Solid-state batteries (SSBs) are considered to be the next-generation lithium-ion battery technology due to their enhanced energy density and safety. However, the high electronic conductivity of solid-state electrolytes (SSEs) leads to Li dendrite nucleation and proliferation. Uneven electric-field distribution resulting from poor interfacial contact can further promote dendritic deposition and lead to rapid short circuiting of SSBs. Herein, we propose a flexible electron-blocking interfacial shield (EBS) to protect garnet electrolytes from the electronic degradation. The EBS formed by an in-situ substitution reaction can not only increase lithiophilicity but also stabilize the Li volume change, maintaining the integrity of the interface during repeated cycling. Density functional theory calculations show a high electron-tunneling energy barrier from Li metal to the EBS, indicating an excellent capacity for electron-blocking. EBS protected cells exhibit an improved critical current density of 1.2 mA cm⁻² and stable cycling for over 400 h at 1 mA cm⁻² (1 mAh cm⁻²) at room temperature. These results demonstrate an effective strategy for the suppression of Li dendrites and present fresh insight into the rational design of the SSE and Li metal interface.

In recent years, the utilization of 3D printing technology in micro and nano device manufacturing has garnered significant attention. Advancements in 3D printing have enabled achieving sub-micron level precision. Unlike conventional micro-machining techniques, 3D printing offers versatility in material selection, such as polymers. 3D printing technology has been gradually applied to the general field of microelectronic devices such as sensors, actuators and flexible electronics due to its adaptability and efficacy in microgeometric design and manufacturing processes. Furthermore, 3D printing technology has also been instrumental in the fabrication of microfluidic devices, both through direct and indirect processes. This paper provides an overview of the evolving landscape of 3D printing technology, delineating the essential materials and processes involved in fabricating microelectronic and microfluidic devices in recent times. Additionally, it synthesizes the diverse applications of these technologies across different domains.

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.