Nano-Micro Letters (Jul 2024)

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

  • Jianye Li,
  • Hao Wang,
  • Yibing Luo,
  • Zijing Zhou,
  • He Zhang,
  • Huizhi Chen,
  • Kai Tao,
  • Chuan Liu,
  • Lingxing Zeng,
  • Fengwei Huo,
  • Jin Wu

DOI
https://doi.org/10.1007/s40820-024-01466-6
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 22

Abstract

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Highlights A novel organohydrogel-based multimodal e-skin with excellent sensing performance for temperature, humidity, pressure, proximity, and NO2 is proposed for the first time, showing powerful sensing capabilities beyond natural skin. The developed multimodal e-skin exhibited extraordinary sensing performance at room temperature, including fast pressure response time (0.2 s), high temperature sensitivity (9.38% °C-1), a wide range of humidity response (22%–98% RH), high NO2 sensitivity (254% ppm-1), a low detection limit (11.1 ppb NO2) and the abilities to sense the proximity of objects accurately, which are yet achieved by previous e-skins. The multimodal e-skin was combined with the deep neural network algorithm and wireless alarm circuit to achieve zero-error classification of different objects and rapid response to NOx leak incidents, proving the feasibility of the e-skin-assisted rescue robot for post-earthquake rescue.

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