Quantitative Structure Tribo-ability Relationship Research Articles

A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair

In this study, a systematic test of 36 organic liquid compounds as lubricants in the SiC/PI friction pair was conducted to investigate their friction-reducing performance. The back propagation neural network (BPNN) method was employed to establish a quantitative structure tribo-ability relationship (QSTR) model for the friction performance of these lubricants. The developed BPNN-QSTR model exhibited excellent fitting and predictive accuracy, with R2 = 0.9700, R2 (LOO) = 0.6570, and q2 = 0.8606. The impact of different descriptors in the model on the friction-reducing performance of the lubricants was explored. The results provide valuable guidance for the design and optimization of lubricants in SiC/PI friction systems, contributing to the development of high-performance lubrication systems.

Review of the State-of-the-Art Application of the Quantitative Structure Tribo-Ability Relationship Model of Lubricants in China

In recent years, lubricant research has developed from empirical to theoretical, from descriptive to rational, from qualitative to quantitative, and from macroscopic to microscopic studies. This review presents the new concept of the quantitative structure tribo-ability relationship (QSTR) derived from the basic principles of quantitative structure activity relationship (QSAR) theory and reviews the latest advances in research on basic problems of the QSTR of lubricants. Specifically, it highlights a series of recent studies conducted by Chinese scholars and future prospects related to these works. It is noted that the study of lubricants involves many related issues, such that there may be omissions in this review. Additionally, the research topics of the quantitative tribo-ability relationship of lubricants covered in this review are mainly mentioned to introduce various modeling methods, and there may be many similar works that are not covered in this review. Despite these limitations, it is hoped that the described QSTR method will become a useful tool and serve as a reference for tribology research groups in the future.

Solvation CoMFA-QSTR and CoMSIA-QSTR Models for Predicting the Antiwear Properties of Lubricant Additives

Abstract The relationship between the molecular structures of lubricant additives and their antiwear properties was assessed using 36 nitrogen-containing heterocyclic organic compounds to generate quantitative structure tribo-ability relationship (QSTR) models. In the modeling process, the molecular structure of the base oil was employed as the solvation factor, employing n-octadecane as a substitute for the liquid paraffin used to generate the original experimental data. Comparative molecular field analysis (CoMFA)-QSTR and comparative molecular similarity indices analysis (CoMSIA)-QSTR models incorporating solvation by n-octadecane were established and compared with QSTR models that did not involve solvation. The solvation CoMFA-QSTR and CoMSIA-QSTR models exhibited excellent fitting abilities and were highly robust when predicting lubricant performance. These models were superior to nonsolvation models in this regard. These models also generated useful information regarding potential changes to molecular structure to improve antiwear properties. Electrostatic fields were found to be very important factors in antiwear models. Because the original experiments used a nonpolar base oil, the solvation factor had no obvious effect on the antiwear characteristics of the polar additives and so the predictive abilities of the solvation and nonsolvation models were similar.

Friction-reducing properties of N-containing ionic liquid additives by using quantitative structure tribo-ability relationship model

The purpose of this paper is to study the friction-reducing properties of 23 N-containing heterocyclic ionic liquid additives in polyethylene glycol 400 (PEG-400) or dimethyl sulfoxide (DMSO). Based on the theory of quantitative structure tribo-ability relationship (QSTR), the quantum chemical parameters of 23 N-containing heterocyclic ionic liquid molecules were calculated by density functional theory, and the friction-reducing properties of the ionic liquid additives in PEG-400 or DMSO were studied by multiple linear regression. The results show that QSTR models with excellent stability and strong prediction abilities were obtained for both polar liquids studied. Heat capacity of the ionic liquid is the main quantum chemical parameter affecting its friction-reducing performance. The friction-reducing performance of the selected ionic liquid additive in DMSO is better than that in PEG-400. A friction-reducing mechanism is also discussed.

A Quantitative Structure Tribo-Ability Relationship Model for Predicting the Antiwear Properties of Ionic Liquids as Lubricant Additives in Dimethyl Sulfoxide

Abstract The structural parameters of 23 ionic liquids were calculated by the Hartree–Fock ab initio method. The relationship between the antiwear properties of ionic liquids dissolved in dimethyl sulfoxide and the structural parameters of the ionic liquids was studied using multiple linear regression analysis. A quantitative structure tribo-ability relationship model with good fitting and prediction ability was established. The results show that entropy is the most important structural parameter affecting the antiwear performance of ionic liquid-dimethyl sulfoxide systems. The entropy of the ionic liquid is related to the hydrogen bonding in the system, and an orderly arrangement of this hydrogen bonding is beneficial in terms of improving antiwear performance.

Estimating Antiwear Properties of Ionic Liquids as Lubricant Additives Using a QSTR Model

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.

BPNN–QSTR Modeling to Develop Isosteres as Sulfur-Free, Anti-Wear Lubricant Additives

The principle of isosterism was employed to design low- or zero-sulfur anti-wear lubricant additives. Thiobenzothiazole compounds and 2-benzothiazole-S-carboxylic acid esters were employed as templates. Sulfur in the thiazole ring or in the branched chain was exchanged with oxygen, CH2, or an NH group. Similarly, the template's benzimidazole ring was replaced with a quinazolinone group. Quantitative structure tribo-ability relationship (QSTR) models by back propagation neural network (BPNN) method were used to study correlations between additive structures and their anti-wear performance. The features of rubbing pairs with different additives were identified by energy dispersive spectrometer-scanning electron microscope analysis. A wide range of samples showed that sulfur substitution in additive molecules was found to be reasonable and feasible. Combined effects of the anti-wear additive and the base oil were able to improve anti-wear performance.

Quantitative structure tribo-ability relationship of ultra-high molecular weight polyethylene modified by inorganic compounds

PurposeUltra-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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsOnly 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.Practical implicationsA 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.Social implicationsBecause the method of studying tribological materials is entirely different from others, the authors want to present the works and discuss it with colleagues.Originality/valueThe 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 ab initio 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.

Isosteric design of friction-reduction and anti-wear lubricant additives with less sulfur content

To reduce harmful sulfur content in lubricant additives, making use of isosterism has been shown to be an effective strategy. When thiobenzothiazole compounds were used as templates, the exchange of sulfur atoms in the thiazole ring with oxygen atoms and NH groups produced twelve isosteres. Similarly, 2-benzothiazole- S-carboxylic acid esters were used as template molecules to produce six isosteres. About 30% of the isosteres exhibited a satisfactory deviation of ±5% relative to the template, ignoring the specific changes in the base oils, the differences in molecular structure, and the friction or wear properties. The template molecules and isosteres in triisodecyl trimellitate exhibited better tribological properties than in trimethylolpropane trioleate or bis(2- ethylhexyl) adipate. Comparative molecular field analysis (CoMFA)- and comparative molecular similarity index analysis (CoMSIA)-quantitative structure tribo-ability relationship (QSTR) models were employed to study the correlation of molecular structures between the base oils and additives. The models indicate that the higher the structural similarities of the base oils and additives are, the more synergetic the molecular force fields of the lubricating system are; the molecular force fields creating synergistic effects will improve tribological performance.

Quantitative Structure Tribo-Ability Relationship for Organic Compounds as Lubricant Base Oils Using CoMFA and CoMSIA

The structures and the wear data of 47 different organic compounds as lubricant base oils were included in a comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)–quantitative structure tribo-ability relationship (QSTR) model. CoMFA- and CoMSIA-QSTR models illustrate good accuracy, robustness, and predictability, with the latter more accurate than the former. CoMFA-QSTR with both steric and electrostatic fields: R2 = 0. 958, R2(LOO) = 0.958, and q2 = 0.625; with only a steric field: R2 = 0.987, R2(LOO) = 0.987, and q2 = 0.692. CoMSIA-QSTR with a steric field: R2 = 0.924, R2(LOO) = 0.923, and q2 = 0.898, whereas CoMSIA-QSTR with a hydrophobic field gave R2 = 0.985, R2(LOO) = 0.985, and q2 = 0.899. QSTR with CoMFA and CoMSIA shows a strong correlation to wear scar diameter scales (WDS), and builds statistical and graphical models that relate the wear properties of molecules to their structures.

Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network

Abstract Quantitative structure-activity relationship methods are used to study the quantitative structure triboability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of structure descriptors. Here, we used the Bayesian regularization neural network (BRNN) to establish a QSTR prediction model. Two-dimensional (2D) BRNN–QSTR models can flexibly and easily estimate lubricant-additive antiwear properties. Our results show that electron transfer and heteroatoms (such as S, P, O, and N) in a lubricant-additive molecule improve the antiwear ability. We also found that molecular connectivity indices are good descriptors of 2D BRNN–QSTR models.

BPNN–QSTR Friction Model for Organic Compounds as Potential Lubricant Base Oils

A series of ball–disk contact friction tests were carried out using a microtribometer to study the tribological characteristics of steel/steel rubbing pairs immersed in 47 different organic compounds as lubricant base oils. The structures and their friction data were included in a back-propagation neural network (BPNN) quantitative structure tribo-ability relationship (QSTR) model. Following leave-one-out (LOO) cross-validation, the BPNN model shows good predictability and accuracy for the friction parameter (R2 = 0.994, R2(LOO) = 0.849, and q2 = 0.935). Connectivity indices (CHI) show the large positive contribution to friction, which imply that friction performance has a strong correlation with molecular structure. The BPNN–QSTR models can flexibly and easily estimate the friction properties of lubricant base oils.

A Three-Dimensional Quantitative Structure Tribo-Ability Relationship Model

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.

A Quantitative Structure Tribo-Ability Relationship Model for Ester Lubricant Base Oils

Friction tests with point–point contact were carried out using a microtribometer to investigate the tribological characteristics of steel/steel rubbing pair immersed in 57 kinds of esters as lubricant base oils. A set of 57 esters and their wear data were included in the back-propagation neural network (BPNN)-quantitative structure tribo-ability relationship (QSTR) model with two-dimensional (2D) and three-dimensional (3D) QSTR descriptors. The predictive performance of the BPNN-QSTR model is acceptable. The findings of the BPNN-QSTR model show that the extent of polar groups cannot be too large in the molecule to achieve good antiwear performance; and the polar groups with a high degree of relative concentrated charge are favorable for antiwear. A low degree of molecular hydrophobicity of lubricant base oil is beneficial for antiwear behavior. Large molecular dipole moment is disadvantageous for antiwear properties. It is necessary to maintain one large molecular surface in one plane, to have a long and short chain length to be present within the same molecule, and to keep small difference between the long and short chain length to enhance the antiwear performance. Finally, lubricant base oil candidate molecules will have beneficial antiwear properties that they should contain more N groups with three single bonds and more C groups with one double bond and two single bonds; the presence of O atoms with any bonds or CH groups with three single bonds leads to a decrease in the wear resistance performance.

Estimating antiwear properties of lubricant additives using a quantitative structure tribo-ability relationship model with back propagation neural network

To be able to predict tribological properties of new lubricant additives as well as clarify lubricating mechanisms, one needs to study the relationship between structures of lubricant additives and their lubricating properties. With a focus on estimating antiwear properties of some heterocyclic additives, we use the quantitative structure tribo-ability relationship (QSTR) model to predict tribological data, which introduces the idea of computer-aided design into tribology. This is combined with back propagation neural network (BPNN), a machine-learning method that offers simplicity and robustness. This study determined the feasibility and predictability of developing the BPNN QSTR model to estimate lubricant additive antiwear properties. For 36 additives, 90 structural descriptors, such as octanol-water partition coefficient, quantum indices, 2D topological indices, and 3D Jurs descriptors, were included as BPNN inputs. Antiwear parameters include wear-scar diameters under three loads. Leave-one-out cross-validation was performed to evaluate accuracy and robustness of this BPNN QSTR model. We also evaluate the descriptor sensitivities, from which we can determine the effects of each descriptor and clarify wearing mechanisms. Given a positive assessment, this method warrants further development and validated integration with other tribological properties.