A method for predicting results of preparing nanolubricant additive molecules based on molecular dynamics

Abstract

The application relates to the field of nanofabrication technology and provides a result prediction method for preparing a nanometer lubricating additive molecule based on molecular dynamics, which comprises the following steps: constructing a first substance molecular structure model according to a molecular dynamics simulation method, performing energy calculation and geometric optimization on the first substance molecular structure model to obtain an optimized first substance molecular structure model, and constructing a modifier amorphous cell model by using the optimized first substance molecular structure model; constructing a second substance molecular structure model according to the molecular dynamics simulation method; simulating different ball milling times to assemble the second substance molecular structure model with the amorphous cell model respectively to obtain interface models; and calculating the theoretical binding energy of the interface models respectively. The second substance molecular structure model is cut by designing different ball milling times, and the binding energy of the interface models obtained by different ball milling times is calculated, so that the optimal ball milling time is determined, and the molecular model with the optimal performance is found.

Description

Technical Field

[0001] This invention relates to the field of nanotechnology, and in particular to a method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics. Background Technology

[0002] In recent years, molecular dynamics simulations have gained increasing attention in the research of lubricating oil additives. The effects and mechanisms of additives typically depend on the chemical reactions between additive molecules and lubricants and friction surfaces; therefore, molecular dynamics simulations are well-suited for studying these issues. Compared to traditional experimental methods, simulation methods facilitate the successful design of additives under different operating conditions, improving efficiency and reducing costs. Furthermore, many friction processes occur under boundary lubrication conditions, and the demand for low-viscosity lubricants continues to increase to improve energy efficiency and save energy consumption. Therefore, applying molecular dynamics simulations to design and prepare nano-lubricating additives suitable for extreme pressure conditions to reduce friction, wear, corrosion, and oxidation of workpieces under boundary lubrication conditions is of great significance.

[0003] Molecular dynamics simulations can be used to study the relationship between the molecular structure of lubricating oils and their tribological properties. Simulations can yield many parameters of additive molecules, including film extension and orientation, atomic position probability distribution, atomic mass density distribution, radial distribution function, and atomic velocity distribution. Molecular dynamics (MD) simulations by Konishi et al. showed that the more complex the lubricant and additive system, the longer it takes for additive molecules to adsorb onto the friction surface. Analyzing diffusion behavior is the best way to reveal the adsorption mechanism and obtain the dynamic process of organic additive molecule adsorption. Ewen et al. used NEMD simulations to study the effect of the atomic structure of organic friction modifiers (OFM) on the lubricating properties of hexadecane under different surface coverage conditions. The results showed that the nature of the head group, the length of the tail group, and the tail structure are the main factors affecting adsorption. Jahanmir et al., through simulations of the mass density distribution of polar atoms and the correlation between RDF, showed that molecules with added -COOH can potentially form a denser monolayer on the friction surface, and molecules with added -OH perform well at low temperatures, while molecules with added -NH2 form a looser adsorption layer. Berro et al. investigated the anomalous tribological properties of low-concentration zinc dithiophosphate (ZDDP) additives in hexadecane base oil, finding that low concentrations of ZDDP in lubricating oil mixed with hexadecane base oil led to an approximately 40% increase in friction. Simulation results showed that when a small concentration of ZDDP was applied, the migration of ZDDP molecules and their adsorption on the friction surface completely inhibited lubricant film sliding, resulting in higher lubricant shear forces and friction. Molecular dynamics simulations can also be used to study and analyze the specific properties of additives in lubricating oils, such as corrosion inhibition, viscosity regulation, and dispersion properties. Xia et al. used CVFF force field MD simulations to study the corrosion inhibition properties of two imidazoline derivatives (containing imidazoline rings, heteroatoms, and alkyl chains) as additives. Analysis of the adsorption behavior of these two inhibitors on the Fe surface showed that both inhibitors were adsorbed onto the Fe surface through the imidazoline rings and heteroatoms in the molecules, while the alkyl chains were approximately perpendicular to the surface. This led to the inference that adding the inhibitor molecules to the solvent resulted in the formation of a waterproof film on the Fe surface. In conclusion, molecular dynamics simulations are very helpful for the design and research of nano-lubricating additives.

[0004] However, existing research on nano-lubricant additives usually requires real experiments. Existing simulation methods are inaccurate in their predictions, which means that the research process requires a lot of manpower, resources and time. In addition, actual experiments are easily affected by external factors, and existing experiments cannot explain the formation mechanism. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics. The method comprises: S100, constructing a first molecular structure model based on molecular dynamics simulation, performing energy calculations and geometric optimization on the first molecular structure model to obtain an optimized first molecular structure model, and using the optimized first molecular structure model to construct an amorphous unit cell model of the modifier; S200, constructing a second molecular structure model based on molecular dynamics simulation; S300, simulating different ball milling times to assemble the second molecular structure model with the amorphous unit cell model to obtain interface models, and optimizing the interface models; S400, calculating the theoretical binding energy of several interface models to predict the binding energy results of the prepared nano-lubricating additive molecules, thereby determining the optimal ball milling time.

[0006] In one embodiment, the specific steps of S100 are as follows: S110, constructing a first molecular structure model of matter; S120, performing energy calculation and geometric optimization on the first molecular structure model of matter through molecular dynamics and quantum mechanics to obtain an optimized first molecular structure model of matter; S130, repeatedly combining and connecting the units of the optimized first molecular structure model of matter to obtain an amorphous unit cell model.

[0007] In one embodiment, the first substance is a modifying organic molecule; the modifying organic molecule is any one of KH550, CTAB, OA, and PEG-400.

[0008] In one embodiment, the specific steps of S200 are as follows: S210, analyze the molecular structure model of the second substance to be constructed; if the molecular structure model of the second substance is a polyhedral crystal model, its representative crystal facets need to be determined; S220, construct the molecular structure model of the second substance, perform energy calculation and geometric optimization on the molecular structure model of the second substance through molecular dynamics and quantum mechanics, and optimize based on molecular dynamics.

[0009] In one embodiment, the second substance is any one of TiO2, graphene, or graphene oxide.

[0010] In one embodiment, the specific steps of S300 are as follows: S310, assembling the second substance molecular structure model with the amorphous unit cell model to obtain an interface model; S320, performing geometric optimization on the interface model; S330, performing molecular dynamics calculations on the optimized interface model.

[0011] In one embodiment, the parameters involved in geometry optimization include energy convergence data, force convergence data, stress convergence data, displacement convergence data, and the maximum number of iterations.

[0012] In one embodiment, the NVT ensemble is used to set the parameters for molecular dynamics simulations.

[0013] In one embodiment, the specific steps of S400 are as follows:

[0014] S410. Perform molecular dynamics equilibrium determination of the interface model;

[0015] S420 Calculate the theoretical binding energy of the interface model corresponding to different ball milling times and determine the optimal ball milling time; S430 Perform energy contribution analysis on the bonding process of the interface model.

[0016] In one embodiment, step S420 specifically involves the following steps:

[0017] S421. When the system is in equilibrium, determine a preset number of samples and calculate the binding energy of the individual trajectory corresponding to each sample;

[0018] The formula for calculating binding energy is:

[0019]

[0020] in, To combine theory with energy, The energy of the interface model, The energy at the surface of the second molecular structure model of matter; The energy of the first molecular structure model of matter;

[0021] S422. Calculate the average binding energy of a preset number of samples;

[0022] The formula for calculating the average binding energy of a sample is:

[0023]

[0024] in, Let n be the average binding energy and n be the number of samples.

[0025] Based on the above, compared with the prior art, the present invention provides a method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics. By designing different ball milling times to cut the molecular structure model of the second substance, and calculating the binding energy of the interface model obtained by different ball milling times, the optimal ball milling time is determined to find the molecular model with the best performance.

[0026] Other features and beneficial effects of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other beneficial effects of the invention can be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Unless otherwise specified, the positional relationships shown in the drawings in the following description are based on the direction in which the components are drawn in the figure.

[0028] Figure 1 This is a flowchart of a method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics, provided in Embodiment 1 of the present invention.

[0029] Figure 2 A schematic diagram of the molecular structure of the modifier KH550 provided in Embodiment 1 of the present invention;

[0030] Figure 3 This is a schematic diagram of the molecular structure of the modifier CTAB provided in Embodiment 1 of the present invention;

[0031] Figure 4 This is a schematic diagram of the structure of the amorphous unit cell model repeatedly constructed from each unit of the modifier KH550 provided in Embodiment 1 of the present invention;

[0032] Figure 5 This is a schematic diagram of the TiO2 crystal model structure provided in Embodiment 1 of the present invention;

[0033] Figure 6 The morphology diagram is obtained by calculating the TiO2 crystal model provided in Embodiment 1 of the present invention;

[0034] Figure 7 This is a schematic diagram of the structure of the TiO2 crystal model after polymerization, provided in Embodiment 1 of the present invention;

[0035] Figure 8 This is a schematic diagram of the TiO2 crystal model ball-milled for 2.5 hours according to Embodiment 1 of the present invention;

[0036] Figure 9 This is a schematic diagram of the structure of the original graphene crystal model after polymerization, provided in Embodiment 1 of the present invention.

[0037] Figure 10 This is a schematic diagram of the graphene oxide model structure after ball milling the original graphene model for 2.5 hours and adding hydroxyl groups, as provided in Embodiment 1 of the present invention.

[0038] Figure 11 This is a schematic diagram of the interface model obtained by combining the representative crystal planes of the KH550 amorphous unit cell model and the TiO2 crystal model according to Embodiment 1 of the present invention.

[0039] Figure 12 This is a schematic diagram of the interface model obtained by combining the KH550 amorphous unit cell model with a representative crystal plane of graphene, as provided in Embodiment 1 of the present invention.

[0040] Figure 13 The MEP diagram of the KH550 molecular hydrolysis product provided in Embodiment 1 of the present invention;

[0041] Figure 14 The temperature fluctuation curve during the KH550-GO (2.5 h) molecular dynamics simulation provided in Embodiment 1 of the present invention;

[0042] Figure 15 The energy fluctuation curve during the KH550-GO (2.5 h) molecular dynamics simulation provided in Embodiment 1 of the present invention;

[0043] Figure 16 This is a schematic diagram of the structure of KH550-TiO2 after adsorption (2.5 h) provided in Embodiment 1 of the present invention;

[0044] Figure 17 This is a schematic diagram of the structure of KH550-GO after adsorption (2.5 h) provided in Embodiment 1 of the present invention;

[0045] Figure 18 The optimized molecular model diagrams of OA, CTAB, and PEG-400 provided in Embodiment 2 of the present invention are shown.

[0046] Figure 19 This is a schematic diagram of the amorphous unit cell structure obtained from OA, CTAB, and PEG-400 molecules provided in Embodiment 2 of the present invention;

[0047] Figure 20 A schematic diagram of a model for adding hydroxyl groups to single-layer graphene at different ball milling times, as provided in Embodiment 2 of the present invention;

[0048] Figure 21 This is a schematic diagram of the interface model structure of OA, CTAB and PEG-400 modifiers and graphene with different ball milling times provided in Embodiment 2 of the present invention;

[0049] Figure 22 This is an interaction diagram of the OA-GO interface model with different ball milling times provided in Embodiment 2 of the present invention;

[0050] Figure 23 This is a diagram of the adsorption process of the OA-GO composite model after ball milling for 5 hours, as provided in Embodiment 2 of the present invention.

[0051] Figure 24This is a molecular model diagram of the TiO2@OA composite system with different ball milling times provided in Embodiment 3 of the present invention;

[0052] Figure 25 This is a diagram showing the intermolecular interaction forces, binding energy, and energy percentage of TiO2@OA at different ball milling times, provided in Embodiment 3 of the present invention.

[0053] Figure 26 This is a diagram showing the adsorption process of OA molecules in TiO2 after ball milling for 7.5 hours, as provided in Example 3 of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The technical features designed in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0055] In the description of this invention, it should be noted that all terms used in this invention (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and should not be construed as limiting the invention; it should be further understood that the terms used in this invention should be understood to have the same meaning as those in the context of this specification and in the relevant field, and should not be understood in an idealized or overly formal sense, except as expressly defined in this invention.

[0056] In the actual preparation of nano-lubricant additive molecules, a first substance and a second substance need to be mixed, and a plasma-assisted ball milling technique (hereinafter referred to as "ball milling") is used to conduct a bonding experiment between the first and second substances. The actual bonding energy of the first and second substances at different ball milling times is collected. Based on the comparison of the actual bonding energy at different ball milling times, the nano-lubricant additive molecules with better performance are obtained.

[0057] Because actual experiments require significant time, manpower, and resource costs, existing molecular dynamics methods can be used to simulate actual ball milling experiments. Based on these simulations, molecular structure models of the first and second substances are constructed to simulate their bonding process, and the theoretical binding energy is calculated. Then, the actual binding energy is compared with the theoretical binding energy to determine if they match. If they match, the molecular dynamics simulation method can replace plasma-assisted ball milling technology for binding energy prediction.

[0058] Specifically, experiments are first conducted using plasma-assisted ball milling technology. For example, using TiO2 and graphene as different systems, different modifier molecules are added, and ball milling experiments are performed for different times to obtain the corresponding actual binding energies. The graphene can be plain graphene without any added groups, or graphene oxide with added oxygen-containing groups. Then, molecular dynamics simulation software is used to simulate the process, constructing corresponding molecular models and simulating the binding models corresponding to different ball milling times to obtain the theoretical binding energy. The actual binding energy is compared with the theoretical binding energy. If the results are consistent, the molecular dynamics simulation software can be used to simulate the research process of nano-lubricant additives. This significantly saves manpower, resources, and time costs. Furthermore, the experimental simulation is unaffected by the external environment, and the real-time data output of the software allows for better monitoring of the entire process, facilitating the determination of the experimental mechanism.

[0059] To address this, this invention proposes a method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics. This method can accurately simulate the actual ball milling process and obtain a theoretical binding energy close to the actual binding energy, including:

[0060] S100. Construct a molecular structure model of the first substance using molecular dynamics simulation methods, and perform energy optimization and geometry optimization on the molecular structure model of the first substance to obtain an optimized molecular structure model of the first substance. Use the optimized molecular structure model of the first substance to construct an amorphous unit cell model of the modifier.

[0061] Specifically, molecular dynamics simulation software is used to accurately construct a first molecular structure model of the substance (e.g., any one of KH550, CTAB, OA, or PEG-400) based on known chemical information. Quantum mechanics methods are used to calculate the energy of the model, and algorithms are employed for geometric optimization until the model reaches the lowest energy and most stable structural form. The optimized molecular structure model is then replicated and arranged to form an amorphous unit cell model of the modifier, providing a foundation for subsequent assembly.

[0062] S200. Construct a molecular structure model of the second substance based on molecular dynamics simulation methods.

[0063] Specifically, a pre-construction analysis of the molecular structure model of the second substance (such as TiO2, graphene, or graphene oxide) is performed to determine its representative crystal planes. Molecular dynamics and quantum mechanics methods are then used to perform energy calculations and geometric optimization on the molecular structure model of the second substance to ensure the model's accuracy and stability.

[0064] S300. Simulate different ball milling times, assemble several molecular structure models of the second substance with the amorphous unit cell model respectively, obtain several interface models corresponding to different ball milling times, and optimize the interface models.

[0065] Specifically, molecular structure models of the second substance after different ball milling times are assembled with amorphous unit cell models to form an interface model. The assembled interface model is then geometrically optimized to ensure its stability at the molecular level.

[0066] S400. Calculate the theoretical binding energy of several interface models corresponding to different ball milling times, and predict the binding energy of the nano-lubricating additive molecules to determine the optimal ball milling time.

[0067] Specifically, molecular dynamics simulations were used to calculate the theoretical binding energy of the interface model under different ball milling times. By comparing the binding energies of different models, the optimal ball milling time was predicted and determined to obtain the molecular model of the nano-lubricant additive with the best performance.

[0068] Specifically, by combining theory with the above methods, predictions can be made that are relatively accurate when compared with actual experimental production results, with an accuracy rate of 60%-65%. This can effectively shorten the experimental cycle, improve production efficiency, and effectively reduce the friction coefficient and wear volume of nano-lubricants.

[0069] It should be noted that the molecular structure model of the first substance is the molecular model obtained by simulating the first substance using molecular dynamics simulation software; the molecular structure model of the second substance is the molecular model obtained by simulating the second substance using molecular dynamics simulation software.

[0070] Specifically, the simulation construction of any molecular model typically includes the following four steps:

[0071] (1) Model building.

[0072] (2) Calculation parameter settings.

[0073] (3) Performing calculations: This step usually needs to be done on a high-performance computer or workstation.

[0074] (4) Analysis of calculation results.

[0075] First, a first molecular structure model of the substance is constructed. The components of the first molecular structure model are any one of the modifying organic molecules selected from KH550, CTAB, OA, and PEG-400, but are not limited to these components; other organic components that can serve as the first molecular structure model can also be used. Next, a second molecular structure model of the substance is constructed. The second molecular structure model can be an inorganic molecule model, such as a TiO2 crystal model, a graphene model, or a graphene oxide model. It should be noted that the inorganic molecules that can be studied are not limited to the components mentioned above.

[0076] After constructing the molecular structure model of the second substance, its representative crystal planes are determined. Alternatively, the representative crystal planes can be determined first, and subsequent operations can be performed by simulating only the representative crystal planes of the molecular structure model of the second substance in the software. Then, several second crystal plane models are assembled with amorphous unit cell models to obtain several interface models, and these interface models are optimized. The molecular binding energy of each interface model is calculated, and the optimal theoretical binding energy is used to determine the optimal first substance, the optimal second substance, and the optimal ball milling time.

[0077] Preferably, the present invention uses Materials Studio (MS) software to complete molecular construction, mainly using four modules: MSVisualizer, Amorphous Cell (AC), DMol3, and Forcite. The functions of these modules are shown in Table 1 below.

[0078] Table 1. Software modules used in this invention and their functions.

[0079]

[0080] Furthermore, the optimization of the first substance molecular structure model, the second substance molecular structure model, and the interface model in this invention includes quantum mechanics-based geometric optimization and molecular mechanics-based crystal morphology calculation, geometric optimization, and molecular dynamics calculation.

[0081] For the geometric optimization in quantum mechanics, the computational parameters used in this paper are shown in Table 2 below:

[0082] Table 2. Geometric Optimization Parameters for Quantum Mechanics

[0083]

[0084] For calculations based on molecular mechanics, the setting of energy calculation parameters is particularly important. These parameters include the calculation methods for force fields and nonbonding forces (van der Waals forces and electrostatic forces). The parameters set in this invention are shown in Table 3 below.

[0085] Table 3. Relevant parameter settings based on molecular mechanics

[0086]

[0087] Among the parameters listed in the table above, the most important is the force field. The COMPASS (Condensed-phase Optimized Molecular Potentials for Atomistic Simulation Studies) series of force fields are obtained based on ab initio fitting, and have advantages such as high computational accuracy and wide coverage. The COMPASSII force field combines quantum mechanical calculation results (B3LYP / 6-31G(d,p) accuracy level), further improving the original COMPASSII force field, not only with a wider coverage but also with higher computational accuracy.

[0088] In this invention, when energy calculations are involved in crystal morphology, geometry optimization, and molecular dynamics simulations based on molecular mechanics, the parameter settings shown in Tables 2 and 3 above are used.

[0089] The specific steps for S100 are as follows:

[0090] S110, Construct the first molecular structure model of matter.

[0091] S120. Energy calculations and geometric optimizations are performed on the first molecular structure model of matter using molecular dynamics and quantum mechanics to obtain the optimized first molecular structure model of matter.

[0092] S130. Repeatedly combine and connect the units of the optimized first material molecular structure model to obtain an amorphous unit cell model.

[0093] In practice, a separate molecular structure model of the first substance is first constructed. Energy calculations and geometric optimizations are then performed on this model using molecular dynamics and quantum mechanics to obtain an optimized molecular structure model. The units of this optimized molecular structure model are then repeatedly combined and connected to obtain an amorphous unit cell model, such as... Figure 2 and Figure 3 The diagram shown is a structural schematic of KH550 and CTAB. Figure 4 A schematic diagram of the structure of the amorphous unit cell model repeatedly constructed for each unit of KH550.

[0094] The specific steps for S200 are as follows:

[0095] S210. Analyze the molecular structure model of the second substance to be constructed; if the molecular structure model of the second substance is a polyhedral crystal model, its representative crystal facets need to be determined.

[0096] S220. Construct a molecular structure model of the second substance, and optimize the energy and geometry of the molecular structure model of the second substance through molecular dynamics and quantum mechanics, while also optimizing based on molecular dynamics.

[0097] In specific implementation, such as Figure 5 As shown, taking the TiO2 crystal model as an example, the TiO2 crystal model is first constructed, and the representative crystal planes of the TiO2 crystal model are determined, such as... Figure 6 As shown, the TiO2 crystal model includes the (1 0 1) crystal plane family and the (0 0 4) crystal plane family. The proportions of the two crystal plane families in the surface area of ​​the TiO2 crystal model are shown in Table 4. The (1 0 1) crystal plane family has a larger proportion, and the (1 0 1) crystal plane family is the representative crystal plane that can represent its interaction with the external environment. When cutting the crystal planes in the future, this crystal plane family will be used as the modeling target.

[0098] Table 4. Percentage of different crystal plane families in the surface area of ​​the TiO2 crystal model

[0099]

[0100] After determining the (1 0 1) crystal plane family as the representative crystal plane, the (1 0 1) crystal plane of the TiO2 crystal model was simulated in Materials Studio software. The measured oxygen content at different ball milling times in actual experiments was collected, and hydroxyl groups were attached to the representative crystal plane. The measured oxygen content is the oxygen content at actual ball milling times of 2.5h, 5h, 7.5h, and 10h, etc. Figure 7 The image shows the original TiO2 crystal model; as shown Figure 8 The image shows a schematic diagram of the structure corresponding to a simulated TiO2 crystal model after ball milling for 2.5 hours. Figure 9 This is a model of the original graphene crystal; such as Figure 10 The image shows the graphene oxide model obtained from the simulation after ball milling the original graphene model for 2.5 hours and adding hydroxyl groups. Furthermore, since both graphene and graphene oxide are two-dimensional structures, it is not necessary to determine representative crystal planes.

[0101] It should be noted that ball milling refers to the process of continuously refining the particle diameter of TiO2 and graphene in actual experiments, during which the two materials can undergo certain surface modifications.

[0102] This invention uses molecular dynamics to simulate and construct TiO2 crystal models and graphene oxide crystal models with different ball milling times, thereby simulating and calculating the theoretical binding energy for different ball milling times, in order to find a molecular model and ball milling time with excellent performance.

[0103] The specific steps for S300 are as follows:

[0104] S310. Assemble the second substance molecular structure model with the amorphous unit cell model to obtain the interface model.

[0105] S320. Perform geometric optimization on the interface model.

[0106] S330. Perform molecular dynamics calculations on the optimized interface model.

[0107] In specific implementation, such as Figure 11 and Figure 12 As shown, the interface model is obtained by ball milling the KH550 amorphous unit cell model with representative crystal planes of the TiO2 crystal model and graphene for 2.5 hours. After the interface model is constructed, geometric optimization is required. The optimization parameters include energy convergence data, force convergence data, stress convergence data, displacement convergence data, and the maximum number of iterations. The parameter settings are shown in Table 5 below. After geometric optimization, molecular dynamics calculations are performed.

[0108] Table 5. Parameters related to interface model geometry optimization

[0109]

[0110] By constructing an interface model, the electrostatic potential of the first molecular structure model can be analyzed. Both the anatase and graphene oxide surfaces carry electrical charges. Electrostatic forces play a crucial role in the interaction between the first molecular structure model and these surfaces, and the charge properties of the first molecular structure model itself directly influence the magnitude of these electrostatic forces. Molecular electrostatic potential (MEP) analysis of the first molecular structure model can provide direction for determining and identifying adsorption sites on the surface.

[0111] like Figure 13 As shown, the electrostatic potential is represented by different colors to indicate the positive and negative charges and their magnitudes in different regions. This can intuitively reflect the charge distribution characteristics of the first molecular structure model of matter. The warmer the color (red), the stronger the positive charge, and the more likely it is to be adsorbed around negatively charged particles; conversely, the cooler the color (blue), the stronger the negative charge, and the more likely it is to be adsorbed around positively charged particles. Figure 13 The MEP diagram of the hydrolysis products of KH550 molecules shows that the KH550 molecule has a large number of active sites. The H atoms in the amino group and the H atoms in the hydroxyl group are the main positively charged regions; the N atoms in the amino group and the O atoms in the hydroxyl group are the main negatively charged regions. In addition to having a large number of active sites, the KH550 molecule is also small in size and highly mobile, and can easily adjust its own configuration for preferential adsorption.

[0112] After geometric optimization, the interface model needs to be subjected to molecular dynamics calculations. The molecular dynamics parameters are set using the NVT ensemble. The initial velocity is set to satisfy the Maxwell-Boltzmann distribution, the temperature is set to 298K, the thermostat is Andersen, the time step is 1fs, the cumulative time is 500ps, and one frame of configuration is output every 5ps, for a total of 101 frames. The first frame is the initial configuration.

[0113] The specific steps for S400 are as follows:

[0114] S410. Perform molecular dynamics equilibrium determination of the interface model.

[0115] S420. Calculate the theoretical binding energy of the interface model corresponding to different ball milling times.

[0116] Energy contribution analysis.

[0117] In one embodiment, step S420 specifically involves the following steps:

[0118] S421. When the system is in equilibrium, determine a preset number of samples and calculate the binding energy of the individual trajectory corresponding to each sample;

[0119] The formula for calculating binding energy is:

[0120]

[0121] in, To combine theory with energy, The energy of the interface model, The energy at the surface of the second molecular structure model of matter; The energy of the first molecular structure model of matter;

[0122] S422. Calculate the average binding energy of a preset number of samples;

[0123] The formula for calculating the average binding energy of a sample is:

[0124]

[0125] in, Let n be the average binding energy and n be the number of samples.

[0126] In practice, the first step is to determine molecular dynamics equilibrium, such as... Figure 14 and Figure 15 As shown, taking the KH550-GO (2.5h) model as an example, the temperature and energy fluctuation curves during its molecular dynamics simulation are presented. It can be seen that the system reaches temperature and energy equilibrium after approximately 50 ps. The data collected after equilibrium can be used for result analysis. Figure 16 and Figure 17As shown, the post-adsorption configurations are presented using KH550-TiO2 (2.5 h) and KH550-GO (2.5 h) as examples. It can be seen that KH550 molecules are enriched on the surface of anatase or graphene oxide, and adsorption occurs on the surface. KH550-GO (2.5 h) is a model of graphene oxide molecules obtained by simulating the combination of KH550 and GO in a 2.5 h ball-milling experiment.

[0127] Once the system is determined to be in equilibrium through molecular dynamics equilibrium, the binding energy corresponding to any trajectory can be calculated. , It can be calculated using the following formula. When <0, it indicates that the modifier has adsorption capacity on the surface of the second substance molecular structure model, and The larger the absolute value when it is less than zero, the stronger the adsorption. The specific calculation formula is as follows:

[0128] (1)

[0129] In formula (1) This represents the energy of the entire interface model; This represents the energy on the surface of a second molecular structure model of matter. This represents the energy of a first-order molecular structure model of matter.

[0130] Equation (1) only calculates the binding energy corresponding to a certain frame configuration. The result obtained from a certain frame configuration is only sample data and does not have statistical significance that can represent the whole. Therefore, a large number of samples need to be selected for statistical analysis. According to the central limit theorem of statistics, when the number of samples is ≥30, the mean of these samples can better reflect the properties of the population.

[0131] This paper takes the trajectory after 300 ps, ​​a total of 40 frames (as can be seen from the temperature and energy curves in section 2.3(1), at which point the temperature and energy are in equilibrium), calculates the binding energy of each frame, performs statistical analysis, and then takes the average value. The specific formula used to evaluate the adsorption capacity of polymer molecules on the calcite surface is as follows:

[0132] (2)

[0133] In actual calculations, due to the large number of output trajectories, the above calculations are mechanical and repetitive operations. This paper uses a Perl script to automatically implement the above calculation process.

[0134] This invention provides a method for predicting the molecular results of nano-lubricating additives based on molecular dynamics simulations. The aim is to optimize the preparation process of nano-lubricating additives by accurately simulating molecular structure and interfacial binding energy. The method first constructs molecular structure models of a first substance (a modifying organic molecule) and a second substance (such as TiO2, graphene, or graphene oxide), and obtains stable molecular structures through energy calculations and geometric optimization. Subsequently, the molecular structure model of the second substance is segmented by simulating different ball milling times, and assembled with an amorphous unit cell model to form an interfacial model, which is then optimized. Finally, the theoretical binding energy of the interfacial model corresponding to different ball milling times is calculated to predict and determine the optimal ball milling time, thereby obtaining the molecular model of the nano-lubricating additive with optimal performance. This method not only improves design efficiency and reduces costs, but also, through accurate molecular simulations, contributes to a deeper understanding of the interaction mechanism between the additive and the friction surface.

[0135] Example 2

[0136] The specific process of modifying graphene oxide surface with oleic acid (OA) is as follows:

[0137] (1) Constructing the first molecular structure model and amorphous unit cell model of matter

[0138] Ball-and-stick molecular models of OA, CTAB, and PEG-400 were constructed using the MS Visualizer module. Gray represents carbon atoms, white represents hydrogen atoms, red represents oxygen atoms, brown represents bromine atoms, and blue represents nitrogen atoms. Then, the Forcite module was used to perform geometric optimization on the ball-and-stick models of these three primary molecular structures, resulting in a more reasonable spatial configuration and optimized energy configuration. The optimized molecular models are shown below. Figure 18 As shown.

[0139] Using the Amorphous Cell module, a rectangular unit cell with a (x×y×z) = 27×26×15ų was constructed. Nineteen OA molecules, eighteen CTAB molecules, and sixteen PEG-400 molecules were respectively filled into the unit cell, with an initial unit cell density set to 1.1 g / cm³. Structural relaxation in the amorphous cell models of the three modifiers occurred between 1098 and 298 K. The COMPASSII force field was selected, and the simulation accuracy was set to smart. The system was then re-optimized and re-equilibrated at 298 K and 500 ps. The unit cells of the three first-order molecular structure models are shown below. Figure 19 As shown.

[0140] (2) Constructing a graphene oxide model

[0141] First, a unit cell (x×y×z) = 27×27×10ų was constructed, and a single-layer graphene model consisting of 242 C atoms was filled inside. The oxygen content on the surface of the nanoparticles was measured using an Eltra ONH-2000 oxygen, nitrogen, and hydrogen analyzer after DBDP-assisted ball milling of expanded graphite for 2.5h, 5h, 7.5h, and 10h, respectively, to add different numbers of hydroxyl groups to the single-layer graphene oxide model. The graphene oxide models before and after the addition of hydroxyl groups are shown below. Figure 20 As shown.

[0142] (3) Constructing the interface model

[0143] Five models were obtained by combining the amorphous unit cell of the modifier and the graphene surface model for subsequent molecular dynamics calculations. Before molecular dynamics, geometry optimization was performed. The convergence criteria for energy and force were set to 1×10−4 kcal / mol and 5×10−3 kcal / mol / Å, respectively; the convergence criterion for stress was set to 5×10−3 GPa. During molecular dynamics simulations, the energy and stress convergence criteria were used to determine whether the system's energy had reached a stable state. When the system energy change was less than the set value, it indicated that the system had reached energy convergence, and the next calculation could proceed. The maximum number of iterations was limited to 5000. The convergence criterion for displacement was 5×10−5 Å, used to determine whether the atomic displacement had reached a stable state. When the change in atomic displacement was less than the set value, it indicated that the displacement had converged, and the next calculation could proceed. The settings of these convergence criteria depend on the actual problem and simulation requirements. By adjusting these parameters, computational efficiency can be improved while ensuring computational accuracy. The interface models obtained by combining three modifiers with graphene oxide of different ball milling times are shown below. Figure 21 As shown.

[0144] (4) Analysis of binding energy and energy contribution

[0145] First, molecular dynamics equilibrium is determined for the interface model. Once the system is in equilibrium according to the molecular dynamics equilibrium determination, the binding energy corresponding to any trajectory can be calculated. , It can be calculated using the following formula. When the value is less than 0, it indicates that the modifier has adsorption capacity on the graphene surface, and The more negative the adsorption, the stronger the adsorption. The specific calculation formula is as follows:

[0146] (1)

[0147] In formula (1) This represents the energy of the entire graphene model; This indicates the energy of the crystal planes in graphene; This represents the energy of the oleic acid molecular model.

[0148] Equation (1) only calculates the binding energy corresponding to a specific configuration. The result obtained from a specific configuration is only sample data and does not have statistical significance representing the whole population. Therefore, a large number of samples need to be selected for statistical analysis. According to the central limit theorem in statistics, when the number of samples is ≥30, the mean of these samples can better reflect the properties of the population. Therefore, when using software simulation, more than 30 oleic acid molecule models and graphene molecule models need to be simulated.

[0149] This article takes the trajectory after 300 ps, ​​a total of 40 frames, from... Figure 14 and Figure 15 The temperature and energy curves show the energy before equilibrium, at which point both temperature and energy are in equilibrium. The binding energy is calculated and statistically analyzed, then the average value is taken to evaluate the adsorption capacity of polymer molecules on the graphene oxide surface. The specific calculation formula is as follows:

[0150] (2)

[0151] Figure 2-10 The intermolecular forces on the surface of graphene oxide prepared by OA molecules at different ball milling times. Figure 22 (a) It can be seen that with the increase of the number of hydroxyl groups on the graphene oxide surface, the electrostatic force first increases and then decreases. The contribution of electrostatic force energy is largest in the first 2.5–5 hours of ball milling, accounting for 34% and 30% respectively. The van der Waals force of the OA-GO interface model also shows a trend of first increasing and then decreasing, with the largest van der Waals force observed after 5 hours of ball milling. After 5 hours of ball milling, the binding energy per unit area in the OA-GO composite interface model is 227.54 mJ / m², the highest among all composite systems. Therefore, it is evident that the binding energy of OA molecules on the graphene oxide surface does not increase indefinitely with increasing ball milling time, but rather there exists an optimal ball milling time. Figure 22 (b) shows the binding energy and energy percentage per unit area in the OA-GO composite system. It can be seen that OA molecules are mainly adsorbed onto the graphene oxide surface by van der Waals forces. Furthermore, with increasing ball milling time, more hydroxyl groups are introduced onto the graphene oxide surface. This increase in the number of hydroxyl groups amplifies the electrostatic effect on the graphene oxide surface, favoring the adsorption of positively charged surface modifiers and hindering the adsorption of negatively charged first-order molecular structure models. The OA molecule surface is generally negatively charged, with only the hydroxyl group (-OH) at the tail being positively charged. The long chain structure of OA molecules easily entangles and intertwines during adsorption, forming large molecules with relatively high molecular weights. Van der Waals forces exist between covalent compounds, and these forces are further divided into orientation forces, induction forces, and dispersion forces. Dispersion forces are generally the most dominant force in covalent compounds; however, dispersion forces are only related to molecular weight, with larger molecular weights resulting in stronger forces. Therefore, the adsorption force of OA molecules on the graphene oxide surface is mainly van der Waals forces.

[0152] To more intuitively explain the adsorption behavior of OA molecules on the graphene oxide surface, the adsorption process of OA molecules on the graphene oxide surface is analyzed, such as... Figure 23 As shown in the figure, at 0 ps, ​​oleic acid molecules are in a relatively disordered state, with molecules entangled and distributed randomly. The -OH group at the tail of the oleic acid molecule carries a positive charge, which guides OA molecules to adsorb onto the graphene oxide surface. After 100 ps of simulation, the local OA molecules adjust their configuration to adsorb onto the graphene oxide surface using the carboxyl group as an anchor, as shown in red in the figure. As the simulation progresses, the number of OA molecules gathered on the graphene oxide surface increases, and the carboxyl groups of the OA molecules adsorbed on the graphene oxide surface are concentrated in the central part. At the same time, the OA molecules also tend to be arranged in an ordered manner, and a complete modification layer begins to form on the upper part of the graphene oxide. When the simulation reaches 500 ps, ​​the OA molecules are arranged in an ordered manner on the upper part of the graphene oxide, and the linear molecules no longer entangle with each other, allowing them to adsorb onto the graphene oxide surface in an optimal configuration. As OA molecules are continuously adsorbed, the thickness of the adsorption layer gradually increases, and the interaction between alkyl chains and carboxyl groups is also enhanced, thereby increasing the stability of the adsorption layer. This also explains why the OA-GO composite interface system has the maximum binding energy per unit area after ball milling for 5 hours.

[0153] Example 3

[0154] (1) The molecular dynamics simulation of TiO2 modified with oleic acid (OA) is similar to the process described above and will not be repeated here. The good dispersibility of TiO2@OA nano-lubricant in base oil is due to the characteristics of its surface oleic acid modification process. The oleic acid modified layer enables the TiO2@OA nano-lubricant to be uniformly dispersed in the liquid through intermolecular interactions with the base oil, such as van der Waals forces and electrostatic forces. This dispersibility allows the nano-lubricant to effectively contact particles and friction surfaces in the base oil, thereby achieving a good lubrication effect.

[0155] Secondly, the oleic acid-modified layer exhibits excellent friction-reducing and anti-wear properties under boundary lubrication conditions. The molecular structure of the oleic acid-modified layer features long alkyl chains and polar carboxylic acid groups, enabling it to effectively adsorb onto metal surfaces and form a stable molecular layer. This molecular layer, through chemical bonds with the metal surface, effectively reduces inter-metal friction and wear, thereby improving the friction-reducing and anti-wear performance of the lubricant. Therefore, in-depth analysis of the oleic acid modification process on the TiO2 surface explains the excellent dispersibility and friction-reducing and anti-wear properties of the TiO2@OA nano-lubricant additive under boundary lubrication conditions. This research result is of great significance for the design and development of high-performance nano-lubricant additives, providing a theoretical basis for achieving optimized lubrication effects.

[0156] (2) The specific process of constructing the TiO2@OA composite interface is as follows:

[0157] As shown in Example 1, the (1,0,1) crystal plane cluster is a representative crystal plane that can represent its interaction with the external environment. This crystal plane cluster is used as the modeling target during subsequent crystal plane cutting. The (1,0,1) crystal plane is cut out, and then, based on the measured oxygen content, hydroxyl groups are attached to the crystal plane. This combines the (1,0,1) plane of the anatase TiO2 crystal with different numbers of added hydroxyl groups and 19 OA molecules to form four stable systems, such as... Figure 24 As shown. The simulation elements were grouped into rectangular elements with periodic boundary conditions, 27×26×15A3 (X×Y×Z). After establishing these contact models, each model was geometrically optimized before performing molecular dynamics calculations, with the calculation parameters set as described in the previous file.

[0158] (3) Analysis of binding energy and energy contribution of TiO2@OA

[0159] Figure 25 (a) represents the van der Waals and electrostatic interaction energies between TiO2@OA molecules. In the COMPASS II force field, the sum of the van der Waals forces and electrostatic interaction energies is the total interaction energy between the additive and TiO2 molecules. Figure 25(b) also provides the proportions of van der Waals and electrostatic interactions to the total interaction energy, as well as the binding energy per unit area. In the TiO2@OA interface model, van der Waals interactions dominate when TiO2 adsorbs additives. The van der Waals force initially decreases and then increases, while the electrostatic force initially increases and then decreases. This is because the TiO2 surface has hydroxyl groups and is negatively charged. As can be seen from the electrostatic potential diagram of the OA molecule, its surface is also negatively charged, thus the electrostatic attraction is relatively small and has little impact on the adsorption process. On the other hand, the OA molecule has only one polar -COOH group at its tail. In the initial process of adsorption, the carbon chains of the OA molecule overlap and become entangled, making it impossible for the OA molecule to adjust its configuration in time, resulting in a weak interaction force with TiO2. It mainly relies on van der Waals forces to adsorb onto the TiO2 surface. However, as the adsorption process progresses, the oleic acid molecules gradually become more ordered, and the electrostatic force begins to increase. The polar group -COOH of the OA molecule adheres to the (1,0,1) crystal surface of TiO2 by electrostatic force. The binding energy per unit area of ​​the composite system reached its maximum after ball milling for 7.5 hours, which was 5.2% higher than that after ball milling for 2.5 hours. Among them, the binding energy per unit area of ​​van der Waals force was 110.06 mJ / m2, accounting for 53% of the total interaction energy; while the electrostatic interaction was relatively small, only 97.59 mJ / m2, accounting for 47% of the total interaction energy.

[0160] Figure 26 This represents a shear snapshot of the adsorption process of OA molecules on the (1,0,1) surface of anatase TiO2 after ball milling for 7.5 h. From... Figure 23 As can be seen from the simulation at 500 ps, ​​oleic acid molecules underwent significant plastic deformation. Under the influence of van der Waals forces and electrostatic forces, a large number of first-order molecular structure models adsorbed near the TiO2 (1,0,1) crystal plane, thereby enhancing the interaction between TiO2 atoms and lubricant molecules. After a 500 ps molecular dynamics simulation, significant adsorption of OA molecules occurred on the TiO2 (1,0,1) crystal plane. The amorphous unit cell system of the hydroxyl-grafted modifier on the anatase TiO2 surface forms a stable mixed state. The main adsorption process is as follows: Figure 26 As shown in (a), the H atoms in the -COOH polar group at the tail of oleic acid form the main positively charged region. Under the influence of electrostatic forces, the polar group molecular chain of OA molecules (-COOH) intertwines and surrounds the hydroxyl molecules on the TiO2 surface, forming stable adsorption sites. Subsequently, more and more carboxyl groups are adsorbed onto the (1,0,1) crystal plane of anatase TiO2 by electrostatic forces, as shown in (a). Figure 26 As shown in (bc), as the number of OA molecules adsorbed on the TiO2 surface gradually increases, the OA molecules also adjust their own configuration for preferential adsorption during the adsorption process. Finally, the composite system of oleic acid molecules and the anatase TiO2 (1,0,1) crystal plane gradually reaches stability, as shown in (bc). Figure 26As shown in (d), oleic acid molecules are initially adsorbed onto the TiO2 surface via van der Waals forces. With increasing ball milling time, oleic acid molecules gradually form a stable and ordered modification layer on the TiO2 surface under the influence of electrostatic forces. Under the action of electrostatic forces, the polar group chains (-COOH) of the OA molecules intertwine and surround the hydroxyl molecules on the TiO2 surface, forming stable adsorption sites and providing conditions for subsequent chemical reactions. The presence of ester groups in the oleic acid modification layer on the TiO2 surface also significantly improves the thermal stability of the TiO2@OA nano-lubricant additive.

[0161] Furthermore, those skilled in the art should understand that although many problems exist in the prior art, each embodiment or technical solution of the present invention can be improved in only one or a few aspects, without necessarily solving all the technical problems listed in the prior art or the background art simultaneously. Those skilled in the art should understand that any content not mentioned in a claim should not be construed as a limitation on that claim.

[0162] Although this paper frequently uses terms such as molecular dynamics, first substance molecular structure model, first substance molecular structure model, modifier amorphous unit cell model, second substance molecular structure model, second crystal plane model, interface model, and molecular binding energy, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of the invention; interpreting them as any additional limitation would contradict the spirit of the invention. The terms "first," "second," etc. (if present) in the specification, claims, and accompanying drawings of the embodiments of the invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics, characterized in that, include: S100. Construct a molecular structure model of the first substance based on molecular dynamics simulation method, and perform energy calculation and geometric optimization on the molecular structure model of the first substance to obtain an optimized molecular structure model of the first substance. Use the optimized molecular structure model of the first substance to construct an amorphous unit cell model of the modifier. S200. Construct a molecular structure model of the second substance based on molecular dynamics simulation methods; S300. Simulate different ball milling times, assemble several molecular structure models of the second substance with the amorphous unit cell model respectively to obtain several interface models corresponding to different ball milling times, and optimize the interface models. S400. Calculate the theoretical binding energy of several interface models corresponding to different ball milling times to complete the prediction of the binding energy of the prepared nano lubricating additive molecules. The components of the first substance molecular structure model are modifying organic molecules; the modifying organic molecules are any one of KH550, CTAB, OA, and PEG-400; The second substance is any one of TiO2, graphene, or graphene oxide; The specific steps for S400 are as follows: S410. Determine the molecular dynamics equilibrium of the interface model. S420. Calculate the theoretical binding energy of the interface model corresponding to different ball milling times, and determine the optimal ball milling time; S430. Perform energy contribution analysis on the bonding process of the interface model; The specific steps of step S420 are as follows: S421. When the system is in equilibrium, a preset number of samples are determined, and the binding energy of the individual trajectory corresponding to each sample is calculated; The formula for calculating binding energy is: in, To combine theory with energy, The energy of the interface model, The energy at the surface of the second molecular structure model of matter; The energy of the first molecular structure model of matter; S422. Calculate the average binding energy of a preset number of samples; The formula for calculating the average binding energy of the sample is: in, Let n be the average binding energy and n be the number of samples.

2. The method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics according to claim 1, characterized in that, The specific steps for S100 are as follows: S110, Construct the first molecular structure model of matter; S120. Energy calculation and geometric optimization of the first molecular structure model of matter are performed using molecular dynamics and quantum mechanics to obtain the optimized molecular structure model of the first matter. S130. Repeatedly combine and connect the units of the optimized first material molecular structure model to obtain an amorphous unit cell model.

3. The method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics according to claim 1, characterized in that, The specific steps for S200 are as follows: S210. Analyze the molecular structure model of the second substance to be constructed; If the molecular structure model of the second substance is a polyhedral crystal model, then its representative crystal facets need to be determined; S220. Construct a second molecular structure model of the substance, and perform energy calculation and geometric optimization on the second molecular structure model of the substance through molecular dynamics and quantum mechanics, while also optimizing based on molecular dynamics.

4. The method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics according to claim 1, characterized in that... The specific steps for S300 are as follows: S310. Assemble the second substance molecular structure model with the amorphous unit cell model to obtain several interface models; S320. Perform geometric optimization on the interface model; S330. Perform molecular dynamics calculations on the optimized interface model.

5. The method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics according to any one of claims 2, 3, and 4, characterized in that, The parameters involved in geometry optimization include energy convergence data, force convergence data, stress convergence data, displacement convergence data, and the maximum number of iterations.

6. The method for predicting the results of preparing nano-lubricating additive molecules based on molecular dynamics according to any one of claims 2, 3, and 4, characterized in that, Molecular dynamics simulation parameters were set using the NVT ensemble.

Images