl (Helmholtz-Zentrum Hereon, Germany)
  • Michael Dickey (North Carolina State University, USA)
  • Peer Fischer (Max Planck Institute for Intelligent Systems, Germany)
  • David H. Gracias (Johns Hopkins University, USA)
  • Sung Hoon Kang (Johns Hopkins University, USA)
  • Kyung-Suk Kim (Brown University, USA)
  • Christopher Lynch (University of California, Riverside, USA)
  • Shlomo Magdassi (The Hebrew University of Jerusalem, Israel)
  • Carmel Majidi (Carnegie Mellon University, USA)
  • Barbara Mazzolai (Istituto Italiano di Tecnologia, Italy)
  • Bradley Nelson (ETH Zürich, Switzerland)
  • Thao (Vicky) Nguyen (Johns Hopkins University, USA)
  • Philippe Poulin (Centre National de la Recherche Scientifique, France)
  • H. Jerry Qi (Georgia Institute of Technology, USA)
  • Patricia Soffiatti (Federal University of Parana State, Brazil)
  • Bozhi Tian (University of Chicago, USA)
  • Victoria Webster-Wood (Carnegie Mellon University, USA)
  • Shu Yang (University of Pennsylvania, USA)
  • Huichan Zhao (Tsinghua University, China)
  • Hongli (Julie) Zhu (Northeastern University, USA)

Symposium Organizers

Andreas Lendlein
University of Potsdam
Institute of Chemistry
Germany

Kris Dorsey
Smith College
USA

Pablo Valdivia y Alvarado
Singapore University of Technology and Design
Singapore

Ruike (Renee) Zhao
Stanford University
USA

Topics

adaptive artificial intelligence bioelectronic energy storage machine learning robotics