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

Author:

Fu Zihong1,Wang Mingcheng1,Huang Chenlin1,Li Zehui1,Yuan Yue1,Hu Shikai1,Zhang Liqun12,Wan Pengbo1ORCID

Affiliation:

1. College of Materials Science and Engineering, State Key Laboratory of Organic‐Inorganic Composites Beijing University of Chemical Technology Beijing China

2. Institute of Emergent Elastomers, School of Materials Science and Engineering South China University of Technology Guangzhou China

Abstract

AbstractFlexible 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.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Wiley

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