Surface Adsorption of Additives


To improve their properties, lubricants usually contain various additives. Anti-wear additives, for example, are important for the formation of the protective film. The interaction of the lubricant components with metallic or oxidized surfaces plays a fundamental role in the film formation – and this can be determined using atomistic simulations. The simulations provide in-depth insights into the adsorption of additives on the surfaces, their binding mode and binding strength. The understanding gained can be used to drive the development of new lubricant additives.

Starting Point

This use case is focused on predicting the properties of a lubricant additive. As an example, the interaction of the additive zinc dialkyldithiophosphate (“ZDDP”, Fig. 1-2) with an iron oxide surface (Fe2O3(0001), Fig. 3) is investigated. The aim is to determine how the additive adsorbs on the surface and to calculate the binding strength. A strong interaction between the additive and the surface, i.e., a high binding energy, is an essential factor for the formation of the protective film and ultimately for a good anti-wear performance.

Fig. 1 Zinc dialkyldithiophosphate “ZDDP” additive

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Fig. 2 ZDDP dithiophosphate fragment

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Fig. 3 Iron oxide Fe2O3(0001) surface model

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Simulation Approach

Starting with the ZDDP dithiophosphate fragment and bulk iron oxide, the first step is to “cut” a surface from the bulk – in this case Fe2O3(0001) – and then determine the structures of the two components. Next, the additive is placed on the surface. Finally, quantum chemical simulations of the entire system (ZDDP fragment on Fe2O3(0001)) as well as of the individual components are performed in order to investigate the adsorption. The binding energy of the ZDDP fragment on the iron oxide surface is calculated from the energetic differences between the entire system and the individual components.

A major advantage is that all these steps can be performed easily and in an automated way using the workflows in the Quantistry Lab. The input consists only of the two components - the ZDDP fragment and iron oxide - and information about which surface to examine.

Key Results

A major benefit of simulations is that they provide fundamental insight into the adsorption of the additive on the surface. Fig. 4 presents the results of the “Surface Adsorption Workflow”. The simulations indicate that the ZDDP dithiophosphate fragment binds well via the sulfur atoms on the iron oxide surface with a binding energy of about 74 kJ/mol (0.77 eV). This provides information on how strongly the fragment interacts with the surface. Promising additives can hence be identified based on their binding energy.

Fig. 4 “Surface Adsorption Workflow”: ZDDP dialkyldithiophosphate fragment adsorbed on Fe2O3(0001), high surface coverage.

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How many additive molecules can be adsorbed on a surface and how the surface coverage affects their binding energy is also of interest for the film formation. The influence of the surface coverage can be easily investigated using simulations, which, for example, provide information on how the binding energy changes with an increasing concentration of adsorbates. In this use case, it is shown that the binding energy of the ZDDP dithiophosphate fragment on iron oxide decreases with increasing coverage from 74 kJ/mol (0.77 eV, large 3x3 cell) to 44 kJ/mol (0.46 eV, smaller 2x3 cell).


The adsorption of additives on metallic or oxidized surfaces can be investigated in a straightforward and simple way with the help of the Quantistry Lab. In this use case, the dissociative adsorption of ZDDP on an iron oxide surface was simulated and the binding energy of the additive fragment was determined. Using our “Surface Adsorption Workflow”, the binding energies of different additives can be compared, or the adsorption on different surfaces or materials can be investigated. To identify optimal lubricant additive candidates, these workflows can be performed as high-throughput screening. A high binding energy and good adsorption capability of an additive - important for the formation of the protective film - may indicate a good anti-wear performance. The optimization of additives can also be carried out regarding their environmental friendliness by predicting the properties and binding energies of novel additives, e.g., without or with a reduced metal, sulfur or phosphorus content.

With QuantistryLab, all you need to run chemical simulations is a web browser. Our cloud-native platform redefines R&D with a holistic computational approach, from quantum to AI. Our customized Use-Case Modules offer tailored solutions to overcome your specific R&D challenges. Get in touch and start simulating today.