Negative electrode material and method for improving coating effect of coating material in preparation process of negative electrode material

Abstract

The invention discloses a negative electrode material and a method for improving the coating effect of a coating material in the preparation process of the negative electrode material, experimental design is carried out in combination with a theoretical calculation method, the experimental design is effectively combined with an actual experiment, a theoretical calculation result is successfully verified, and a breakthrough from theory to experiment is realized. And a universal and efficient research method is provided for directional design and optimization of the electrode material structure on the microscale.

Description

Technical Field

[0001] This invention relates to the field of battery anode material technology, specifically to an anode material and a method for improving the coating effect of the coating material during its preparation. Background Technology

[0002] With the rapid global adoption of personal electric vehicles and the continuous expansion of various mobile electronic devices in people's daily lives, the market's demands for battery performance are becoming increasingly diversified and standardized. Although current electrode material research has achieved phased results, improving energy density and cycle life, from the perspective of long-term development and practical application needs, existing material systems still cannot fully meet the comprehensive requirements of high-performance, high-stability energy storage systems in terms of multi-functional integration and multi-scenario adaptability. Therefore, developing novel composite electrode materials with excellent comprehensive performance has become a research hotspot and key breakthrough direction in the field of energy storage.

[0003] Among numerous candidate materials, silicon particle anode materials are considered to have significant potential for constructing highly stable electrodes due to their unique core-shell structure. However, the electrochemical performance of Si particle materials is limited by the structural collapse caused by particle expansion. Therefore, it largely depends on the precise control and optimization of its coating process—the uniformity, thickness, interfacial bonding state, and chemical stability of the coating layer directly affect the conductivity, structural integrity, and cycle durability of Si anode materials. Furthermore, current silicon-carbon coating processes suffer from problems such as uneven coating, uncontrollable interfacial reactions, and poor process repeatability.

[0004] However, using traditional experimental methods to screen coating processes can lead to problems such as resource waste and long research and development cycles.

[0005] Therefore, how to use material simulation calculation methods to accurately predict and optimize the material selection process during the coating process, so as to provide clear theoretical guidance and data support for the coating process in the experimental stage, has become one of the urgent development needs. Summary of the Invention

[0006] To address the issues of resource waste and long development cycles in the design and optimization of existing traditional experimental schemes, one of the objectives of this invention is to provide a method for improving the coating effect of coating materials during the preparation of anode materials.

[0007] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for improving the coating effect of coating materials during the preparation of negative electrode materials is provided, comprising the following steps: Step 1: Selection of coating materials Step S1: Construct a contact interface model between the coating material and the core material. The most thermodynamically stable crystal plane in the crystal surface model of the coating material is combined with the most thermodynamically stable crystal plane in the crystal surface model of the core material to construct a contact interface model. First-principles calculation software is used to perform ab initio molecular dynamics simulation on the model until the system reaches equilibrium or stability. Then the interface formation energy between the coating material and the core material is calculated. Step S2: Construct a contact interface model between the covering material and the shell material. The molecular model of the shell material is added to the thermodynamically stable crystal surface of the crystal surface model of the coating material to construct a contact interface model. The model is then subjected to ab initio molecular dynamics simulation using first-principles calculation software until the system reaches equilibrium or stability. Subsequently, the interaction energy between the coating material and the shell material is calculated. Step S3: Based on the calculation results in Step S1 and Step S2, select the coating material with the lowest interface formation energy and interaction energy. Step 2: Preparation of negative electrode material The selected coating material and core material are mixed evenly under a protective gas to form a liquid slurry. The liquid slurry is stirred, ground, and then granulated to obtain the anode material precursor. The core and shell material is coated onto the anode material precursor and then sintered at high temperature to obtain the anode material.

[0008] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the construction method of the crystal surface model of the coating material and the crystal surface model of the core material in step S1 is as follows: after the material crystal model is optimized by first principles, it is slicing to obtain the surface model of the corresponding index plane, a vacuum layer is set, and the bottom atoms are fixed at the same time. Then, the first principles optimization is performed again to obtain the surface model. The vacuum layer is 15–30 Å.

[0009] Furthermore, when the coating material or core material is a surface reconstruction material, the surface atoms of the surface model corresponding to the index surface need to be manually adjusted in a small random manner.

[0010] Furthermore, the conditions for the ab initio molecular dynamics simulations in steps S1 and S2 are as follows: The total simulation duration is: The simulation will terminate when the system reaches equilibrium and the total simulation time reaches 5 ps. or If the total simulation time does not reach equilibrium within 20,000 fs, the simulation continues until the system stabilizes. The time step is 0.5 fs to 1 fs. or There exists an element H with a time step of 0.5 fs; The simulation temperature ranges from 298.15 K to 1200 K. The NPT ensemble is used to simulate the interface between the cladding material and the core material, while the NVT ensemble is used to simulate the interface between the cladding material and the shell material. The thermal bath method includes any one of CSVR, Nose-Hoover, Adaptive-Langevin, and Generalized Langevin Equation. The time constant is 200–500. The exchange-correlation functional is selected from any one of PBE, Pade, and B3LYP. The basis set includes any one of DZVP-MOLOPT-SR-GTH, TZVP-MOLOPT-GTH, TZV2P-MOLOPT-GTH, and TZV2PX-MOLOPT-GTH. The cutoff energy is 300 Ry to 600 Ry. The SCF convergence criterion is 1 × 10⁻⁶. -4 ~1×10 -8 The iterative calculation methods are: diagonalization method or orbital exchange method.

[0011] Furthermore, the interface formation in step S1 can be calculated according to Equation 1, which is: (Equation 1) Among them, E sum E represents the system energy of the interface model. sur1 and E sur2 Let A represent the system energy under the surface models of the coating material and the core material, respectively, and let A be the area of ​​the interface model.

[0012] Furthermore, the interaction energy in step S2 is calculated according to Equation 2, which is: (Equation 2) Among them, E con E represents the interaction between the coating material and the bitumen. tot E represents the system energy of the shell material and the coating material under coating behavior. sur and E es n represents the surface model energy of the coating material and the energy of the shell material itself, respectively. es This refers to the atomic number or molecular weight of the shell material.

[0013] Furthermore, the coating material includes any one of Al2O3, TiO2, graphite, and an amorphous carbon layer formed by organic matter; the core material is Si particles; and the shell material is asphalt.

[0014] Furthermore, in step 2, the coating material accounts for 0.5% of the mass of the core material, and the shell material accounts for 30%-60% of the mass of the anode material precursor.

[0015] Furthermore, in step 2, the conditions for stirring and grinding are: stirring time 1-5h, stirring speed 300-900r; the conditions for high-temperature sintering are: temperature 800-1100℃, time 1-3h; and the protective gas is nitrogen.

[0016] A second objective of this invention is to provide a negative electrode material, which is prepared by the method described in the first aspect.

[0017] The present invention has the following beneficial effects: 1. This invention combines theoretical calculations with experimental design, effectively integrating them with practical experiments. It successfully verifies the results of theoretical calculations, achieving a breakthrough from theory to experiment. This provides a universal and efficient research method for the targeted design and optimization of electrode material structures at the microscale. Furthermore, the method in this invention effectively solves the problem of screening coating materials in electrode material preparation processes. This method can effectively predict the coating stability of electrode materials, quickly and efficiently screen coating materials, design the microstructure of complex electrode materials, evaluate their coating stability and efficiency, and effectively guide the coating structure design in practical experimental processes. In addition, this invention also enables the design of microstructures of complex electrode materials, evaluates their coating stability and efficiency, and effectively guides the coating structure design in practical experimental processes. To a certain extent, it effectively overcomes the high cost and blind spots of traditional experimental methods, providing key theoretical basis and design criteria for the formulation of experimental schemes.

[0018] 2. Simulation results show that the Si particles and Al2O3 particles in the negative electrode material prepared by the method of this invention form an alloy, which improves the stability of the coating layer on the surface of the Si particles and prevents the coating layer from falling off during the expansion of the Si particles. Furthermore, the asphalt molecules in this invention not only form new bonds (such as H bonds) with the Al2O3 particles, but also generate chemical bonds, which to a certain extent improves the stability of the asphalt coating on the surface of the Al2O3 particles. This results in a negative electrode material with good stability, exhibiting higher capacity utilization, stable initial coulombic efficiency, and high capacity retention. Attached Figure Description

[0019] Figure 1 The diagram shows the theoretical structure of the contact interface model before and after AIMD simulation calculations. Figure 1 Figure (a) in the figure is a model of the contact interface before AIMD simulation calculation. Figure 1 Figure (b) shows the adsorption of silicon particles with Al2O3; Figure 2 The adsorption diagram of coal tar pitch components and Al2O3; Figure 3 Adsorption diagram of cycloalkane aromatic molecules with Al2O3; Figure 4The adsorption diagram shows the adsorption of oxygen-containing polar aromatic molecules with Al2O3. Figure 5 The adsorption diagram shows the adsorption of nitrogen-containing polar aromatic molecules with Al2O3. Figure 6 The adsorption diagram shows the adsorption of sulfur-containing polar aromatic molecules with Al2O3. Figure 7 SEM image of the negative electrode material precursor; Figure 8 This is a SEM image of the intermediate material for the negative electrode. Detailed Implementation

[0020] The following describes, with reference to embodiments, a negative electrode material of this application and a method for improving the coating effect of the coating material during its preparation process.

[0021] However, this application may be exemplified in many different forms and should not be construed as limited to the specific embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the scope of this application to those skilled in the art.

[0022] The inventors' research revealed that traditional methods of screening chemical experimental reaction conditions (including raw materials, time, and temperature) result in resource waste and long research and development cycles.

[0023] Based on this, this invention starts from the atomic scale and utilizes molecular dynamics simulation technology to provide a solid theoretical basis for the design of coating process experimental schemes. By screening and comparing various candidate coating materials, calculating the stability of each material to the coating process, effectively optimizing the coating material formulation, and finally accurately identifying the optimal coating material formulation and combination, the theoretical calculation guides the experimental scheme.

[0024] A first aspect of the present invention provides a method for improving the coating effect of coating materials during the preparation of negative electrode materials, comprising the following steps: Step 1: Selection of coating materials Step S1: Construct a contact interface model between the coating material and the core material. The most thermodynamically stable crystal plane in the crystal surface model of the coating material is combined with the most thermodynamically stable crystal plane in the crystal surface model of the core material to construct a contact interface model. First-principles calculation software is used to perform ab initio molecular dynamics simulation on the model until the system reaches equilibrium or stability. Then the interface formation energy between the coating material and the core material is calculated. Step S2: Construct a contact interface model between the covering material and the shell material. The molecular model of the shell material is added to the thermodynamically stable crystal surface of the crystal surface model of the coating material to construct a contact interface model. The model is then subjected to ab initio molecular dynamics simulation using first-principles calculation software until the system reaches equilibrium or stability. Subsequently, the interaction energy between the coating material and the shell material is calculated. Step S3: Based on the calculation results in Step S1 and Step S2, select the coating material with the lowest interface formation energy and interaction energy. Step 2: Preparation of negative electrode material The selected coating material and core material are mixed evenly under a protective gas to form a liquid slurry. The liquid slurry is stirred, ground, and then granulated to obtain the anode material precursor. The core and shell material is coated onto the anode material precursor and then sintered at high temperature to obtain the anode material.

[0025] In this embodiment, first-principles calculation software is used to perform ab initio molecular dynamics (AIMD) simulations on the constructed model. This analyzes the interaction forces between the coating material and the shell material, as well as the interfacial forces (interface formation energy) between the coating material and the core material, evaluating the stability of the coating performance and obtaining coating behavior data. Based on this data, the optimal coating material scheme is selected, significantly reducing resource waste that may occur in traditional experiments. Furthermore, this embodiment experimentally verifies the selected coating material, successfully achieving efficient coating. This significantly shortens the experimental condition selection time and provides a novel theoretical and experimental approach for the development of high-performance electrode materials.

[0026] Furthermore, in this embodiment, the first-principles calculation software is preferably cp2k.

[0027] In some embodiments, the method for constructing the crystal surface model of the coating material and the crystal surface model of the core material in step S1 is as follows: after the material crystal model is optimized using first principles, it is slicing to obtain the surface model of the corresponding index plane. A vacuum layer is set, and the bottom atoms are fixed. Then, the first principles optimization is performed again to obtain the surface model. Preferably, the vacuum layer is 15–30 Å, and the bottom 3 to 6 layers of atoms are fixed. The crystal structure data (i.e., crystal model) of the coating material and the core material in this embodiment can be built by oneself or obtained and imported from the international mainstream material crystallography database (such as ICSD). It is usually preferred to import from the crystal database. The most easily exposed crystal planes in the crystal structure can also be obtained from the crystal database. For example, for Si particles, the most easily exposed crystal planes can be obtained from the crystal database as (111) crystal plane and (100) crystal plane. The most easily exposed crystal plane of Al2O3 is (100) crystal plane. In this embodiment, by converting the crystal structure model into a surface model, the behavior of the two substances in a real environment (such as coating and adsorption) can be simulated more realistically. This allows for the calculation of more accurate interface formation energy and interaction energy, which is beneficial for the precise selection of coating materials.

[0028] Furthermore, the methods for constructing the crystal surface models of the coating material and the core material in this embodiment can refer to the method for obtaining the crystal material surface model in the article "Patent No.: 202410212519.X, Patent Name: Method for Predicting the Adsorption Capacity of Crystal Material Surface with Organic Macromolecules". Specifically, taking Si and Al2O3 as examples, the index of the most stable surface of both Si and Al2O3 can be found in the crystallography database. The index surface is cut out using Materials Studio software or scripts to obtain the surface model of the corresponding index surface. A vacuum layer of 15-30 Å is set to prevent mutual influence between adjacent periodic atoms. If the index surface of the crystal has surface reconstruction phenomenon, it is necessary to manually and randomly move atoms on the surface and then perform structural optimization. The optimization parameters are as follows: the DFT calculation method of cp2k is used, the crystal size is fixed for optimization, and the dispersion correction effect is considered, using DFT-D3 (BJ). The commutative correlation functional is selected from any one of PBE, Pade, and B3LYP; the basis set includes any one of DZVP-MOLOPT-SR-GTH, TZVP-MOLOPT-GTH, TZV2P-MOLOPT-GTH, and TZV2PX-MOLOPT-GTH, with a cutoff energy of 300 Ry to 600 Ry, and the SCF convergence criterion is 1 × 10⁻⁶. -5 ~1×10 -8 The BFGS or CG optimization method is adopted, and the iterative calculation method is: diagonalization method or orbital exchange method. When using the diagonalization method, the K point should also be considered. The K point is set to be greater than 50 as the product of the lattice constant and the K point.

[0029] In some embodiments, when the coating material or core material is a surface-reconstructed material, the surface atoms of the surface model corresponding to the exponential surface need to be manually adjusted slightly and randomly. For example, in Si, when constructing the surface model, the surface atoms of the surface model corresponding to the exponential surface need to be adjusted slightly and randomly, typically fixing the bottom four layers of atoms. In this embodiment, through surface reconstruction, atomic rearrangement, and partial saturation of dangling bonds, the total energy of the system is significantly reduced, thereby obtaining the lowest energy stable structure. This ensures that the calculated interface formation energy and interaction energy are closer to the true values, which is beneficial for selecting suitable coating materials. In addition, surface reconstruction will change the atomic arrangement, charge distribution, and electronic structure on the exposed surfaces, which is conducive to chemical reactions or the formation of new bonds (such as hydrogen bonds) between the coating material and the core material, as well as between the coating material and the shell material. This improves the bonding force between the coating material and the core material, as well as between the coating material and the shell material, avoiding the problem of detachment during the subsequent anode material preparation process, and also improving the stability of the anode material.

[0030] In some embodiments, the conditions for ab initio molecular dynamics simulations in steps S1 and S2 are as follows: The total simulation duration is: The simulation will terminate when the system reaches equilibrium and the total simulation time reaches 5 ps. or If the total simulation time does not reach equilibrium within 20,000 fs, the simulation continues until the system stabilizes. The time step is 0.5 fs to 1 fs. or There exists an element H with a time step of 0.5 fs; The simulation temperature ranges from 298.15 K to 1200 K. The NPT ensemble is used to simulate the interface between the cladding material and the core material, while the NVT ensemble is used to simulate the interface between the cladding material and the shell material. The thermal bath method includes any one of CSVR, Nose-Hoover, Adaptive-Langevin, and Generalized Langevin Equation. The time constant is 200–500. The exchange-correlation functional is selected from any one of PBE, Pade, and B3LYP. The basis set includes any one of DZVP-MOLOPT-SR-GTH, TZVP-MOLOPT-GTH, TZV2P-MOLOPT-GTH, and TZV2PX-MOLOPT-GTH. The cutoff energy is 300 Ry to 600 Ry. The SCF convergence criterion is 1 × 10⁻⁶. -4 ~1×10 -8 The iterative calculation methods are: diagonalization method or orbital exchange method.

[0031] In this embodiment, the parameters used in the head calculation molecular dynamics simulation in steps S1 and S2 are the same. The purpose of this is to avoid unexpected occurrences during the simulation process and to enhance the accuracy of the simulation results.

[0032] In some embodiments, the interface formation in step S2 can be calculated according to Equation 1, which is: (Equation 1) Among them, E sum E represents the system energy of the interface model. sur1 and E sur2 Let A represent the system energy under the surface models of the coating material and the core material, respectively, and let A be the area of ​​the interface model.

[0033] In some embodiments, the interaction energy in step S3 is calculated according to Equation 2, which is: (Equation 2) Among them, E con E represents the interaction between the coating material and the bitumen. tot E represents the system energy of the shell material and the coating material under coating behavior. sur and E es n represents the surface model energy of the coating material and the energy of the shell material itself, respectively. es This refers to the atomic number or molecular weight of the shell material.

[0034] In the above embodiments, when calculating the interaction energy between the shell material and the coating material surface using Equation 2, the average energy of the system is typically calculated based on its kinetically stable state, and then the interaction energy is calculated using Equation 2. Lower energy indicates a stronger interaction, and the final structural model allows us to examine whether asphalt molecules react chemically with the coating material, and whether old chemical bonds are broken and new chemical bonds are formed. Similarly, a lower interface formation energy between the coating material and the core material also indicates greater stability.

[0035] In practice, a dynamically stable state refers to the uniform selection of 10 frames of structure as technical samples from the ab initio molecular dynamics (AIMD) simulation trajectory that has reached dynamic equilibrium.

[0036] In some embodiments, the coating material includes any one of Al2O3, TiO2, graphite, and an amorphous carbon layer formed from organic matter; the core material is Si particles; and the shell material is pitch. In this embodiment, the structures of Al2O3, TiO2, graphite, the amorphous carbon layer, and the Si particles can be downloaded from a crystallography database. In this embodiment, the coating material is preferably Al2O3, and more preferably, the coating material is nanoscale Al2O3, for example, Al2O3 with an average particle size of 10~100 nm; the core material is preferably Si particles, and more preferably, the core material is micron-sized Si particles.

[0037] In some embodiments, the coating material in step 2 is 0.5% of the mass of the core material, and the shell material is 30%-60% of the mass of the negative electrode material precursor.

[0038] In some embodiments, in step 2, the stirring and grinding conditions are: stirring time 1-5 hours, stirring speed 300-900 rpm, which allows the coating material to adhere better to the core material; the high-temperature sintering conditions are: temperature 800-1100℃, time 1-3 hours; preferably, the high-temperature sintering conditions are: temperature 1050℃, time 2 hours. The protective gas is nitrogen.

[0039] An embodiment of the second aspect of the present invention provides a negative electrode material prepared according to the first aspect embodiment.

[0040] Example The technical solution of the present invention will be further explained and illustrated below through specific embodiments.

[0041] Example 1 A method for improving the coating effect of coating materials during the preparation of negative electrode materials includes the following steps: Step 1: Selection of coating materials Step S1: Construct Si crystal surface model and Al2O3 crystal surface model (1) The silicon (111) structure can be obtained by downloading the silicon (111) model from the ICSD crystallography database. The silicon (111) model is optimized using first-principles calculations to obtain the structure with the lowest energy and most reasonable structure. Then, the surface model corresponding to the exponential plane is obtained by faceting using Materials Studio software. A vacuum layer of 20 Å is set to prevent mutual interference between adjacent periodic atoms. Since Si has the characteristic of surface reconstruction, the surface atoms are manually adjusted slightly to introduce perturbations so that the optimization can achieve the structure of surface reconstruction. At the same time, the bottom 4 layers of atoms are fixed to simulate the bulk phase properties, and the rest are relaxed. Then, the surface model of silicon is calculated using the first-principles calculation module of cp2k software. High precision is set, and then first-principles optimization is performed to obtain the optimized silicon (111) surface model. The optimization parameters are as follows: the Gaussian plane wave basis set is DZVP-MOLOPT-GTH, the exchange correlation functional is PBE, the BFGS optimization method and the diagonalization (Dig) algorithm are used, the K point is set to 331, and the convergence criterion is 10. -5 The cutoff energy is set to 500Ry, and the density standard at K point is set to be combined with the weak interaction force description method DFT-D3(BJ).

[0042] (2) The crystal structure of Al2O3 was downloaded from the ICSD crystal database. After first-principles optimization, the surface structure of sheet Al2O3(100) can be obtained by cross-sectioning using Materials Studio software. A vacuum layer of 20 Å was set, and the bottom 4 layers of atoms were fixed to simulate bulk phase properties. The rest were relaxed. Then, a second optimization was performed to obtain the optimized Al2O3(100) surface model. The calculation parameters of the two first-principles optimizations were the same as those in Example (1).

[0043] Step S2: Construct a contact interface model between silicon particles and Al2O3. The silicon (111) surface model and the Al2O3 (100) surface model from step S1 are combined to construct a contact junction interface model. The obtained structural model is then used for AIMD simulation calculations. The calculation ends and proceeds to the next step once the energy balance of the entire system is reached. The conditions for AIMD simulation calculations are as follows: the NVT ensemble is selected, the temperature is set to 800 K to accelerate the system to reach the reaction equilibrium state; the time step is set to 1 fs, and the total time is set to 20000 fs; the CSVR heat bath is used in the molecular dynamics simulation, and the time constant is set to 200; the exchange correlation functional is PBE, and the basis set is DZVP-MOLOPT-SR-GTH; the cutoff energy is set to 300 Ry, the SCF convergence criterion is set to 1E-5, and the diagonalization method is used for the calculation.

[0044] The theoretical structure diagrams of the constructed contact interface model before and after AIMD simulation calculations are as follows: Figure 1 As shown, where, Figure 1 Figure (a) in the figure is a model of the contact interface before AIMD simulation calculation. Figure 1 Figure (b) shows the adsorption of silicon particles with Al2O3. The steady-state model (i.e., ...) is derived from simulation calculations. Figure 1 As can be seen in Figure (b), Al and Si in Al2O3 form an AlSi alloy (the Si particles and Al2O3 particles undergo interfusion). This phenomenon is sufficient to prove at the atomic scale that the Al2O3 as a coating material is not simply physically attached to the silicon particles, but rather undergoes a strong chemical bond, thus providing a microscopic theoretical basis for the structural stability and interface strength of the coating layer.

[0045] Furthermore, the interface formation energy can be calculated as -6.42 eV / Å using Equation 2. 2 It can be observed that the interface formation energy between Si particles and Al2O3 particles is very low, further indicating that the bonding between Si particles and Al2O3 particles is very stable.

[0046] Step S3: Construct an interface model between Al2O3 and asphalt molecules. (1) Construction of the molecular model of asphalt Using the molecular modeling software Materials Studio, molecular models were constructed based on the molecular structural formulas of each component in asphalt. The components contained in asphalt include: coal tar pitch, cycloalkane aromatics, oxygen-containing polar aromatics, nitrogen-containing polar aromatics, and sulfur-containing polar aromatics.

[0047] (2) Add the molecular models of each component constructed in (1) to the Al2O3(100) surface model obtained in step S1 to construct the interface model. Perform AIMD simulation calculations on the interface model structure. The simulation conditions are the same as those for the AIMD simulation calculation of the contact interface model between silicon particles and Al2O3 in step S2.

[0048] The theoretical structure diagram of the interface model when it reaches a steady state after simulation by AIMD is shown below. Figures 2-6 ,in, Figure 2 This is an adsorption diagram of coal tar pitch components and Al2O3. Figure 3 This is an adsorption diagram of cycloalkane aromatic molecules with Al2O3. Figure 4 This is an adsorption diagram of oxygen-containing polar aromatic molecules with Al2O3. Figure 5 This is an adsorption diagram of nitrogen-containing polar aromatic molecules with Al2O3. Figure 6 This is an adsorption diagram of sulfur-containing polar aromatic molecules with Al2O3.

[0049] from Figures 2-6It can be observed that the molecules of each component in asphalt form H bonds or other chemical bonds with Al2O3. This is beneficial for further coating asphalt on the coating material, improving the stability between asphalt and Al2O3, and to a certain extent improving the bonding force between the carbon layer formed by asphalt after high-temperature calcination and Al2O3, thus obtaining a negative electrode material with good stability and enhancing the conductivity and coating stability of the negative electrode material.

[0050] Meanwhile, the interaction energies between the molecules of each component in asphalt and Al2O3, calculated using Equation 1, are shown in Table 1. Table 1 reveals that all component molecules interact with Al2O3. Based on the principle that lower energy equates to stronger interaction forces, Table 1 shows that coal tar pitch molecules and sulfur-containing polar aromatic molecules exhibit the strongest relatively strong interactions with Al2O3.

[0051] Table 1. Interaction energies between various molecules in asphalt and Al2O3 Step 2, preparation of the negative electrode material, as follows: Step B1: Under a nitrogen atmosphere, 100 g of micron-sized silicon particles (average particle size of 3 μm) and 0.5% Al2O3 nanoparticles (average particle size of 50 nm) were added to 1.5 L of ethanol solvent and stirred until homogeneous to obtain a nano-silicon liquid slurry. The slurry was then stirred, milled, and dried to obtain a negative electrode material precursor. The stirring and milling conditions were: stirring speed of 600 rpm and stirring time of 2 h.

[0052] Step B2: Add asphalt to the negative electrode material precursor. The mass of the asphalt is 40% of the negative electrode material precursor. After thorough mixing, dry to obtain the negative electrode material intermediate. The mixing time is 5 h and the drying temperature is 500℃. Step B3: Calcine the anode material intermediate at 1050°C for 2 hours to completely carbonize the asphalt and obtain the anode material.

[0053] Test Analysis: 1. Morphological analysis (1) The negative electrode material precursor prepared in step B1 of Example 1 was analyzed by SEM, and the results are as follows: Figure 7 As shown, where, Figure 7 This is a SEM image of the precursor material for the negative electrode.

[0054] from Figure 7 As can be seen, the white Al2O3 particles are uniformly distributed on the Si particles.

[0055] (2) The intermediate anode material prepared in step B2 of Example 1 was subjected to SEM testing and analysis. The test results are detailed in [link to SEM analysis]. Figure 8 ,in, Figure 8This is a SEM image of the intermediate material for the negative electrode.

[0056] from Figure 8 As can be seen, asphalt molecules form a perfect coating layer on the surface of the negative electrode precursor particles, making the entire surface relatively smooth and possessing the characteristics of asphalt. The surface has a complete asphalt coating structure with some layers, forming a Si-Al2O3-asphalt microstructure.

[0057] 2. Performance Analysis of Anode Materials The obtained negative electrode material was prepared into electrode slurries (referred to as experimental group numbers 1-5). Simultaneously, Si particles without Al2O3 coating were used as control materials to prepare electrode slurries (referred to as control group numbers 6-10). These were coated onto copper foil current collectors, sliced, and assembled into 2032 coin cells using lithium metal as the counter electrode and EC:DEC:DMC = 1:1:1 as the electrolyte. The electrochemical performance of the assembled coin cells was tested, and the results are shown in Table 2.

[0058] Table 2. Performance Test Results of Button Cells As can be seen from Table 2, the silicon particle coating structure is very good when coated with Al2O3, resulting in higher capacity performance (1800-2000 mAh / g) and more stable first coulombic efficiency (close to 90%). Moreover, the capacity retention rate can reach about 88% after 100 cycles.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for improving the coating effect of coating materials during the preparation of negative electrode materials, characterized in that, Includes the following steps: Step 1: Selection of coating materials Step S1: Construct a contact interface model between the coating material and the core material. The most thermodynamically stable crystal plane in the crystal surface model of the coating material is combined with the most thermodynamically stable crystal plane in the crystal surface model of the core material to construct a contact interface model. First-principles calculation software is used to perform ab initio molecular dynamics simulation on the model until the system reaches equilibrium or stability. Then the interface formation energy between the coating material and the core material is calculated. Step S2: Construct a contact interface model between the covering material and the shell material. The molecular model of the shell material is added to the thermodynamically stable crystal surface of the crystal surface model of the coating material to construct a contact interface model. The model is then subjected to ab initio molecular dynamics simulation using first-principles calculation software until the system reaches equilibrium or stability. Subsequently, the interaction energy between the coating material and the shell material is calculated. Step S3: Based on the calculation results in Step S1 and Step S2, select the coating material with the lowest interface formation energy and interaction energy. Step 2: Preparation of negative electrode material The selected coating material and core material are mixed evenly under a protective gas to form a liquid slurry. The liquid slurry is stirred, ground, and then granulated to obtain the anode material precursor. The core and shell material is coated onto the anode material precursor and then sintered at high temperature to obtain the anode material.

2. The method according to claim 1, characterized in that, The method for constructing the surface model of the coating material crystal and the surface model of the core material crystal in step S1 is as follows: after the material crystal model is optimized using first principles, it is slicing to obtain the surface model of the corresponding index plane. A vacuum layer is set, and the bottom atoms are fixed. Then, the first principles optimization is performed again to obtain the surface model. The vacuum layer is 15–30 Å.

3. The method according to claim 2, characterized in that, When the coating material or core material is a surface reconstruction material, the surface atoms of the surface model corresponding to the index surface need to be manually adjusted in a small random manner.

4. The method according to claim 1, characterized in that, The conditions for the ab initio molecular dynamics simulations in steps S1 and S2 are as follows: The total simulation duration is: The simulation will terminate when the system reaches equilibrium and the total simulation time reaches 5 ps. or If the total simulation time does not reach equilibrium within 20,000 fs, the simulation continues until the system stabilizes. The time step is 0.5 fs to 1 fs. or There exists an element H with a time step of 0.5 fs; The simulation temperature ranges from 298.15 K to 1200 K. The NPT ensemble is used to simulate the interface between the cladding material and the core material, while the NVT ensemble is used to simulate the interface between the cladding material and the shell material. The thermal bath method includes any one of CSVR, Nose-Hoover, Adaptive-Langevin, and Generalized Langevin Equation. The time constant is 200–500. The exchange-correlation functional is selected from any one of PBE, Pade, and B3LYP. The basis set includes any one of DZVP-MOLOPT-SR-GTH, TZVP-MOLOPT-GTH, TZV2P-MOLOPT-GTH, and TZV2PX-MOLOPT-GTH. The cutoff energy is 300 Ry to 600 Ry. The SCF convergence criterion is 1 × 10⁻⁶. -4 ~1×10 -8 The iterative calculation methods are: diagonalization method or orbital exchange method.

5. The method according to claim 1, characterized in that, The interface formation in step S1 can be calculated according to Equation 1, which is: (Equation 1) Among them, E sum E represents the system energy of the interface model. sur1 and E sur2 Let A represent the system energy under the surface models of the coating material and the core material, respectively, and let A be the area of ​​the interface model.

6. The method according to claim 1, characterized in that, The interaction energy in step S2 is calculated according to Equation 2, which is: (Equation 2) Among them, E con E represents the interaction between the coating material and the bitumen. tot E represents the system energy of the shell material and the coating material under coating behavior. sur and E es n represents the surface model energy of the coating material and the energy of the shell material itself, respectively. es This refers to the atomic number or molecular weight of the shell material.

7. The method according to any one of claims 1 to 6, characterized in that, The coating material includes any one of Al2O3, TiO2, graphite, and an amorphous carbon layer formed by organic matter; the core material is Si particles; and the shell material is asphalt.

8. The method according to claim 7, characterized in that, In step 2, the coating material is 0.5% of the core material mass, and the shell material is 30%-60% of the negative electrode material precursor mass.

9. The method according to claim 8, characterized in that, In step 2, the stirring and grinding conditions are: stirring time 1-5h, stirring speed 300-900r; the high-temperature sintering conditions are: temperature 800-1100℃, time 1-3h; the protective gas is nitrogen.

10. A negative electrode material, characterized in that, The negative electrode material is prepared by the method described in any one of claims 1 to 9.

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