Self-powered mechanoresponsive fibers with human-like visual-digital bimodality and mechano-memory.

Authors

Zibin Wang, Hao Ouyang, Yin Cheng, Fei Wu, Liangjing Shi, Jing Sun, Ranran Wang

Published in

Materials horizons. Sep 15, 2026. Epub Sep 15, 2026.

Abstract

Bioinspired mechanoresponsive optical-electrical dual-mode sensors hold significant potential in future human-machine interaction (HMI) applications owing to their complementary visual-digital signal coupling. However, existing mechanoluminescence (ML)-based bimodal technologies suffer from low luminescence intensity, limited differentiation towards multi-type mechano-stimuli, and the absence of human-like mechano-memory retention. Herein, we report a scalable manufacturing strategy for self-powered opto-electrical dual-mode fibers (SOEDFs), which are soft, stretchable, and compatible with industrial weaving techniques. Distinct from conventional material design, the SOEDF integrates ZnS:Cu-based mechanoluminescence with ultrahigh-dielectric-constant BaTiO₃ and thermoluminescent BaSi₂O₂N₂:Eu (BSON). The BaTiO₃ provides stress transfer enhancement and charge polarization to collectively amplify the energy band tilting, contributing to remarkably boosted ML performance (4-fold intensity promotion). BSON with the force-induced charge carrier storage (FICS) effect endows the SOEDF with on-demand mechano-memory recurrence for historical mechano-information analysis. The synergistic material design together with fiber-based compliance makes SOEDF a versatile real-time/historical sensing platform, including multi-stimuli discrimination (stretch/press/bend), a chipless skin-integrated gesture recognition system, and retrospective mechano-analysis such as handwriting identification and break-in footprint memorizing. Our SOEDFs hold great potential for advancing next-generation intelligent HMIs with human-like sensory experience.

PMID:
42742191
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.

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