Heat-vision based drone surveillance augmented by deep learning for critical industrial monitoring.

This study examines the application of drone-assisted infrared (IR) imaging with vision grayscale imaging and deep learning for enhanced abnormal detection in nuclear power plants. A scaled model, replicating the modern pressurized water reactor, facilitated the data collection for normal and abnormal conditions. A drone, equipped with dual vision and IR cameras, captured detailed operational imagery, crucial for detecting subtle anomalies within the plant's primary systems. Deep learning algorithms were deployed to interpret these images, aiming to identify component abnormals not easily discernible by traditional monitoring. The object detection model was trained to classify normal and abnormal component states within the facility, marked by color-coded bounding boxes for clarity. Models like YOLO and Mask R-CNN were evaluated for their precision in anomaly detection. Results indicated that the YOLO v8m model was particularly effective, showcasing high accuracy in both detecting and adapting to system anomalies, as validated by high mAP scores. The integration of drone technology with IR imaging and deep learning illustrates a significant stride toward automating abnormal detection in complex industrial environments, enhancing operational safety and efficiency. This approach has the potential to revolutionize real-time monitoring in safety-critical settings by providing a comprehensive, automated solution to abnormal detection.

Robust 2D layered MXene matrix-boron carbide hybrid films for neutron radiation shielding.

Large-scale fabrication of neutron-shielding films with flexible or complex shapes is challenging. Uniform and high boron carbide (B4C) filler loads with sufficient workability are needed to achieve good neutron-absorption capacity. Here, we show that a two-dimensional (2D) Ti3C2Tx MXene hybrid film with homogeneously distributed B4C particles exhibits high mechanical flexibility and anomalous neutron-shielding properties. Layered and solution-processable 2D Ti3C2Tx MXene flakes serve as an ideal robust and flexible matrix for high-content B4C fillers (60 wt.%). In addition, the preparation of a scalable neutron shielding MXene/B4C hybrid paint is demonstrated. This composite can be directly integrated with various large-scale surfaces (e.g., stainless steel, glass, and nylon). Because of their low thickness, simple and scalable preparation method, and an absorption capacity of 39.8% for neutrons emitted from a 241Am-9Be source, the 2D Ti3C2Tx MXene hybrid films are promising candidates for use in wearable and lightweight applications.

Hybrid Graphene and Single-Walled Carbon Nanotube Films for Enhanced Phase-Change Heat Transfer.

Nucleate boiling is an effective heat transfer method in power generation systems and cooling devices. In this letter, hybrid graphene/single-walled carbon nanotube (SWCNT), graphene, and SWCNT films deposited on indium tin oxide (ITO) surfaces were fabricated to investigate the enhancement of nucleate boiling phenomena described by the critical heat flux and heat transfer coefficient. The graphene films were grown on Cu foils and transferred to ITO surfaces. Furthermore, SWCNTs were deposited on the graphene layer to fabricate hybrid graphene/SWCNT films. We determined that the hybrid graphene/SWCNT film deposited on an ITO surface is the most effective heat transfer surface in pool boiling because of the interconnected network of carbon structures.

Optimal synthesis and characterization of Ag nanofluids by electrical explosion of wires in liquids.

Silver nanoparticles were produced by electrical explosion of wires in liquids with no additive. In this study, we optimized the fabrication method and examined the effects of manufacturing process parameters. Morphology and size of the Ag nanoparticles were determined using transmission electron microscopy and field-emission scanning electron microscopy. Size and zeta potential were analyzed using dynamic light scattering. A response optimization technique showed that optimal conditions were achieved when capacitance was 30 µF, wire length was 38 mm, liquid volume was 500 mL, and the liquid type was deionized water. The average Ag nanoparticle size in water was 118.9 nm and the zeta potential was -42.5 mV. The critical heat flux of the 0.001-vol.% Ag nanofluid was higher than pure water.