Dynamic Electromechanical Co‐Stimulation Based Enhancement of Skeletal Muscle Tissues for Fast Biosyncretic Robots Actuation
Abstract Biosyncretic robots composed of living and synthetic materials have garnered significant attention due to their high energy conversion efficiency, good biocompatibility and human‐robot interaction safety. Among common living actuation materials, artificial skeletal muscle tissue (ASMT) stands out for its good size scalability, controllability, and potential high driving force. However, due to the low differentiation efficiency of myoblasts, the performance of ASMT lags behind that of natural skeletal muscle tissue, thereby hindering the progress of biosyncretic robots. Here, inspired by the training mode of human skeletal muscle, an electromechanical co‐stimulation system for enhancing the performance of ASMTs is proposed. This system is capable of simultaneously applying electrical and mechanical stimulation to ASMTs. Moreover, the mechanical resistance can be dynamically adjusted during ASMT growth based on real‐time measurements of the contractile force of the ASMT. The results show that the enhanced ASMTs demonstrate improved differentiation and performance and can actuate a robot at a maximum speed of 2.38 mm s −1 , which is faster than those of most currently reported ASMT‐based robots. This study introduces a novel approach for enhancing the performance of ASMTs, with substantial implications for the fields of biosyncretic robots and tissue engineering.
Authors
- Hengshen Qin (ORCID: https://orcid.org/0009-0004-3443-7750)
- Ruiqian Wang (ORCID: https://orcid.org/0000-0001-8822-7346)
- Lianqing Liu (ORCID: https://orcid.org/0000-0002-2271-5870)
- Qi Zhang (ORCID: https://orcid.org/0009-0003-1841-6346)
- Chuang Zhang (ORCID: https://orcid.org/0000-0003-3709-8294)
- Lianchao Yang (ORCID: https://orcid.org/0009-0002-2792-2841)
- Yiwei Zhang (ORCID: https://orcid.org/0000-0003-1257-8435)
Institutions
- Shenyang Institute of Automation (CN)
- Chinese Academy of Sciences (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1002/adfm.202410334
- Citations
- 13
- Primary Topic
- Advanced Sensor and Energy Harvesting Materials
- Type
- article
- Field-Weighted Citation Impact
- 2.40
Funders
- National Natural Science Foundation of China
- State Key Laboratory of Robotics
- National Key Research and Development Program of China