Converting solar energy into fuels/chemicals through photochemical approaches holds significant promise for addressing global energy demands. Currently, semiconductor photocatalysis combined with redox techniques has been intensively researched in pollutant degradation and secondary energy generation owing to its dual advantages of oxidizability and reducibility; however, challenges remain, particularly with improving conversion efficiency. Since graphene’s initial introduction in 2004, three-dimensional (3D) graphene-based photocatalysts have garnered considerable attention due to their exceptional properties, such as their large specific surface area, abundant pore structure, diverse surface chemistry, adjustable band gap, and high electrical conductivity. Herein, this review provides an in-depth analysis of the commonly used photocatalysts based on 3D graphene, outlining their construction strategies and recent applications in photocatalytic degradation of organic pollutants, H₂ evolution, and CO₂ reduction. Additionally, the paper explores the multifaceted roles that 3D graphene plays in enhancing photocatalytic performance. By offering a comprehensive overview, we hope to highlight the potential of 3D graphene as an environmentally beneficial material and to inspire the development of more efficient, versatile graphene-based aerogel photocatalysts for future applications.

Chromium nitride (Cr-N) coatings fabricated by physical vapor deposition (PVD) have gained significant interest in the field of surface protection with the exceptional hardness, robust adhesion to substrates, and superior wear resistance. The mechanical properties of Cr-N coatings are predominantly determined by their chemical composition, phase structure, and microstructure. The selection of deposition technique and regulation of process parameters, such as N₂ flow rate, play a crucial role in optimizing coating performance. This review systematically summarizes recent research advancements in PVD-fabricated Cr-N coatings with a specific focus on both monolayer and multilayer architectures. It explores the impact of process parameters on the hardness, adhesion strength, and tribological properties. Furthermore, it outlines the design strategies and fabrication methodologies for high-performance Cr-N coatings. Results indicate that the mechanical properties of monolayer Cr-N coating are primarily governed by the process parameters. As for multilayer coatings, the incorporation of ductile Cr layers can enhance the coating-substrate adhesion strength and wear resistance while preserving a relatively high hardness. This study aims to provide a theoretical foundation and technical reference for future research and applications of the Cr-N coating material system.

Remote sensing platforms provide crucial data for applications such as urban planning and traffic monitoring. However, detecting small objects remains challenging due to low resolution, cluttered backgrounds, and scale variations. In this work, we propose efficient feature aggregation framework YOLO(EFAF-YOLO), a lightweight and efficient architecture for real-time detection in remote sensing imagery. The model incorporates two novel modules: the Multi-scale Feature Enhancement (MFE) module, which expands receptive fields via multi-branch and dilated convolutions to improve small-object representation, and the Adaptive Multi-scale Feature Fusion (AMFF) module, which selectively integrates shallow and deep features to recover targets often overlooked in deeper layers. Additionally, an Efficient Multi-scale Attention (EMA) module is embedded in the backbone to emphasize small targets in cluttered scenes. Evaluated on VisDrone2019, AI-TOD, and RS-STOD, EFAF-YOLOs achieves 45.4% mAP50 on VisDrone2019 (+6.8 over YOLO11s), 53.7% mAP50 on AI-TOD (+4.0), and 77.3% mAP on RS-STOD (+5.2), with only 1.2M additional parameters, while maintaining real-time inference at 5.2 ms per image (192 FPS on RTX 5070), well above the 30 FPS requirement. These results demonstrate the method’s effectiveness for efficient and real-time interpretation of remote sensing images.