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<p>Triboelectric nanogenerators (TENGs) are emerging as self-powered sensors, while relying on (i) non-sustainable, oil-based materials and (ii) external timing circuits or machine learning back ends. Here, we report a bio-based, 4D-printed α-helix shape memory TENG (H-STENG) that integrates sustainable material design, programmable architecture, and interpretation ready multimodal sensing. A PLA/PHA/chitosan composite was engineered through meso-scale morphology regulation and interfacial hydrogen-bonding and amide interactions, yielding improved toughness and shape memory recovery (~90% within &lt; 10 s). Filament material extrusion programmed the composite into a biomimetic α-helix architecture, enabling mechanically adaptive triboelectric and resistive outputs. The real-time H-STENG directly encodes loading, holding, unloading, and resting states into phase resolved peak and plateau waveforms; holding duration is linearly extracted from the plateau width without external timing circuits or machine learning post-processing. The α-helix structure further provides an intrinsic resistive sensing mode, where deformation amplitude, rate, holding time, and recovery state are resolved through conductive network reconstruction. The device shows stable energy harvesting, temperature triggered shape recovery, environmental robustness, Morse-code communication, and human motion monitoring. This work establishes a sustainable, structurally programmable, and algorithm-free sensing platform for interpretation ready wearable electronics.</p>

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Sustainable interpretation-ready multifunctional sensing by Bio-based α-Helix biomimetic shape memory material and manufacturing design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月17日

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  • Ning Zhu,
  • Jing Li,
  • Maofan Zhou,
  • Ludwig Cardon,
  • Qi Zhang,
  • Pablo Reyes,
  • Dagmar R. D’hooge,
  • Mariya Edeleva

摘要

Triboelectric nanogenerators (TENGs) are emerging as self-powered sensors, while relying on (i) non-sustainable, oil-based materials and (ii) external timing circuits or machine learning back ends. Here, we report a bio-based, 4D-printed α-helix shape memory TENG (H-STENG) that integrates sustainable material design, programmable architecture, and interpretation ready multimodal sensing. A PLA/PHA/chitosan composite was engineered through meso-scale morphology regulation and interfacial hydrogen-bonding and amide interactions, yielding improved toughness and shape memory recovery (~90% within < 10 s). Filament material extrusion programmed the composite into a biomimetic α-helix architecture, enabling mechanically adaptive triboelectric and resistive outputs. The real-time H-STENG directly encodes loading, holding, unloading, and resting states into phase resolved peak and plateau waveforms; holding duration is linearly extracted from the plateau width without external timing circuits or machine learning post-processing. The α-helix structure further provides an intrinsic resistive sensing mode, where deformation amplitude, rate, holding time, and recovery state are resolved through conductive network reconstruction. The device shows stable energy harvesting, temperature triggered shape recovery, environmental robustness, Morse-code communication, and human motion monitoring. This work establishes a sustainable, structurally programmable, and algorithm-free sensing platform for interpretation ready wearable electronics.