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Biomimetic microstructure design for ultrasensitive piezoionic mechanoreceptors in multimodal object recognition

Author

Listed:
  • Mingqi Ding

    (City University of Hong Kong)

  • Pengshan Xie

    (City University of Hong Kong)

  • Jingwen Wang

    (Central South University
    Shanghai Institute of Microsystem and Information Technology)

  • Wu Guo

    (Polymer Research Institute of Sichuan University)

  • Haifan Li

    (City University of Hong Kong)

  • Siliang Hu

    (City University of Hong Kong)

  • Dengji Li

    (City University of Hong Kong)

  • Bowen Li

    (City University of Hong Kong
    City University of Hong Kong)

  • Nan Wang

    (City University of Hong Kong)

  • Chun-Yuen Wong

    (City University of Hong Kong)

  • Jia Sun

    (Central South University
    Shanghai Institute of Microsystem and Information Technology)

  • Johnny C. Ho

    (City University of Hong Kong
    City University of Hong Kong
    City University of Hong Kong
    Kyushu University)

Abstract

The challenge of achieving high recognition accuracy in artificial mechanoreceptors arises from the trade-off between sensitivity and stability in the sensing unit. Inspired by human skin, we developed a biomimetic approach that involves structural and engineering enhancements for ionic-conducting polyvinyl alcohol/Ti3C2Tx (PVA/MXene) composite hydrogel microneedles (HM) to enhance the sensitivity. By integrating the HM with a polyethylene terephthalate/indium tin oxide (PET/ITO) film, we create a non-faradaic junction that ensures stable electrical output without transmission loss under stimulation. Furthermore, the significant alteration in nanosheet spacing facilitates proton transport along the MXene microchannels, increasing the plasmonic gradient between the junction and the hydrogel’s center, thereby boosting piezoionic efficiency. Consequently, the biomimetic sensing unit achieves a high power density of 165.6 mW m-2 and exceptional sensing stability over 10,000 cycles. When combined with vertical memristor units, this system effectively captures and transforms characteristic signals from various objects, achieving a recognition accuracy of 90%.

Suggested Citation

  • Mingqi Ding & Pengshan Xie & Jingwen Wang & Wu Guo & Haifan Li & Siliang Hu & Dengji Li & Bowen Li & Nan Wang & Chun-Yuen Wong & Jia Sun & Johnny C. Ho, 2025. "Biomimetic microstructure design for ultrasensitive piezoionic mechanoreceptors in multimodal object recognition," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63115-9
    DOI: 10.1038/s41467-025-63115-9
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