Abstract
Recent advancements in soft robotics have catalyzed significant progress in smart materials research, positioning hydrogel-based actuators as promising candidates for bioinspired soft robotic systems owing to their tunable mechanical properties and inherent flexibility. A central challenge lies in balancing the retention of desirable mechanical properties with achieving high responsiveness in hydrogel-based soft robotic devices. Entangled polymer networks have emerged as a groundbreaking class of materials for soft actuators, offering distinctive mechanical behavior and precisely adjustable responsiveness. This review aims to establish design principles for engineering soft actuators with tailored functionalities by elucidating the underlying mechanisms governing controlled chain entanglement dynamics during material processing. Furthermore, current technical limitations and future research directions are examined from a materials science perspective. This review provides guidance for the design and fabrication of soft actuators with desired properties through a rational route involving controlled chain entanglement.
Funding source: Innovation and Entrepreneurship Team of Jiangsu Province
Award Identifier / Grant number: JSSCTD202241
Funding source: the National Natural Science Foundation of China
Award Identifier / Grant number: 22120102001
Acknowledgments
This work was supported by the National Natural Science Foundation of China (grant no. 22120102001) and the Innovation and Entrepreneurship Team of Jiangsu Province (grant no. JSSCTD202241). In addition, the work is sponsored by the interdisciplinary platform of science and engineering at China University of Mining and Technology.
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Research ethics: Not applicable. 
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Informed consent: Not applicable. 
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Author contributions: Zhenfang Cheng and Qingyu Gao conceived the ideas and wrote the manuscript. Qingyu Gao and Ling Yuan supervised the research, and wrote the manuscript. Meng Zhang and Hui Wang analyzed and post-proofread the content in the manuscript. Yinghua Xie and Qizhai Sha pre-sorted and summarized the literature in the manuscript. Qingyu Gao defined the topic, designed the structural framework, and proofread the final version of the manuscript. 
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Use of Large Language Models, AI and Machine Learning Tools: None declared. 
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Conflict of interest: The author states no conflict of interest. 
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Research funding: This work was supported by the National Natural Science Foundation of China (grant no. 22120102001) and the Innovation and Entrepreneurship Team of Jiangsu Province (grant no. JSSCTD202241). In addition, the work is sponsored by the interdisciplinary platform of science and engineering at China University of Mining and Technology. 
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Data availability: Not applicable. 
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