Synergistic Tribological and Rheological Performance Enhancement of Sustainable Karanja-Based Trimethylolpropane Esters Using Reduced Graphene Oxide/Molybdenum Disulfide Hybrid Nanoparticles

Abstract

Abstract. This study addresses the growing environmental concerns associated with petroleum-based lubricants by developing high-performance, sustainable biolubricants derived from nonedible karanja oil. Karanja trimethylolpropane esters (KTMPEs) were synthesized through a three-step esterification–transesterification process, with Fourier transform infrared spectroscopy (FTIR) analysis confirming successful synthesis of biolubricant-grade esters. To enhance tribological properties, reduced graphene oxide/molybdenum disulfide (rGO/MoS2) hybrid nanoparticles (3:2 mass ratio) were integrated into the biolubricants at concentrations ranging from 0.1 to 1.0 wt%. Raman spectroscopy, X-ray diffractometry (XRD) analysis, and transmission electron microscopy (TEM) imaging verified the structural integrity and successful hybridization of the nanoparticles. Rheological testing revealed that all formulations exhibited Newtonian behavior across tested conditions. The incorporation of nanoparticles improved the viscosity index and enhanced thermal stability of the KTMPE. Tribological evaluation using a four-ball tribometer demonstrated that 0.3–0.6 wt% rGO/MoS2 concentration yielded optimal performance, producing marked reductions in friction coefficient and wear scar diameter due to improved dispersion stability and robust tribofilm formation. Comparative analysis with synthetic PAO4 confirmed that the enhanced biolubricants exhibited comparable antiwear and friction-reducing performance. Field emission-scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy (EDAX) analyses further validated protective tribofilm development through the presence of carbon, molybdenum, and sulfur on worn surfaces. An artificial neural network model, optimized using Bayesian regularization, identified a 14-neuron hidden layer architecture as optimal. The model achieved a correlation coefficient of 0.999698 and a mean squared error of 0.00279, demonstrating high predictive accuracy. Overall, the results establish rGO/MoS2-enhanced KTMPE biolubricants as a promising renewable alternative to synthetic oils for automotive lubrication.