Bioinspired Adaptive, Elastic, and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception - Wang, Qiling; Xiao, Peng; Zhou, Wei; Liang, Yun; Yin, Guangqiang; Yang, Qiu; Kuo, Shiao‑Wei; Chen, Tao (Nano-Micro Letters, 2022)
Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans. In recent years, extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing. However, there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient, controllable, and scalable way. Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli, an ultrathin, elastic, and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated. The graphene structured thin film is endowed with favourable adaptive and morphable features, which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure. Owing to the introduction of the graphene-based layer, the integrated sensing system can actively detect the water depth with a wide range of 0.3–1.8 m. Furthermore, similar to the fish, the mechanical stimuli from land (e.g. knocking, stomping) and water (e.g. wind blowing, raining, fishing) can also be sensitively captured in real time. This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring, communication, and risk avoidance. Highlights: 1 The lateral-line-like underwater mechanical sensor (LUMS) realizes the imitation of the structure and function of the lateral line. 2 The detection range of water depth can be controlled by adjusting the size of the graphene/Ecoflex Janus film on LUMS. The maximum measured depth is 1.8 m. 3 Similar to the fish, the mechanical stimuli from land and water can be sensitively captured by LUMS in real time.