Flexible antibacterial degradable bioelastomer nanocomposites for ultrasensitive human-machine interaction sensing enabled by machine learning

被引:5
|
作者
Fu, Zihong [1 ]
Wang, Mingcheng [1 ]
Huang, Chenlin [1 ]
Li, Zehui [1 ]
Yuan, Yue [1 ]
Hu, Shikai [1 ]
Zhang, Liqun [1 ,2 ]
Wan, Pengbo [1 ]
机构
[1] Beijing Univ Chem Technol, Coll Mat Sci & Engn, State Key Lab Organ Inorgan Composites, Beijing 100029, Peoples R China
[2] South China Univ Technol, Inst Emergent Elastomers, Sch Mat Sci & Engn, Guangzhou, Peoples R China
来源
AGGREGATE | 2024年 / 5卷 / 03期
基金
中国国家自然科学基金;
关键词
antibacterial; degradable bioelastomer nanocomposites; MXene; skin-inspired flexible electronic sensor; ultrasensitive intelligent wearable human-interactive sensing; MXENE; STIFFNESS; FACILE;
D O I
10.1002/agt2.522
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Flexible wearables have attracted extensive interests for personal human motion sensing, intelligent disease diagnosis, and multifunctional electronic skins. However, the reported flexible sensors, mostly exhibited narrow detection range, low sensitivity, limited degradability to aggravate environmental pollution from vast electronic wastes, and poor antibacterial performance to hardly improve skin discomfort and skin inflammation from bacterial growth under long-term wearing. Herein, bioinspired from human skin featuring highly sensitive tactile sensation with spinous microstructures for amplifying sensing sensitivity between epidermis and dermis, a wearable antibacterial degradable electronics is prepared from degradable elastomeric substrate with MXene-coated spinous microstructures templated from lotus leaf assembled with the interdigitated electrode. The degradable elastomer is facilely obtained with tunable modulus to match the modulus of human skin with improved hydrophilicity for rapid degradation. The as-obtained sensor displays ultra-low detection limit (0.2 Pa), higher sensitivity (up to 540.2 kPa(-1)), outstanding cycling stability (>23,000 cycles), a wide detection range, robust degradability, and excellent antibacterial capability. Facilitated by machine learning, the collected sensing signals from the integrated sensors on volunteer's fingers to the related American Sign Language are effectively recognized with an accuracy up to 99%, showing excellent potential in wireless human movement sensing and smart machine learning-enabled human-machine interaction.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Machine-induced Coordination Behavior in Human-Machine Interaction
    de Guzman, Gonzalo C.
    Tognoli, Emmanuelle
    Kelso, J. A. Scott
    2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO 2009), VOLS 1-4, 2009, : 510 - 515
  • [42] A Flexible Bidirectional Interface with Integrated Multimodal Sensing and Haptic Feedback for Closed-Loop Human-Machine Interaction
    Feng, Kai
    Lei, Ming
    Wang, Xianli
    Zhou, Bingpu
    Xu, Qingsong
    ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (11)
  • [43] Robust human-machine interfaces enabled by a skin-like, electromyogram sensing system
    Kim, Yun-Soung
    Mahmood, Musa
    Kwon, Shinjae
    Herbert, Robert
    Yeo, Woon-Hong
    NANO-, BIO-, INFO-TECH SENSORS AND 3D SYSTEMS III, 2019, 10969
  • [44] Interaction force modeling and analysis of the human-machine kinematic chain based on the human-machine deviation
    Zhou, Xin
    Duan, Zhisheng
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [45] Learning Algorithms for Human-Machine Interfaces
    Danziger, Zachary
    Fishbach, Alon
    Mussa-Ivaldi, Ferdinando A.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2009, 56 (05) : 1502 - 1511
  • [46] The Role of Explanations in Human-Machine Learning
    Holmberg, Lars
    Generalao, Stefan
    Hermansson, Adam
    2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2021, : 1006 - 1013
  • [47] Tough, Antifreezing, and Piezoelectric Organohydrogel as a Flexible Wearable Sensor for Human-Machine Interaction
    Shi, Yongdong
    Guan, Youjun
    Liu, Mingjie
    Kang, Xinchang
    Tian, Yu
    Deng, Weicheng
    Yu, Peng
    Ning, Chengyun
    Zhou, Lei
    Fu, Rumin
    Tan, Guoxin
    ACS NANO, 2024, 18 (04) : 3720 - 3732
  • [48] Human-Machine Interfacing Enabled by Triboelectric Nanogenerators and Tribotronics
    Ding, Wenbo
    Wang, Aurelia C.
    Wu, Changsheng
    Guo, Hengyu
    Wang, Zhong Lin
    ADVANCED MATERIALS TECHNOLOGIES, 2019, 4 (01)
  • [49] Deep Learning for EMG-based Human-Machine Interaction: A Review
    Dezhen Xiong
    Daohui Zhang
    Xingang Zhao
    Yiwen Zhao
    IEEE/CAAJournalofAutomaticaSinica, 2021, 8 (03) : 512 - 533
  • [50] Effects of human-machine interaction on employee's learning: A contingent perspective
    Wang Sen
    Zhao Hong
    Zhu Xiaomei
    FRONTIERS IN PSYCHOLOGY, 2022, 13