High-throughput fabrication of soft magneto-origami machines

Shengzhu Yi, Liu Wang, Zhipeng Chen, Jian Wang, Xingyi Song, Pengfei Liu, Yuanxi Zhang, Qingqing Luo, Lelun Peng, Zhigang Wu (), Chuan Fei Guo () and Lelun Jiang ()
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Shengzhu Yi: Sun Yat-Sen University
Liu Wang: University of Science and Technology of China
Zhipeng Chen: Sun Yat-Sen University
Jian Wang: Sun Yat-Sen University
Xingyi Song: Sun Yat-Sen University
Pengfei Liu: Sun Yat-Sen University
Yuanxi Zhang: Sun Yat-Sen University
Qingqing Luo: Sun Yat-Sen University
Lelun Peng: Sun Yat-Sen University
Zhigang Wu: Huazhong University of Science and Technology
Chuan Fei Guo: Southern University of Science and Technology
Lelun Jiang: Sun Yat-Sen University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Soft magneto-active machines capable of magnetically controllable shape-morphing and locomotion have diverse promising applications such as untethered biomedical robots. However, existing soft magneto-active machines often have simple structures with limited functionalities and do not grant high-throughput production due to the convoluted fabrication technology. Here, we propose a facile fabrication strategy that transforms 2D magnetic sheets into 3D soft magneto-active machines with customized geometries by incorporating origami folding. Based on automated roll-to-roll processing, this approach allows for the high-throughput fabrication of soft magneto-origami machines with a variety of characteristics, including large-magnitude deploying, sequential folding into predesigned shapes, and multivariant actuation modes (e.g., contraction, bending, rotation, and rolling locomotion). We leverage these abilities to demonstrate a few potential applications: an electronic robot capable of on-demand deploying and wireless charging, a mechanical 8-3 encoder, a quadruped robot for cargo-release tasks, and a magneto-origami arts/craft. Our work contributes for the high-throughput fabrication of soft magneto-active machines with multi-functionalities.

Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1038/s41467-022-31900-5

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