1,721,014 research outputs found
Selenium Chemisorption Makes Iron Surfaces Slippery
No full textIn the effort to reduce the energy consumption due to friction, finding new effective lubricants is of primary importance. Here we suggest selenium as a possible element for a highly effective lubricant on iron/iron interfaces by means of density functional theory. The adsorption properties of Se on the most stable iron surface are studied and the metal–adsorbate interaction is characterized. The adsorption reveals that selenium behaves similarly to sulfur and phosphorus, two key elements for high-pressure, anti-wear lubricant additives. The tribological properties of the Fe–Se/Se–Fe interface and the electronic modifications induced by the additive are then investigated and compared with Fe–P/P–Fe and Fe–S/S–Fe interfaces. The charge rearrangement at the interface and the density of states reveal the formation of strong covalent interactions inside the adsorbed layer of selenium atoms that weaken the metal–metal interaction. The calculated work of adhesion and ideal interfacial shear strength show that, with respect to P and S, Se possesses superior lubricating properties
Monitoring water and oxygen splitting at graphene edges and folds: Insights into the lubricity of graphitic materials
Full text linkThe functionality of graphene as lubricant material is affected by extrinsic factors, such as the film thickness and the environmental conditions. Graphite lubricating capability depends as well on air humidity. To accurately describe the tribochemistry mechanisms underlying these behaviours we adopt a Quantum Mechanics/Molecular Mechanics approach. We show that reactive edges are able to cause a huge friction increase, which is quantified for graphene flakes between sliding diamond surfaces. Moreover, folds spontaneously formed in single layer graphene under tribological conditions are shown to be highly reactive due to carbon re-hybridization. This observation offers a new hint for interpreting the dependence of graphene friction on the number of layers. Both water and oxygen molecules are found to be effective in quenching the reactivity of defects by dissociative chemisorption. However, peculiar mechanisms of water molecules makes humidity more effective than oxygen for enabling the lubricity of graphitic media. They include collective processes as Grotthus-like proton diffusion enhanced by confinement, and the strong change in hydrophilic character of the passivated media. This comprehensive study sheds a new light on debated issues of graphene and graphite tribology, and highlights the potentiality of these materials for metal-free catalysis, e.g., for H production by water splitting
Tuning the adhesion of diamond/copper interfaces through surface chemical modifications and reconstruction
No full textDiamond and diamond-like carbon (DLC) coatings are well-known for their exceptional combination of tribological and mechanical properties, such as low friction coefficients and wear rates, together with high hardness and elastic modulus. A significant limitation in their employment concerns their spallation from the substrate; it is thus interesting to explore how DLCs adhesion can be tuned through chemical modifications of its surfaces. We employ ab initio simulations to study the effect of surface reconstruction and chemical species intercalation (B, P, O, F, N, S, H) on the adhesion of non-reconstructed and Pandey-reconstructed C(111)/Cu(111) interfaces. We found that the increment of graphitization at the diamond surface decreases the adhesion. Moreover, when a high degree of surface graphitization is present the best way to increase adhesion is to select atoms able to act as chemical bridges (e.g., B and N), compensating for the lack of interaction between the surfaces. Conversely, adhesion reduction of ∼100% can be achieved, regardless of the degree of surface graphitization, by intercalating an atomic species that does not bond with the countersurface and prevents the interaction between the slabs, i.e. F and S
Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling
Full text linkMachine Learning Interatomic Potentials (MLIPs) offer a powerful way to overcome the limitations of ab initio and classical molecular dynamics simulations. However, a major challenge is the generation of high-quality training data sets, which typically require extensive ab initio calculations and intensive user intervention. Here, we introduce Strategic Configuration Sampling (SCS), an active learning framework to construct compact and comprehensive data sets for MLIP training. SCS introduces the usage of workflows for the automated generation and exploration of systems, collections of MD simulations where geometries and run conditions are set up automatically based on high-level, user defined inputs. To explore nontrivial atomic environments, initial geometries can be assembled dynamically via collaging of structures harvested from preceding runs. Multiple automated exploration workflows can be run in parallel, each with its own resource budget according to the computational complexity of each system. Besides leveraging the MLIP models trained iteratively, SCS also incorporates pretrained models to steer the exploration MD, thereby eliminating the need for an initial data set. By integrating widely used software, SCS provides a fully open-source, automatic, active learning framework for the generation of data sets in a high-throughput fashion. Case studies demonstrate its versatility and effectiveness to accelerate the deployment of MLIP in diverse materials science applications
First principles study of organophosphorus additives in boundary lubrication conditions: Effects of hydrocarbon chain length
No full textWe apply first principle calculations to investigate the effects of the hydrocarbon chain length in additive molecules under boundary lubrication conditions. In these conditions, occurring in high-pressure applications, the thickness of the oil film becomes extremely thin, and the additive molecules remain confined between the two solid surfaces in contact. We consider two types of organophosphorous additives having the same phosphite group but hydrocarbon chains of different lengths. By comparing the molecular behavior under uniaxial stress applied, we elucidate the atomistic mechanisms that control the molecular capacity of maintaining an interfacial spacing under compression and the load-induced molecular dissociation. This insight is relevant not only for a rational design of lubricant additives but also to provide understanding on the activation mechanisms of tribochemical reactions
High-throughput screening of the static friction and ideal cleavage strength of solid interfaces
Full text linkWe present a comprehensive ab initio, high-throughput study of the frictional and cleavage strengths of interfaces of elemental crystals with different orientations. It is based on the detailed analysis of the adhesion energy as a function of lateral, γ(x, y), and perpendicular displacements, γ(z), with respect to the considered interface plane. We use the large amount of computed data to derive fundamental insight into the relation of the ideal strength of an interface plane with its adhesion. Moreover, the ratio between the frictional and cleavage strengths is provided as good indicator for the material failure mode – dislocation propagation versus crack nucleation. All raw and curated data are made available to be used as input parameters for continuum mechanic models, benchmarks, or further analysis
Insigths into the Tribochemistry of Silicon-doped Carbon-Based Films by Ab Initio Analysis of Water-Surface Interactions
Full text linkDiamond and diamond-like carbon are used as coating materials for numerous applications, ranging from biomedicine to tribology. Recently, it has been shown that the hydrophilicity of the carbon films can be enhanced by silicon doping, which highly improves their biocompatibility and frictional performances. Despite the relevance of these properties for applications, a microscopic understanding on the effects of silicon is still lacking. Here, we apply ab initio calculations to study the interaction of water molecules with Si-incorporating C(001) surfaces. We find that the presence of Si dopants considerably increases the energy gain for water chemisorption and decreases the energy barrier for water dissociation by more than 50 %. We provide a physical rational for the phenomenon by analyzing the electronic charge displacements occurring upon adsorption. We also show that once hydroxylated, the surface is able to bind further water molecules much strongly than the clean surface via hydrogen bond networks. This two-step process is consistent with and can explain the enhanced hydrophilic character observed in carbon-based films doped by silicon
Interfacial Charge Density and Its Connection to Adhesion and Frictional Forces
Full text linkWe derive a connection between the intrinsic tribological properties and the electronic properties of a solid interface. In particular, we show that the adhesion and frictional forces are dictated by the electronic charge redistribution occurring due to the relative displacements of the two surfaces in contact. We define a figure of merit to quantify such a charge redistribution and show that simple functional relations hold for a wide series of interactions including metallic, covalent, and physical bonds. This suggests unconventional ways of measuring friction by recording the evolution of the interfacial electronic charge during sliding. Finally, we explain that the key mechanism to reduce adhesive friction is to inhibit the charge flow at the interface and provide examples of this mechanism in common lubricant additives
Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics
No full textThe need to move toward more sustainable lubricant materials has sparked an ever growing interest on the tribological performances of additives based on environmentally friendly molecules, such as carbon-based compounds, and green liquid media as aqueous solutions. The prediction of the solubility of the additives into the liquid and the tribochemistry of decomposition and polymerization of the additive molecules under harsh conditions is essential for understanding the atomistic mechanisms leading to the formation in situ of the carbon-based lubricious tribofilms so effective in reducing friction and wear at solid interfaces. To this extent, the application of tools like ab initio molecular dynamics based on first-principle density functional theory is severely hindered by the size of the systems of interests and the need to simulate their dynamics over relatively long times. To enable tribological simulations with quantum accuracy for a first time, we develop a workflow for smart configuration sampling in active learning, to obtain machine learning interatomic potentials which are shown to be sufficiently robust and accurate also in the characteristic harsh conditions generated by high loads and shear rates. Focusing on glycerol rich lubricants, we apply this active learning strategy to generate a neural network potential to simulate the formation and behavior of nanometer thick molecular tribofilms. The simulations reveal the superior accuracy of the machine learning approach with respect to classical molecular dynamics with reactive force fields, and pave the way for more promising in depth exploration of novel carbon-based lubricants
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