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Biomimetic microstructure design for ultrasensitive piezoionic mechanoreceptors in multimodal object recognition

Mingqi Ding (Co-first Author), Pengshan Xie (Co-first Author), Jingwen Wang (Co-first Author), Wu Guo, Haifan Li, Siliang Hu, Dengji Li, Bowen Li, Nan Wang, Chun-Yuen Wong, Jia Sun*, Johnny C. Ho*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Downloads (CityUHK Scholars)

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%. © The Author(s) 2025.
Original languageEnglish
Article number8129
Number of pages11
JournalNature Communications
Volume16
Online published30 Aug 2025
DOIs
Publication statusPublished - 2025

Funding

This research was financially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CRS_CityU101/24), the Innovation and Technology Fund (MHP/044/23) from the Innovation Technology Commission of the Hong Kong Special Administrative Region, China, the Science Technology and Innovation Committee of Shenzhen Municipality (Project No. JCYJ20230807114910021), and Guangdong Basic and Applied Basic Research Fund (Project no. 2024A1515011922). Open Access made possible with partial support from the Open Access Publishing Fund of the City University of Hong Kong.

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

RGC Funding Information

  • RGC-funded

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