Ultra-Stretchable Polymer Fibers Anchored with a Triple-Level Self-Assembled Conductive Network for Wide-Range Strain Detection
Year: 2025
Type: article
Abstract: Numerous strategies have been demonstrated to enhance the mechanical stretchability of electromechanical sensors for widespread applications in wearable electronics. However, ranging from composite to microstructural materials, their electromechanical sensing performances are usually vulnerable to large stretching deformations due to the low-ductility of the infilled conductive components and the modulus mismatch between the flexible polymer substrate and conductive fillers. Here, a novel design strategy is proposed to fabricate ultra-stretchable electromechanical composites constructed by a triple-level interaction conductive network (Tri-LICN) in buckled-TPU microfibers for strain sensors. The Tri-LICN is established by bridging one-dimensional cellulose nanocrystals (CNC) with zero-dimensional gold-nanoparticles (AuNPs) and two-dimensional MXene sheets using interface self-assembly and ultrasound-assisted anchoring to eliminate the modulus mismatching between the conductive material and polymer substrate. The buckled-TPU microfibers are introduced to improve the mechanical stretchability of composites by the external-stimuli-induced imbalance of the stretching conformation of TPU macromolecules. The Tri-LICN MXene/CNC/AuNPs@TPU composite sensor displays an enhanced strain sensitivity (GF~2514) with a fast response time (~150 ms) over a wide operational strain up to 200% and excellent durability over 1000 tensile cycles. Our finding offers a promising approach to enhancing the performance of stretchable sensors based on polymer materials, providing new opportunities for the development of next-generation electronics.
Source: Polymers
Authors: Zhong Zheng, Shuyi Song, Xun Chen, Xixing Li, Jing Li
Institutions: Hubei University of Technology, Hubei University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Awards: G625758156, G7551591270
OpenAlex ID: W4408330822
