Researchers from the Technical University of Munich have developed URNet, a novel artificial intelligence model that helps autonomous driving systems perceive their surroundings more clearly—even in dark, fast-changing environments. By combining an unconventional “event camera” with a self-aware framework, URNet allows vehicles to build reliable 3D maps that measure how far objects are—a process known as depth estimation—while understanding how confident they should be about what they “see.” This innovation could make next-generation self-driving cars safer and more capable of navigating complex real-world conditions.

A research paper by scientists at the Harbin Institute of Technology proposed a novel centimeter-scale quadruped piezo robot. The robot’s locomotion is generated by multi-dimensional vibration trajectories at the feet, which are produced through a novel built-in actuation method.

The research paper, published on Jul. 22, 2025 in the journal Cyborg and Bionic Systems, presented a novel centimeter-scale quadruped piezoelectric robot with high integration and strong robustness, which promises to bring new perspectives for the construction and application of centimeter-scale robots.

In the study, (Hf_(1-X)/4Zr_(1-X)/4Nb_(1-X)/4Ta_(1-X)/4Co_X)C (X=0.14, 0.18, and 0.20) high-entropy ceramic powders were successfully synthesized via a polymer-derived ceramic (PDC) method at 1700-1900 °C. Structural analysis (XRD, SEM, TEM, and XPS) confirmed the formation of single-phase rock-salt structures with homogeneous elemental distribution and significant lattice distortion. The (Hf_0.215Zr_0.215Nb_0.215Ta_0.215Co_0.140)C ceramic prepared at 1700°C exhibited excellent reflection loss (RL) of -37.95 dB at 14.01 GHz with a thickness of 3.10 mm. The introduction of the magnetic element cobalt optimized the permeability and dielectric constant of the sample, significantly enhancing the dielectric-magnetic loss synergy. This work bridges the gap in systematic research on incorporating Co into high-entropy carbide ceramics and provides new insights for designing high-performance electromagnetic wave absorbing materials.

In a study published in Nature, Prof. XIAO Zhengguo’s team from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences has proposed a novel strategy based on weakly space-confined, large-grain crystals of all-inorganic perovskite to prepare perovskite films with larger crystalline grains and higher temperature resistance. Researchers increased the brightness of perovskite LEDs (PeLEDs) to over 1.16 million nits and extended their lifespan to more than 180,000 hours.

In a study published in Advanced Materials, a research team led by Academician YU Shuhong from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences reported a structure-function integrated design of nacre-mimetic alumina (Al2O3)-based (NMA) composites. This NMA composite material not only has unique color tunability and excellent wave transparency, but also achieves lightweight, high strength, high toughness, and outstanding impact resistance.

A research team from Yunnan University has developed a novel liquid metal-assisted heteroepitaxy method to grow high-quality perovskite crystals within mesoporous scaffolds. This breakthrough enables printable mesoscopic perovskite solar cells to reach a champion efficiency of 20.2% while maintaining 97% performance after 3000 hours under harsh conditions. The approach offers a scalable pathway to efficient, stable, and low-cost printable photovoltaics.

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers investigated a fine-scale lightning forecasting approach based on weather foundation models (WFMs) and proposed a dual-source data-driven forecasting framework that integrates the strengths of both WFMs and recent lightning observations to enhance predictive performance. Furthermore, a gated spatiotemporal fusion network (gSTFNet) is designed to address the challenges of cross-temporal and cross-modal fusion inherent in dual-source data integration. Experimental results demonstrate that the dual-source framework significantly improves forecasting performance compared to models trained solely on WFMs and outperforms both the ECMWF HRES lightning product and other deep-learning spatiotemporal forecasting models.