Adhesion systems with dynamically adjustable adhesion states have broad application prospects. In this work, for the first time, strong adhesion and easy detachment are achieved solely through the movement speed of the gripper without the use of any external stimulation. The flexible gripper based on the core–shell structure with a corn-starch suspension encapsulated by polydimethylsiloxane (PDMS) achieved a grasping ability of 33.71 g/cm², a ratio of adhesion to detachment of 203.78, and a response time of only 0.11 s. The rheological properties of non-Newtonian fluid (NNF) provide the regulatory ability of adhesion states. The designed speed-control method is superior to the strategies of changing the temperature through photothermal and Joule heating, applying an external magnetic field, and adjusting the stiffness of the backplate. This scheme not only breaks through the traditional external field-driving mode, successfully resolving the inherent contradiction between strong adhesion and easy detachment of the flexible adhesion gripper, but also enables universal grasping and rapid release of various materials and various target shapes. The gripper shows significant application potential in scenarios such as rapid transfer of electronic components, nondestructive picking of flexible materials, climbing and adhesion of small robots, and transportation of biomedical samples.

The energy crisis and environmental pollution are worsening. Therefore, water-based hydraulic fluids, i.e., aqueous ethylene glycol-based, fire-retardant hydraulic fluid concentrates (HFCs), are becoming increasingly common. However, seawater intrusion inevitably occurs under marine conditions, generating hazards, such as corrosion and friction, within hydraulic system components, pipelines, and materials. Moreover, HFCs have several drawbacks, including low viscosity, inadequate lubrication, and high corrosivity. Therefore, the tribological characteristics and corrosivity of HFCs must be improved and reduced, respectively. This can be achieved using additives. Herein, we summarize the fundamental characteristics of HFCs and their modifications for use in the marine environment, focusing on the optimal water–ethylene glycol proportion and its influence on the physicochemical, lubricating, and tribological properties of this HFC under varying conditions. We discuss the latest progress on the effects of seawater on the tribological corrosion of HFCs and the reduction in corrosivity in the presence of different additives. Finally, we highlight challenges and propose future research to improve performance in the marine environment.

Machine learning (ML) is transforming material research and development (R&D), driving a fundamental shift from experience-driven approaches to data-driven frameworks. This review systematically highlights the transformative breakthroughs brought by machine learning throughout the entire process of intelligent material innovation. And it provides a comprehensive full chain analysis, from atomic scale design to macroscopic applications, emphasizing multi-scale modeling that combines physical mechanisms with data-driven methods, running through all stages of material innovation. In the design phase, ML promotes performance-oriented structural optimization through inverse design systems and generative models. For synthesis and processing, closed-loop autonomous systems and green controllable synthesis strategies significantly improve efficiency and sustainability. In terms of advanced representation, ML-powered techniques can help proactively tackle key challenges of complex structures. Performance prediction models enable precise correlations between material properties and extreme properties (such as auxiliary structures) by revealing catalytic descriptors and decoding biological interface mechanisms. Ultimately, these ML-driven advancements are unlocking practical applications in key fields, such as energy, biomedicine, environmental remediation, and structural engineering. This article aims to provide a comprehensive technological roadmap for the next generation of smart material development by integrating cross scale insights and autonomous strategies, and to outline future directions for this rapidly developing paradigm.