Self-powered flexible sensors hold revolutionary potential for wearable electronics and human-machine interfaces, yet their widespread deployment remains constrained by dynamic mechanical mismatch at the device-skin interface and energy dissipation during complex deformation. Now, researchers from Shaanxi University of Science and Technology, led by Professor Xuechuan Wang, Professor Ouyang Yue, and Professor Xinhua Liu, have developed a bioinspired auxetic triboelectric nanogenerator (Auxetic-TENG) that resolves these fundamental challenges through negative Poisson's ratio engineering.
Why This Sensor Matters
Conventional flexible materials exhibit positive Poisson's ratios, meaning they contract laterally when stretched or bent—causing edge curling and delamination from curved surfaces like joints. This mechanical mismatch drastically reduces effective contact area, signal fidelity, and energy harvesting efficiency. The novel auxetic metastructure overcomes this limitation by expanding laterally under axial strain, maintaining intimate conformal contact through synclastic curvature.
Innovative Design and Mechanism
Drawing inspiration from the re-entrant lattice architecture of lacewing wings, the device features a hexagonal metastructure with triangular ligaments that create negative Poisson's ratio behavior. When bent, the structure undergoes dome-like synclastic expansion rather than saddle-shaped anticlastic contraction. This "conformal self-adaptation" mechanism ensures gap-free contact with biological tissues while minimizing mechanical energy loss. The metastructure integrates PEI-modified collagen as the positive triboelectric layer and micropatterned fluorinated ethylene propylene (FEP) as the negative layer, unified by the auxetic silicone framework.
Outstanding Performance
The Auxetic-TENG delivers 478V output voltage with 13.8% energy conversion efficiency in linear contact-separation mode. Under complex bending conditions—critical for wearable applications—it achieves 7.58% efficiency, representing a 3.2-fold improvement over non-auxetic controls (2.37%). The device maintains stable output (58V) and demonstrates 3.175 V kPa -1 sensitivity with rapid 47ms response time. Coupled with a convolutional neural network (CNN) deep learning model, the system achieves 98.7% accuracy in object recognition tasks, enabling intelligent tactile perception for diverse material classification.
Applications and Future Outlook
This bioinspired design establishes a universal strategy for mechanically adaptive self-powered sensors capable of monitoring dynamic joint motions without signal degradation. The technology opens avenues for advanced prosthetics, robotic skin, and human-machine interfaces where mechanical compliance, energy efficiency, and intelligent sensing converge. By transforming mechanical mismatch into conformal adaptation, this work paves the way for next-generation wearable electronics that seamlessly integrate with the human body.
Nano-Micro Letters
News article
Bioinspired Auxetic Metastructures Enable Biomechanically Adaptive, Machine Learning‑Enhanced Self‑Powered Sensing with Ultrahigh Efficiency
18-Mar-2026