Thermally trainable dual network hydrogels

Shanming Hu, Yuhuang Fang, Chen Liang, Matti Turunen, Olli Ikkala () and Hang Zhang ()
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Shanming Hu: Aalto University
Yuhuang Fang: Aalto University
Chen Liang: Aalto University
Matti Turunen: Aalto University
Olli Ikkala: Aalto University
Hang Zhang: Aalto University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-39446-w

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