Transmembrane Inspired Mechano‐Responsive Elastomers with Synergized Traction‐Assisted Healing and Dual‐Channel Sensing

Author:

Chen Chao12,Yu Zhe3,Tian Ying4,Li Fenglong12,Kong Zhengyang5,Ran Xu3,Wu Xing3,Lee Kyung Jin6,Kim Do Hwan5,Lee Jung‐Yong7,Zhu Jin1,Ying Wu Bin7ORCID

Affiliation:

1. Key Laboratory of Bio‐Based Polymeric Materials Technology and Application of Zhejiang Province Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 P. R. China

2. University of Chinese Academy of Sciences Beijing 100049 P. R. China

3. In Situ Devices Center School of Integrated Circuits East China Normal University Shanghai 200241 P. R. China

4. Department of Nano Engineering Department of Nano Science and Technology SKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea

5. Department of Chemical Engineering Hanyang University Seoul 04763 Republic of Korea

6. Department of Chemical Engineering and Applied Chemistry Chungnam National University Daejeon 34134 Republic of Korea

7. School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro, Yuseong‐gu Daejeon 34141 Republic of Korea

Abstract

AbstractIn the burgeoning field of bioinspired materials, the principles governing biological perception and self‐healing drive advancements in biomimetic mechano‐responsive materials, seamlessly integrating ionic signal sensing with self‐healing. While current research often emphasizes individual functionalities, the concurrent enhancement of both self‐healing and sensitivity in iontronic skins is often overlooked. Drawing inspiration from transmembrane proteins like TSP‐15, Piezo 1 and Piezo 2, renowned for recruiting repair factors, multifunctional molecular‐ionic regulatory sites are constructed within a polyurethane/ionic liquid composite system, leading to the development of a mechano‐responsive elastomer (i‐DAPU) that exhibited both rapid self‐healing (72 µm min−1) and impressive sensitivity (7012.05 kPa−1). Leveraging the dual functionalities of i‐DAPU in tandem with deep learning algorithms, a sophisticated system is devised for intelligently analyzing neural conditions in comatose patients based on muscle strength, achieving a remarkable 99.2% accuracy rate, holding significant promise for healthcare applications.

Funder

National Research Foundation of Korea

National Natural Science Foundation of China

Publisher

Wiley

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