A microneedle patch that uses snake fang–inspired multiple open grooves can rapidly deliver liquid-phase drugs and vaccines under capillary action.

Wide-Bandgap (WBG) semiconductors—silicon carbide (SiC) and gallium nitride (GaN)— enable high-power-density conversion, but performance is limited by where heat is generated and how it is removed. This review links device-level loss mechanisms (conduction and switching, including output-capacitance hysteresis and dynamic on-resistance) to structure-driven hot spots within the ultra-thin (tens of nanometers) two-dimensional electron gas (2DEG) channel of GaN HEMTs and to thermal boundary resistance at layer interfaces. We compare wire-bondless package concepts—double-sided cooling, embedded packaging, and interleaved planar layouts—and survey system-level cooling that shortens the conduction path and raises heat-transfer coefficients. The impact on reliability is discussed using temperature-sensitive electrical parameters (e.g., on-state VDS, threshold voltage, drain leakage, di/dt, and gate current) for real-time junction-temperature estimation and compact electro-thermal RC models for remaining-useful-life prediction. Evidence from recent literature points to interface resistance in GaN-on-SiC as a primary bottleneck, while near-junction cooling and advanced packages are effective mitigations. We argue for integrated co-design—devices, packaging, electromagnetic interference (EMI)-aware layout, and cooling—together with interface engineering and health monitoring to deliver reliable, high-density WBG systems.

Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems.

Phenomena observed in nature can be considered to be the result of optimized outcomes that have been obtained through trial and error for many years. Researchers have thus attempted to apply the lessons provided by nature to real scientific problems. In particular, in nature, the fluidic behavior of eddy currents is frequently observed, such as in a river, whereby the eddy develops into a whirlpool with an increased flow velocity, vorticity, and inner pressure. However, in this study on the design of a cathode for Li–air batteries (LABs), the eddy current of oxygen gas as a fluid can also be found in the gas diffusion layer (GDL) in the cathode fabricated using an imprinting process with micro-scale patterned metal molds. Herein, the cathode with the patterned GDL containing an increased density of carbon paper exhibits increased velocity, vorticity, and inner pressure of O₂ flow due to the increased eddy current effect (the inner structuring). In addition, the patterned cathode increases the catalytic active sites for the oxygen reduction reaction due to the outer structuring effect such as an increased roughness factor. According to the simulated data from COMSOL, an average O₂ gas velocity of the patterned cathode prepared through inner/outer structuring is increased by 45.8%, compared to that of the bare electrode. In the LAB test, the micro-scale patterned cathode shows an increased energy density and decreased IR drop by 79.6% and 23.2%, respectively. Consequently, the inner and outer structured GDL in the cathode for the ORR could be predominantly responsible for the superior LAB performance.

We present a simple method of fabricating a hierarchically nanostructured CuO–Cu current collector by using laser ablation and metal mold imprinting to maximize the surface area. The laser ablation of the Cu current collector created the CuO nanostructure on the Cu-collector surface. The microstructure was transferred by subsequent imprinting of the microstructure metal mold on the Cu collector. Then, the laser-ablation nanostructure was formed. Consequently, a hierarchical structure is generated. The laser-ablated hierarchical CuO–Cu current collector exhibited an improved capacity while maintaining a cyclability that is similar to those of conventional graphite batteries.