Although hydrogels containing large amounts of water are similar to natural muscles, they are a challenge to be used in artificial muscles because of their poor mechanical properties and low work capacities. The current paper describes the design and fabrication of tendril-inspired hydrogel artificial muscles via a consecutive shaping process. Tunicate cellulose nanocrystals (TCNCs) are incorporated into polymeric networks via host−guest interactions to reinforce the hydrogel. Tendril-inspired hydrogels are obtained by treating the TCNC-reinforced hydrogels with a consecutive stretching, twisting, and coiling process and locking the shaped structure through Fe 3+ /−COO − ionic coordination. These hydrogel muscles exhibit a high actuation rate, large actuation strain, and shape memory property in response to solvents. The actuation performances of hydrogel muscles are affected by their chirality, twist density, applied stress, and temporary shape. Moreover, a homochiral hydrogel muscle with temporary shape II shows comparable contractile work capacity with a natural muscle, which can be applied as the engine to actuate the movement of a car model. This work demonstrates a simple and effective strategy for the fabrication of hydrogel artificial muscles that have great potential for biomedical application as a result of their comparable water content and contractile work capacity with natural muscles.
Abstract. In recent years, with the advancement of marine resources and environment research, the ecological functions of reef-building coral reef ecosystems distributed in warm shallow waters of the ocean are being continuously discovered and valued by people. It is important for ecosystem protection to monitor the population of marine animals. Besides, many projects of Autonomous Underwater Vehicle (AUV) also need technology to perceive and understand environment information in real-time for better decision-making. Therefore, marine animal detection has become a challenge for researchers to study nowadays. Deep neural network models have been used to solve fish-related tasks and gained encouraging achievements, but there are still many problems in this field. In this paper, several YOLO-based methods are chosen for comparison. Experiment results indicate that these methods can recognize the marine animals in coral reef quickly and accurately. Finally, several recommendations for model improvement according to assessment results are presented.
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