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Thermally trainable dual network hydrogels

Author

Listed:
  • Shanming Hu

    (Aalto University)

  • Yuhuang Fang

    (Aalto University)

  • Chen Liang

    (Aalto University)

  • Matti Turunen

    (Aalto University)

  • Olli Ikkala

    (Aalto University)

  • Hang Zhang

    (Aalto University)

Abstract

Inspired by biological systems, trainable responsive materials have received burgeoning research interests for future adaptive and intelligent material systems. However, the trainable materials to date typically cannot perform active work, and the training allows only one direction of functionality change. Here, we demonstrate thermally trainable hydrogel systems consisting of two thermoresponsive polymers, where the volumetric response of the system upon phase transitions enhances or decreases through a training process above certain threshold temperature. Positive or negative training of the thermally induced deformations can be achieved, depending on the network design. Importantly, softening, stiffening, or toughening of the hydrogel can be achieved by the training process. We demonstrate trainable hydrogel actuators capable of performing increased active work or implementing an initially impossible task. The reported dual network hydrogels provide a new training strategy that can be leveraged for bio-inspired soft systems such as adaptive artificial muscles or soft robotics.

Suggested Citation

  • Shanming Hu & Yuhuang Fang & Chen Liang & Matti Turunen & Olli Ikkala & Hang Zhang, 2023. "Thermally trainable dual network hydrogels," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39446-w
    DOI: 10.1038/s41467-023-39446-w
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    References listed on IDEAS

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    1. C. Kaspar & B. J. Ravoo & W. G. Wiel & S. V. Wegner & W. H. P. Pernice, 2021. "The rise of intelligent matter," Nature, Nature, vol. 594(7863), pages 345-355, June.
    2. Hang Zhang & Hao Zeng & Arri Priimagi & Olli Ikkala, 2019. "Programmable responsive hydrogels inspired by classical conditioning algorithm," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    3. Yunlei Zhang & Weiyi Zhao & Shuanhong Ma & Hui Liu & Xingwei Wang & Xiaoduo Zhao & Bo Yu & Meirong Cai & Feng Zhou, 2022. "Modulus adaptive lubricating prototype inspired by instant muscle hardening mechanism of catfish skin," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Feng Wu & Yan Pang & Jinyao Liu, 2020. "Swelling-strengthening hydrogels by embedding with deformable nanobarriers," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Dmitrii V. Kriukov & Jurriaan Huskens & Albert S. Y. Wong, 2024. "Exploring the programmability of autocatalytic chemical reaction networks," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    2. Wenfei Ai & Kai Hou & Jiaxin Wu & Yue Long & Kai Song, 2024. "Miniaturized and untethered McKibben muscles based on photothermal-induced gas-liquid transformation," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Kexin Guo & Xuehan Yang & Chao Zhou & Chuang Li, 2024. "Self-regulated reversal deformation and locomotion of structurally homogenous hydrogels subjected to constant light illumination," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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