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Nanoparticles have generated interest in the oil industry as potential additives. Many nanoparticles have been shown to improve fuel economy and engine efficiency. However, there are an infinite number of nanoparticle formulations, and to test all these would be impossible. The objective of this project was to develop a predictive model which demonstrates the impact of factors such as size, concentration, and hardness on the performance of nanoparticles as oil additives.
The concentrations tested were 0.01% and 0.5% by mass in PAO4 base oil. The sizes tested were 5-15nm and 20-50nm. Using four compositions to approximate hardness, a 4 x 2 x 2 DOE matrix generated a predictive model of nanoparticle performance. Nanoparticles can reduce friction and physical wear, in turn improving fuel economy. A high-frequency reciprocating rig (HFRR) was used to obtain friction coefficients and wear scar area, and a mini-traction machine with space layer imaging method (MTMSLIM) was used to observe tribofilm growth.
Large, concentrated, and hard nanoparticles demonstrated the best performance as friction modifiers and wear reducers. The hardest, zinc oxide, showed the greatest friction reduction at 74% below base oil and also exhibited 79% reduction of wear scar area below base oil as compared to, respectively, 62% and 35% for the current industry standard. This project examines a limited set of concentrations and compositions, including alloys, and studies of these trends could better indicate mechanisms. Future study of zinc oxide and elements of comparable hardness are recommended and could provide a class of promising nanoparticle additives.
Chemical and life science engineering, nanotechnology, tribology, metal oxide nanoparticles, engine oil performance
Chemical Engineering | Engineering
Dr. Nastassja Lewinski
Dr. Mark Devlin
VCU Capstone Design Expo Posters
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