Rapidly self-healing electronic skin for machine learning-assisted physiological and movement evaluation.

Journal: Science advances
Published Date: Feb 12, 2025

Abstract

Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Current materials have slow recovery times, impeding reliable signal measurement. The inability to heal within 1 minute is a major barrier to commercialization. A composition achieving 80% recovery within 1 minute has not yet been reported. Here, we present a rapidly self-healing E-Skin tailored for real-time monitoring of physical and physiological bioinformation. The E-Skin recovers more than 80% of its functionality within 10 seconds after physical damage, without the need of external stimuli. It consistently maintains reliable biometric assessment, even in extreme environments such as underwater or at various temperatures. Demonstrating its potential for efficient health assessment, the E-Skin achieves an accuracy exceeding 95%, excelling in wearable muscle strength analytics and on-site AI-driven fatigue identification. This study accelerates the advancement of E-Skin through rapid self-healing capabilities.

Authors

  • Yongju Lee
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea.
  • Xinyu Tian
    Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
  • Jaewon Park
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, Seoul 02504, Republic of Korea.
  • Dong Hyun Nam
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, Seoul 02504, Republic of Korea.
  • Zhuohong Wu
    Department of Nanoengineering, University of California, San Diego, La Jolla, CA 92093, USA.
  • Hyojeong Choi
    Terasaki Institute for Biomedical Innovation, Los Angeles, CA 91367, USA.
  • Juhwan Kim
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, Seoul 02504, Republic of Korea.
  • Dong-Wook Park
  • Keren Zhou
    Terasaki Institute for Biomedical Innovation, Los Angeles, CA 91367, USA.
  • Sang Won Lee
    Harvard Medical School, Boston, Massachusetts.
  • Tanveer A Tabish
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, British Heart Foundation (BHF) Centre of Research Excellence, University of Oxford, Headington, Oxford OX3 7BN, UK.
  • Xuanbing Cheng
    Department of Electrical and Computer Engineering and Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Sam Emaminejad
    Interconnected & Integrated Bioelectronics Lab (IBL), Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA. emaminejad@ucla.edu dicarlo@ucla.edu.
  • Tae-Woo Lee
    Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 790-784, Republic of Korea.; Department of Chemical Engineering, Division of Advanced Materials Science, School of Environmental Science and Engineering, POSTECH, 77 Cheongam-Ro, Nam-Gu, Pohang 790-784, Republic of Korea.
  • Hyeok Kim
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
  • Ali Khademhosseini
    Center for Minimally Invasive Therapeutics, University of California, Los Angeles, CA, 90095, USA.
  • Yangzhi Zhu
    Terasaki Institute for Biomedical Innovation, Los Angeles, CA 91367, USA.

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