Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (6/6 displayed)

  • 2024Quantifying efficient shape-shifting4citations
  • 2024Quantifying efficient shape-shifting:Energy barrier measurement in multi-stable lattice metamaterials4citations
  • 2021Liquid metal logic for soft robotics9citations
  • 2021B:Ionic Glove: A Soft Smart Wearable Sensory Feedback Device for Upper Limb Robotic Prostheses35citations
  • 2021B:Ionic Glove: A Soft Smart Wearable Sensory Feedback Device for Upper Limb Robotic Prostheses35citations
  • 2019A soft matter computer for soft robots77citations

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Chart of shared publication
Shen, Jiajia
2 / 40 shared
Scarpa, Fabrizio L.
1 / 33 shared
Groh, Rainer Mj
1 / 45 shared
Pirrera, Alberto
2 / 85 shared
Zhang, Qicheng
2 / 5 shared
Scarpa, Fabrizio
1 / 100 shared
Groh, Rainer
1 / 5 shared
Rossiter, Jonathan M.
4 / 34 shared
Hauser, Helmut
2 / 2 shared
Conn, Andrew T.
4 / 10 shared
Chen, Hsing-Yu
3 / 4 shared
Diteesawat, Richard Suphapol
2 / 2 shared
Zaghloul, Nouf
2 / 2 shared
Carreira, Sara Correia
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Le, Hao
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Kent, Chris
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Digumarti, Krishna Manaswi
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Le, Anthony N.
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Simons, Melanie F.
1 / 1 shared
Correia Carreira, Sara
1 / 1 shared
Soter, G.
1 / 1 shared
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2024
2021
2019

Co-Authors (by relevance)

  • Shen, Jiajia
  • Scarpa, Fabrizio L.
  • Groh, Rainer Mj
  • Pirrera, Alberto
  • Zhang, Qicheng
  • Scarpa, Fabrizio
  • Groh, Rainer
  • Rossiter, Jonathan M.
  • Hauser, Helmut
  • Conn, Andrew T.
  • Chen, Hsing-Yu
  • Diteesawat, Richard Suphapol
  • Zaghloul, Nouf
  • Carreira, Sara Correia
  • Le, Hao
  • Kent, Chris
  • Digumarti, Krishna Manaswi
  • Le, Anthony N.
  • Simons, Melanie F.
  • Correia Carreira, Sara
  • Soter, G.
OrganizationsLocationPeople

article

Liquid metal logic for soft robotics

  • Rossiter, Jonathan M.
  • Garrad, Martin S.
  • Hauser, Helmut
  • Conn, Andrew T.
  • Chen, Hsing-Yu
Abstract

While there are many soft matter sensing and actuation technologies, there is far less choice when it comes to soft material devices for control and computation. One solution is the Soft Matter Computer (SMC) which can perform both analogue and digital computations in soft materials. This computer processes a fluidic input pattern, consisting of alternating regions of conducting and insulating fluids into an electronic output signal. However, the use of salt water as the conductive fluid means that the Soft Matter Computer has high electrical resistance and requires an AC voltage, making untethered operation impractical. In this paper, we introduce the liquid metal Soft Matter Computer (LM-SMC), which uses galinstan as an alternative conductive fluid. We show that by switching to a liquid metal-sodium hydroxide fluidic input, we reduce the electrical resistance of the SMC by three orders of magnitude, allowing operation at DC voltages of 2 Volts and under. We characterise the stability of the liquid metal input patterns and demonstrate the potential of the LM-SMC by using it to control bipolar ionic polymer metal composite and shape memory alloy actuators. By enabling fully soft computation and control of multiple actuators from a single low voltage DC source, the LM-SMC enables a new class of intelligent and untethered soft machines.

Topics
  • impedance spectroscopy
  • polymer
  • laser emission spectroscopy
  • Sodium
  • composite