A common belief proposed by Peierls and Landau that two-dimensional material cannot exist freely in a three-dimensional world has been proved false when graphene was first synthesized in 2004. Graphene, which is the base structure of other carbon materials, has drawn much attention of scholars and researchers due to its extraordinary electrical, mechanical and thermal properties. Moreover, methods for its synthesis have developed greatly in recent years. This review focuses on the mechanism of the thermal growth method and the different synthesis methods, where epitaxial growth, chemical vapor deposition, plasma-enhanced chemical vapor deposition and combustion are discussed in detail based on this mechanism. Meanwhile, to improve the quality and control the number of graphene layers, the latest research progress in optimizing growth parameters and developmental technologies has been summarized. The strategies for synthesizing high-quality and large-scale graphene are proposed and an outlook on the future synthesis direction is also provided.

A polystyrene (PS)/paraffin wax composite coating with excellent hydrophobic property was prepared on stainless steel strainer using a simple, inexpensive immersion drying method. The thickness and bonding strength of the composite coating were measured. Scanning electron microscopy (SEM) was used to characterise the coating surface of the stainless steel strainer. The effects of the paraffin concentration in the PS composite coating and the drying temperature of the coating were studied. The contact angles for oil and water were investigated. The results indicated that the composite coating on stainless steel strainer obtained the best hydrophobic property under paraffin concentration 5 mg mL^− 1 and drying temperature 50°C. The water contact angle reached 154 ± 1°, and the coating of the stainless steel strainer also showed a distinct oleophilic characteristic, which could be used for oil–water separation.

Graphene, as the earliest discovered two-dimensional (2D) material, possesses excellently physical and chemical properties. Vast synthetic strategies, including chemical vapor deposition, mechanical exfoliation, and chemical reduction, are proposed. In this paper, a method to synthesize multilayer graphene in a semi-opened environment is presented by introducing arc-discharge plasma technology. Compared with previous technologies, the toxic gases and hazardous chemical components are not generated in the whole process. The synthesized carbon materials were characterized by transmission electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectra technologies. The paper offers an idea to synthesize multilayer graphene in a semi-opened environment, which is a development to produce graphene with arc-discharge plasma.

The magnetic flux leakage (MFL) defect detection of oil and gas pipelines faces two tasks, defect type identification and defect size and shape estimation. However, there are few pieces of research on defect shape estimation, especially fewer researches on the defect cross-sectional profile estimation. The complex nonlinear relationship between the defect profile and the MFL signal makes the defect profile difficult to be estimated. In this paper, we propose a novel visual deep transfer learning neural network (VDTL), which not only predicts the defect size but also estimates the defect cross-sectional profile. VDTL network consists of a visual data transformation layer, a transfer learning convolutional neural network (CNN) layer, and a fully connected layer. In addition, we propose an augmentation data to figure (ADF) transformation method for one-dimensional MFL signals, and a fusion algorithm for two-dimensional radial and axial MFL images, which enriches the defect information in the images. Based on the Alexnet network, the multi-kernel maximum mean discrepancy (MK-MMD) transfer learning algorithm is introduced to improve the accuracy. Experiments are carried out on the data collected in the laboratory and on the data simulated by the finite element method. The results show that the prediction errors for defect length, depth, and defect cross-sectional profile are 0.67mm, 0.97%, and 2.67%, which are the smallest among the other methods. The research provides a theoretical basis for accurate defect prediction and the safe maintenance of oil and gas pipelines.