3D Single-Layer-Dominated Graphene Foam for High-Resolution Strain Sensing and Self-Monitoring Shape Memory Composite

Autor: Jiasheng Rong, Jianxin Zhou, Yucheng Zhou, Cong Hu, Luxian Li, Wanlin Guo
Rok vydání: 2022
Předmět:
Zdroj: Small (Weinheim an der Bergstrasse, Germany).
ISSN: 1613-6829
Popis: Flexible intelligent materials are desired to effectively regulate their own deformation and accurately sense their immediate morphology at the same time. Graphene foam is an attractive material for strain sensing and electrical/thermal performance control due to its outstanding mechanical, electrical, and thermal properties. However, graphene-foam-based materials with both strain sensing and deformation control capabilities are rarely reported. Here, a multiscale design of graphene foam with a single-layer-graphene-dominated microstructure and resilient 3D network architecture, which leads to exceptional strain sensing performance as well as modulation ability of the electrical and thermal conductivity for shape memory polymers, is reported. The graphene foams exhibit a strain detection limit of 0.033%, a rapid response of 53 ms, long-term stability over 10 000 cycles, significant thermoacoustic effect, and great heat-generation and heat-diffusion ability. By combining these advantages, an electro-activated shape-memory composite that is capable of monitoring its own shape state during its morphing process, is demonstrated.
Databáze: OpenAIRE