Electrical contact materials are increasingly widely used, but the existing electric contact lubricants still have lots of room for improvement, such as anti-wear performance and lubrication life. Due to the excellent electrical and lubrication properties, graphene shows great potential in lubricating the sliding electrical contact interface, but there is a lack of relevant research. Some researchers have studied the lubrication performance of graphene between the gold-coated/TiN-coated friction pair at an ultra-low current. However, the lubrication performance of graphene on more widely used electrical contact materials such as copper and its alloys under larger and more commonly used current or voltage conditions has not been reported. In this paper, we study the lubrication performance of graphene in the copper and its alloys sliding electrical contact interface under usual parameters, which is explored through four aspects: different substrates—copper and brass, different test methods—constant voltage and constant current, different normal loads and durability test. The experiments demonstrate that graphene can significantly reduce the friction and wear on brass and copper under the above test methods and parameters, with low contact resistance at the same time. Our work is expected to provide a new lubricant for electrical contact materials and contribute to enriching the tribological theory of graphene.

For training artificial neural network (ANN), big data either generated by machine or measured from experiments are used as input to "learn" the unspecified functions defining the ANN. The experimental data are fed directly into the optimizer allowing training to be performed according to a predefined loss function. To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. After the training procedure, the ANN is capable to predict the contact and hydrodynamic pressure by adapting the output data according to the tribological condition implemented in the optimization algorithm.

Corrosion and corrosive wear occur commonly on metals surface in aqueous solutions. External electric field is usually considered as one of the factors to accelerate corrosion or corrosive wear of materials in the presence of conventional electrolytes. This work aims to reposition widely believed perspective by experimental justification which have been conducted in aqueous solutions containing surfactants. Electric potential of metal surfaces was modulated externally within the electrochemical potential window of the metal electrode-solution-counter electrode system, which actively regulated the adsorption or desorption of surfactant molecule in the aqueous solution over the electrodes to form a molecular barrier of electron transportation across the electrode–electrolyte interface. The advantage of the approach over the anodic passivation is negligible redox reactions on the protected electrode surface while a better lubricious and wear resistant film than oxide is maintained in the meantime. Tribopairs of several metal/metal and metal/ceramic were tested by employing a ball-on-disc tribometer with anionic and cationic surfactants solutions. For anionic surfactant as the modifier, positive surface potential enables coefficient of friction to be decreased by promoting the formation of adsorption film on metal surface in aqueous solutions. For cationic surfactant, negative surface potential plays a role in decreasing the coefficient of friction. Phase diagrams of friction and wear in wide ranges of surfactant concentration and surface potential were plotted for the tested metal/metal and metal/ceramic tribopairs. These results indicate that the adsorption behavior of molecules or ions at the metal–aqueous interface can be well regulated when an external electric field is present without inducing corrosion or corrosive wear.

The combined effect of boundary layer formation and surface smoothing on friction and wear rate of metallic surfaces under lubricated point contact condition was investigated. The double trend of friction coefficient variations was revealed during running-in and sub-running-in processes. The evolution of surface topography was measured on-site using white-light interference profilometer and analyzed using bearing area curves. Comprehensive theoretical equations that explicitly express the contributions of boundary friction, adhesive friction and wear have been derived, and results obtained by these equations were compared with experimental observations. It is concluded that the theoretical models are quantitatively adequate to describe the combined effect of surface smoothing due to mechanical wear and formation of boundary films on the changes in friction and wear rate during normal running-in processes.

Rotary near-field lithography (RNFL) technology provides a route to overcome the diffraction limit with a high throughput and low cost for nanomanufacturing. Utilizing the advantage of the passive flying of a plasmonic head, RNFL can achieve a 10 m/s processing speed with a perfect near-field condition at dozens of nanometers. The flying performance of the plasmonic flying head (PFH) is the pivotal issue in the system. The linewidth has a strong correlation with the near-field gap, and the manufacturing uniformity is directly influenced by the dynamic performance. A more serious issue is that the unexpected contact between the PFH and substrate will result in system failure. Therefore, it is important to model and analyze the flying process of the PFH at the system level. In this study, a novel full-coupled suspension-PFH-air-substrate (SPAS) model that integrates a six-degree of freedom suspension-PFH dynamics, PFH-air-substrate air bearing lubrication, and substrate vibration, is established. The pressure distribution of the air bearing is governed by the molecular gas lubrication equation that is solved by the finite element method (FEM) with a local pressure gradient based adaptive mesh refinement algorithm using the COMSOL Multiphysics software. Based on this model, three designs of the air bearing surface are chosen to study the static, dynamic, and load/unload performance to verify whether it satisfies the design requirements of RNFL. Finally, a PFH analysis solver SKLY.app is developed based on the proposed model.

A complete understanding of the mechanism of boundary lubrication is a goal that scientists have been striving to achieve over the past century. Although this complicated process has been far from fully revealed, a general picture and its influencing factors have been elucidated, not only at the macroscopic scale but also at the nanoscale, which is sufficiently clear to provide effective instructions for a lubrication design in engineering and even to efficiently control the boundary lubrication properties. Herein, we provide a review on the main advances, especially the breakthroughs in uncovering the mysterious but useful process of boundary lubrication by adsorption film. Despite the existence of an enormous amount of knowledge, albeit unsystematic, acquired in this area, in the present review, an effort was made to clarify the mainline of leading perspectives and methodologies in revealing the fundamental problems inherent to boundary lubrication. The main content of this review includes the formation of boundary film, the effects of boundary film on the adhesion and friction of rough surfaces, the behavior of adsorption film in boundary lubrication, boundary lubrication at the nanoscale, and the active control of boundary lubrication, generally sequenced based on the real history of our understanding of this process over the past century, incorporated by related modern concepts and prospects.