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In Silico Modeling, Prediction, and Designing of Some Anti-wear Lubricant Additives

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Abstract

For many decades, scientists in the field of boundary tribology have been working to find an excellent and environment friendly anti-wear additives to minimize wearing of the boundary equipment by improving the anti-wearing ability of the boundary lubricants. Four (4) anti-wear lubricant additives with better properties were carefully designed with the aids of QSPR and MD simulation methodologies. Out of the four newly designed additives, 2, 3, 5-trimethylheptyl acetate with the anti-wear property of 2.0802 mm was found to have better anti-wear lubricant property than its co-additives as well as the standard AW additive, Zinc dipropyl dithiophosphate (ZDDP). All the additives were found to have better boundary dynamic binding energy as well as high dynamic binding temperatures on steel-simulated coated surface than on DLC-coated surface. The boundary dynamic binding energy of all these anti-wear additives was found to be better than the ZDDP. And they could improve the anti-wear property of lubricant at elevated temperature without being decomposed since they have a high dynamic binding tribological temperature. Moreover, all these new anti-wear lubricant additives structures do not contain zinc (catalytic converters deactivator), sulfur (acidic oxide), and phosphorus (exhaust pipe ashes producer) and could be used to replace the widely used additive, ZDDP, which contains zinc and phosphorus and has less active (3.284 mm) additive property. Due to their better structures and properties correlation ability, these two methods could be used to provide a theoretical framework for engineers and other researchers to design a better anti-wear base oil additive before laboratory synthesis.

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Authors and Affiliations

  1. Department of Chemistry, Ahmadu Bello University, P.M.B. 1044, Zaria, Nigeria

    Usman Abdulfatai, Adamu Uzairu, Gideon Adamu Shallangwa & Sani Uba

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  1. Usman Abdulfatai
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  2. Adamu Uzairu
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  3. Gideon Adamu Shallangwa
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  4. Sani Uba
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Correspondence to Usman Abdulfatai.

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Abdulfatai, U., Uzairu, A., Shallangwa, G.A. et al. In Silico Modeling, Prediction, and Designing of Some Anti-wear Lubricant Additives. J Bio Tribo Corros 6, 100 (2020). https://doi.org/10.1007/s40735-020-00399-y

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  • DOI: https://doi.org/10.1007/s40735-020-00399-y

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