PDF
Abstract
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.
Keywords
electromagnetic interference shielding
/
healable
/
hydrogel nanocomposite
/
machine learning
/
ultrasensitive human-interactive sensing
Cite this article
Download citation ▾
Yunfei Zhang, Zehui Li, Zhishan Xu, Mingyue Xiao, Yue Yuan, Xiaolong Jia, Rui Shi, Liqun Zhang, Pengbo Wan.
Flexible healable electromagnetic-interference-shielding bioelastic hydrogel nanocomposite for machine learning-assisted highly sensitive sensing bioelectrode.
Aggregate, 2024, 5(5): e566 DOI:10.1002/agt2.566
| [1] |
A. Chortos, J. Liu, Z. Bao, Nat. Mater. 2016, 15, 937.
|
| [2] |
Y. Wang, H. Haick, S. Guo, C. Wang, S. Lee, T. Yokota, T. Someya, Chem. Soc. Rev. 2022, 51, 3759.
|
| [3] |
A. Libanori, G. Chen, X. Zhao, Y. Zhou, J. Chen, Nat. Electron. 2022, 5, 142.
|
| [4] |
J. Kim, A. S. Campbell, B. E. de Avila, J. Wang, Nat. Biotechnol. 2019, 37, 389.
|
| [5] |
Z. Shen, Z. Zhang, N. Zhang, J. Li, P. Zhou, F. Hu, Y. Rong, B. Lu, G. Gu, Adv. Mater. 2022, 34, 2203650.
|
| [6] |
Z. Sun, M. Zhu, X. Shan, C. Lee, Nat. Commun. 2022, 13, 5224.
|
| [7] |
J. Park, D.-H. Kang, H. Chae, S. K. Ghosh, C. Jeong, Y. Park, S. Cho, Y. Lee, J. Kim, Y. Ko, J. J. Kim, H. Ko, Sci. Adv. 2022, 8, eabj9220.
|
| [8] |
X. Zhao, L. Y. Wang, C. Y. Tang, X. J. Zha, Y. Liu, B. H. Su, K. Ke, R. Y. Bao, M. B. Yang, W. Yang, ACS Nano 2020, 14, 8793.
|
| [9] |
Y. Zhao, Z. Li, S. Song, K. Yang, H. Liu, Z. Yang, J. Wang, B. Yang, Q. Lin, Adv. Funct. Mater. 2019, 29, 1901474.
|
| [10] |
Z. Lei, W. Zhu, X. Zhang, X. Wang, P. Wu, Adv. Funct. Mater. 2021, 31, 2008020.
|
| [11] |
H. Yuk, J. Wu, X. Zhao, Nat. Rev. Mater. 2022, 7, 935.
|
| [12] |
Y. Guo, J. Bae, Z. Fang, P. Li, F. Zhao, G. Yu, Chem. Rev. 2020, 120, 7642.
|
| [13] |
L. Hu, P. L. Chee, S. Sugiarto, Y. Yu, C. Shi, R. Yan, Z. Yao, X. Shi, J. Zhi, D. Kai, H.-D. Yu, W. Huang, Adv. Mater. 2023, 35, 2205326.
|
| [14] |
H. Liao, X. Guo, P. Wan, G. Yu, Adv. Funct. Mater. 2019, 29, 1904507.
|
| [15] |
Y. Z. Zhang, J. K El-Demellawi, Q. Jiang, G. Ge, H. Liang, K. Lee, X. Dong, H. N. Alshareef, Chem. Soc. Rev. 2020, 49, 7229.
|
| [16] |
A. VahidMohammadi, J. Rosen, Y. Gogotsi, Science 2021, 372, eabf1581.
|
| [17] |
L. Wang, N. Li, Y. Zhang, P. Di, M. Li, M. Lu, K. Liu, Z. Li, J. Ren, L. Zhang, P. Wan, Matter 2022, 5, 3417.
|
| [18] |
Y. Zhang, M. Gong, P. Wan, Matter 2021, 4, 2655.
|
| [19] |
C. Wang, Y. Liu, X. Qu, B. Shi, Q. Zheng, X. Lin, S. Chao, C. Wang, J. Zhou, Y. Sun, G. Mao, Z. Li, Adv. Mater. 2022, 34, 2105416.
|
| [20] |
M. Liao, P. Wan, J. Wen, M. Gong, X. Wu, Y. Wang, R. Shi, L. Zhang, Adv. Funct. Mater. 2017, 27, 1703852.
|
| [21] |
P. Bertsch, M. Diba, D. J. Mooney, S. C. G. Leeuwenburgh, Chem. Rev. 2023, 123, 834.
|
| [22] |
J. Goecks, V. Jalili, L. M. Heiser, J. W. Gray, Cell 2020, 181, 92.
|
| [23] |
J. Banerjee, J. N. Taroni, R. J. Allaway, D. V. Prasad, J. Guinney, C. Greene, Nat. Methods 2023, 20, 803.
|
| [24] |
J. G. Greener, S. M. Kandathil, L. Moffat, D. T. Jones, Nat. Rev. Mol. Cell. Bio. 2022, 23, 40.
|
| [25] |
J. Wang, B. Hao, K. Xue, H. Fu, M. Xiao, Y. Zhang, L. Shi, C. Zhu, Adv. Mater. 2022, 34, 2205653.
|
| [26] |
J. Huo, Q. Jia, H. Huang, J. Zhang, P. Li, X. Dong, W. Huang, Chem. Soc. Rev. 2021, 50, 8762.
|
| [27] |
Y. Liu, P. Bhattarai, Z. Dai, X. Chen, Chem. Soc. Rev. 2019, 48, 2053.
|
| [28] |
D. Yang, Y. Ni, X. Kong, S. Li, X. Chen, L. Zhang, Z. L. Wang, ACS Nano 2021, 15, 14653.
|
| [29] |
A. Maleki, J. He, S. Bochani, V. Nosrati, M. A. Shahbazi, B. Guo, ACS Nano 2021, 15, 18895.
|
| [30] |
N. Rohaizad, C. C Mayorga-Martinez, M. Fojtů, N. M. Latiff, M. Pumera, Chem. Soc. Rev. 2021, 50, 619.
|
| [31] |
R. Li, L. Zhang, L. Shi, P. Wang, ACS Nano 2017, 11, 3752.
|
| [32] |
H. Lin, X. Wang, L. Yu, Y. Chen, J. Shi, Nano. Lett. 2017, 17, 384.
|
| [33] |
T. Zhou, C. Cao, S. Yuan, Z. Wang, Q. Zhu, H. Zhang, J. Yan, F. Liu, T. Xiong, Q. Cheng, L. Wei, Adv. Mater. 2023, 35, 2305807.
|
| [34] |
A. Iqbal, F. Shahzad, K. Hantanasirisakul, M.-K. Kim, J. Kwon, J. Hong, H. Kim, Y. Gogotsi, C. Koo, Science 2020, 369, 446.
|
| [35] |
Y. Zhang, K. Ruan, K. Zhou, J. Gu, Adv. Mater. 2023, 35, 2211642.
|
| [36] |
Y. Yang, N. Wu, B. Li, W. Liu, F. Pan, Z. Zeng, J. Liu, ACS Nano 2022, 16, 15042.
|
| [37] |
Y. Zhu, J. Liu, T. Guo, J. J. Wang, X. Tang, V. Nicolosi, ACS Nano 2021, 15, 1465.
|
| [38] |
Y. Z. Zhang, K. H. Lee, D. H. Anjum, R. Sougrat, Q. Jiang, H. Kim, H. N. Alshareef, Sci. Adv. 2018, 4, eaat0098.
|
| [39] |
Y. Cai, J. Shen, C.-W. Yang, Y. Wan, H.-L. Tang, A. Aljarb Areej, C. Chen, J.-H. Fu, X. Wei, K.-W. Huang, Y. Han, J. Jonas Steven, X. Dong, V. Tung, Sci. Adv. 2020, 6, eabb5367.
|
| [40] |
M. Li, Y. Zhang, L. Lian, K. Liu, M. Lu, Y. Chen, L. Zhang, X. Zhang, P. Wan, Adv. Funct. Mater. 2022, 32, 2208141.
|
| [41] |
Z. Wei, J. H. Yang, J. Zhou, F. Xu, M. Zrínyi, P. H. Dussault, Y. Osada, Y. M. Chen, Chem. Soc. Rev. 2014, 43, 8114.
|
| [42] |
J. Chen, J. Zhang, J. Hu, N. Luo, F. Sun, H. Venkatesan, N. Zhao, Y. Zhang, Adv. Mater. 2022, 34, 2104313.
|
| [43] |
Z. Li, S. Zhang, Y. Chen, H. Ling, L. Zhao, G. Luo, X. Wang, M. C. Hartel, H. Liu, Y. Xue, R. Haghniaz, K. Lee, W. Sun, H. Kim, J. Lee, Y. Zhao, Y. Zhao, S. Emaminejad, S. Ahadian, N. Ashammakhi, M. R. Dokmeci, Z. Jiang, A. Khademhosseini, Adv. Funct. Mater. 2020, 30, 2003601.
|
| [44] |
Y. Lee, J. Park, S. Cho, Y. E. Shin, H. Lee, J. Kim, J. Myoung, S. Cho, S. Kang, C. Baig, H. Ko, ACS Nano 2018, 12, 4045.
|
| [45] |
W. W. Nichols, Am. J. Hypertens. 2005, 18, 3S.
|
| [46] |
Y. Zhang, Z. Xu, Y. Yuan, C. Liu, M. Zhang, L. Zhang, P. Wan, Adv. Funct. Mater. 2023, 33, 2300299.
|
| [47] |
G. Minervini, R. Franco, M. M. Marrapodi, L. Fiorillo, G. Cervino, M. Cicciù, J. Oral. Rehabil. 2023, 50, 522.
|
| [48] |
V. R. Feig, H. Tran, M. Lee, Z. Bao, Nat. Commun. 2018, 9, 2740.
|
| [49] |
X. Li, L. He, Y. Li, M. Chao, M. Li, P. Wan, L. Zhang, ACS Nano 2021, 15, 7765.
|
| [50] |
Z. Wu, L. Wei, S. Tang, Y. Xiong, X. Qin, J. Luo, J. Fang, X. Wang, ACS Nano 2021, 15, 18880.
|
| [51] |
Y. Cheng, Y. Zhou, R. Wang, K. H. Chan, Y. Liu, T. Ding, X.-Q. Wang, T. Li, G. W. Ho, ACS Nano 2022, 16, 18608.
|
| [52] |
Y. Liu, C. Wang, J. Xue, G. Huang, S. Zheng, K. Zhao, J. Huang, Y. Wang, Y. Zhang, T. Yin, Z. Li, Adv. Healthcare Mater. 2022, 11, 2200653.
|
| [53] |
M. Dias, L. Silva, D. Folgado, M. L. Nunes, C. Cepeda, M. Cheetham, H. Gamboa, Int. J. Ind. Ergonom. 2023, 96, 103476.
|
| [54] |
J. Dong, Y. Peng, X. Nie, L. Li, C. Zhang, F. Lai, G. He, P. Ma, Q. Wei, Y. Huang, T. Liu, Adv. Funct. Mater. 2022, 32, 2209762.
|
| [55] |
D. Song, G. Ye, Y. Zhao, Y. Zhang, X. Hou, N. Liu, ACS Nano 2022, 16, 17168.
|
| [56] |
S. Ji, C. Wan, T. Wang, Q. Li, G. Chen, J. Wang, Z. Liu, H. Yang, X. Liu, X. Chen, Adv. Mater. 2020, 32, 2001496.
|
| [57] |
H. Tang, Y. Li, B. Chen, X. Chen, Y. Han, M. Guo, H.-q. Xia, R. Song, X. Zhang, J. Zhou, ACS Nano 2022, 16, 17931.
|
| [58] |
C. Wang, T. Yokota, T. Someya, Chem. Rev. 2021, 121, 2109.
|
| [59] |
K. Nan, V. R. Feig, B. Ying, J. G. Howarth, Z. Kang, Y. Yang, G. Traverso, Nat. Rev. Mater. 2022, 7, 908.
|
| [60] |
J. Lee, K.H. Cho, K. Cho, Adv. Mater. 2023, 35, 2209673.
|
| [61] |
J. Liu, L. McKeon, J. Garcia, S. Pinilla, S. Barwich, M. Möbius, P. Stamenov, J. N. Coleman, V. Nicolosi, Adv. Mater. 2022, 34, 2106253.
|
RIGHTS & PERMISSIONS
2024 The Author(s). Aggregate published by SCUT, AIEI, and John Wiley & Sons Australia, Ltd.