MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces
Polymeric materials systems developed for actuators and human-machine interfaces suffer from limitations associated with effective force output due to their low mechanical modulus. New material solutions which can provide intrinsic multi-modal responses are needed to reversibly modulate rigidity; to...
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sg-ntu-dr.10356-1529752021-11-06T20:11:01Z MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces Ankit Krisnadi, Febby Pethe, Shreyas Lim, Ryan Kwang Jen Kulkarni, Mohit Rameshchandra Accoto, Dino Mathews, Nripan School of Materials Science and Engineering School of Mechanical and Aerospace Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Materials Rigidity Modulation Adaptive Surfaces Polymeric materials systems developed for actuators and human-machine interfaces suffer from limitations associated with effective force output due to their low mechanical modulus. New material solutions which can provide intrinsic multi-modal responses are needed to reversibly modulate rigidity; to be flexible, stretchable and bendable one moment, and to be rigid, able to bear load and resist deformation at another moment. Thermally modulated phase transition materials are promising for modulation of mechanical properties; however, they have not been explored for electrically driven shape morphing and responsive surfaces which require favourable electrical properties too. Polymers like polyethylene glycol (PEG) allow for low melting point (56 ℃) and high dielectric constant (10), however they are limited by slow crystallization kinetics and large temperature window. We architect an MXene incorporated PEG-water hybrid which allows for both reduction in melting point and rapid heterogeneous nucleation, which in turn increases the crystallization point. Multimodal response is demonstrated via thermal and electrical input, resulting in modulation of 700 times in Young's modulus, 100 times in flexural modulus and 10 times in hardness as well as large actuation strains (~28%) at low electric fields (~0.7 V/µm). They can be printed to create hardness domains, allowing for local and programmable modulation. An all-printed haptic device with an array of 3 × 3 pixels has been demonstrated, capable of independently varying the hardness values for each pixel. Ministry of Education (MOE) Accepted version The authors would like to acknowledge the funding support for this project from Ministry of Education (MOE) Tier 1 grant (MOE2018-T1- 002-179) (Singapore). 2021-10-27T05:18:07Z 2021-10-27T05:18:07Z 2021 Journal Article Ankit, Krisnadi, F., Pethe, S., Lim, R. K. J., Kulkarni, M. R., Accoto, D. & Mathews, N. (2021). MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces. Nano Energy, 90(Part A), 106548-. https://dx.doi.org/10.1016/j.nanoen.2021.106548 2211-2855 https://hdl.handle.net/10356/152975 10.1016/j.nanoen.2021.106548 2-s2.0-85116041071 Part A 90 106548 en MOE2018-T1- 002-179 Nano Energy © 2021 Elsevier Ltd. All rights reserved. This paper was published in Nano Energy and is made available with permission of Elsevier Ltd. application/pdf application/pdf |
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Engineering::Materials Rigidity Modulation Adaptive Surfaces Ankit Krisnadi, Febby Pethe, Shreyas Lim, Ryan Kwang Jen Kulkarni, Mohit Rameshchandra Accoto, Dino Mathews, Nripan MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
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Polymeric materials systems developed for actuators and human-machine interfaces suffer from limitations associated with effective force output due to their low mechanical modulus. New material solutions which can provide intrinsic multi-modal responses are needed to reversibly modulate rigidity; to be flexible, stretchable and bendable one moment, and to be rigid, able to bear load and resist deformation at another moment. Thermally modulated phase transition materials are promising for modulation of mechanical properties; however, they have not been explored for electrically driven shape morphing and responsive surfaces which require favourable electrical properties too. Polymers like polyethylene glycol (PEG) allow for low melting point (56 ℃) and high dielectric constant (10), however they are limited by slow crystallization kinetics and large temperature window. We architect an MXene incorporated PEG-water hybrid which allows for both reduction in melting point and rapid heterogeneous nucleation, which in turn increases the crystallization point. Multimodal response is demonstrated via thermal and electrical input, resulting in modulation of 700 times in Young's modulus, 100 times in flexural modulus and 10 times in hardness as well as large actuation strains (~28%) at low electric fields (~0.7 V/µm). They can be printed to create hardness domains, allowing for local and programmable modulation. An all-printed haptic device with an array of 3 × 3 pixels has been demonstrated, capable of independently varying the hardness values for each pixel. |
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School of Materials Science and Engineering |
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School of Materials Science and Engineering Ankit Krisnadi, Febby Pethe, Shreyas Lim, Ryan Kwang Jen Kulkarni, Mohit Rameshchandra Accoto, Dino Mathews, Nripan |
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Article |
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Ankit Krisnadi, Febby Pethe, Shreyas Lim, Ryan Kwang Jen Kulkarni, Mohit Rameshchandra Accoto, Dino Mathews, Nripan |
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Ankit |
title |
MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
title_short |
MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
title_full |
MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
title_fullStr |
MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
title_full_unstemmed |
MXene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
title_sort |
mxene incorporated polymeric hybrids for stiffness modulation in printed adaptive surfaces |
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2021 |
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https://hdl.handle.net/10356/152975 |
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1718368046164213760 |