Nature-inspired self-cleaning surfaces have attracted considerable attention from both fundamental research and practical applications. This review adopts a chemical-engineering point of view and focuses on mechanisms, modelling, and manufacturing (M3) of nature-inspired self-cleaning surfaces. We will introduce six nature-inspired self-cleaning mechanisms: The Lotus-effect, superhydrophobic-induced droplet jumping, superhydrophobic-induced unidirectional movement of water droplet, underwater-superoleophobic-based self-cleaning, slippery-based self-cleaning, and dry self-cleaning. These mechanisms of nature self-cleaning examples are popular and well-known as well as have been widely applied or exhibited potential applications in our daily life and industrial productions. The mathematical and numerical modelling of the identified self-cleaning mechanisms will be carefully introduced, which will contribute to the rational design and reproducible construction of these functional self-cleaning surfaces. Finally, we will discuss how these materials can be produced, with a focus on scalable manufacturing. We hope this review will strengthen the understanding on nature-inspired self-cleaning surfaces and stimulate interdisciplinary collaboration of material science, biology and engineering.@en

Black phosphorus (BP) has received increasing research attention as an anode material in sodium-ion batteries (SIBs), owing to its high capacity, electronic conductivity, and chemical stability. However, it is still challenging for BP-based SIB anodes to achieve a high electrochemical performance utilizing cost-effective materials and synthetic methods. This work presents a sodium-ion anode based on a BP-carbon nanocomposite synthesized from commercial red phosphorus and low-cost super P carbon black. Intimate interactions between BP and carbon are present, which helps to maintain the electrical conduction during cycling and, therefore, a high cycling stability is achieved. It exhibits a high capacity retention of 1381mAhg-1 for sodium-ion storage after 100 cycles, maintaining 90.5% of the initial reversible capacity. Such high performance/materials cost ratio may provide direction for future phosphorus-based anodes in high energy density SIBs.@en

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about defects in the catenary support devices. A series of experiments in an unified test environment for detection of components are performed using Faster-CNN, R-FCN, SSD, and YOLOv2. Through the comparison of different assessment indicators, such as precision, recall, average precision and mean average precision, the detection performance of the different DCNNs methods for the components of the catenary support devices is analyzed, discussed and evaluated. The experiment results show that among all considered methods, R-FCN is the more suitable for the detection of catenary support components.@en