Wang Siyuan, Liang Zhao, Liu Ling, Wan Peng, Qian Qihao, Chen Yaotong, Jia Shuo, Chen Ding
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, China.
Institute of Micro/Nano Materials and Devices, Ningbo University of Technology, Ningbo315211, China.
Langmuir. 2023 Jan 10;39(1):647-658. doi: 10.1021/acs.langmuir.2c03006. Epub 2022 Dec 23.
Rapid chemical functionalization of additives and efficient determination of their optimum concentrations are important for designing high-performance lubricants, especially under multi-additive conditions. Herein, chemically functionalized graphene (FGR) and carbon nanotubes (FCNTs) were rapidly prepared by microwave-assisted ball milling and subsequently introduced into grease as additives. The tribological properties of the additives in grease at different concentrations and ratios were measured using a four-ball test. A reliable artificial neural network (ANN) model was established according to a few test results. Subsequently, the optimal concentration of multiple additives in the grease was predicted using a genetic algorithm and experimentally validated. The results indicated that the introduction of FGR (0.14 wt %) and FCNT (0.16 wt %) improved the antifriction and anti-wear performance of the base grease by 25.66 and 29.34%, respectively. The results of the ANN model analysis and friction interface characterization indicate that such performance is principally attributed to the synergistic lubrication of the FGR and FCNT.
添加剂的快速化学功能化及其最佳浓度的有效测定对于设计高性能润滑剂至关重要，尤其是在多添加剂条件下。在此，通过微波辅助球磨快速制备了化学功能化石墨烯（FGR）和碳纳米管（FCNT），随后将其作为添加剂引入润滑脂中。使用四球试验测量了不同浓度和比例的添加剂在润滑脂中的摩擦学性能。根据少数试验结果建立了可靠的人工神经网络（ANN）模型。随后，使用遗传算法预测了润滑脂中多种添加剂的最佳浓度，并进行了实验验证。结果表明，引入FGR（0.14 wt%）和FCNT（0.16 wt%）分别使基础润滑脂的减摩和抗磨性能提高了25.66%和29.34%。人工神经网络模型分析和摩擦界面表征结果表明，这种性能主要归因于FGR和FCNT的协同润滑作用。
