Quantitative Structure-Properties Relationship of Lubricating Oil Additives and Molecular Dynamic Simulations Studies of Diamond-Like-Carbon (DLC)

Document Type : Research Article

Authors

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

Abstract

Quantitative Structure-Properties Relationship (QSPR) and molecular dynamics simulations studies were carried out on the 53 lubricating oil additives and hydrogen-containing DLC (a-C: H). Good QSPR model was developed along with squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), leave one out cross-validation coefficient (Q2) and the external validation (R2ext) of values 0.807208, 0.763674, 0.68867 and 0.6297 respectively which shows that model I was reliable and satisfactory. Molecular dynamics simulation binding energy calculations between the lubricant additives and the hydrogen-containing DLC (a-C: H) crystals surface revealed that the best molecular dynamic binding energy was found to be -2112.06 kcal/mol and was found to be better than the one reported by other researchers. Moreover, the lubricant additive with compound number 50** (S-(2-(benzo[d]thiazol-2-ylamino)-2-oxoethyl) O-hexyl carbonodithioate) was found to have the best molecular dynamic binding energy of -2112.06 kcal/mol which conformed with excellent best-normalized onset temperature (Tonset) 2.467K of the same compound. Moreover, Table 7 revealed that the time(s) used for every simulation varies from 12683.13s to 138841.09s for all the studied additives. This investigation will help in rational additive design and synthesis of new and better lubricant additives with predetermined promising binding energy and onset temperature (Tonset) and will provide valuable information for the understanding of dynamic binding energy between DLC substrate and the new compounds and will give the way toward the discovery of novel lubricating oil additives that can withstand high dynamic working temperature and also resist wearing and frictions.

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References

[1] Andrzej M., The Aftermarket Additives Used In Lubricating Oils. Journal of Kones Powertrain and Transport. 20: 4 (2013).
[2] Abdulfatai, U., Uzairu, A., Uba, S., et al., Quantitative Structure-Properties Relationship, Molecular Dynamic Simulations and Designs of Some Novel Lubricant Additives, Egyptian Journal of Petroleum, 05:001 (2019).
[3] Holmberg K., Andersson, P., Erdemir, A., Global Energy Consumption Due to Friction in Passenger Cars, Tribology International, 47; 221 (2012).
[4] Tung S.C., McMillan, M.L., Automotive Tribology Overview of Current Advances and Challenges
for the Future, Tribology Internationa, l37: 517–536 (2004).
[5] Hideki M., Hisanori O., Masanori T., History and Applications of Diamond-Like Carbon Manufacturing Processes. Sei Technical Review, 52-58 (2016).
[6] Neville A., Morina A.,  Haque T., Voong M., Compatibility between Tribological Surfaces and Lubricant Additives—How Friction and Wear Reduction can be Controlled by Surface/Lube Synergies, Tribology International, 40; 1680–1695 (2007).
[7] Kano M., Yasuda, Y., Okamoto, Y., Mabuchi Y., Hamada T., Ueno T., Ye J., Konishi S., Takeshima S., Martin J.M., De Barros Bouchet M.I., Le Mognee T., Ultralow Friction of DLC in Presence of Glycerol Mono-Oleate (GMO), Tribology Letters, 18 (2005).
[8] Kalin, M., Kosovšek J., Remškar M., Nanoparticle as Novel Lubricating Additives in a Green, Physically Based Lubrication Technology for DLC Coatings, Wear, 303: 480–485 (2013).
[9] Kosarieh S., Morina A., Laině E., Flemming J., Neville A., The Effect of MoDTC- Type Friction Modifier on the Wear Performance of Hydrogenated DLC Coating, Wear,  302: 890–898 (2013).
[10] Forsberg P., Gustavsson F., Renman V., Hieke A., Jacobson S., Performance of DLC Coating Sin Heated Commercial Engine Oils, Wear, 304: 211–222 (2013).
[11] Kalin M., Velkavrh I., Non-Conventional Inverse-Stribeck-Curve Behaviour and Other Characteristics of DLC Coating Sin All Lubrication Regimes, Wear, 297: 911–918 (2013).
[12] Kubinyi H., QSAR and 3D QSAR in Drug Design, Drug Disc. Today, 457–467. (2014).
[13] Robinson D.D., Winn P.J., Lyne P.D., Richards W.G., Self-Organizing Molecular Field Analysis: A Tool for Structure-Activity Studies. J. Med. Chem., 42: 573–583 (1997).
[14] Ivanciuc, O., Ivanciuc, T., Cabrol-Bass, 3D Quantitative Structure-Activity Relationships with CoRSA. Comparative Receptor Surface Analysis. In: Application to Calcium Channel Agonists. Analysis, 28: 637–642 (2000).
[15] Wong Kai Y., Andrew G., Mercader L.M., Saavedra B.H., Gustavo P., Romanelli P.R., SAR Analysis on Tacrine-Related Acetylcholinesterase Inhibitors, J. Biomed Sci., 21:84- (2014).
[16] Fatemi S.M., Foroutan, M., Molecular Dynamics Simulations of Freezing Behavior of Pure Water and 14% Water-NaCl Mixture Using the Coarse-Grained Model. Iran. J. Chem. Chem. Eng. (IJCCE), 35(1): 1-10 (2016)
[17] Deng B., “Synthesis, Characterization and Properties of Novel Compounds Contain Nitrogen and Sulfur as Lubricant Additives”, Xiangtan University, Xiangtan (2009).
[18] Jeng W., “Synthesis, Characterization and Properties of Novel Heterocyclic Compounds Containing Nitrogen and Sulfur as Lubricant Additives”, Xiangtan University; Xiangtan (2010).
[19] Jiao Y., “Synthesis, characterization and Lubrication Performance of N, S-Containing Compounds as Lubricant Additives”, Xiangtan University; Xiangtan (2011).
[20] Anonymous, Wavefunction. Inc., Spartan’14, Version 1.1.2. Irvine, California, USA; (2013).
[21] Todeschini R., Consonni V., Mauri A., Pavan M. DRAGON for Widows (Software for the Calculation of Molecular Descriptors), Version 6.0. Milan, Italy: TALETE srl; (2012).
[22] Tropsha A, Gramatica P, Gombar VK. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models, QSAR Comb Sci. Apil; 22:69-77 (2003).
[23] Wymyslowski A., Iwamoto N., Yuen M., Fan H, “Molecular Modeling and Multiscaling Issues for Electronic Material Applications”, Springer, New York, (2014).
[24] Zhao H., X. Zhang, Ji L., Hu H., Li Q., Quantitative Structure-Activity Relationship Model for Amino Acids as Corrosion Inhibitors Based on the Support Vector Machine and Molecular Design, Corrosion Science. (2014)
[25] David A.B, Edwin K., Roy E.W., “Regression Diagnostics: Identifying Influential Data and Source of Collinearity”, Wiley-Interscience: John Wiley & Sons, lnc., New Jersey 85-93.ISBN 0-47 1- 69 1 17-8 (2004).
[26] Belsley D.A. “Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Hoboken”, Wiley-Interscience; N.J: 292 (2004).
[27] Paula de Castro Costa R., Fernanda R.M., Deiler Antônio L.O., Vladimir T.A., Enhanced DLC Wear Performance by the Presence of Lubricant Additives. Materials Research, 14 (2): 222-226 (2011).
Iranian Journal of Chemistry and Chemical Engineering
Volume 39, Issue 4 - Serial Number 102
July and August 2020
Pages 281-295

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