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Hybrid magnetorheological elastomers enable versatile soft actuators
npj Computational Materials, Volume: 8, Issue: 1
Swansea University Author: Mokarram Hossain
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DOI (Published version): 10.1038/s41524-022-00844-1
Recent advances in magnetorheological elastomers (MREs) have posed the question on whether the combination of both soft- and hard-magnetic particles may open new routes to design versatile multifunctional actuators. Here, we conceptualise ultra-soft hybrid MREs (≈1–10 kPa stiffness) combining experi...
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Recent advances in magnetorheological elastomers (MREs) have posed the question on whether the combination of both soft- and hard-magnetic particles may open new routes to design versatile multifunctional actuators. Here, we conceptualise ultra-soft hybrid MREs (≈1–10 kPa stiffness) combining experimental and computational approaches. First, a comprehensive experimental characterisation is performed. The results unravel that the magneto-mechanical performance of hybrid MREs can be optimised by selecting an adequate mixing ratio between particles. Then, a multi-physics computational framework provides insights into the synergistic magneto-mechanical interactions at the microscale. Soft particles amplify the magnetisation and hard particles contribute to torsional actuation. Our numerical results suggest that the effective response of hybrid MREs emerges from these intricate interactions. Overall, we uncover exciting possibilities to push the frontiers of MRE solutions. These are demonstrated by simulating a bimorph beam that provides actuation flexibility either enhancing mechanical bending or material stiffening, depending on the magnetic stimulation.
Data availability: The data generated during the current study are available from the corresponding author upon reasonable request.
Faculty of Science and Engineering
The authors acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP). M.H. and D.G.G. acknowledge support from MCIN/AEI/10.13039/501100011033 under Grant number PID2020-117894GA-I00. M.A.M.M. acknowledges support from the Ministerio de Ciencia, Innovacion y Universidades, Spain (FPU19/03874). D.G.G. acknowledges support from the Talent Attraction grant (CM 2018- 2018-T2/IND-9992) from the Comunidad de Madrid. M.H. acknowledges the funding through an EPSRC Impact Acceleration Award (EP/R511614/1).