Lubricant shear thinning behavior correlated with variation of radius of gyration via molecular dynamics simulations

Enriching Anticancer Drug Pipeline with Potential Inhibitors of Cyclin-Dependent Kinase-8 Identified from Natural Products

Cyclin-dependent kinase 8 (CDK8) is highly expressed in various cancers and common complex human diseases, and an important therapeutic target for drug discovery and development. The CDK8 inhibitors are actively sought after, especially among natural products. We performed a virtual screening using the ZINC library comprising approximately 90,000 natural compounds. We applied Lipinski's rule of five, absorption, distribution, metabolism, excretion, and toxicity properties, and pan-assay interference compounds filter to eliminate promiscuous binders. Subsequently, the filtered compounds underwent molecular docking to predict their binding affinity and interactions with the CDK8 protein. Interaction analysis were carried out to elucidate the interaction mechanism of the screened hits with binding pockets of the CDK8. The ZINC02152165, ZINC04236005, and ZINC02134595 were selected with appreciable specificity and affinity with CDK8. An all-atom molecular dynamic (MD) simulation followed by essential dynamics was performed for 200 ns. Taken together, the results suggest that ZINC02152165, ZINC04236005, and ZINC02134595 can be harnessed as potential leads in therapeutic development. Moreover, the binding of the molecules brings change in protein conformation in a way that blocks the ATP-binding site of the protein, obstructing its kinase activity. These new findings from natural products offer insights into the molecular mechanisms underlying CDK8 inhibition. CDK8 was previously associated with behavioral and neurological diseases such as autism spectrum disorder, and cancers, for example, colorectal, prostate, breast, and acute myeloid leukemia. Hence, we call for further research and experimental validation, and with an eye to inform future clinical drug discovery and development in these therapeutic fields.

Boron Nitride's Morphological Role in the Design of Injectable Hyaluronic Acid Based Hybrid Artificial Synovial Fluid

The treatment process of osteoarthritis (OA) is challenging as it affects not only cartilage but also subchondral bone, ligament attachment capsules, synovium, and surrounding muscle tissue. Therefore, the search for preventive treatment or methods to slow the onset of the condition. Hexagonal boron nitride (hBN) has a graphite-like lamellar structure and is thought to facilitate cartilage movement for biomedical applications, just like in bearing systems. Hyaluronic acid (HA) is one of the natural polymers that can be used to transport boron nitride and maintain its presence in joints for a long time. In this study, hybrid hydrogels were formulated by using boron nitride nanoparticles and nanosheets. The rheological properties of the hydrogels were evaluated according to the structural differences of hBN. Characterizations have shown that hybrid hydrogels can be produced in injectable form, and the rheological properties are strongly related to the structural properties of the added particle. It has been determined that hBN added to the hydrogel structure reduces the dynamic viscosity of the zero-shear point and the deformation rate of the hydrogel and also changes the viscoelastic properties of the hydrogel depending on boron nitride's structural differences. The suggested mechanism is the hybrid hydrogel that exhibits lower viscosity as the layers detach from each other or disperses the agglomerates under applied shear stress. hBN, which has been proposed as a new strategy for joint injections, is thought to be a promising candidate for the treatment of OA due to its lamellar structures.

Discovery of new thieno[2,3-d]pyrimidines as EGFR tyrosine kinase inhibitors for cancer treatment

Background: EGFR has been considered a vital molecular target in cancer management. Aim: The discovery of new thieno[2,3-d]pyrimidine derivatives as EGFR tyrosine kinase inhibitors. Methods: Nine derivatives were designed, synthesized and subjected to in vitro and in silico studies. Results: Compound 7a significantly inhibited the growth of HepG2 and PC3 cells for both EGFR wild-type and EGFRT790M. Compound 7a caused a significant apoptotic effect, arresting HepG2 cells' growth in the S and G2/M phases. Docking and molecular dynamics simulation studies confirmed the correct and stable binding modes of the synthesized compounds against the active sites. Conclusion: Compound 7a is a promising dual EGFR inhibitor for cancer treatment.

Comparison of Force Fields for the Prediction of Thermophysical Properties of Long Linear and Branched Alkanes

The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations. Nine transferable force fields from three different classes were considered (all-atom, united-atom, and coarse-grained force fields). Three linear alkanes (n-decane, n-icosane, and n-triacontane) and two branched alkanes (1-decene trimer and squalane) were studied. Simulations were carried out in a pressure range between 0.1 and 400 MPa at 373.15 K. For each state point, density, viscosity, and self-diffusion coefficient were sampled, and the results were compared to experimental data. The Potoff force field yielded the best results.

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

Discovery of new quinoline and isatine derivatives as potential VEGFR-2 inhibitors: design, synthesis, antiproliferative, docking and MD simulation studies

A new set of quinoline and isatine derivatives were synthesized as antiangiogenic VEGFR-2 inhibitors. On a biological level, the in vitro ability of the obtained candidates to inhibit VEGFR-2 was found to be strong with IC50 values in the range of 76.64-175.50 nM. To investigate the cytotoxicity and safety, all compounds were tested against a panel of four cancer cell lines (A549, Caco2, HepG2 and MDA) as well as two normal cell lines (Vero and WI-38). Interestingly, compound 12 exhibited noticeable cytotoxicity against A549, Caco2 and MDA with IC50 values of 5.40, 0.58 and 0.94 µM, respectively. These results were better and comparable to that of doxorubicin (0.70, 0.82 and 0.90 µM, respectively) with more than three folds higher selectivity index against the Caco2 cell lines. Compound 9 prevented the healing of the cancer cells at a low concentration. Also, the compound's potential to induce programmed cell death in Caco-2 was proved through the significant down regulating of the expression of Bcl2, Bcl-xl and Survivin in addition to the slight upregulation of the TGF-β gene. The cell cycle analysis indicated that compound 9 arrested the Caco-2 cells in the G2/M phase. Interestingly, the molecular docking studies against VEGFR-2 revealed the correct binding of the targeted compounds similar to sorafenib. Furthermore, MD experiments validated the binding of compound 12 with VEGFR-2 over 100 ns, as well as MM-PBSA analysis that confirmed the precise binding with optimum energy. Finally, ADMET analysis showed the general drug-likeness and confirmed the safety of the tested compounds.Communicated by Ramaswamy H. Sarma.

New quinoline and isatin derivatives as apoptotic VEGFR-2 inhibitors: design, synthesis, anti-proliferative activity, docking, ADMET, toxicity, and MD simulation studies

New quinoline and isatin derivatives having the main characteristics of VEGFR-2 inhibitors was synthesised. The antiproliferative effects of these compounds were estimated against A549, Caco-2, HepG2, and MDA-MB-231. Compounds 13 and 14 showed comparable activities with doxorubicin against the Caco-2 cells. These compounds strongly inhibited VEGFR-2 kinase activity. The cytotoxic activities were evaluated against Vero cells. Compound 7 showed the highest value of safety and selectivity. Cell migration assay displayed the ability of compound 7 to prevent healing and migration abilities in the cancer cells. Furthermore, compound 7 induced apoptosis in Caco-2 through the expressive down-regulation of the apoptotic genes, Bcl2, Bcl-xl, and Survivin, and the upregulation of the TGF gene. Molecular docking against VEGFR-2 emerged the interactions of the synthesised compounds in a similar way to sorafenib. Additionally, seven molecular dynamics simulations studies were applied and confirmed the stability of compound 13 in the active pocket of VEGFR-2 over 100 ns.

The Discovery of Potential SARS-CoV-2 Natural Inhibitors among 4924 African Metabolites Targeting the Papain-like Protease: A Multi-Phase In Silico Approach

Four compounds, hippacine, 4,2'-dihydroxy-4'-methoxychalcone, 2',5'-dihydroxy-4-methoxychalcone, and wighteone, were selected from 4924 African natural metabolites as potential inhibitors against SARS-CoV-2 papain-like protease (PLpro, PDB ID: 3E9S). A multi-phased in silico approach was employed to select the most similar metabolites to the co-crystallized ligand (TTT) of the PLpro through molecular fingerprints and structural similarity studies. Followingly, to examine the binding of the selected metabolites with the PLpro (molecular docking. Further, to confirm this binding through molecular dynamics simulations. Finally, in silico ADMET and toxicity studies were carried out to prefer the most convenient compounds and their drug-likeness. The obtained results could be a weapon in the battle against COVID-19 via more in vitro and in vivo studies.

Discovery of New VEGFR-2 Inhibitors: Design, Synthesis, Anti-Proliferative Evaluation, Docking, and MD Simulation Studies

Four new nicotinamide-based derivatives were designed as antiangiogenic VEGFR-2 inhibitors. The congeners were synthesized possessing the pharmacophoric essential features to bind correctly with the VEGFR-2 active pocket. All members were evaluated for their cytotoxic and VEGFR-2 inhibitory potentialities. Compound 6 was the most potent showingIC50 values of 9.3 ± 0.02 and 7.8 ± 0.025 µM against HCT-116 and HepG-2 cells, respectively, and IC50 of 60.83 nM regarding VEGFR-2 enzyme inhibition. Compound 6 arrested the growth of HCT-116 cells at the pre-G1 and G2-M phases. Further, it induced both early and late apoptosis. Additionally, compound 6 caused a significant decrease in TNF-α and IL6 by 66.42% and 57.34%, respectively. The considered compounds had similar docking performances to that of sorafenib against the VEGFR-2 (PDB ID: 2OH4). The correct binding of compound 6 with VEGFR-2 was validated using MD simulations, and MM-GPSA calculations.

Correlation between ordering and shear thinning in confined OMCTS liquids

Despite the extensive research that has been conducted for decades on the behavior of confined liquids, detailed knowledge of this phenomenon, particularly in the mixed/boundary lubrication regime, remains limited. This can be attributed to several factors including the difficulty of direct experimental observations of the behavior of lubricant molecules under non-equilibrium conditions, the high computational cost of molecular simulations to reach steady state, and the low signal-to-noise ratio at extremely low shear rates corresponding to actual operating conditions. To this end, we studied the correlation between the structure formation and shear viscosity of octamethylcyclotetrasiloxane confined between two mica surfaces in a mixed/boundary lubrication regime. Three different surface separations corresponding to two-, three-, and five-layered structures were considered to analyze the effect of confinement. The orientational distributions with one specific peak for n=2 and two distributions, including a parallel orientation with the surface normal for n>2, were observed at rest. The confined liquids exhibited a distinct shear-thinning behavior independent of surface separations for a relatively low shear rate, \dot{\gamma}<10^{8} s^{-1}. However, the shear viscosities at \dot{\gamma}<10^{8} s^{-1} depended on the number of layered structures. Newtonian behavior was observed with a further increase in the shear rate. Furthermore, we found a strong correlation between the degree of molecular orientation and the shear viscosity of the confined liquids. The magnitude of the shear viscosity of the confined liquids can primarily be determined by the degree of molecular orientation, and shear-thinning originates from the vanishing of specific orientational distributions with increasing shear rate.

Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine

Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.

The Inhibitory Potential of 2'-dihalo Ribonucleotides against HCV: Molecular Docking, Molecular Simulations, MM-BPSA, and DFT Studies

Sofosbuvir is the first approved direct-acting antiviral (DAA) agent that inhibits the HCV NS5B polymerase, resulting in chain termination. The molecular models of the 2′-dihalo ribonucleotides used were based on experimental biological studies of HCV polymerase inhibitors. They were modeled within HCV GT1a and GT1b to understand the structure–activity relationship (SAR) and the binding interaction of the halogen atoms at the active site of NS5B polymerase using different computational approaches. The outputs of the molecular docking studies indicated the correct binding mode of the tested compounds against the active sites in target receptors, exhibiting good binding free energies. Interestingly, the change in the substitution at the ribose sugar was found to produce a mild effect on the binding mode. In detail, increasing the hydrophobicity of the substituted moieties resulted in a better binding affinity. Furthermore, in silico ADMET investigation implied the general drug likeness of the examined derivatives. Specifically, good oral absorptions, no BBB penetration, and no CYP4502D6 inhibitions were expected. Likely, the in silico toxicity studies against several animal models showed no carcinogenicity and high predicted TD50 values. The DFT studies exhibited a bioisosteric effect between the substituents at the 2′-position and the possible steric clash between 2′-substituted nucleoside analogs and the active site in the target enzyme. Finally, compound 6 was subjected to several molecular dynamics (MD) simulations and MM-PBSA studies to examine the protein-ligand dynamic and energetic stability.

Molecular Origin of Driving-Dependent Friction in Fluids

The friction coefficient of fluids may become a function of the velocity at increased external driving. This non-Newtonian behavior is of general theoretical interest and of great practical importance, for example, for the design of lubricants. Although the effect has been observed in large-scale atomistic simulations of bulk liquids, its theoretical formulation and microscopic origin are not well understood. Here, we use dissipation-corrected targeted molecular dynamics, which pulls apart two tagged liquid molecules in the presence of surrounding molecules, and analyze this nonequilibrium process via a generalized Langevin equation. The approach is based on a second-order cumulant expansion of Jarzynski's identity, which is shown to be valid for fluids and therefore allows for an exact computation of the friction profile as well of the underlying memory kernel. We show that velocity-dependent friction in fluids results from an intricate interplay of near-order structural effects and the non-Markovian behavior of the friction memory kernel. For complex fluids such as the model lubricant C₄₀H₈₂, the memory kernel exhibits a stretched-exponential long-time decay, which reflects the multitude of timescales of the system.

Isolation and In Silico SARS-CoV-2 Main Protease Inhibition Potential of Jusan Coumarin, a New Dicoumarin from Artemisia glauca

A new dicoumarin, jusan coumarin, (1), has been isolated from Artemisia glauca aerial parts. The chemical structure of jusan coumarin was estimated, by 1D, 2D NMR as well as HR-Ms spectroscopic methods, to be 7-hydroxy-6-methoxy-3-[(2-oxo-2H-chromen-6-yl)oxy]-2H-chromen-2-one. As the first time to be introduced in nature, its potential against SARS-CoV-2 has been estimated using various in silico methods. Molecular similarity and fingerprints experiments have been utilized for 1 against nine co-crystallized ligands of COVID-19 vital proteins. The results declared a great similarity between Jusan Coumarin and X77, the ligand of COVID-19 main protease (PDB ID: 6W63), Mpro. To authenticate the obtained outputs, a DFT experiment was achieved to confirm the similarity of X77 and 1. Consequently, 1 was docked against Mpro. The results clarified that 1 bonded in a correct way inside Mpro active site, with a binding energy of -18.45 kcal/mol. Furthermore, the ADMET and toxicity profiles of 1 were evaluated and showed the safety of 1 and its likeness to be a drug. Finally, to confirm the binding and understand the thermodynamic characters between 1 and Mpro, several molecular dynamics (MD) simulations studies have been administered. Additionally, the known coumarin derivative, 7-isopentenyloxycoumarin (2), has been isolated as well as β-sitosterol (3).

Molecular Simulations of Surfactant Adsorption on Iron Oxide from Hydrocarbon Solvents

The performance of organic friction modifiers (OFMs) depends on their ability to adsorb onto surfaces and form protective monolayers. Understanding the relationship between OFM concentration in the base oil and the resulting surface coverage is important for improving lubricant formulations. Here, we use molecular dynamics (MD) simulations to study the adsorption of three OFMs─stearic acid (SA), glycerol monoostearate (GMS), and glycerol monooleate (GMO)─onto a hematite surface from two hydrocarbon solvents─n-hexadecane and poly(α-olefin) (PAO). We calculate the potential of mean force of the adsorption process using the adaptive biasing force algorithm, and the adsorption strength increases in the order SA < GMS < GMO. We estimate the minimum area occupied by OFM molecules on the surface using annealing MD simulations and obtained a similar hard-disk area for GMS and GMO but a lower value for SA. Using the MD results, we determine the adsorption isotherms using the molecular thermodynamic theory (MTT), which agree well with one previous experimental data set for SA on hematite. For two other experimental data sets for SA, lateral interactions between surfactant molecules need to be accounted for within the MTT framework. SA forms monolayers with lower surface coverage than GMO and GMS at low concentrations but also has the highest plateau coverage. We validate the adsorption energies from the MD simulations using high-frequency reciprocating rig friction experiments with different concentrations of the OFMs in PAO. For OFMs with saturated tailgroups (SA and GMS), we obtain good agreement between the simulations and the experiments. The results deviate for OFMs containing Z-unsaturated tailgroups (GMO) due to the additional steric hindrance, which is not accounted for in the current simulation framework. This study demonstrates that MD simulations, alongside MTT, are an accurate and efficient tool to predict adsorption isotherms at solid-liquid interfaces.

Molecular insight into optimizing the N- and P-doped fullerenes for urea removal in wearable artificial kidneys

Urea is the result of the breakdown of proteins in the liver, the excess of which circulates in the blood and is adsorbed by the kidneys. However, in the case of kidney diseases, some products, specifically urea, cannot be removed from the blood by the kidneys and causes serious health problems. The end-stage renal disease (ESRD) patients are not able to purify their blood, which endangers their life. ESRD patients require dialysis, a costly and difficult method of urea removal from the blood. Wearable artificial kidneys (WAKs) are consequently designed to remove the waste from blood. Regarding the great amount of daily urea production in the body, WAKs should contain strong and selective urea adsorbents. Fullerenes-which possess fascinating chemical properties-have been considered herein to develop novel urea removal adsorbents. Molecular dynamics (MD) has enabled researchers to study the interaction of different materials and can pave the way toward facilitating the development of wearable devices. In this study, urea adsorption by N-doped fullerenes and P-doped fullerenes were assessed through MD simulations. The urea adsorption was simulated by five samples of fullerenes, with phosphorous and different nitrogen dopant contents. For comparing the urea adsorption capacity in the performed simulations, detailed characteristics-including the energy analysis, radius of gyration, radial distribution function (RDF), root-mean-square fluctuation (RMSD), and H-bond analyses were investigated. It had been determined that the fullerene containing 8% nitrogen-with the highest reduction in the radius of gyration, the maximum RDF, a high adsorption energy, and a high number of hydrogen bonds-adsorbs urea more efficiently.

Molecular Dynamics Simulation on Thin-Film Lubrication of a Mixture of Three Alkanes

We used the COMPASS forcefield to perform molecular dynamics (MD) simulation of a mixture composed of three alkanes as the lubricant for the thin-film lubrication. The viscosity of the lubrication film in the non-working state, the final film thickness, and density distribution were investigated. The results reveal that the viscosity error among different initial film thicknesses in the non-working state is within 5%, which confirms the applicability of the model and the forcefield. The viscosity decreases oscillating as temperature increases. Whatever the initial film thickness is, the film thickness change rate with respect to pressure load is almost the same. When pressure increases, the density peaks increase. As the initial film thickness increases, the normalized thicknesses of adsorption and ordered layers decrease. In nanoscale, the density predicted by the MD simulation is higher than the prediction of the Tait equation, even if the adsorption layers is excluded.

Understanding Adsorption Behaviors of Organic Friction Modifiers on Hydroxylated SiO2 (001) Surfaces: Effects of Molecular Polarity and Temperature

Molecular dynamics simulations are used to investigate the physisorption of organic friction modifiers (OFMs) lubricated by 1-decene trimer (PAO4) representing a base oil and confined between hydroxylated SiO₂ (001) surfaces. The results indicate that OFM molecules form dense, tendentiously vertical monolayer films at low temperature but loose adsorption layers at high temperature, particularly for R-NH₂ with weaker molecular polarity. The structural information is quantitatively clarified by mass density profiles, radial distribution function, and probability distributions of an end-to-end distance at a perpendicular-to-surface direction. The movement performance of lubricant, reflected by the thickness of the organic part and radius of gyration of PAO4 molecules, strongly depends on temperature. The adsorption amount of OFM molecules decreases dramatically with lowering OFM polarity and increasing temperature above the critical desorption temperatures of about 320, 373, and 453 K for amine (R-NH₂), alcohol (R-OH), and acid (R-COOH), respectively. The interaction energies of the OFM-surface decrease continuously for the R-NH₂ system with temperature and decrease rapidly as temperature exceeds a critical value for both R-OH and R-COOH systems. The single-molecule geometry optimization validates the significant role of the electrostatic and hydrogen-bond attractions in molecular adsorption. Therefore, the OFMs with stronger polarity (like R-COOH) present stronger adsorption and better temperature resistance. The findings in this work are of particular value and provide a guideline in designing and engineering novel OFM additives for extreme lubrication conditions.

Enhancing NEMD with automatic shear rate sampling to model viscosity and correction of systematic errors in modeling density: Application to linear and light branched alkanes

We perform molecular dynamics simulations to model density as a function of temperature for 74 alkanes with 5-10 carbon atoms and non-equilibrium molecular dynamics simulations in the NVT ensemble to model the kinematic viscosity of 10 linear alkanes as a function of molecular weight, pressure, and temperature. To model density, we perform simulations in the NPT ensemble before applying correction factors to exploit the systematic error in the SciPCFF force field and compare the results to experimental values, obtaining an average absolute deviation of 3.4 gl at 25 °C and of 7.2 gl at 100 °C. We develop a sampling algorithm that automatically selects good shear rates at which to perform viscosity simulations in the NVT ensemble and use the Carreau model with weighted least squares regression to extrapolate Newtonian viscosity. Viscosity simulations are performed at experimental densities and show an excellent agreement with experimental viscosities, with an average percent deviation of -1% and an average absolute percent deviation of 5%. Future plans to study and apply the sampling algorithm are outlined.

Nonempirical Free Volume Viscosity Model for Alkane Lubricants under Severe Pressures

Viscosities η and diffusion coefficients D_{s} of linear and branched alkanes at pressure 0<P<0.7  GPa and temperature T=500-600  K are calculated from molecular dynamics simulations. Combining Stokes-Einstein, free volume, and random walk concepts results in an accurate viscosity model for the considered P and T. All model parameters (hydrodynamic radius, random walk step size, and step frequency) are extracted from equilibrium molecular dynamics via microscopic ensemble averages rendering η(P,T) a parameter-free predictor for lubrication simulations.

Shear heating, flow, and friction of confined molecular fluids at high pressure

Understanding the molecular-scale behavior of fluids confined and sheared between solid surfaces is important for many applications, particularly tribology where this often governs the macroscopic frictional response. In this study, nonequilibrium molecular dynamics simulations are performed to investigate the effects of fluid and surface properties on the spatially resolved temperature and flow profiles, as well as friction. The severe pressure and shear rate conditions studied are representative of the elastohydrodynamic lubrication regime. In agreement with tribology experiments, flexible lubricant molecules give low friction, which increases linearly with logarithmic shear rate, while bulky traction fluids show higher friction, but a weaker shear rate dependence. Compared to lubricants, traction fluids show more significant shear heating and stronger shear localization. Models developed for macroscopic systems can be used to describe both the spatially resolved temperature profile shape and the mean film temperature rise. The thermal conductivity of the fluids increases with pressure and is significantly higher for lubricants compared to traction fluids, in agreement with experimental results. In a subset of simulations, the efficiency of the thermostat in one of the surfaces is reduced to represent surfaces with lower thermal conductivity. For these unsymmetrical systems, the flow and the temperature profiles become strongly asymmetric and some thermal slip can occur at the solid-fluid interface, despite the absence of velocity slip. The larger temperature rises and steeper velocity gradients in these cases lead to large reductions in friction, particularly at high pressure and shear rate.

Molecular Dynamics Investigation of Graphene Nanoplate Diffusion Behavior in Poly-α-Olefin Lubricating Oil

Graphene as a type of novel additive significantly enhanced the tribological performance of blended lubricating oil. However, the dispersibility of graphene with long-term stability in lubricating oil is still a challenge. Chemical modification for graphene, rather than using surfactants, provided a better method to improve the dispersibility of graphene in lubricants. In this study, the equilibrium molecular dynamics (EMD) simulations were carried out to investigate the diffusion behavior of graphene nanoplates in poly-α-olefin (PAO) lubricating oil. The effects of graphene-size, edge-functionalization, temperature, and pressure on the diffusion coefficient were studied. In order to understand the influence of edge-functionalization, three different functional groups were grafted to the edge of graphene nanoplates: COOH, COON(CH3)2, CONH(CH2)8CH3 (termed GO, MG, and AG, respectively). The EMD simulations results demonstrated that the relationships between diffusion coefficient and graphene-size and number of functional groups were linear while the temperature and pressure had a nonlinear influence on the diffusion coefficient. It was found that the larger dimension and more functional groups provided the lower diffusion coefficient. AG with eight CONH(CH2)8CH3 groups exhibited the lowest diffusion coefficient. Furthermore, the experimental results and radial distribution function for graphene-PAO illustrated that the diffusion coefficient reflected the dispersibility of nanoparticles in nanofluids to some degree. To our best knowledge, this study is the first time the diffusion behavior of graphene in PAO lubricating oil was investigated using EMD simulations.