We present a numerical investigation of the influence of water content on the dynamic properties of a family of phosphonium-based room-temperature ionic liquids. The study presents a compelling correlation between structural changes in water-ionic liquid solutions and thermodynamic and transport properties across diverse systems. The results for phosphonium ionic liquids are compared with 1-butyl-3-methylimidazolium hexaphosphate ([bmim]PF6) as a reference. Through this approach, phosphonium cation structure-related characteristics can be identified and placed within the broader context of ionic liquids. These insights are underpinned by observed changes in interaction energy, boiling point, diffusion rate, and viscosity, highlighting the crucial role of water molecules in weakening the strength of interactions between ions within the ionic liquid. The investigation also explains temperature-dependent trends in phosphonium cations, showing that alkyl group length and molecular symmetry are important tuning parameters for the strength of Coulomb interactions. These results contribute to a refined understanding of phosphonium ionic liquid behavior in the presence of water, offering valuable insights for optimizing their use in diverse fields.
The use of ionic liquids (ILs) as lubricants or additives has been studied extensively over the past few decades. However, the ILs considered for lubricant applications have been part of a limited structural class of phosphonium- or imidazolium-type compounds. Here, new pyrylium- and pyridinium-based ILs bearing long alkyl chains were prepared and evaluated as friction- and wear-reducing additives in naphthenic greases. The physical properties of the synthetic ILs and additized naphthenic grease were measured. The tribological performance of the greases was measured by using standard benchtop tests. The addition of ILs was detrimental to wear, causing an increase in the amount of material removed by sliding relative to the base greases in most cases. In contrast, the friction performance improved under nearly all conditions tested due to the IL additives. The compatibility of the synthetic ILs with the naphthenic greases and its potential influence upon miscibility and tribological performance are tentatively proposed to be a result of the molecular structure.
Mechanical stress can directly activate chemical reactions by reducing the reaction energy barrier. A possible mechanism of such mechanochemical activation is structural deformation of the reactant species. However, the effect of deformation on the reaction energetics is unclear, especially, for shear stress-driven reactions. Here, we investigated shear stress-driven oligomerization reactions of cyclohexene on silica using a combination of reactive molecular dynamics simulations and ball-on-flat tribometer experiments. Both simulations and experiments captured an exponential increase in reaction yield with shear stress. Elemental analysis of ball-on-flat reaction products revealed the presence of oxygen in the polymers, a trend corroborated by the simulations, highlighting the critical role of surface oxygen atoms in oligomerization reactions. Structural analysis of the reacting molecules in simulations indicated the reactants were deformed just before a reaction occurred. Quantitative evidence of shear-induced deformation was established by comparing bond lengths in cyclohexene molecules in equilibrium and prior to reactions. Nudged elastic band calculations showed that the deformation had a small effect on the transition state energy but notably increased the reactant state energy, ultimately leading to a reduction in the energy barrier. Finally, a quantitative relationship was developed between molecular deformation and energy barrier reduction by mechanical stress.
While metal nanoparticles are widely used, their small size makes them mechanically unstable. Extensive prior research has demonstrated that nanoparticles with sizes in the range of 10-50 nm fail by the surface nucleation of dislocations, which is a thermally activated process. Two different contributions have been suggested to cause the weakening of smaller particles: first, geometric effects such as increased surface curvature reduce the barrier for dislocation nucleation; second, surface diffusion happens faster on smaller particles, thus accelerating the formation of surface kinks which nucleate dislocations. These two factors are difficult to disentangle. Here we use in situ compression testing inside a transmission electron microscope to measure the strength and deformation behavior of platinum particles in three groups: 12 nm bare particles, 16 nm bare particles, and 12 nm silica-coated particles. Thermodynamics calculations show that, if surface diffusion were the dominant factor, the last two groups would show equal strengthening. Our experimental results refute this, instead demonstrating a 100% increase in mean yield strength with increased particle size and no statistically significant increase in strength due to the addition of a coating. A separate analysis of stable plastic flow corroborates the findings, showing an order-of-magnitude increase in the rate of dislocation nucleation with a change in particle size and no change with coating. Taken together, these results demonstrate that surface diffusion plays a far smaller role in the failure of nanoparticles by dislocations as compared to geometric factors that reduce the energy barrier for dislocation nucleation.
Reactive molecular dynamics simulations are used to model the covalent functionalization of amorphous silica with aromatic hydrocarbons. Simulations show that the surface density of silanol-terminated phenyl, naphthyl, and anthracenyl molecules is lower than the maximum value calculated based on molecule geometry, and the simulation densities decrease faster with the number of aromatic rings than the geometric densities. The trends are analyzed in terms of the surface-silanol bonding configurations, tilt angles, local conformational ordering, and aggregation of surface-bound molecules under steady-state conditions. Results show that the surface density is affected by both the size and symmetry of the aromatic hydrocarbons. The correlations among bonding, orientation, and surface density identified here may guide the selection or design of molecules for functionalized surfaces.
The growth of protective tribofilms from lubricant antiwear additives on rubbing surfaces is initiated by mechanochemically promoted dissociation reactions. These processes are not well understood at the molecular scale for many important additives, such as tricresyl phosphate (TCP). One aspect that needs further clarification is the extent to which the surface properties affect the mechanochemical decomposition. Here, we use nonequilibrium molecular dynamics (NEMD) simulations with a reactive force field (ReaxFF) to study the decomposition of TCP molecules confined and pressurised between sliding ferrous surfaces at a range of temperatures. We compare the decomposition of TCP on native iron, iron carbide, and iron oxide surfaces. We show that the decomposition rate of TCP molecules on all the surfaces increases exponentially with temperature and shear stress, implying that this is a stress-augmented thermally activated (SATA) process. The presence of base oil molecules in the NEMD simulations decreases the shear stress, which in turn reduces the rate constant for TCP decomposition. The decomposition is much faster on iron surfaces than iron carbide, and particularly iron oxide. The activation energy, activation volume, and pre-exponential factor from the Bell model are similar on iron and iron carbide surfaces, but significantly differ for iron oxide surfaces. These findings provide new insights into the mechanochemical decomposition of TCP and have important implications for the design of novel lubricant additives for use in high-temperature and high-pressure environments.
Molecular dynamics simulations of the tensile ultimate properties of polymer crystals require the use of empirical potentials that model bond dissociation. However, fully reactive potentials are computationally expensive such that reactive simulations cannot reach the low strain rates of typical experiments. Here, we present a hybrid approach that uses the simplicity of a classical, nonreactive potential, information from bond dissociation energy calculations, and a probabilistic expression that mimics bond breaking. The approach is demonstrated for poly(p-phenylene terephthalamide) and, with one tunable parameter, the calculated tensile ultimate stress matches that obtained using a fully reactive simulation at high strain rates. Then, the hybrid simulations are run at much lower strain rates where the ultimate tensile stress is strain rate-independent and consistent with the expected experimental range.
Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.
Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.
The lubricity of hydrogenated diamond-like carbon (HDLC) films is highly sensitive to the hydrogen (H) content in the film and the oxidizing gas in the environment. The tribochemical knowledge of HDLC films with two different H-contents (mildly hydrogenated vs highly hydrogenated) was deduced from the analysis of the transfer layers formed on the counter-surface during friction tests in O2 and H2O using Raman spectroscopic imaging and X-ray photoelectron spectroscopy (XPS). The results showed that, regardless of H-content in the film, shear-induced graphitization and oxidation take place readily. By analyzing the O2 and H2O partial pressure dependence of friction of HDLC with a Langmuir-type reaction kinetics model, the oxidation probability of the HDLC surface exposed by friction as well as the removal probability of the oxidized species by friction were determined. The HDLC film with more H-content exhibited a lower oxidation probability than the film with less H-content. The atomistic origin of this H-content dependence was investigated using reactive molecular dynamics simulations, which showed that the fraction of undercoordinated carbon species decreased as the H-content in the film increased, corroborating the lower oxidation probability of the highly-hydrogenated film. The H-content in the HDLC film influenced the probabilities of oxidation and material removal, both of which vary with the environmental condition.
Challenges in quantifying how force affects bond formation have hindered the widespread adoption of mechanochemistry. We used parallel tip-based methods to determine reaction rates, activation energies, and activation volumes of force-accelerated [4+2] Diels-Alder cycloadditions between surface-immobilized anthracene and four dienophiles that differ in electronic and steric demand. The rate dependences on pressure were unexpectedly strong, and substantial differences were observed between the dienophiles. Multiscale modeling demonstrated that in proximity to a surface, mechanochemical trajectories ensued that were distinct from those observed solvothermally or under hydrostatic pressure. These results provide a framework for anticipating how experimental geometry, molecular confinement, and directed force contribute to mechanochemical kinetics.
Two-dimensional (2D) lamellar materials are normally capable of rendering super-low friction, wear protection, and adhesion reduction in nanoscale due to their ultralow shear strength between two basal plane surfaces. However, high friction at step edges prevents the 2D materials from achieving super-low friction in macroscale applications and eventually leads to failure of lubrication performance. Here, taking graphene as an example, the authors report that not all step edges are detrimental. The armchair (AC) step edges are found to have only a minor topographic effect on friction, while the zigzag (ZZ) edges cause friction two orders of magnitude larger than the basal plane. The AC step edge is less reactive and thus more durable. However, the ZZ structure prevails when step edges are produced mechanically, for example, through mechanical exfoliation or grinding of graphite. The authors found a way to make the high-friction ZZ edge superlubricious by reconstructing the (6,6) hexagon structure to the (5,7) azulene-like structure through thermal annealing in an inert gas environment. This will facilitate the realization of graphene-based superlubricity over a wide range of industrial applications in which avoiding the involvement of step edges is difficult.
Achieving high-resolution images using dynamic atomic force microscopy (AFM) requires understanding how chemical and structural features of the surface affect image contrast. This understanding is particularly challenging when imaging samples in water. An initial step is to determine how well-characterized surface features interact with the AFM tip in wet environments. Here, we use molecular dynamics simulations of a model AFM tip apex oscillating in water above self-assembled monolayers (SAMs) with different chain lengths and functional groups. The amplitude response of the tip is characterized across a range of vertical distances and amplitude set points. Then relative image contrast is quantified as the difference of the amplitude response of the tip when it is positioned directly above a SAM functional group vs positioned between two functional groups. Differences in contrast between SAMs with different lengths and functional groups are explained in terms of the vertical deflection of the SAMs due to interactions with the tip and water during dynamic imaging. The knowledge gained from simulations of these simple model systems may ultimately be used to guide selection of imaging parameters for more complex surfaces.
The mechanical behavior of nanostructures is known to transition from a Hall-Petch-like "smaller-is-stronger" trend, explained by dislocation starvation, to an inverse Hall-Petch "smaller-is-weaker" trend, typically attributed to the effect of surface diffusion. Yet recent work on platinum nanowires demonstrated the persistence of the smaller-is-stronger behavior down to few-nanometer diameters. Here, we used in situ nanomechanical testing inside of a transmission electron microscope (TEM) to study the strength and deformation mechanisms of platinum nanoparticles, revealing the prominent and size-dependent role of surfaces. For larger particles with diameters from 41 nm down to approximately 9 nm, deformation was predominantly displacive yet still showed the smaller-is-weaker trend, suggesting a key role of surface curvature on dislocation nucleation. For particles below 9 nm, the weakening saturated to a constant value and particles deformed homogeneously, with shape recovery after load removal. Our high-resolution TEM videos revealed the role of surface atom migration in shape change during and after loading. During compression, the deformation was accommodated by atomic motion from lower-energy facets to higher-energy facets, which may indicate that it was governed by a confined-geometry equilibration; when the compression was removed, atom migration was reversed, and the original stress-free equilibrium shape was recovered.
A method for the acyclic diene metathesis polymerization of semiaromatic amides is described. The procedure uses second-generation Grubbs' catalyst and N-cyclohexyl-2-pyrrolidone (CHP), a high boiling, polar solvent capable of solubilizing both monomer and polymer. The addition of methanol to the reaction was found to significantly increase polymer molar mass although the role of the alcohol is currently not understood. Hydrogenation with hydrogen gas and Wilkinson's catalyst resulted in near-quantitative saturation. All polymers synthesized here exhibit a hierarchical semicrystalline morphology driven by ordering of aromatic amide groups via strong nonbonded interactions. Furthermore, the melting points can be tuned over a >100 °C range by precise substitution at just one of the backbone positions on each mer (<5% of the total).
Mechanochemical activation has created new opportunities for applications such as solvent-free chemical synthesis, polymer processing, and lubrication. However, mechanistic understanding of these processes is still limited because the mechanochemical response of a system is a complex function of many variables, including the direction of applied stress and the chemical features of the reactants in non-equilibrium conditions. Here, we studied shear-activated reactions of simple cyclic organic molecules to isolate the effect of chemical structure on reaction yield and pathway. Reactive molecular dynamics simulations were used to model methylcyclopentane, cyclohexane, and cyclohexene subject to pressure and shear stress between silica surfaces. Cyclohexene was found to be more susceptible to mechanochemical activation of oxidative chemisorption and subsequent oligomerization reactions than either methylcyclopentane or cyclohexane. The oligomerization trend was consistent with shear-driven polymerization yield measured in ball-on-flat sliding experiments. Analysis of the simulations showed the distribution of carbon atom sites at which oxidative chemisorption occurred and identified the double bond in cyclohexene as being the origin of its shear susceptibility. Lastly, the most common reaction pathways for association were identified, providing insight into how the chemical structures of the precursor molecules determined their response to mechanochemical activation.
The elastic behavior of nanoparticles depends strongly on particle shape, size, and crystallographic orientation. Many prior investigations have characterized the elastic modulus of nanoscale particles using experiments or simulations; however their reported values vary widely depending on the methods for measurement and calculation. To understand these discrepancies, we used classical molecular dynamics simulation to model the compression of platinum nanoparticles with two different polyhedral shapes and a range of sizes from 4 to 20 nm, loaded in two different crystal orientations. Multiple standard methods were used to calculate the elastic modulus from stress-vs-strain data for each nanoparticle. The magnitudes and particle-size dependence of the resulting moduli varied with calculation method and, even for larger nanoparticles where bulk-like behavior may be expected, the effective elastic modulus depended strongly on shape and orientation. Analysis of per-atom stress distributions indicated that the shape- and orientation-dependence arise due to stress triaxiality and inhomogeneity across the particle. When the effective elastic modulus was recalculated using a representative volume element in the center of a large nanoparticle, the elastic modulus had the expected value for each orientation and was shape independent. It is only for single-digit nanoparticles that meaningful differences emerged, where even the very center of the particle had a lower modulus due to the effect of the surface. These findings provide better understanding of the elastic properties of nanoparticles and disentangle geometric contributions (such as stress triaxiality and spatial inhomogeneity) from true changes in elastic properties of the nanoscale material.
While it is well established that nanoparticle shape can depend on equilibrium thermodynamics or growth kinetics, recent computational work has suggested the importance of thermal energy in controlling the distribution of shapes in populations of nanoparticles. Here, we used transmission electron microscopy to characterize the shapes of bare platinum nanoparticles and observed a strong dependence of shape distribution on particle size. Specifically, the smallest nanoparticles (<2.5 nm) had a truncated octahedral shape, bound by ã111ã and ã100ã facets, as predicted by lowest-energy thermodynamics. However, as particle size increased, the higher-energy ã110ã facets became increasingly common, leading to a large population of non-equilibrium truncated cuboctahedra. The observed trends were explained by combining atomistic simulations (both molecular dynamics and an empirical square-root bond-cutting model) with Boltzmann statistics. Overall, this study demonstrates experimentally how thermal energy leads to shape variation in populations of metal nanoparticles, and reveals the dependence of shape distributions on particle size. The prevalence of non-equilibrium facets has implications for metal nanoparticles applications from catalysis to solar energy.
The adhesion between nanoscale components has been shown to increase with applied load, contradicting well-established mechanics models. Here, we use in situ transmission electron microscopy and atomistic simulations to reveal the underlying mechanism for this increase as a change in the mode of separation. Analyzing 135 nanoscale adhesion tests on technologically relevant materials of anatase TiO2, silicon, and diamond, we demonstrate a transition from fracture-controlled to strength-controlled separation. When fracture models are incorrectly applied, they yield a 7-fold increase in apparent work of adhesion; however, we show that the true work of adhesion is unchanged with loading. Instead, the nanoscale adhesion is governed by the product of adhesive strength and contact area; the pressure dependence of adhesion arises because contact area increases with applied load. By revealing the mechanism of separation for loaded nanoscale contacts, these findings provide guidance for tailoring adhesion in applications from nanoprobe-based manufacturing to nanoparticle catalysts.
Adhesives , Physical Phenomena
