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REVIEW OF VEGETABLE NANOLUBRICANTS FOR TRIBOLOGICAL APPLICATIONS

Research Output:
Contribution to journal
Article
Peer-review

Open access

Publication metrics

Metrics

SciVal
Citations
6
SciVal
FWCI
0.17
SciVal
Author count
6
SciVal
Paper percentile
30
Scopus
Citations

Abstract

About 40 million tons of lubricants are used every year for various industry purposes and applications, where most of them are petroleum based oils. These oils are difficult and expensive to dispose of, have low biodegradability and are contamination risks. Recent efforts have been focused on reducing the environmental impact of petroleum based lubricants through the use of vegetable oils since they are biodegradable and have good lubricity. A drawback of vegetable oils is their poor thermal stability and oxidation, which causes them to decrease their properties at higher loads. Nanoparticle (NP) additives have been explored for improving the tribological performance of vegetable. This literature review seeks to compare and analise the impact of the different NP types, concentrations and vegetable oil type on the coefficient of friction. The vegetable oils that have shown to provide the best tribological behaviour were coconut oil, sunflower oil, palm sesame oil, canola oil, among others. The NP with the best performance were SiO2 and CuO and the concentration with the highest improvement was between 0.01-3.0 wt.%. The results of this study provide an insight on the areas of opportunity for developing new lubricant formulations with vegetable oils and NP additives for industrial applications.

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 333-343 (11 pages)

Journal (Volume, Issue Number)

International Journal of Modern Manufacturing Technologies (Volume 14, Issue 3)

Publication milestones

  • Published - 2022

Publication status

Published - 2022

ISSN

2067-3604

Publication IDs

  • Scopus: 85145318048

Funding Details

Acknowledgments: This works was partially supported by Universidad de Monterrey research grant No. UIN21523.
FundersFunding numbersUDEM
UIN21523