Fine-Tuning Additives of Carbon Nanotubes to Enhance Simulated Lubricity of PAO Oils
L Van Sang, LANGMUIR, 41, 31073-31084 (2025).
DOI: 10.1021/acs.langmuir.5c03355
Extensive research has demonstrated the potential of carbon nanotube (CNT) additives to enhance the lubricating properties of poly alpha- olefin (PAO) base oils. However, the systematic optimization of CNT parameters, specifically molecular length, layering configuration, and additive concentration, remains insufficiently explored, particularly in relation to performance under varying pressure conditions. This study employs molecular dynamics simulations to investigate the tribological behavior of PAO oils containing CNT additives. The analysis considers PAO molecules with one, three, and seven branches; CNT lengths of 10, 20, 40, and 60 & Aring;; CNT layering configurations of one, two, and three layers; and applied pressures ranging from 0.5 to 4 GPa. Key findings reveal that the lubricant formulation comprising seven-branched PAO molecules and the longest single-walled carbon nanotube, measuring 60 & Aring;, exhibits superior stability and achieves the lowest friction coefficient across all test conditions. These results highlight the critical roles of molecular elongation, additive length, and concentration in optimizing lubricating performance. Furthermore, lubrication efficiency is inversely correlated with CNT layering: single-layer CNTs consistently outperform two-layer and three-layer configurations. CNTs with fewer layers also demonstrate enhanced stability, maintaining low friction coefficients and resisting performance degradation across a wide pressure range. This study identifies optimal combinations of molecular architecture and CNT configuration for improved lubrication and elucidates the underlying mechanisms, namely, molecular interactions, lubricant structure, and mechanical stability, driving these enhancements. The insights gained provide a strategic foundation for the design of high-performance nanolubricants based on CNT-enhanced PAO formulations.
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