Search

The present paper aims to study the influence of pore-scale fiber structure on the Cassie-Baxter contact angle of droplets on PTFE fibrous hydrophobic membranes. The fiber structure is obtained by the Confocal Laser Scanning Microscope (CLSM), and an optical test bench is built to measure the Cassie-Baxter contact angles of a droplet on a PTFE membrane. The free energy of the solid-liquid interface is measured by the surface tension integral on the droplet profile. An iterative algorithm for liquid-vapor interface shrinking is brought forward to analyze the liquid-vapor interface area between the droplet and the membrane, and the interface parameters of the Cassie-Baxter model are recognized. The experiment shows that the Cassie-Baxter contact angles and the contact line radii along the droplet circumference are in a normal distribution due to random fiber structures, and the variance of the normal distribution increases with the membrane pore diameter. The normal distribution of Cassie-Baxter's contact angle indicates the constraint of the solid-liquid interface to the liquid-vapor interface. On the same normal contact pressure condition, the liquid-vapor area ratio of the droplet and membrane interface changes little, and Cassie-Baxter's contact angle increases with the solid-liquid interface area ratio. [ABSTRACT FROM AUTHOR]

Environment perception technology is the most important part of driverless technology, and driverless vehicles need to realize decision planning and control by virtue of perception feedback. This paper summarizes the most promising technology methods in the field of perception, namely visual perception technology, radar perception technology, state perception technology, and information fusion technology. Regarding the current development status in the field, the development of the main perception technology is mainly the innovation of information fusion technology and the optimization of algorithms. Multimodal perception and deep learning are becoming popular. The future of the field can be transformed by intelligent sensors, promote edge computing and cloud collaboration, improve system data processing capacity, and reduce the burden of data transmission. Regarding driverless vehicles as a future development trend, the corresponding technology will become a research hotspot. [ABSTRACT FROM AUTHOR]

Widespread integration of carbon‐based membranes into prevailing fields like energy storage and material engineering is thwarted by a lack of functionality, customization, and compatibility, making the development of such carbon‐based membranes urgency yet challenging. Herein, a "graphene bubble bridging" strategy is adopted to fabricate the multifunctional carbon fiber membrane, in which graphene bubbles with optimized assembling not only endow the carbon fiber with superior flexibility and integrity by embedded bridging function, but also enhance the fast electron transport capability and electrolyte wettability. Few‐layer MoSSe nanosheets with expanded interlayer spacing as a typical energy storage material contribute to plentiful ion intercalation/deintercalation that can be well buffered within the robust fibers. The interlaced carbon fibers enable a self‐supporting framework that ensures good conductivity and mechanical stability as well. Such functional, customized, and compatible membranes with synergies can boost the attributes of advanced K+ storage devices, such as flexible K‐ion capacitors and K‐based dual‐ion batteries. This work can afford new insights for designing flexible electrode in the energy storage field, as well as various types of conformations that can bring more opportunities on developing flexible functional membranes for other fields (e.g., catalysts, electromagnetic shielding). [ABSTRACT FROM AUTHOR]

Airborne array-interferometric synthetic aperture radar (array-InSAR), one of the implementation methods of tomographic SAR (TomoSAR), has the advantages of all-time, all-weather, high consistency, and exceptional timeliness. As urbanization continues to develop, the utilization of array-InSAR data for building detection holds significant application value. Existing methods, however, face challenges in terms of automation and detection accuracy, which can impact the subsequent accuracy and quality of building modeling. On the other hand, deep learning methods are still in their infancy in SAR point cloud processing. Existing deep learning methods do not adapt well to this problem. Therefore, we propose a Position-Feature Attention Network (PFA-Net), which seamlessly integrates positional encoding with point transformer for SAR point clouds building target segmentation tasks. Experimental results show that the proposed network is better suited to handle the inherent characteristics of SAR point clouds, including high noise levels and multiple scattering artifacts. And it achieves more accurate segmentation results while maintaining computational efficiency and avoiding errors associated with manual labeling. The experiments also investigate the role of multidimensional features in SAR point cloud data. This work also provides valuable insights and references for future research between SAR point clouds and deep learning. [ABSTRACT FROM AUTHOR]