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Effects of Lubricating Oil Metallic Content on Morphology, Nanostructure and Graphitization Degree of Diesel Engine Exhaust Particles
Technical Paper
2017-01-1009
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
In this paper, the influences of metallic content of lubricating oils on diesel particles were investigated. Three lubricating oils with different levels of metallic content were used in a 2.22 Liter, two cylinders, four stroke, and direct injection diesel engine. 4.0 wt. % and 8 wt. % antioxidant and corrosion inhibitor (T202) were added into baseline lubricating oil to improve the performance respectively. Primary particle diameter distributions and particle nanostructure were compared and analyzed by Transmission Electron Microscope. The graphitization degrees of diesel particles from different lubricating oils were analyzed by Raman spectroscopy. Conclusions drawn from the experiments indicate that the metallic content increases the primary particles diameter at 1600 rpm and 2200 rpm. The primary particles diameter ranges from 5 nm to 65 nm and the distribution conformed to Gaussian distribution. Besides, particles from high metallic content lubricating oils show same chain-like and branch-like cluster and core-shell nanostructure with particles from the baseline lubricating oil. The layer fringe length and tortuosity were used to quantify the particle nanostructure. The analysis results of particle nanostructure show that metallic content leads to shorter fringes with higher tortuosity. As to the graphitization degree, four curves (D1, D3, D4, G) were used to fit the Raman spectrum curves. And the area ratio of D1 band to G band increased when the high metallic content lubricating oil was used at 1600 rpm and 2200 rpm, which means that metallic content leads to the disordered particle structure.
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Wang, Y., Liang, X., Wang, Y., Sun, X. et al., "Effects of Lubricating Oil Metallic Content on Morphology, Nanostructure and Graphitization Degree of Diesel Engine Exhaust Particles," SAE Technical Paper 2017-01-1009, 2017, https://doi.org/10.4271/2017-01-1009.Data Sets - Support Documents
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