Issue

Vol. 18 (2026)

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

https://doi.org/10.1007/s40820-026-02125-8
Wei Wang
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Xuechuan Wang
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Linbin Li
College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Yi Zhou
College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Wenlong Zhang
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Long Xing
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Long Xie
College of Chemistry and Chemical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Yitong Wang
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Ouyang Yue
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China
Xinhua Liu
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, People’s Republic of China

Corresponding Author: Xinhua Liu

liuxinhua@sust.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 286

Abstract

Self-powered flexible sensors exhibit revolutionary potential in next-generation wearable technologies owing to their exceptional sensitivity and self-sustaining energy harvesting capabilities. Nevertheless, their widespread deployment remains constrained by three fundamental challenges: dynamic mechanical mismatch between biological tissues and rigid devices, suboptimal energy conversion efficiency, and interfacial impedance fluctuation under deformation. Drawing inspiration from the unique negative Poisson’s ratio mesh architecture of lacewing wings, we present a bioinspired auxetic metastructure-engineered triboelectric nanogenerator. This innovative design integrates engineered collagen and micropatterned fluorinated ethylene propylene as triboelectric layers, unified by an auxetic framework with re-entrant hexagonal unit cells interconnected via triangular ligaments. The metastructure enables exceptional lateral expansion under longitudinal strain while simultaneously enhancing structural rigidity and deformation adaptability. This dual functionality effectively minimizes tissue–device mechanical mismatch, thereby significantly improving signal fidelity, sensitivity, and mechanical-to-electrical conversion efficiency during multi-axial deformations. The optimized device achieves remarkable performance metrics, delivering 478 V output voltage with 13.8% energy conversion efficiency in linear configuration, while demonstrating threefold enhanced stability (58 V, 7.58% efficiency) under complex bending compared to conventional designs. Integrated with a convolutional neural network-based machine learning enables exceptional classification accuracy (> 99%) across diverse material recognition tasks, validating its robustness as a next-generation platform for adaptive self-powered wearable sensing.


Highlights:
1 Bioinspired auxetic triboelectric nanogenerator utilizes negative Poisson’s ratio to resolve interfacial mechanical mismatch. A “conformal self-adaptation” mechanism via synclastic curvature maximizes contact area and signal stability on curvilinear surfaces.
2 The optimized structure achieves a 3.2-fold increase in bending-mode energy conversion efficiency compared to non-auxetic controls, ensuring robust energy harvesting performance under dynamic deformation.
3 An integrated self-powered sensor array coupled with a convolutional neural network deep learning model enables intelligent object recognition with 98.7% accuracy, demonstrating precise human–machine interaction capabilities.

Keywords

Tissue–device matching Adaptive self-powered sensors Auxetic effect Energy conversion efficiency Neural network model
Wang, Wei, Xuechuan Wang, Linbin Li, Yi Zhou, Wenlong Zhang, Long Xing, Long Xie, Yitong Wang, Ouyang Yue, and Xinhua Liu. 2026. “Bioinspired Auxetic Metastructures Enable Biomechanically Adaptive, Machine Learning-Enhanced Self-Powered Sensing With Ultrahigh Efficiency”. Nano-Micro Letters 18 (March):286. https://doi.org/10.1007/s40820-026-02125-8.
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