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.
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This work was supported by the National Nature Science Foundation of China (NSFC, No. 51675395).
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Zhan WANG. She received her M.S. degree in food science and engineering in 2002 from Henan University of Technology, and graduated from Huazhong University of Science and Technology in biomedical engineering with PhD degree in 2009. Currently she is an associate professor at Wuhan Polytechnic University. Her research interests include organic chemistry and chemical computing.
Xinei GAO. She received her M.S. degree in 1996 from Huazhong Normal University in organic chemistry, and graduated from Wuhan Research Institute of Materials Protection in mechanical design and theory with PhD degree in 2006. Currently she is a full professor at Wuhan Polytechnic University, member of Chinese Tribology Association. She is interested in tribology chemistry, chemical computing, and designation of lubricant.
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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 6, 289–296 (2018). https://doi.org/10.1007/s40544-017-0175-5
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DOI: https://doi.org/10.1007/s40544-017-0175-5