TY - JOUR
T1 - Flexible healable electromagnetic-interference-shielding bioelastic hydrogel nanocomposite for machine learning-assisted highly sensitive sensing bioelectrode
AU - Zhang, Yunfei
AU - Li, Zehui
AU - Xu, Zhishan
AU - Xiao, Mingyue
AU - Yuan, Yue
AU - Jia, Xiaolong
AU - Shi, Rui
AU - Zhang, Liqun
AU - Wan, Pengbo
N1 - Publisher Copyright:
© 2024 The Author(s). Aggregate published by SCUT, AIEI, and John Wiley & Sons Australia, Ltd.
PY - 2024/10
Y1 - 2024/10
N2 - The prosperous evolution of conductive hydrogel-based skin sensors is attracting tremendous attention nowadays. Nevertheless, it remains a great challenge to simultaneously integrate excellent mechanical strength, desirable electrical conductivity, admirable sensing performance, and brilliant healability in hydrogel-based skin sensors for high-performance diagnostic healthcare sensing and wearable human-machine interface, as well as robust photothermal performance for promptly intelligent photothermal therapy followed by the medical diagnosis and superior electromagnetic interference (EMI) shielding performance for personal protection. Herein, a flexible healable MXene hydrogel-based skin sensor is prepared through a delicate combination of MXene (Ti3C2Tx) nanosheets network with the polymeric network. The as-prepared skin sensor is featured with significantly enhanced mechanical, conducting, and sensing performances, along with robust self-healability, good biocompatibility, and reliable injectability, enabling ultrasensitive human motion monitoring and teeny electrophysiological signals sensing. As a frontier technology in artificial intelligence, machine learning can facilitate to efficiently and precisely identify the electromyography signals produced by various human motions (such as variable finger gestures) with up to 99.5% accuracy, affirming the reliability of the machine learning-assisted gesture identification with great potential in smart personalized healthcare and human-machine interaction. Moreover, the MXene hydrogel-based skin sensor displays prominent EMI shielding performance, demonstrating the great promise of effective personal protection.
AB - The prosperous evolution of conductive hydrogel-based skin sensors is attracting tremendous attention nowadays. Nevertheless, it remains a great challenge to simultaneously integrate excellent mechanical strength, desirable electrical conductivity, admirable sensing performance, and brilliant healability in hydrogel-based skin sensors for high-performance diagnostic healthcare sensing and wearable human-machine interface, as well as robust photothermal performance for promptly intelligent photothermal therapy followed by the medical diagnosis and superior electromagnetic interference (EMI) shielding performance for personal protection. Herein, a flexible healable MXene hydrogel-based skin sensor is prepared through a delicate combination of MXene (Ti3C2Tx) nanosheets network with the polymeric network. The as-prepared skin sensor is featured with significantly enhanced mechanical, conducting, and sensing performances, along with robust self-healability, good biocompatibility, and reliable injectability, enabling ultrasensitive human motion monitoring and teeny electrophysiological signals sensing. As a frontier technology in artificial intelligence, machine learning can facilitate to efficiently and precisely identify the electromyography signals produced by various human motions (such as variable finger gestures) with up to 99.5% accuracy, affirming the reliability of the machine learning-assisted gesture identification with great potential in smart personalized healthcare and human-machine interaction. Moreover, the MXene hydrogel-based skin sensor displays prominent EMI shielding performance, demonstrating the great promise of effective personal protection.
KW - electromagnetic interference shielding
KW - healable
KW - hydrogel nanocomposite
KW - machine learning
KW - ultrasensitive human-interactive sensing
UR - https://www.scopus.com/pages/publications/85196765429
U2 - 10.1002/agt2.566
DO - 10.1002/agt2.566
M3 - 文章
AN - SCOPUS:85196765429
SN - 2766-8541
VL - 5
JO - Aggregate
JF - Aggregate
IS - 5
M1 - e566
ER -