Wang S, Liang Z, Liu L, Wan P, Qian Q, Chen Y, Jia S, Chen D. Artificial Intelligence-Based Rapid Design of Grease with Chemically Functionalized Graphene and Carbon Nanotubes as Lubrication Additives.
LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023;
39:647-658. [PMID:
36563178 DOI:
10.1021/acs.langmuir.2c03006]
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Abstract
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.
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