Synthesis method of oil-soluble trinickel disulfide nanoparticles

Abstract

The invention discloses a synthesis method of oil-soluble trinickel disulfide nanoparticles. The synthesis method comprises the following steps: adding nickel stearate and sulfur powder into a reaction container; a solvent and a surface modifier are added into the reaction container to obtain an initial mixed solution, the solvent is octadecene, and the surface modifier is oleylamine; performing ultrasonic treatment on the initial mixed solution to uniformly mix the initial mixed solution to obtain a mixed solution; introducing inert gas into the reaction container to exhaust air in the reaction container so as to ensure that the reaction is carried out in an inert atmosphere; and carrying out a hot stirring reaction on the mixed solution, and after the reaction is finished, carrying out solid-liquid separation and washing to obtain the oil-soluble trinickel disulfide nanoparticles. Particularly, an intelligent technology is also adopted to help to quickly determine proper synthesis parameters according to actual synthesis conditions and requirements, so that the intelligent degree of the synthesis process of the oil-soluble trinickel disulfide nanoparticles is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent synthesis, and more specifically, to a method for synthesizing oil-soluble nickel disulfide nanoparticles. Background Art

[0002] In modern industry, friction and wear issues are inevitable challenges in mechanical design and maintenance. Energy loss is associated with friction in many cases, with most of the energy used to overcome friction. In mechanical equipment, such as engine components, high friction and wear can lead to reduced equipment performance and may even cause equipment failure. Currently, nearly one-fifth of the power loss in automobile engines is caused by overcoming friction. In addition, friction and wear of mechanical parts are not only one of the important reasons for energy efficiency loss and failure of mechanical equipment, but also cause serious pollution to the environment and bring huge economic losses.

[0003] In order to meet this challenge, the development of lubrication technology is particularly important. Lubricating additives, as an effective means to improve friction performance, have received widespread attention in recent years. However, traditional lubricating additives often cannot fully meet the stringent requirements of modern mechanical equipment. Nanoparticles, due to their unique physical and chemical properties, can easily penetrate into the friction contact area to form a protective layer, thereby reducing direct contact between metal surfaces, effectively reducing friction coefficient and wear rate, and improving the tribological properties of lubricants.

[0004] Nevertheless, the application of nanomaterials in lubricants also faces some difficulties. In particular, nanomaterials are prone to agglomeration in lubricants due to their shape, size and weight, which limits their dispersibility and stability in the lubrication system. In addition, some commonly used sulfide nano-lubricating additives, such as MoS2 (molybdenum disulfide), although they have good lubrication properties, are prone to aggregation or sintering under high temperature conditions, resulting in a decrease in lubrication effect. More importantly, MoS2 nanoparticles are difficult to recycle after use, and may accumulate in the environment and cause long-term impacts on the ecosystem.

[0005] Ni3S2 (nickel disulfide) nanoparticles not only have good dispersibility and thermal stability, but also remain stable under high temperature and high pressure due to their magnetic properties. They can also be recycled by applying an external magnetic field, reducing potential environmental pollution. However, the current preparation method of Ni3S2 nanomaterials is relatively complicated and the preparation cost is high.

[0006] Therefore, an optimized synthesis scheme for oil-soluble nickel disulfide nanoparticles is desired. Summary of the invention

[0007] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method for synthesizing oil-soluble nickel disulfide nanoparticles, which encodes the addition amount of nickel stearate, sulfur powder and solvent, and combines the user's performance requirements for the target product, uses data processing and semantic understanding algorithms based on artificial intelligence and deep learning to embed and associate the addition amount of nickel stearate, sulfur powder and solvent, and further recommends the appropriate amount of oleylamine based on the principal component interactive response characterization information between the semantics of the target product performance requirements. In this way, not only can the controllability and repeatability of the synthesis process of oil-soluble nickel disulfide nanoparticles be improved, but also it can help to quickly determine the appropriate synthesis parameters according to the actual synthesis conditions and requirements, thereby improving the intelligence level of the synthesis process of oil-soluble nickel disulfide nanoparticles.

[0008] According to one aspect of the present application, a method for synthesizing oil-soluble nickel disulfide nanoparticles is provided, comprising:

[0009] Add nickel stearate and sulfur powder into a reaction vessel;

[0010] Adding a solvent and a surface modifier into the reaction container to obtain an initial mixed solution, wherein the solvent is octadecene and the surface modifier is oleylamine;

[0011] Performing ultrasonic treatment on the initial mixed solution to make it uniformly mixed to obtain a mixed solution;

[0012] Passing an inert gas into the reaction container to exhaust the air in the reaction container to ensure that the reaction is carried out under an inert atmosphere;

[0013] The mixed solution is heated and stirred for reaction, and after the reaction is completed, solid-liquid separation and washing are performed to obtain oil-soluble nickel disulfide nanoparticles.

[0014] Compared with the prior art, the present application provides a method for synthesizing oil-soluble nickel disulfide nanoparticles, which encodes the addition amount of nickel stearate, sulfur powder and solvent, and combines the user's performance requirements for the target product, uses data processing and semantic understanding algorithms based on artificial intelligence and deep learning to embed and associate the addition amount of nickel stearate, sulfur powder and solvent, and further recommends the appropriate amount of oleylamine based on the principal component interactive response characterization information between the semantics of the target product performance requirements. In this way, not only can the controllability and repeatability of the synthesis process of oil-soluble nickel disulfide nanoparticles be improved, but also the appropriate synthesis parameters can be quickly determined according to the actual synthesis conditions and requirements, thereby improving the intelligence level of the synthesis process of oil-soluble nickel disulfide nanoparticles. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 is a flow chart of a method for synthesizing oil-soluble nickel disulfide nanoparticles according to an embodiment of the present application;

[0017] Figure 2 It is a data flow diagram of the synthesis method of oil-soluble nickel disulfide nanoparticles according to an embodiment of the present application;

[0018] Figure 3 Flow chart of sub-step S2 of the method for synthesizing oil-soluble nickel disulfide nanoparticles according to an embodiment of the present application;

[0019] Figure 4 is a Tyndall effect diagram of oil-soluble nickel disulfide nanoparticles prepared in Example 1 dissolved in n-hexane;

[0020] Figure 5 is the XRD pattern of the oil-soluble nickel disulfide nanoparticles prepared in Example 1;

[0021] Figure 6 TEM images of oil-soluble nickel disulfide nanoparticles prepared in Example 1 at different sizes (500 nm, 200 nm, 100 nm, 50 nm, 10 nm, 5 nm) dissolved in n-hexane;

[0022] Figure 7 This is a comparison chart of the friction coefficients of the base oil PAO6 and the base oil PAO6+0.6wt% nickel disulfide additive in Example 1 (experimental conditions: 75°C, 392N, 1200r / min);

[0023] Figure 8 The wear spot image (a) of the base oil PAO6 of Example 1 and the wear spot image (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive; (experimental conditions: 75°C, 392N, 1200r / min);

[0024] Fig. 9 This is a comparison chart of the friction coefficients of the base oil PAO6 and the base oil PAO6+0.6wt% nickel disulfide additive in Example 1 (experimental conditions: 75°C, 300N, 1200r / min);

[0025] Fig.10The wear spot image (a) of the base oil PAO6 of Example 1 and the wear spot image (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive; (experimental conditions: 75°C, 300N, 1200r / min);

[0026] Fig.11 This is a comparison chart of the friction coefficients of the base oil PAO6 and the base oil PAO6 + 0.6wt% nickel disulfide additive in Example 1 (experimental conditions: 75°C, 500N, 1200r / min);

[0027] Fig.12 The wear spot image (a) of the base oil PAO6 of Example 1 and the wear spot image (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive; (experimental conditions: 75°C, 500N, 1200r / min);

[0028] Fig.13 This is a comparison chart of the friction coefficient of the base oil PAO6 and the base oil PAO6 + 0.6wt% nickel disulfide additive in Example 1; (experimental conditions: 100°C, 392N, 1200r / min);

[0029] Fig.14 The wear spot image (a) of the base oil PAO6 of Example 1 and the wear spot image (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive; (experimental conditions: 100°C, 392N, 1200r / min). DETAILED DESCRIPTION

[0030] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0031] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0032] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0033] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0034] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0035] The present application relates to a method for synthesizing oil-soluble nickel disulfide nanoparticles, which uses nickel stearate as a nickel source and sulfur powder as a sulfur source. Under the conditions of a surface modifier and a solvent, the temperature is raised to 350-360°C in an inert gas atmosphere (argon) and the reaction is stirred at a constant temperature for 70-90 minutes. After the reaction is completed, the solid-liquid separation and washing are performed to obtain oil-soluble flaky nickel disulfide nanoparticles. The nickel disulfide nanoparticles have magnetism and can be used as a lubricant additive to significantly improve lubrication performance. They can form a protective film on the friction surface, reduce wear, reduce the friction coefficient, improve the load-bearing capacity, and show excellent extreme pressure performance under high loads. In addition, the nano magnetic particles can be accelerated to deposit on the wear surface under the action of a magnetic field, accelerate the formation of a lubricating film, and enhance the lubrication effect. The magnetic liquid lubricant also has self-sealing properties to prevent leakage and pollution, and is suitable for use under harsh working conditions. The oil-soluble nickel disulfide nanoparticles are prepared by a one-pot method, which consumes less energy, is low in cost, and is suitable for mass production; they have good dispersibility and stability in lubricating oils, and as lubricating oil additives, they can significantly improve the tribological properties of lubricating oils, showing a smaller friction coefficient and wear spot diameter. Some sulfides, such as MoS2 nanoparticles, are non-magnetic, and molybdenum disulfide (MoS2) nanoparticle lubricating additives after service are difficult to recycle. The nickel disulfide of the present application is magnetic and more stable under high temperature conditions. The magnetic nanoparticles can be recycled by applying an external magnetic field, reducing pollution to the environment, achieving a lower friction coefficient and a smaller wear scar diameter and economic benefits.

[0036] A method for synthesizing oil-soluble nickel disulfide nanoparticles, which uses nickel stearate as a nickel source and sulfur powder as a sulfur source, reacts at 350-360°C in an inert gas (such as argon) atmosphere with constant temperature stirring for 70-90 minutes under the conditions of a surface modifier and a solvent, and after the reaction is completed, separates the solid from the liquid and washes with anhydrous ethanol three times to obtain the oil-soluble nickel disulfide nanoparticles.

[0037] Specifically, in the technical solution of the present application, a method for synthesizing oil-soluble nickel disulfide nanoparticles is proposed. Figure 1Flow chart of a method for synthesizing oil-soluble nickel disulfide nanoparticles according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the method for synthesizing oil-soluble nickel disulfide nanoparticles according to an embodiment of the present application. Figure 1 and Figure 2 As shown, the method for synthesizing oil-soluble nickel disulfide nanoparticles according to the embodiment of the present application includes the following steps: S1, adding nickel stearate and sulfur powder into a reaction container; S2, adding a solvent and a surface modifier into the reaction container to obtain an initial mixed solution, wherein the solvent is octadecene and the surface modifier is oleylamine; S3, ultrasonically treating the initial mixed solution to uniformly mix it to obtain a mixed solution; S4, introducing an inert gas into the reaction container to exhaust the air in the reaction container to ensure that the reaction is carried out under an inert atmosphere; S5, heating and stirring the mixed solution for reaction, and after the reaction is completed, performing solid-liquid separation and washing to obtain oil-soluble nickel disulfide nanoparticles.

[0038] Particularly, in S1, nickel stearate and sulfur powder are added into a reaction container, wherein the molar ratio of the nickel stearate to the sulfur powder is 3:1-3. Particularly, the nickel stearate and the sulfur are both powders.

[0039] In particular, the S2, a solvent and a surface modifier are added to the reaction vessel to obtain an initial mixed solution, wherein the solvent is octadecene and the surface modifier is oleylamine. It should be understood that the synthesis scheme of the oil-soluble nickel disulfide nanoparticles is to synthesize oil-soluble flaky nickel disulfide nanoparticles by a one-step method, which has low energy consumption, economical cost and is suitable for large-scale production. At the same time, long-chain organic matter is selected to surface-modify the nanomaterials so that the nanoparticles show excellent dispersibility and stability in the lubricating oil. By using this lubricating oil additive, not only the tribological properties of the lubricating oil can be improved, but also nickel disulfide has magnetism and is more stable when used under high temperature conditions. The magnetic nanoparticles can be recovered by applying an external magnetic field, which solves the problem of recovery of the lubricant after use to a certain extent, and meets the requirements of energy saving and environmental protection. It should be understood that in order to improve the dispersibility of the nanoparticles and their stability in the oil phase, a surface modifier is usually introduced during the synthesis process. Oleylamine, as a common surfactant, can not only prevent the aggregation between particles during the synthesis of oil-soluble nickel disulfide nanoparticles, but also enhance the surface activity of the nanoparticles through chemical adsorption and improve their physical and chemical properties. Reasonable selection and adjustment of the amount of surface modifier is crucial for preparing oil-soluble Ni3S2 nanoparticles with excellent performance. In a specific example of the present application, Figure 3As shown, the S2 includes: S21, obtaining the addition amount of the nickel stearate, the addition amount of the sulfur powder and the addition amount of the solvent; S22, obtaining the target performance requirements of the oil-soluble nickel disulfide nanoparticles determined by the user; S23, embedding semantic association coding of the addition amount of the nickel stearate, the addition amount of the sulfur powder and the addition amount of the solvent to obtain a reactant addition amount cascade representation vector; S24, semantic coding of the target performance requirements of the oil-soluble nickel disulfide nanoparticles to obtain a product target performance requirement semantic coding vector; S25, performing principal component-based feature interaction response coupling processing on the reactant addition amount cascade representation vector and the product target performance requirement semantic coding vector to obtain a reactant-product target performance principal component granularity significant interaction coupling feature; S26, determining a recommended decoding value for the addition amount of the surface modifier based on the reactant-product target performance principal component granularity significant interaction coupling feature.

[0040] Specifically, the S21 and S22 obtain the addition amount of the nickel stearate, the addition amount of the sulfur powder and the addition amount of the solvent; and obtain the target performance requirements of the oil-soluble nickel disulfide nanoparticles determined by the user. It should be understood that nickel stearate will decompose and produce nickel ions during the reaction, and these nickel ions react with sulfur to form nickel disulfide. In addition, the stearic acid part can be attached to the surface of the nanoparticles, giving them oil-soluble properties and preventing particle agglomeration. The amount of stearic acid added directly affects the nickel content in the final product and the size and morphology of the nanoparticles. Sulfur powder is another key component in the formation of nickel disulfide, which reacts with nickel ions to generate target compounds. The target performance requirements refer to the specific performance indicators of the required nanomaterials determined according to actual application requirements, such as particle size distribution, specific surface area, magnetism or catalytic activity.

[0041] Specifically, the S23, the amount of nickel stearate added, the amount of sulfur powder added and the amount of solvent added are embedded semantically associated and coded to obtain a cascade representation vector of reactant addition. In the technical solution of the present application, the amount of nickel stearate added, the amount of sulfur powder added and the amount of solvent added are embedded and coded to obtain an embedded coding vector of nickel stearate added, an embedded coding vector of sulfur powder added and an embedded coding vector of solvent added; by means of embedded coding, these raw material addition parameters can be converted into digital information, that is, the amount of nickel stearate added, the amount of sulfur powder added and the amount of solvent added are converted into a form that can be understood and processed by a computer. The advantage of doing this is that it is convenient to use machine learning or deep learning algorithms for subsequent data analysis and model training, and to learn and capture the added embedded semantic information in the amount of nickel stearate added, the amount of sulfur powder added and the amount of solvent added, thereby providing a data basis for the task of recommending the amount of surface modifier added. Furthermore, in order to ensure that the embedded semantic information of all input reactant raw material addition parameters is completely retained and can be considered as a whole, thereby providing a basis for the subsequent prediction of the optimal addition amount of the surface modifier, in the technical solution of the present application, the nickel stearate addition amount embedded coding vector, the sulfur powder addition amount embedded coding vector and the solvent addition amount embedded coding vector are cascaded to obtain a reactant addition amount cascade representation vector.

[0042] Specifically, in S24, the target performance requirement of the oil-soluble nickel disulfide nanoparticles is semantically encoded to obtain a semantic encoding vector of the product target performance requirement. That is, by semantically encoding the target performance requirement of the oil-soluble nickel disulfide nanoparticles, semantic feature information of the target performance requirement of the oil-soluble nickel disulfide nanoparticles is extracted, thereby obtaining a semantic encoding vector of the product target performance requirement.

[0043] Specifically, the S25 performs a principal component-based feature interaction response coupling process on the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector to obtain a reactant-product target performance principal component granularity significant interaction coupling feature. It should be understood that since the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector respectively contain embedded semantic cascade representations and product target performance requirement semantic features related to different reactant addition amounts, considering that there is an interaction between the features of the two, this interaction reflects the relationship between the user's target performance requirements for the product and the actual reactant addition amount. In addition, it is also considered that in the interactive information between the reactant addition amount cascade feature and the product target performance requirement semantic feature, some information is crucial to the subsequent surface modifier addition amount recommendation task, while some is redundant information or noise information. Therefore, in order to capture and characterize the interactive representation information between the reactant addition amount cascade feature and the product target performance requirement semantic feature, and extract the significant key interactive semantics related to the subsequent task, in the technical solution of the present application, the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector are further subjected to the principal component-based feature interactive response coupling processing to obtain the reactant-product target performance principal component granularity significant interactive coupling feature. The process of principal component-based feature interactive response coupling processing aims to extract and enhance the significant interactive information between the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector through multi-step data analysis and fusion technology. The network first uses principal component analysis (PCA) to reduce the dimension of the two input feature vectors to obtain a set of reactant addition amount cascade representation principal component feature components and a set of product target performance requirement semantic principal component feature components. Through the PCA dimensionality reduction processing, the redundant information in the reactant addition amount cascade representation feature and the product target performance requirement semantic feature can be removed respectively, thereby retaining the most important feature information. Accordingly, principal component analysis can simplify the computational complexity of subsequent processing while focusing on the information that best represents the original feature space. Subsequently, an optimal matching algorithm is used to establish a one-to-one correspondence between these principal component features of the reactant addition cascade representation and the product target performance requirement semantics to form the best matching pair.

[0044] In an embodiment of the present application, the specific steps of the reactant addition cascade representation vector and the product target performance requirement semantic encoding vector for the principal component-based feature interaction response coupling processing are as follows: First, each reactant addition cascade representation principal component feature component in the set of the reactant addition cascade representation principal component feature components is used as a query vector and the set of the product target performance requirement semantic principal component feature components is used as a query library, and the product target performance requirement semantic principal component feature components that best match each query vector are matched from the query library to obtain the set of {reactant addition cascade representation principal component feature components; product target performance requirement semantic principal component feature components} best matching pairs; that is, for each query vector selected from the set of the reactant addition cascade representation principal component feature components, the most similar corresponding item is found in the set of the product target performance requirement semantic principal component feature components. Such a pairing process is not just a simple element association, but a one-to-one mapping relationship between the two sets of principal component features of the reactant addition cascade representation feature and the product target performance requirement semantic feature is established, which further optimizes the expressiveness of the model and provides a basis for the subsequent surface modifier addition amount recommendation task. Next, each best matching pair of {reactant addition amount cascade representation principal component characteristic component; product target performance requirement semantic principal component characteristic component} in the set of best matching pairs is input into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of reactant-product target performance requirement semantic best matching pairs; the multi-scale interactive response coupling module automatically learns and generates a comprehensive representation of each pair of combinations through deep learning technology (such as convolutional neural network or recurrent neural network), that is, multi-scale interactive coupling representation of reactant-product target performance requirement semantic best matching pairs, which can capture the multi-level feature interaction pattern between reactant addition features and target performance requirement semantic features in each set of best matching pairs. The set of multi-scale interactive coupling representation vectors of reactant-product target performance requirement semantic best matching pairs generated at this stage not only provides a rich comprehensive representation, but also promotes the improvement of model generalization ability, so that the system can better perform the subsequent surface modifier addition amount recommendation task when facing unseen data. Subsequently, the set of multi-scale interaction coupling representation vectors of the best semantic match of the reactant-product target performance requirements is cascaded to obtain a reactant-product target performance principal component granularity significant interaction coupling feature vector as the reactant-product target performance principal component granularity significant interaction coupling feature.Here, by performing a cascade operation on the multi-scale interactive coupling representation vectors for all the best semantic matches of the reactant-product target performance requirements, a unified, high-dimensional feature vector is obtained, that is, the reactant-product target performance principal component granularity significant interactive coupling feature vector as the reactant-product target performance principal component granularity significant interactive coupling feature. The final feature vector obtained in this way not only contains the reactant addition amount cascade representation feature and the product target performance requirement semantic feature, but also maintains the consistency of the overall structure. In this way, it is possible to reduce the risk of overfitting while maintaining key information, thereby improving the performance of the model on unknown samples. In this way, the amount of surface modifier added can be more accurately recommended to meet the requirements of the product target performance.

[0045] Among them, each reactant addition amount cascade representation principal component feature component in the set of reactant addition amount cascade representation principal component feature components is used as a query vector and the set of product target performance requirement semantic principal component feature components is used as a query library, and the product target performance requirement semantic principal component feature components that best match each of the query vectors are matched from the query library to obtain a set of best matching pairs of {reactant addition amount cascade representation principal component feature components; product target performance requirement semantic principal component feature components}, including: extracting a predetermined reactant addition amount cascade representation principal component feature component from the set of reactant addition amount cascade representation principal component feature components; calculating the Mahalanobis distance between the predetermined reactant addition amount cascade representation principal component feature component and each product target performance requirement semantic principal component feature component in the set of product target performance requirement semantic principal component feature components to obtain a set of matching distances, wherein a pair of {reactant addition amount cascade representation principal component feature components; product target performance requirement semantic principal component feature components} with the smallest matching distance in the set of matching distances is taken as the best matching pair. Among them, the specific steps of calculating the Mahalanobis distance between each product target performance requirement semantic main component feature component in the set of the principal component feature component of the predetermined reactant addition amount cascade representation and the product target performance requirement semantic main component feature component to obtain a set of matching distances include: calculating the positional difference between the principal component feature component of the predetermined reactant addition amount cascade representation and the product target performance requirement semantic main component feature component to obtain a reactant-product target performance requirement semantic positional difference representation vector; after calculating the covariance matrix between the principal component feature component of the predetermined reactant addition amount cascade representation and the product target performance requirement semantic main component feature component, taking the inverse of each positional eigenvalue in the covariance matrix to obtain a reactant-product target performance requirement semantic covariance representation matrix; calculating the multiplication between the transposed vector of the reactant-product target performance requirement semantic positional difference representation vector and the reactant-product target performance requirement semantic covariance representation matrix, and then multiplying it with the reactant-product target performance requirement semantic positional difference representation vector to obtain a matching distance.

[0046] More specifically, each best matching pair of {principal component feature component of cascade representation of reactant addition amount; semantic principal component feature component of product target performance requirement} in the set of best matching pairs is input into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of best matching pairs of reactant-product target performance requirement semantics, including: respectively calculating the L2 norm of the principal component feature component of cascade representation of reactant addition amount and the principal component feature component of semantic product target performance requirement in the best matching pair of {principal component feature component of cascade representation of reactant addition amount; semantic principal component feature component of product target performance requirement} to obtain a reactant norm value and a product target performance requirement semantic norm value; extracting the maximum value from the reactant norm value, the product target performance requirement semantic norm value and the modulation parameter to obtain an interactive maximum value; respectively calculating the positional relationship between the principal component feature component of cascade representation of reactant addition amount and the semantic principal component feature component of product target performance requirement Subtract, multiply by position and add by position to obtain the reactant-product target performance requirement semantic principal component difference vector, the reactant-product target performance requirement semantic principal component dot product vector and the reactant-product target performance requirement semantic principal component sum vector; cascade the reactant-product target performance requirement semantic principal component difference vector, the reactant-product target performance requirement semantic principal component dot product vector and the reactant-product target performance requirement semantic principal component sum vector to obtain the reactant-product target performance requirement semantic principal component cascade vector; perform one-dimensional convolution encoding on the reactant-product target performance requirement semantic principal component cascade vector to obtain the reactant-product target performance requirement semantic principal component convolution encoding vector; perform local window-based maximum pooling processing on the reactant-product target performance requirement semantic principal component convolution encoding vector, and divide the obtained reactant-product target performance requirement semantic temporal interaction representation vector and the interaction maximum value by position point to obtain the reactant-product target performance requirement semantic best match pair multi-scale interaction coupling representation vector.

[0047] In summary, in the above embodiment, the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector are subjected to principal component-based feature interaction response coupling processing to obtain the reactant-product target performance principal component granularity significant interaction coupling feature, including: the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector are subjected to principal component-based feature interaction response coupling processing to obtain the reactant-product target performance principal component granularity significant interaction coupling feature according to the following feature interaction response formula; wherein the feature interaction response formula is:

[0048] C1=U1Λ1U1 T

[0049] C2=U2Λ2U2 T

[0050] U1=[v 11 ,v 12 ,…,v 1m ]

[0051]

[0052] U2=[v 21 ,v 22 ,…,v 2m ]

[0053]

[0054] v f =[v p1 ;v p2 ;...;v pi ...;v pm ]

[0055] Among them, C1 and C2 are the reactant sample covariance matrix and the product target performance requirement semantic sample covariance matrix, U1 and U2 are the reactant principal component orthogonal matrix and the product target performance requirement semantic principal component orthogonal matrix, v 11 ,v 12 ,…,v 1m The cascade representation of the principal component characteristic components of the reactant addition amount is each reactant addition amount cascade representation of the principal component characteristic components in the set, v 21 ,v 22 ,…,v 2m is the semantic principal component characteristic component of each product target performance requirement in the set of semantic principal component characteristic components of the product target performance requirement, Λ1 and Λ2 are the reactant diagonal matrix and the product target performance requirement semantic diagonal matrix respectively, λ 11 , 1m are the weight values ​​of the first and mth reactant addition amount cascade representing the principal component characteristic components in the set of reactant addition amount cascade representing the principal component characteristic components, λ 21 , 2m are the weight values ​​of the first and mth semantic principal component feature components of the product target performance requirement in the set of semantic principal component feature components of the product target performance requirement, respectively, U1 T and U2 T are the transposed matrices of U1 and U2, respectively, 1i is the i-th reactant addition amount cascade representation of the principal component characteristic component in the set of reactant addition amount cascade representation of the principal component characteristic component, S ij v 1iand v 2j The covariance matrix between To return the j value corresponding to the minimum value, k is the minimum matching distance, ⊙ and are position difference, position dot multiplication and position addition respectively, [·; ·; ·] is cascade processing, conv1D(·) is a one-dimensional convolutional coding operation, MaxPool(·) is a maximum pooling operation, max(·,·,·) is to extract the maximum value, ||·||2 is the L2 norm of the vector, ε is the modulation parameter, and v p1 、v p2 、v pi and v pm are the first, second, i-th and m-th multi-scale interactive coupling representation vectors of the best semantic matching of reactant-product target performance requirements in the set of multi-scale interactive coupling representation vectors, v f is the reactant-product target performance principal component granularity significant interaction coupling eigenvector.

[0056] Specifically, the S26 determines the recommended decoded value of the amount of surface modifier added based on the significant interactive coupling characteristics of the principal component granularity of the reactant-product target performance. In the technical solution of the present application, the reactant-product target performance principal component granularity significant interactive coupling feature vector is input into a surface modifier addition amount recommender based on a decoder to obtain a recommendation result, and the recommendation result is the recommended decoded value of the amount of surface modifier added. That is, the principal component significant interactive characteristics between the cascade representation of the reactant addition amount and the product target performance description semantics are used to perform decoding regression, so as to recommend a suitable amount of oleylamine. In this way, not only the controllability and repeatability of the synthesis process of oil-soluble nickel disulfide nanoparticles can be improved, but also suitable synthesis parameters can be quickly determined according to actual synthesis conditions and requirements, thereby improving the intelligence of the synthesis process of oil-soluble nickel disulfide nanoparticles.

[0057] Preferably, the reactant-product target performance principal component particle size significant interaction coupling feature vector is input into a decoder-based surface modifier addition amount recommender to obtain a recommendation result, which includes:

[0058] Calculate the first characteristic and value of all eigenvalues ​​of the reactant-product target performance principal component granularity significant interactive coupling eigenvector, the second characteristic and value of all eigenvalues ​​of the reactant addition amount cascade representation vector, and the third characteristic and value of all eigenvalues ​​of the product target performance requirement semantic encoding vector respectively;

[0059] After dividing the first characteristic sum value by the second characteristic sum value and the third characteristic sum value respectively, the square root of the sum of the squares of the two quotients is calculated to obtain a probability value of significant interaction coupling distribution of the principal component particle size of the reactant-product target performance;

[0060] Calculate the product of the first reactant-product target performance principal component granularity significant interactive coupling characteristic value of the reactant-product target performance principal component granularity significant interactive coupling characteristic vector and the reactant-product target performance principal component granularity significant interactive coupling distribution probability value to obtain the first reactant-product target performance principal component granularity significant interactive coupling characteristic distribution probability product;

[0061] Calculate the product of the second reactant-product target performance principal component particle size significant interactive coupling characteristic value of the reactant-product target performance principal component particle size significant interactive coupling characteristic vector and the absolute value of the difference between one and the reactant-product target performance principal component particle size significant interactive coupling distribution probability value to obtain the second reactant-product target performance principal component particle size significant interactive coupling characteristic inverse distribution probability product;

[0062] Calculate the quotient of the first reactant-product target performance principal component particle size significant interactive coupling characteristic value divided by the absolute value of the difference between one and the reactant-product target performance principal component particle size significant interactive coupling distribution probability value to obtain the first reactant-product target performance principal component particle size significant interactive coupling characteristic inverse distribution probability quotient;

[0063] Calculate the quotient of the second reactant-product target performance principal component particle size significant interactive coupling characteristic value and the reactant-product target performance principal component particle size significant interactive coupling distribution probability value to obtain the second reactant-product target performance principal component particle size significant interactive coupling characteristic distribution probability quotient;

[0064] After adding the probability product of the significant interactive coupling characteristic distribution of the first reactant-product target performance principal component particle size and the probability product of the significant interactive coupling characteristic inverse distribution of the second reactant-product target performance principal component particle size, the difference between the probability quotient of the significant interactive coupling characteristic inverse distribution of the first reactant-product target performance principal component particle size and the probability quotient of the significant interactive coupling characteristic distribution of the second reactant-product target performance principal component particle size is subtracted to obtain a reactant-product target performance principal component particle size significant interactive coupling correction characteristic value;

[0065] For each first reactant-product target performance principal component granularity significant interactive coupling eigenvalue and second reactant-product target performance principal component granularity significant interactive coupling eigenvalue of the reactant-product target performance principal component granularity significant interactive coupling eigenvalue, a reactant-product target performance principal component granularity significant interactive coupling correction characteristic matrix composed of the reactant-product target performance principal component granularity significant interactive coupling correction eigenvalues ​​is matrix-multiplied with the reactant-product target performance principal component granularity significant interactive coupling eigenvector to obtain a corrected reactant-product target performance principal component granularity significant interactive coupling eigenvector;

[0066] The corrected reactant-product target performance principal component particle size significant interaction coupling feature vector is input into a decoder-based surface modifier addition amount recommender to obtain a recommendation result.

[0067] Among them, the correction process of the reactant-product target performance principal component particle size significant interactive coupling eigenvector V is expressed as:

[0068]

[0069] and,

[0070]

[0071] Wherein, V1 is the cascade representation vector of the reactant addition amount, V2 is the semantic encoding vector of the product target performance requirement, p is the probability value of the significant interactive coupling distribution of the principal component granularity of the reactant-product target performance, v i is the first reactant-product target performance principal component granularity significant interactive coupling eigenvalue of the reactant-product target performance principal component granularity significant interactive coupling eigenvalue, v j is the second reactant-product target performance principal component granularity significant interactive coupling eigenvalue for calculating the reactant-product target performance principal component granularity significant interactive coupling eigenvalue, m is the reactant-product target performance principal component granularity significant interactive coupling correction eigenvalue, M is the reactant-product target performance principal component granularity significant interactive coupling correction characteristic matrix, V is the reactant-product target performance principal component granularity significant interactive coupling eigenvector, is the matrix multiplication, V' is the corrected reactant-product target property principal component size significant interaction coupling eigenvector.

[0072] That is, when the reactant addition amount cascade representation vector and the product target performance requirement semantic coding vector respectively represent the joint embedded coding features of the nickel stearate addition amount, the sulfur powder addition amount and the solvent addition amount and the product target performance requirement semantic coding features, when performing interactive response coupling based on the optimal matching of feature principal component granularity, the reactant-product target performance principal component granularity significant interactive coupling feature vector will also cause interactive response diversity based on the optimal matching due to the difference in the temporal pattern of the feature principal component granularity, affecting the iterative consistency of the decoding mapping and reducing the accuracy of the decoding result.

[0073] Therefore, the applicant of the present application aims at the region-boundary integral relationship of the high-dimensional characteristic manifold of the main component granularity of the reactant-product target performance significant interactive coupling feature vector, and defines the spatial probability distribution of the main component granularity of the reactant-product target performance significant interactive coupling feature vector as a manifold constraint boundary relative to the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector, to approximate the high-dimensional manifold of the feature set of the main component granularity of the reactant-product target performance significant interactive coupling feature vector composed of eigenvalue pairs, so as to avoid the mapping ambiguity of the local manifold representation of the diversified feature representation of the main component granularity of the reactant-product target performance significant interactive coupling feature vector to the high-dimensional feature decoding regression convergence space, improve the execution iteration consistency of each local feature distribution in the mapping task, improve the feature decoding regression convergence effect, and improve the accuracy of the recommendation results obtained by the decoder-based surface modifier addition amount recommender. In this way, not only can the controllability and repeatability of the oil-soluble nickel disulfide nanoparticle synthesis process be improved, but also it can help to quickly determine the appropriate synthesis parameters according to the actual synthesis conditions and requirements.

[0074] In particular, the S3 is to ultrasonically treat the initial mixed solution to make it uniformly mixed to obtain a mixed solution. It should be understood that ultrasound can produce strong mechanical vibrations, which can effectively destroy large particle clusters in the liquid and make various components (such as precursors, stabilizers, etc.) more evenly dispersed in the solution. This is very important for ensuring the size uniformity and morphological consistency of the nanoparticles.

[0075] In particular, in S4, an inert gas is introduced into the reaction vessel to exhaust the air in the reaction vessel to ensure that the reaction is carried out under an inert atmosphere. Here, an inert gas is introduced into the reaction vessel to exhaust the air to ensure that the reaction is carried out under an inert atmosphere. This measure can effectively control the reaction environment and improve the selectivity, safety and purity of the reaction product.

[0076] In particular, in S5, the mixed solution is subjected to a heat stirring reaction, and after the reaction is completed, the mixed solution is subjected to solid-liquid separation and washing to obtain oil-soluble nickel disulfide nanoparticles. It should be understood that by subjecting the mixed solution to a heat stirring reaction, the reaction can be better controlled by controlling the temperature and stirring speed, thereby affecting the size, morphology and crystallinity of the nanoparticles. After the reaction is completed, the generated nickel disulfide nanoparticles will be suspended in the solution. Therefore, the solid nanoparticles are separated from the liquid phase by solid-liquid separation (such as centrifugation, filtration, etc.). Furthermore, the impurities, solvents and unreacted precursors adsorbed on the surface of the nanoparticles can be further removed by the washing step, thereby improving the purity and quality of the product.

[0077] In summary, the synthesis method of oil-soluble nickel disulfide nanoparticles according to the embodiment of the present application is explained, which encodes the addition amount of nickel stearate, sulfur powder and solvent, and combines the user's performance requirements for the target product, uses data processing and semantic understanding algorithms based on artificial intelligence and deep learning to embed and associate the addition amount of nickel stearate, sulfur powder and solvent, and further recommends the appropriate amount of oleylamine based on the principal component interactive response characterization information between the semantics of the target product performance requirements. In this way, not only can the controllability and repeatability of the synthesis process of oil-soluble nickel disulfide nanoparticles be improved, but also it can help to quickly determine the appropriate synthesis parameters according to the actual synthesis conditions and requirements, thereby improving the intelligence of the synthesis process of oil-soluble nickel disulfide nanoparticles.

[0078] Preferably, the specific steps of the synthesis method of oil-soluble nickel disulfide nanoparticles are as follows:

[0079] 1) Add nickel stearate and sulfur powder into a three-necked flask, add octadecene and oleylamine, and perform ultrasonication to obtain a mixed solution;

[0080] 2) Stir the mixed solution thoroughly and introduce argon gas to exhaust the air in the bottle;

[0081] 3) The mixed solution is then heated to 350-360°C. After the reaction is completed, it is centrifuged to obtain a black paste with a layer of transparent attachment. After washing three times with anhydrous ethanol, the attachment disappears:

[0082] 4) Drying in a vacuum drying oven at 60-80° C. for 12-16 hours to obtain black solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0083] The nano nickel disulfide lubricating additive of the present application is a flaky structure, which exhibits excellent friction reduction and anti-wear properties under high temperature and high pressure conditions, and can improve the stability of friction workpieces in extreme environments. The experimental operation steps of the present application are simple and safe, and the obtained oil-soluble nickel disulfide nanoparticles can be well dispersed in the base oil. The oil-soluble flaky nickel disulfide nanoparticles synthesized in the present application have multiple properties and can be well dispersed in the base oil. They can be used as a new type of lubricating oil additive, and can effectively improve the friction reduction and anti-wear properties of grease, improve the load-bearing capacity of friction pairs, and extend the service life of equipment.

[0084] Compared with the prior art, this application has the following beneficial effects:

[0085] The synthesis method of the present application has many characteristics such as simple process, cheap and easy-to-obtain raw materials, low cost, environmental protection and pollution-free, high yield and low equipment requirements, and is suitable for large-scale industrial production. The oil-soluble flaky nickel disulfide nanoparticles synthesized in the present application have multiple properties and can be well dispersed in base oil, can be used as a new type of lubricating oil additive, and can effectively improve the friction and anti-wear performance of grease, improve the load-bearing capacity of friction pairs, and extend the service life of equipment.

[0086] The oil-soluble flaky nickel disulfide nanoparticles provided in the present application are prepared by a one-pot method, which consumes less energy, is low in cost, and is suitable for mass production; the process is relatively simple. The nanoparticles have good dispersibility and stability in lubricating oils, and can significantly improve the tribological properties of lubricating oils as lubricating oil additives, showing a smaller friction coefficient and wear spot diameter.

[0087] Example 1

[0088] A method for synthesizing oil-soluble nickel disulfide nanoparticles, the specific steps are as follows:

[0089] 1) In a three-necked flask, 1.25 g of nickel stearate and 0.096 g of sulfur powder were added in sequence, and then 30 ml of octadecene and 30 ml of oleylamine were added to obtain a mixed solution;

[0090] 2) Stir the mixed solution thoroughly and introduce argon to exhaust the air in the three-necked flask;

[0091] 3) The mixed solution was heated to 360°C and stirred for 70 minutes. After the reaction was completed, a black paste was obtained by centrifugation with a layer of transparent attachments. The attachments disappeared after washing with anhydrous ethanol:

[0092] 4) Drying in a vacuum drying oven at 75° C. for 12 h to obtain red solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0093] Figure 4A Tyndall effect diagram of the oil-soluble nickel disulfide nanoparticles prepared in Example 1 dissolved in n-hexane is given; the diagram proves that the synthesized nickel disulfide is of nanometer scale.

[0094] Figure 5 The XRD pattern of the oil-soluble nickel disulfide nanoparticles prepared in Example 1 is given; it can be seen from the figure that the main diffraction peaks at 21.755°, 31.101°, 37.795°, 38.276°44.349°, 49.725°, 54.612°55.155° correspond to the characteristic diffraction patterns of (101), (110), (003), (021), (202), (113), (104) and (122) of the standard card PDF#44-1418Ni3S2 phase.

[0095] Figure 6 The TEM image of the oil-soluble nickel disulfide nanoparticles prepared in Example 1 dissolved in n-hexane is given; it can be seen from the figure that the synthesized nickel disulfide is a flaky nanoparticle, which can be measured using Digital Micrograph software. Figure 6 The lattice spacing shown is 0.13 nm. The average particle size is 30-50 nm.

[0096] Figure 7 Example 1 Comparison of the friction coefficients of base oil PAO6 and base oil PAO6+0.6wt% nickel disulfide additive; (experimental conditions: 75°C, 392N, 1200r / min); As can be seen from the figure: the average friction coefficient of base oil PAO6 is 0.085, the average friction coefficient of base oil PAO6+0.6wt% nickel disulfide additive is 0.053, and the average friction coefficient of base oil PAO6+0.6wt% nickel disulfide additive is 37.64% lower than that of PAO6 base oil, and the friction reduction performance is relatively excellent.

[0097] Figure 8 The wear spot diagram (a) of the base oil PAO6 of Example 1 and the wear spot diagram (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive are given; (experimental conditions: 75°C, 392N, 1200r / min); it can be seen from the figure that the average wear spot diameter of the base oil PAO6 is 0.648mm, and the average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 0.511mm. The average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 21.14% lower than that of the PAO6 base oil, and the anti-wear performance is excellent.

[0098] Fig. 9A comparison chart of the friction coefficients of the base oil PAO6 of Example 1 and the base oil PAO6+0.6wt% nickel disulfide additive is given; (experimental conditions: 75°C, 300N, 1200r / min); it can be seen from the figure that the average friction coefficient of the base oil PAO6 is 0.066, and the average friction coefficient of the base oil PAO6+0.6wt% nickel disulfide additive is 0.05, and the base oil PAO6+0.6wt% nickel disulfide additive has a 24.24% lower average friction coefficient than that of the PAO6 base oil, and has excellent friction reduction performance.

[0099] Fig.10 The wear spot diagram (a) of the base oil PAO6 of Example 1 and the wear spot diagram (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive are given; (experimental conditions: 75°C, 300N, 1200r / min); it can be seen from the figure that the average wear spot diameter of the base oil PAO6 is 0.634mm, and the average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 0.472mm. The average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 25.55% lower than that of the PAO6 base oil, and the anti-wear performance is excellent.

[0100] Fig.11 A comparison chart of the friction coefficients of the base oil PAO6 of Example 1 and the base oil PAO6+0.6wt% nickel disulfide additive is given; (experimental conditions: 75°C, 500N, 1200r / min); it can be seen from the figure that the average friction coefficient of the base oil PAO6 is 0.097, and the average friction coefficient of the base oil PAO6+0.6wt% nickel disulfide additive is 0.07, and the base oil PAO6+0.6wt% nickel disulfide additive is 27.83% lower than the average friction coefficient of the PAO6 base oil, and the friction reduction performance is relatively excellent.

[0101] Fig.12 The wear spot diagram (a) of the base oil PAO6 of Example 1 and the wear spot diagram (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive are given; (experimental conditions: 75°C, 500N, 1200r / min); it can be seen from the figure that the average wear spot diameter of the base oil PAO6 is 0.781mm, and the average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 0.620mm. The average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 20.61% lower than that of the PAO6 base oil, and the anti-wear performance is excellent.

[0102] Fig.13A comparison chart of the friction coefficients of the base oil PAO6 of Example 1 and the base oil PAO6+0.6wt% nickel disulfide additive is given; (experimental conditions: 100°C, 392N, 1200r / min); it can be seen from the figure that the average friction coefficient of the base oil PAO6 is 0.078, and the average friction coefficient of the base oil PAO6+0.6wt% nickel disulfide additive is 0.057, and the base oil PAO6+0.6wt% nickel disulfide additive has a 26.92% lower average friction coefficient than that of the PAO6 base oil, and has excellent friction reduction performance.

[0103] Fig.14 The wear spot diagram (a) of the base oil PAO6 of Example 1 and the wear spot diagram (b) of the base oil PAO6 + 0.6wt% nickel disulfide additive are given; (experimental conditions: 100°C, 392N, 1200r / min); it can be seen from the figure that the average wear spot diameter of the base oil PAO6 is 0.796mm, and the average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 0.565mm. The average wear spot diameter of the base oil PAO6 + 0.6wt% nickel disulfide additive is 29.08% lower than that of the PAO6 base oil, and the anti-wear performance is excellent.

[0104] Depend on Figure 4 , Figure 5 and Figure 6 It can be seen that nickel disulfide was successfully prepared. Figure 7-14 Under different loads and temperatures, the friction coefficient of base oil PAO6+0.6wt% nickel disulfide additive is relatively stable, showing anti-wear and friction-reducing properties.

[0105] Example 2

[0106] A method for synthesizing oil-soluble nickel disulfide nanoparticles, the specific steps are as follows:

[0107] 1) In a three-necked flask, 0.626 g of nickel stearate and 0.032 g of sulfur powder were added in sequence, and then 30 ml of octadecene and 30 ml of oleylamine were added to obtain a mixed solution;

[0108] 2) Stir the mixed solution thoroughly and introduce argon to exhaust the air in the three-necked flask;

[0109] 3) The mixed solution was heated to 350-360°C and stirred for 70 minutes. After the reaction was completed, a black paste was obtained by centrifugation with a layer of transparent attachments. After washing three times with anhydrous ethanol, the attachments disappeared:

[0110] 4) Drying in a vacuum drying oven at 75° C. for 14 h to obtain black solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0111] Example 3

[0112] A method for synthesizing oil-soluble nickel disulfide nanoparticles, the specific steps are as follows:

[0113] 1) In a three-necked flask, 1.252 g of nickel stearate and 0.32 g of sulfur powder were added in sequence, followed by 25 ml of octadecene and 30 ml of oleylamine to obtain a mixed solution;

[0114] 2) Stir the mixed solution thoroughly and introduce argon to exhaust the air in the three-necked flask;

[0115] 3) The mixed solution was heated to 350-360°C and stirred for 70 minutes. After the reaction was completed, a black paste was obtained by centrifugation with a layer of transparent attachments. After washing three times with anhydrous ethanol, the attachments disappeared:

[0116] 4) Drying in a vacuum drying oven at 75° C. for 14 h to obtain black solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0117] Example 4

[0118] A method for synthesizing oil-soluble nickel disulfide nanoparticles, the specific steps are as follows:

[0119] 1) In a three-necked flask, 1.252 g of nickel stearate and 0.64 g of sulfur powder were added in sequence, and then 40 ml of octadecene and 20 ml of oleylamine were added to obtain a mixed solution;

[0120] 2) Stir the mixed solution thoroughly and introduce argon to exhaust the air in the three-necked flask;

[0121] 3) The mixed solution was heated to 350-360°C and stirred for 70 minutes. After the reaction was completed, a black paste was obtained by centrifugation with a layer of transparent attachments. The attachments disappeared after washing with anhydrous ethanol:

[0122] 4) Drying in a vacuum drying oven at 70° C. for 12 h to obtain red solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0123] Example 5

[0124] A method for synthesizing oil-soluble nickel disulfide nanoparticles, the specific steps are as follows:

[0125] 1) In a three-necked flask, 1.880 g of nickel stearate and 0.128 g of sulfur powder were added in sequence, and then 30 ml of octadecene and 30 ml of oleylamine were added to obtain a mixed solution;

[0126] 2) Stir the mixed solution thoroughly and introduce argon to exhaust the air in the three-necked flask;

[0127] 3) The mixed solution was heated to 350-360°C and stirred for 70 minutes. After the reaction was completed, a black paste was obtained by centrifugation with a layer of transparent attachments. After washing three times with anhydrous ethanol, the attachments disappeared:

[0128] 4) Drying in a vacuum drying oven at 70° C. for 12 h to obtain black solid particles, which are oil-soluble nickel disulfide nanoparticles.

[0129] Comparative Example

[0130] Referring to Example 1, the surfactant was replaced by oleic acid from oleylamine. After the reaction was completed, centrifugation was performed and no product was obtained. After acetone was added to the solution and centrifuged, the product obtained was a white paste instead of a black paste, that is, no oil-soluble nickel disulfide nanoparticles were synthesized.

[0131] In summary, it can be concluded that the oil-soluble nickel disulfide nanoparticles of the present application can significantly improve the tribological properties of the lubricating oil as a lubricating oil additive, and exhibit a smaller friction coefficient and wear spot diameter.

[0132] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for synthesizing oil-soluble nickel disulfide nanoparticles, characterized in that: include: Add nickel stearate and sulfur powder into a reaction vessel; Adding a solvent and a surface modifier into the reaction container to obtain an initial mixed solution, wherein the solvent is octadecene and the surface modifier is oleylamine; Performing ultrasonic treatment on the initial mixed solution to make it uniformly mixed to obtain a mixed solution; Passing an inert gas into the reaction container to exhaust the air in the reaction container to ensure that the reaction is carried out under an inert atmosphere; The mixed solution is heated and stirred for reaction, and after the reaction is completed, solid-liquid separation and washing are performed to obtain oil-soluble nickel disulfide nanoparticles.

2. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 1, characterized in that: A solvent and a surface modifier are added into the reaction container to obtain an initial mixed solution, wherein the solvent is octadecene and the surface modifier is oleylamine, comprising: Obtaining the addition amount of the nickel stearate, the addition amount of the sulfur powder and the addition amount of the solvent; Obtaining target performance requirements of the oil-soluble nickel disulfide nanoparticles determined by a user; Performing embedded semantic association coding on the addition amount of the nickel stearate, the addition amount of the sulfur powder and the addition amount of the solvent to obtain a cascade representation vector of the addition amount of the reactants; Semantically encoding the target performance requirements of the oil-soluble nickel disulfide nanoparticles to obtain a semantic encoding vector of the product target performance requirements; The reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector are subjected to principal component-based feature interactive response coupling processing to obtain a reactant-product target performance principal component granularity significant interactive coupling feature, including: principal component analysis is performed on the reactant addition amount cascade representation vector and the product target performance requirement semantic encoding vector to obtain a set of reactant addition amount cascade representation principal component feature components and a set of product target performance requirement semantic principal component feature components; optimal feature interactive matching coupling is performed on the set of reactant addition amount cascade representation principal component feature components and the set of product target performance requirement semantic principal component feature components to obtain a reactant-product target performance principal component granularity significant interactive coupling feature; Based on the significant interactive coupling characteristics of the main component particle size of the reactant-product target performance, the recommended decoding value of the addition amount of the surface modifier is determined.

3. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 2, characterized in that: The amount of nickel stearate added, the amount of sulfur powder added, and the amount of solvent added are embedded in semantic association coding to obtain a cascade representation vector of the amount of reactant added, including: Embedding and coding the amount of nickel stearate added, the amount of sulfur powder added, and the amount of solvent added to obtain an embedded coding vector of the amount of nickel stearate added, an embedded coding vector of the amount of sulfur powder added, and an embedded coding vector of the amount of solvent added; The nickel stearate addition amount embedded coding vector, the sulfur powder addition amount embedded coding vector and the solvent addition amount embedded coding vector are cascaded to obtain the reactant addition amount cascade representation vector.

4. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 3, characterized in that: The set of the reactant addition amount cascade representation principal component feature components and the set of the product target performance requirement semantic principal component feature components are subjected to optimal feature interactive matching coupling to obtain the reactant-product target performance principal component granularity significant interactive coupling feature, including: Using each reactant addition amount cascade representation principal component feature component in the set of reactant addition amount cascade representation principal component feature components as a query vector and using the set of product target performance requirement semantic principal component feature components as a query library, matching the product target performance requirement semantic principal component feature components that best match each query vector from the query library to obtain a set of best matching pairs of {reactant addition amount cascade representation principal component feature component; product target performance requirement semantic principal component feature component}; Input each best matching pair of {principal component characteristic component of reactant addition amount cascade representation; product target performance requirement semantics principal component characteristic component} in the set of best matching pairs of {principal component characteristic component of reactant addition amount cascade representation; product target performance requirement semantics principal component characteristic component} into a multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of best matching pairs of reactant-product target performance requirement semantics; The set of multi-scale interaction coupling representation vectors of the best semantic match of the reactant-product target performance requirement is cascaded to obtain a reactant-product target performance principal component granularity significant interaction coupling feature vector as the reactant-product target performance principal component granularity significant interaction coupling feature.

5. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 4, characterized in that: Each reactant addition amount cascade representation principal component characteristic component in the set of reactant addition amount cascade representation principal component characteristic components is used as a query vector and the set of product target performance requirement semantic principal component characteristic components is used as a query library, and the product target performance requirement semantic principal component characteristic components that best match each query vector are matched from the query library to obtain {reactant addition amount cascade representation principal component characteristic components; The product target performance requires a set of the best matching pairs of semantic principal component feature components, including: Extracting a predetermined reactant addition amount cascade representation principal component characteristic component from the set of reactant addition amount cascade representation principal component characteristic components; The Mahalanobis distances between the principal component characteristic components represented by the cascade of the predetermined reactant addition amount and the semantic principal component characteristic components of the product target performance requirement in the set of semantic principal component characteristic components of the product target performance requirement are calculated to obtain a set of matching distances, wherein the pair of {principal component characteristic components represented by the cascade of the reactant addition amount; semantic principal component characteristic components of the product target performance requirement} with the smallest matching distance in the set of matching distances is taken as the best matching pair.

6. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 5, characterized in that: Calculating the Mahalanobis distances between the principal component feature components of the predetermined reactant addition amount cascade representation and the semantic principal component feature components of the product target performance requirement in the set to obtain a set of matching distances includes: Calculating the positional difference between the principal component characteristic component of the cascade representation of the predetermined reactant addition amount and the principal component characteristic component of the semantic principal component of the product target performance requirement to obtain a reactant-product target performance requirement semantic positional difference representation vector; After calculating the covariance matrix between the principal component characteristic component of the cascade representation of the predetermined reactant addition amount and the semantic principal component characteristic component of the product target performance requirement, taking the inverse of each position eigenvalue in the covariance matrix to obtain a reactant-product target performance requirement semantic covariance representation matrix; The transposed vector of the positional difference representation vector of the reactant-product target performance requirement semantics is calculated and multiplied with the covariance representation matrix of the reactant-product target performance requirement semantics, and then multiplied with the positional difference representation vector of the reactant-product target performance requirement semantics to obtain the matching distance.

7. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 6, characterized in that: The {reactant addition amount cascade represents the principal component characteristic component; Each {reactant addition amount cascade represents the principal component characteristic component in the set of product target performance requirements semantic principal component characteristic component} best matching pairs; The product target performance requirement semantic principal component characteristic component} best matching pair is input into the multi-scale interactive response coupling module to obtain a set of multi-scale interactive coupling representation vectors of the best matching pair of reactant-product target performance requirement semantics, including: Respectively calculating the L2 norms of the principal component characteristic component of the reactant addition amount cascade representation and the principal component characteristic component of the product target performance requirement semantics in the best matching pair of {principal component characteristic component of the reactant addition amount cascade representation; semantic principal component characteristic component of the product target performance requirement} to obtain the reactant norm value and the product target performance requirement semantics norm value; Extracting the maximum value from the reactant norm value, the product target performance requirement semantic norm value and the modulation parameter to obtain the interaction maximum value; Respectively calculating the positional subtraction, positional dot product and positional addition between the principal component characteristic component of the reactant addition amount cascade representation and the semantic principal component characteristic component of the product target performance requirement to obtain a reactant-product target performance requirement semantic principal component difference vector, a reactant-product target performance requirement semantic principal component dot product vector and a reactant-product target performance requirement semantic principal component sum vector; Cascade processing is performed on the reactant-product target performance requirement semantic principal component difference vector, the reactant-product target performance requirement semantic principal component dot product vector and the reactant-product target performance requirement semantic principal component sum vector to obtain a reactant-product target performance requirement semantic principal component cascade vector; Performing one-dimensional convolution encoding on the reactant-product target performance requirement semantic principal component cascade vector to obtain a reactant-product target performance requirement semantic principal component convolution encoding vector; The principal component convolutional coding vector of the reactant-product target performance requirement semantics is subjected to local window-based maximum pooling processing, and the obtained reactant-product target performance requirement semantics temporal interaction representation vector is divided by the interaction maximum value at each position point to obtain the multi-scale interaction coupling representation vector of the reactant-product target performance requirement semantics best match.

8. The method for synthesizing oil-soluble nickel disulfide nanoparticles according to claim 7, characterized in that: Based on the significant interaction coupling characteristics of the main component particle size of the reactant-product target performance, the recommended decoding value of the addition amount of the surface modifier is determined, including: The reactant-product target performance principal component particle size significant interaction coupling feature vector is input into a decoder-based surface modifier addition amount recommender to obtain a recommendation result, which is a recommended decoded value of the surface modifier addition amount.

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