Shi, Hongfei; Zhang, Jingchun; Hu, Wenjing; Wang, Jing; Li, Jiusheng

TRIBOLOGY INTERNATIONAL

2025, ,

Shi, Hongfei; Zhang, Jingchun; Hu, Wenjing; Wang, Jing; Li, Jiusheng

Lubricating oils contain a variety of functional additives, which often exhibit complex structure-activity relationships due to differences in microstructure. Understanding the influence of molecular structure parameters on lubricating properties is essential for lubricant additives design. In this study, cyanuric acid (CA)-based organic friction modifiers were found to significantly enhance the tribological performance of lubricants. Three lubricants were formulated with CA friction modifiers that featured subtle differences in alkyl chain structures and polar group distributions. Tribological tests were carried out using a UMT tribometer. Based on density functional theory (DFT), the chemical activity and electrostatic potential of the molecules were calculated. The adsorption energies of the additives on metal surfaces were determined via molecular dynamics (MD) simulations. A predictive model was constructed by machine learning (ML) to quantify the influence of molecular configuration factors on tribological performance. The results show that the polar groups with more uniform dispersion will bring more adsorption sites to CA molecules, which is conducive to the interfacial adsorption of additives. CA molecules with the straight alkane chain length has a more powerful electrostatic force, giving them an advantage in competitive adsorption with base oils and other friction modifiers. This study provides a new paradigm for guiding the design of lubricant additives by quantifying the effects of molecular conformation.