Advance in Tribology Study of Polyelectrolyte Multilayers

This review introduced the preparation and structural characterization of polyelectrolyte multilayers in recent years and also summarized the tribology research progress of the polyelectrolyte multilayers, including tribological properties, surface adhesion characteristics, and wear resistance properties. Statistics analysis indicated that nanoparticles‐doped polyelectrolyte multilayers present better friction and wear performance than pristine polyelectrolyte multilayers. Furthermore, the in situ growth method resulted in improved structural order of nanoparticles composite molecular deposition film. In situ nanoparticles not only reduced the molecular deposition film surface adhesion force and friction force but also significantly improved the life of wear resistance. That was due to the nanoparticles that possessed a good load‐carrying capacity and reduced the mobility of the polymer‐chain segments, which can undergo reversible shear deformation. Based on this, further research direction of in situ nanoparticles molecular deposition film was proposed

A Strategy to Synthesize Multilayer Graphene in Arc-Discharge Plasma in a Semi-Opened Environment

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

Thermal Growth of Graphene: A Review

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 Novel Damage Identification Method for Steel Catenary Risers Based on a Novel CNN-GRU Model Optimized by PSO

As a new type of riser connecting offshore platforms and submarine pipelines, steel catenary risers (SCRs) are generally subject to waves and currents for a long time, thus it is significant to fully evaluate the SCR structure’s safety. Aiming at the damage identification of the SCR, the acceleration time series signals at multiple locations are taken as the damage characteristics. The damage characteristics include spatial information of the measurement point location and time information of the acquisition signal. Therefore, a convolutional neural network (CNN) is employed to obtain spatial information. Considering the variable period characteristics of the acceleration time series of the SCR, a gated recurrent unit (GRU) neural network is utilized to study these characteristics. However, neither a single CNN nor GRU model can simultaneously obtain temporal and spatial data information. Therefore, by combining a CNN with a GRU, the CNN-GRU model is established. Moreover, the hyperparameters of deep learning models have a significant influence on their performance. Therefore, particle swarm optimization (PSO) is applied to solve the hyperparameter optimization problem of the CNN-GRU. Thus, the PSO-CNN-GRU (PCG) model is established. Subsequently, an SCR damage identification method based on the PCG model is presented to predict the damage location and degree by SCR acceleration time series. By analyzing the SCR acceleration data, the prediction performances of the PCG model and the PSO optimization capacity are verified. The experimental results indicate that the identification result of the proposed PCG model is better than that of several existing models (CNN, GRU, and CNN-GRU)

Effect of pv values on dry fretting and wear characteristics of aromatic thermosetting co-polyester (ATSP)-MoS2 coatings

For components affected by vibration, fretting wear is one of the most common failure types. In this work, the fretting behavior of ATSP-MoS2 coating was investigated and analyzed in detail. The tested coating only has two fretting states: partial slip regime (PSR) and gross slip regime (GSR). The coefficient of friction (COF) can be maintained at 0.1 when the pv<1 MPa⋅m⋅s− 1, meanwhile the COF and wear rate are significantly affected by the Hertzian contact stress p. The wear mechanism of the coating mainly includes three aspects, the mechanical damage of the original coating, especially the abrasive wear, the densification of surface film, as well as the formation and rupture of blisters on the surface film