摘要：
采用比较分子力场分析(Comparative Molecular Field Analysis,CoMFA)和比较分子相似性指数分析(Comparative Molecular Similarity Indices Analysis,CoMSIA),对36种酰肼类及部分磷酸类润滑油添加剂的抗磨性能进行摩擦学三维定量构效关系的研究,在静电场和立体场分别建立添加剂的CoMSIA模型,对比分析2种模型的稳定性和预测能力。结果表明:在静电场构建的CoMSIA模型的预测能力较好,表明分子静电场对该类型类化合物的抗磨性能影响最大;基于该模型构建的三维等高线图可较为直观地解释静电场对化合物抗磨性能的影响,即当官能团或原子的电负性和所在区域性质一致时,抗磨性能最好。因此在特定区域引入带有正电荷或负电荷的基团或原子将有助于提高化合物的抗磨效果。
关键词：
ionic liquids;Hartree-Fock ab initio method;multiple linear regression;quantitative structure tribo-ability relationship;antiwear;hydrogen bond
摘要：
The antiwear properties of ionic liquids (ILs) as lubricant additives were studied with polyethylene glycol (PEG) used as the lubricant base oil. The quantum parameters of the ILs were calculated using a Hartree–Fock ab initio method. Correlation between the scale of the wear scar diameter and quantum parameters of the ILs was studied by multiple linear regression (MLR) analysis. A quantitative structure tribo-ability relationship (QSTR) model was built with a good fitting effect and predictive ability. The results show that the entropy of the ILs is the main descriptor affecting the antiwear performance of the lubricant system. To improve the antiwear performance of the lubricants, the entropy of the system should be decreased, reducing the system randomness and increasing the system regularity. A major influencing factor on the entropy of a system is the intra- and intermolecular hydrogen bonds present. Therefore, enhanced antiwear properties of lubricants could be achieved with a three-dimensional netlike structure of lubricant formed by hydrogen bonding.
摘要：
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.
关键词：
Model;Anti-wear and friction-reducing;Ultra-high molecular weight polyethylene
摘要：
<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>Ultra-high molecular weight polyethylene (UHMWPE) has an excellent performance and application value; however, as a tribological material, its main drawback is its poor performance under dry friction, impacting its ability to work in high-speed dry friction conditions. Modification of UHMWPE can be carried out to overcome these issues. A significant number of inorganic materials have been used to modify UHMWPE and provide it with good tribological performance. However, thus far, there has been no systematic investigation into the methodology of modifying UHMWPE. The authors take a quantitative approach to determine the structure tribo-ability relationship and basic principles of screening of inorganic compounds suited to modify UHMWPE.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>The tribological properties of modified UHMWPE using a series of inorganic additives have been qualitatively studied by the authors’ research group previously. In this study, basic quantitative structure tribo-ability relationships (QSTRs) of inorganic additives for modifying UHMWPE were studied to predict tribological properties. A set of 15 inorganic compounds and their tribological data were used to study the predictive capability of QSTR towards inorganic additives properties.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>The results show that the anti-wear and friction-reducing properties of these inorganic compounds correlate with the calculated parameters of entropy and dipole moment. Increased entropy and smaller dipole moment can effectively improve the anti-wear and friction-reducing ability of inorganic compounds as UHMWPE additives. Additives with larger molecular weight, lower hardness and lower melting and boiling points provide good tribological properties for UHMWPE. For inorganic compounds to act as UHMWPE additives, the chemical bond should be less covalent and have more ionic character.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Research limitations/implications</jats:title>
<jats:p>Only 15 inorganic compounds and their tribological data were used to study the predictive capability of QSTR towards inorganic additives properties. If the samples number is more than 30, the other QSTR methodology can be used to study the modified UHMWPE, and the models finding can be more precise.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Practical implications</jats:title>
<jats:p>A QSTR model for modified UHMWPE has been studied systematically. While the results are not more precise and detailed, the model provides a new way to explore the modified UHMWPE characteristics and to reveal new insight into the friction and wear process.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Social implications</jats:title>
<jats:p>Because the method of studying tribological materials is entirely different from others, the authors want to present the works and discuss it with colleagues.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The paper presents a new method to study the modified UHMWPE. A QSTR is used to study the tribology capability of compounds from calculated structure descriptors. This study uses the Hartree–Fock <jats:italic>ab initio</jats:italic> method to establish a QSTR prediction model to estimate the ability of 15 inorganic compounds to act as anti-wear and friction-reducing additives for UHMWPE.</jats:p>
</jats:sec>
作者机构：
[王婷婷; 高新蕾] College of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[戴康] College of Pharmacy, South-Central University for Nationalities, Wuhan, Hubei 430074, China;[王展] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[彭浩] Key Laboratory of Pesticides and Chemical Biology, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
College of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, Hubei, China
作者机构：
[刘登辉; 王锐涛; 王越; 高新蕾] School of Chemical and Environment Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[杨奇; 贺军波] School of Food science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China;[戴康] School of Pharmacy, South-Central University for Nationalities, Wuhan, Hubei 430074, China
通讯机构：
School of Chemical and Environment Engineering, Wuhan Polytechnic University, Wuhan, Hubei, China
摘要：
The prediction of lubrication characteristics for compounds through tribological models would aid in the discovery of new lubricant additives and improved lubricant design. But until recently, the field of tribological prediction has been sparse and not systematic. Tribological processes are complex and involve molecular energy exchange as well as conformation transitions. We have developed a platform of a "quantitative structure tribo-ability relationship (QSTR)," which enables us to introduce well-developed quantitative structure-activity relationships (QSAR) methods into tribology systematically. The present study applies "evaluation of infrared vibration-based" (EVA) descriptors, which are three-dimensional (3D) QSAR descriptors to simulate infrared (IR) vibration properties of molecules, in order to establish the QSTR prediction model. As structural changes take place under friction loads, the EVA descriptors characterize both molecular energy and conformations. The results show a strong correlation and robust predictability of the EVA model to tribological parameters. The approach paves a way to a systematic QSTR.
