Hovering at any depth, is one of the most important requirements for underwater robotics, which calls for large‐range buoyancy control ability. Although various underwater robotics have been proposed and developed, the requirements of noise‐free, environmental tolerance, and low energy consumption in hovering manipulation, are still attracting a lot of attention for underwater tasks. Herein, a submersible soft‐robotic platform driven by the self‐contained liquid–vapor phase transition is developed. The proposed soft‐robotic platform precisely modulates its buoyancy, showing an excellent positioning ability underwater. The proposed soft‐robotic platform performs reversible rise‐and‐sink motions underwater, and generate a buoyancy force of 0.93 N (131% of its weight) with an accuracy of ±2.5 mN, at a heating temperature of 63 °C. It shows that more than 40% of the total buoyancy change is achieved within 55 s, and the platform hovers in any depth in a range of 450 mm (limited by the adopted water container) in 18 s, with positioning fluctuation of 17.42 mm. This soft‐robotic platform demonstrates active vertical motions in water and suggests a feasible approach to develop noisy‐free and high‐reliability underwater robots, which guide the further design of autonomous underwater vehicles (AUVs).

Nowadays, digital images are confronted with notable privacy and security issues, and many research works have been accomplished to countermeasure these risks. In this article, a novel scheme for data hiding in encrypted domain is proposed using fuzzy C-means clustering and compressive sensing technologies to protect privacy of the host image. The original image is preprocessed first to generate multiple highly correlated classes with fuzzy C-means clustering algorithm. Then, all classes are further divided into two parts according to proper threshold. One is encrypted by stream cipher, and the other is encrypted and compressed simultaneously with compressive sensing technology for easy data embedding by information hider. The receiver can extract additional data and recover the original image with data-hiding key and encryption key. Experiments and analysis demonstrate that the proposed scheme can achieve higher embedding rate about additional data and better visual quality of recovered image than other state-of-the-art schemes.

A liquid deposited on a solid surface exhibits various wetting properties, primarily influenced by solid–liquid intermolecular interactions. Achieving a well-estimation of wetting behaviors on textured surfaces remains challenging because the influence of surface textures on intermolecular interactions is unclear. Here, an equivalent surface model is proposed to unify the wettability of both plane and textured substrates through intermolecular interactions. We show that by incorporating intermolecular interactions, lattice structures, and surface topographies, the substrate wettability can be tailored in an equivalent plane with the same average Lennard–Jones potential energy field E. The wettability of one substrate (plane or patterned) is transferred from non-wetting, partial wetting to complete wetting by adjusting the potential well depth of E, εa. With enhancing εa, complete wetting is achieved under the critical potential well depth of E, εac. Before εac, the spreading radius grows as R ∼ t1/1.47 until reaches equilibrium. In this scenario, the equilibrium contact angle θ and spreading factor β are strongly dependent on εa, and cos θ is linear to εa. When exceeding εac, complete wetting is achieved, and droplets spread with a precursor film as R ∼ t1/2.3 latterly. This model builds a bridge to link intermolecular interactions and substrate wettability normalization. This strategy offers a framework for substrate surface design and wettability manipulation, catering to applications such as photoresist-drop dispensing in nanoimprint lithography, surface design for grating antifouling, and anti-icing on aircraft surfaces.