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Research Article | Open Access

Estimating antiwear properties of esters as potential lubricant- based oils using QSTR models with CoMFA and CoMSIA

Zhan WANG1, Tingting WANG2, Guoyan YANG1, Xinlei GAO2( ), Kang DAI3
 College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
 School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, China
 College of Pharmacy, South-Central University for Nationalities, Wuhan 430074, China
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Abstract

Comparative molecular field analysis and comparative molecular similarity indices analysis were employed to analyze the antiwear properties of a series of 57 esters as potential lubricant-based oils. Predictive 3D-quantitative structure tribo-ability relationship models were established using the SYBYL multifit molecular alignment rule with a training set and a test set. The optimum models were all shown to be statistically significant with cross-validated coefficients q2 > 0.5 and conventional coefficients r2 > 0.9, indicating that the models are sufficiently reliable for activity prediction, and may be useful in the design of novel ester-based oils.

Keywords

quantitative structure tribo-ability relationshipantiwear propertieslubricant-based oils

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Friction
Volume 6 Issue 3,
September 2018
Pages 289-296
DOI: 10.1007/s40544-017-0175-5
Cite this article:
WANG Z, WANG T, YANG G, et al. Estimating antiwear properties of esters as potential lubricant- based oils using QSTR models with CoMFA and CoMSIA. Friction, 2018, 6(3): 289-296. https://doi.org/10.1007/s40544-017-0175-5

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Received: 06 January 2017
Revised: 03 May 2017
Accepted: 19 June 2017
Published: 05 December 2017
© The author(s) 2017

This article is published with open access at Springerlink.com

Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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