Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

Materials Map under construction

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Coulais, Corentin

  • Google
  • 9
  • 24
  • 557

University of Amsterdam

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (9/9 displayed)

  • 2024Thermoresponsive oil-continuous gels based on double-interpenetrating colloidal-particle networks4citations
  • 2023Shape Memory Soft Robotics with Yield Stress Fluids2citations
  • 2022The extreme mechanics of viscoelastic metamaterials26citations
  • 2021Inverted and Programmable Poynting Effects in Metamaterials23citations
  • 2021Inverted and Programmable Poynting Effects in Metamaterials23citations
  • 2017A nonlinear beam model to describe the postbuckling of wide neo-Hookean beams26citations
  • 2016Periodic cellular materials with nonlinear elastic homogenized stress-strain response at small strains34citations
  • 2016Combinatorial design of textured mechanical metamaterials365citations
  • 2014Shear modulus and dilatancy softening in granular packings above jamming54citations

Places of action

Chart of shared publication
Velikov, Krassimir Petkov
1 / 13 shared
Gouzy, Roland
1 / 2 shared
Macias-Rodriguez, Braulio A.
1 / 2 shared
Wilt, Jackson Kyle
1 / 1 shared
Overvelde, Johannes T. B.
1 / 2 shared
Dykstra, D. M. J.
1 / 3 shared
Janbaz, S.
1 / 4 shared
Ghorbani, Aref
1 / 1 shared
Dykstra, David
1 / 1 shared
Bonn, Daniel
1 / 23 shared
Habibi, Mehdi
1 / 9 shared
Bonn, D.
1 / 34 shared
Habibi, M.
1 / 6 shared
Dykstra, D.
1 / 1 shared
Linden, E. Van Der
1 / 2 shared
Ghorbani, A.
1 / 2 shared
Hecke, M. Van
1 / 2 shared
Lubbers, L. A.
1 / 2 shared
Teomy, Eial
1 / 1 shared
Shokef, Yair
1 / 1 shared
Reus, Koen De
1 / 1 shared
Hecke, Martin Van
1 / 1 shared
Dauchot, Olivier
1 / 2 shared
Seguin, Antoine
1 / 2 shared
Chart of publication period
2024
2023
2022
2021
2017
2016
2014

Co-Authors (by relevance)

  • Velikov, Krassimir Petkov
  • Gouzy, Roland
  • Macias-Rodriguez, Braulio A.
  • Wilt, Jackson Kyle
  • Overvelde, Johannes T. B.
  • Dykstra, D. M. J.
  • Janbaz, S.
  • Ghorbani, Aref
  • Dykstra, David
  • Bonn, Daniel
  • Habibi, Mehdi
  • Bonn, D.
  • Habibi, M.
  • Dykstra, D.
  • Linden, E. Van Der
  • Ghorbani, A.
  • Hecke, M. Van
  • Lubbers, L. A.
  • Teomy, Eial
  • Shokef, Yair
  • Reus, Koen De
  • Hecke, Martin Van
  • Dauchot, Olivier
  • Seguin, Antoine
OrganizationsLocationPeople

article

Shape Memory Soft Robotics with Yield Stress Fluids

  • Wilt, Jackson Kyle
  • Overvelde, Johannes T. B.
  • Coulais, Corentin
Abstract

Biological movement is a source of inspiration for designing soft robots that use fluidic actuation for adaptive gripping and locomotion. While many biological systems use networks of non-Newtonian fluid for movement, to date, most soft robots use Newtonian fluids or pneumatics. Herein, yield stress fluids to manufacture and operate soft devices are exploited, particularly to create soft actuators that exhibit shape memory. Our soft robots are fabricated through embedded 3D printing where the suspension media is a yield stress fluid. Moreover, this complex fluid is encapsulated and used as the hydraulic transmission fluid. Diagnostic designs are developed to characterize the force and shape memory of the yield stress fluid, and the findings are used to create a gripper common in modern soft robotic applications. The diagnostic devices have deformable reservoirs that demonstrate force response, flow behavior, and deformation profiles dependent on the yield stress features of the transmission fluid. The actuation using the yield stress fluid from the retained suspension media creates avenues for partial shape retention and unconventional expansion from localized fluid flow. Looking toward the future of soft robotics, these fabrication and operational approaches using yield stress fluids can provide greater tunability for applications requiring nonlinear actuation and shape memory.

Topics
  • impedance spectroscopy
  • complex fluid