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Bioinspired Auxetic Metastructures Enable Biomechanically Adaptive, Machine Learning-Enhanced Self-Powered Sensing

Wei Wang1, Xuechuan Wang2, Linbin Li3

  • 1College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi'an, 710021, People's Republic of China.

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|March 18, 2026
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Summary
This summary is machine-generated.

This study introduces a bioinspired auxetic triboelectric nanogenerator for self-powered wearable sensors. The novel design enhances mechanical adaptability and energy conversion efficiency, overcoming key challenges in flexible electronics.

Keywords:
Adaptive self-powered sensorsAuxetic effectEnergy conversion efficiencyNeural network modelTissue–device matching

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Area of Science:

  • Materials Science
  • Biomimetics
  • Wearable Technology

Background:

  • Self-powered flexible sensors are crucial for advanced wearable technologies.
  • Challenges include mechanical mismatch, low energy efficiency, and impedance fluctuations.
  • Existing designs struggle with dynamic deformations.

Purpose of the Study:

  • To develop a bioinspired auxetic triboelectric nanogenerator (TENG).
  • To address mechanical mismatch and improve energy conversion efficiency in wearable sensors.
  • To enhance signal fidelity and stability under multi-axial deformations.

Main Methods:

  • Engineered collagen and fluorinated ethylene propylene as triboelectric layers.
  • Developed an auxetic metastructure with re-entrant hexagonal unit cells.
  • Integrated the TENG with a convolutional neural network (CNN) for material recognition.

Main Results:

  • Achieved 478 V output voltage and 13.8% energy conversion efficiency in linear configuration.
  • Demonstrated threefold enhanced stability under complex bending (58 V, 7.58% efficiency).
  • CNN integration enabled >99% classification accuracy for material recognition.

Conclusions:

  • The bioinspired auxetic TENG effectively minimizes tissue-device mechanical mismatch.
  • The design significantly improves sensitivity, signal fidelity, and energy conversion efficiency.
  • This technology offers a robust platform for adaptive, self-powered wearable sensing.