Preparation method of high-thermal-conductivity hexagonal boron nitride film

Abstract

The invention relates to a preparation method of a high-thermal-conductivity hexagonal boron nitride film in the technical field of new materials, which comprises the following steps: acquiring particle size distribution data of hexagonal boron nitride powder treated by silane, and measuring the distribution condition of hexagonal boron nitride particles with different particle sizes by a particle size analyzer to obtain a particle size distribution curve; the influence of key process parameters on the heat-conducting property, the mechanical strength and the flame-retardant property is verified through experiments, performance data under different process parameters are obtained, and the influence rule of silanization treatment conditions on the surface property of hexagonal boron nitride and the performance of the composite material is focused on; and according to the heat-conducting property, the mechanical strength and the flame-retardant property data, optimizing process parameters by adopting a particle swarm optimization algorithm in a multi-objective optimization algorithm to obtain an optimal process parameter combination.

Description

Technical Field

[0001] The present invention relates to the technical field of new materials, specifically to a hexagonal boron nitride soft sheet, and particularly to a preparation method of a high-thermal-conductivity hexagonal boron nitride soft sheet. The thermal conductivity of the hexagonal boron nitride soft sheet prepared by this preparation method can be increased to more than 12 W / (m·K). Background Art

[0002] In the process of preparing a solid hexagonal boron nitride soft sheet with a high thermal conductivity, we face multiple interrelated technical challenges. First, although silanization treatment can improve the surface properties of hexagonal boron nitride particles, how to precisely control the reaction conditions to obtain the best surface modification effect still needs to be explored. Second, the arrangement of hexagonal boron nitride particles with different particle sizes in the silica gel matrix directly affects the heat conduction path, and how to optimize the particle arrangement to form an efficient heat conduction path is a key issue. Third, the interfacial bonding force between silanized hexagonal boron nitride and the silica gel matrix is crucial for the performance of the composite material, and it is necessary to deeply study the influence mechanism of silanization treatment on the interfacial action. In addition, there is a trade-off between ensuring the mechanical properties and increasing the filling amount of hexagonal boron nitride to obtain a high thermal conductivity. Finally, the complex relationship between process parameters such as mixing conditions, molding pressure, and curing time and the material formula, and how to synergistically optimize them to achieve the best comprehensive performance is the core problem in the preparation of high-performance composite soft sheets. Summary of the Invention

[0003] The present invention provides a preparation method of a high-thermal-conductivity hexagonal boron nitride soft sheet, mainly including the following steps:

[0004] S1. Obtain the particle size distribution data of the silane-treated hexagonal boron nitride powder, measure the distribution of hexagonal boron nitride particles with different particle sizes through a particle size analyzer, and obtain a particle size distribution curve;

[0005] S2. According to the particle size distribution curve, use the clustering analysis method in machine learning algorithms to divide the silanized hexagonal boron nitride particles into multiple categories according to the particle size, and determine the particle size range of each category. The silanization treatment improves the surface properties of hexagonal boron nitride particles with different particle sizes, which is beneficial to the subsequent preparation of composite materials;

[0006] S3. For the silanized hexagonal boron nitride particles of different particle size categories obtained by clustering analysis, use the finite element analysis method to simulate their arrangement in the silica gel matrix, obtain the heat conduction path structure under different arrangements, including the heat flow direction, thermal resistance distribution, and heat flux density distribution, and analyze the influence of silanization treatment on the interfacial bonding force between hexagonal boron nitride particles and the silica gel matrix;

[0007] S4. According to the simulation results of the heat conduction path structure, use the genetic algorithm in the optimization algorithm to optimize the arrangement of silanized hexagonal boron nitride particles to obtain the optimal arrangement plan. The optimal arrangement plan should make full use of the high thermal conductivity characteristics of silanized hexagonal boron nitride particles to form an efficient heat conduction path;

[0008] S5. On the basis of the optimal arrangement plan, verify the influence of different material formulations on the thermal conductivity through experiments, and obtain the thermal conductivity data under different formulations. The material formulations include the content of silanized hexagonal boron nitride, the type and proportion of the silica gel matrix, and the type and content of other additives. While ensuring the mechanical properties, it is necessary to maximize the filling amount of silanized hexagonal boron nitride to obtain a high thermal conductivity;

[0009] S6. According to the thermal conductivity data, use the regression analysis method to establish a mathematical model between the thermal conductivity and the material formulation, determine the best material formulation, and comprehensively consider the influence of the content of silanized hexagonal boron nitride on the thermal conductivity and mechanical properties to optimize the material formulation;

[0010] S7. Analyze the relationship between the best material formulation and the process parameters, and determine the key process parameters, including silanization reaction conditions, mixing time, mixing temperature, molding pressure, molding temperature, and curing time. The silanization reaction conditions will affect the surface properties of hexagonal boron nitride, and thus affect its compatibility and interfacial bonding force with the silica gel matrix;

[0011] S8. Verify the influence of the key process parameters on the thermal conductivity, mechanical strength, and flame retardancy through experiments, and obtain the performance data under different process parameters, focusing on the influence law of the silanization treatment conditions on the surface properties of hexagonal boron nitride and the properties of the composite material;

[0012] S9. According to the thermal conductivity, mechanical strength, and flame retardancy data, use the particle swarm optimization algorithm in the multi-objective optimization algorithm to optimize the process parameters to obtain the best combination of process parameters;

[0013] S10. Prepare the silanized hexagonal boron nitride / silica gel composite soft sheet according to the best combination of process parameters, verify its comprehensive performance, obtain the final thermal conductivity, mechanical strength, and flame retardancy data, determine the final preparation plan of the composite soft sheet, and improve the compatibility of hexagonal boron nitride and the silica gel matrix through silanization treatment. Combining the optimized material formulation and process parameters, prepare a solid hexagonal boron nitride soft sheet with a high thermal conductivity.

[0014] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0015] The present invention discloses a preparation method of a high - thermal - conductivity hexagonal boron nitride soft sheet. By subjecting hexagonal boron nitride powder to silanization treatment, its surface properties are improved, and its compatibility with the silica gel matrix is enhanced. The silanized hexagonal boron nitride particles are classified using particle size analysis and machine - learning clustering methods. The arrangement mode of the particles in the silica gel matrix is simulated by finite - element analysis, and a genetic algorithm is used to optimize the particle arrangement to form an efficient heat - conduction path. The influence of the material formula on the thermal conductivity is verified through experiments, and a mathematical model is established to determine the optimal formula. Further, the relationship between process parameters and performance is analyzed, and a multi - objective optimization algorithm is used to optimize the process parameters. Finally, a composite soft sheet with both high thermal conductivity, good mechanical strength, and flame - retardant properties is prepared. Through silanization treatment, particle - arrangement optimization, and process - parameter optimization, the comprehensive performance of the hexagonal boron nitride / silica gel composite soft sheet is significantly improved in the present invention. Detailed implementation manners

[0016] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0017] The preparation method of a high - thermal - conductivity hexagonal boron nitride soft sheet in this embodiment may specifically include:

[0018] S1. Obtain the particle - size distribution data of the silane - treated hexagonal boron nitride powder, measure the distribution of hexagonal boron nitride particles with different particle sizes through a particle size analyzer, and obtain a particle - size distribution curve.

[0019] Obtain the particle - size data of the silane - treated hexagonal boron nitride powder particles; according to the particle - size data, calculate the number of particles in different particle - size ranges to obtain a particle - size distribution statistical result; input the particle - size distribution statistical result into a preset curve - fitting algorithm to generate a particle - size distribution curve; if the particle - size distribution curve shows a multi - peak distribution, use a Gaussian mixture model to decompose the particle - size distribution curve to determine the particle - size range corresponding to each peak; according to the decomposition result of the Gaussian mixture model, calculate the proportion of each particle - size range to obtain the proportion data of particles with different particle sizes; compare the particle - size proportion data with a preset powder - property database to judge the influence degree of silanization treatment on the powder particle - size distribution; according to the comparison result, output a specific influence analysis report on the silanization treatment of the hexagonal boron nitride powder particle - size distribution.

[0020] Specifically, a particle size analyzer is an important instrument for measuring the particle size of powder. Through the principle of laser diffraction, the particle size distribution data of silane-treated hexagonal boron nitride powder can be quickly obtained. Taking a batch of silane-treated hexagonal boron nitride powder as an example, the original data measured by the particle size analyzer may contain the size information of tens of thousands of particles. These data are grouped and statistically analyzed according to the particle size range, such as 0-1μm, 1-2μm, 2-5μm, etc., to obtain the number of particles in each particle size range, thus forming a preliminary statistical result of the particle size distribution. The statistical result is input into a preset curve fitting algorithm, such as the least squares method, to generate a continuous particle size distribution curve. If the curve shows a multi-peak distribution, it indicates that there are multiple size concentration intervals in the powder, and a Gaussian mixture model needs to be used for decomposition. The Gaussian mixture model can decompose the complex multi-peak distribution into the superposition of several single-peak Gaussian distributions, and each Gaussian distribution represents a particle size concentration interval. Suppose the decomposition result shows that there are three main peaks, corresponding to the particle size ranges of 0.5-1μm, 2-3μm, and 4-5μm respectively. By calculating the area ratio under each peak, the proportion data of particles with different particle sizes can be obtained, such as small particles (0.5-1μm) accounting for 30%, medium particles (2-3μm) accounting for 50%, and large particles (4-5μm) accounting for 20%. These proportion data reflect the particle size distribution characteristics of the silane-treated hexagonal boron nitride powder. Comparing the obtained particle size proportion data with the preset powder property database can determine the influence degree of silane treatment on the particle size distribution of the powder. The database may contain data of untreated hexagonal boron nitride powder, such as small particles accounting for 20%, medium particles accounting for 60%, and large particles accounting for 20%. By comparison, it is found that silane treatment increases the proportion of small particles and decreases the proportion of medium particles, which may be due to the formation of a nanoscale coating on the surface of the powder by silane or the promotion of the dispersion of some particles. Based on the comparison result, a specific impact analysis report on the particle size distribution of silane-treated hexagonal boron nitride powder can be output. The report may point out that silane treatment increases the proportion of small particles in the powder and improves the dispersion of the powder, which is beneficial to improving the uniform distribution of the powder in the composite material, thereby enhancing the thermal conductivity and mechanical properties of the material. At the same time, the report may also remind attention to the possible surface chemical changes brought about by silane treatment and suggest further surface analysis to comprehensively evaluate the treatment effect. Through this systematic analysis process, the influence of silane treatment on the particle size distribution of hexagonal boron nitride powder can be deeply understood, providing an important basis for optimizing the treatment process and improving the material properties.

[0021] S2. According to the particle size distribution curve, using the clustering analysis method in machine learning algorithms, the silanized hexagonal boron nitride particles are divided into multiple categories according to the particle size, and the particle size range of each category is determined. The silanization treatment improves the surface properties of hexagonal boron nitride particles with different particle sizes, which is beneficial to the subsequent preparation of composite materials.

[0022] Obtain the particle size distribution data of silanized hexagonal boron nitride particles, generate a particle size distribution curve based on the particle size distribution data; use the K-means clustering algorithm to perform clustering analysis on the particle size distribution data, and determine the optimal number of clusters through clustering analysis; classify the silanized hexagonal boron nitride particles according to the clustering results to obtain particle categories in different particle size ranges; extract the surface property data corresponding to each particle size category and establish a particle size-surface property correlation model; set corresponding composite material preparation process parameters for particle categories in different particle size ranges; use the principal component analysis method to evaluate the influence of different particle size categories on the properties of the composite material; optimize the silanization process parameters according to the particle size distribution data and surface property data.

[0023] Specifically, obtaining the particle size distribution data of silanized hexagonal boron nitride particles is a key step in the preparation of composite materials. The particle size of powder particles can be measured by a laser diffraction particle size analyzer to obtain a particle size distribution curve. For example, for a batch of silanized hexagonal boron nitride powder, a bimodal distribution curve may be obtained, with the main peak at about 2 μm and the secondary peak near 0.5 μm. This distribution reflects that the silanization treatment may cause partial particle agglomeration. To analyze the particle size distribution characteristics in depth, the K-means clustering algorithm is used for data classification. This algorithm iteratively optimizes and groups data points with similar particle sizes into one category. In this example, three optimal clusters may be obtained: less than 1 μm, 1-3 μm, and greater than 3 μm. This classification method helps to more accurately describe the particle size composition of the powder. Based on the clustering results, the surface characteristics of particles in different particle size ranges can be further studied. For example, the specific surface area is measured by the nitrogen adsorption method, and it is found that the specific surface area of particles less than 1 μm can reach 50 m 2 / g, while that of particles greater than 3 μm is only 10 m 2 / g or so. This difference will directly affect the interfacial bonding strength between the particles and the matrix. Establishing a correlation model between particle size and surface characteristics is an important means to optimize the properties of composite materials. Multiple regression analysis can be used, taking the particle size as the independent variable and the specific surface area, surface energy, etc. as the dependent variables, to construct a mathematical model. This helps to predict the surface properties of particles with different particle sizes and provides guidance for the subsequent preparation of composite materials. For particles in different particle size ranges, it is necessary to adjust the preparation parameters of the composite material. For example, for particles smaller than 1μm, due to their large specific surface area, it may be necessary to increase the dosage of silane coupling agent to ensure sufficient surface modification. For particles larger than 3μm, it may be necessary to extend the mixing time to improve their dispersion uniformity in the matrix. The principal component analysis method can be used to evaluate the influence of particle size classification on the properties of composite materials. Through dimensionality reduction, it may be found that the first principal component is highly correlated with the thermal conductivity of the material, while the second principal component is closely related to the mechanical properties. This analysis helps to identify the key particle size factors and provides a direction for material design. Finally, based on the particle size distribution and surface characteristic data, the silanization treatment process can be optimized. For example, if it is found that the surface modification effect of particles in the range of 1-3μm is the best, the proportion of particles in this particle size range can be increased by adjusting the grinding time or selective sedimentation, etc. At the same time, it may be necessary to adjust the silane concentration or reaction time to ensure that particles with different particle sizes can obtain ideal surface modification effects. This optimization process will help to improve the overall properties of the composite material, such as enhancing the thermal conductivity or improving the mechanical strength.

[0024] S3. For the silanized hexagonal boron nitride particles of different particle size categories obtained by cluster analysis, use the finite element analysis method to simulate their arrangement patterns in the silica gel matrix, and obtain the thermal conduction path structures under different arrangement patterns, including the heat flow direction, thermal resistance distribution, and heat flux density distribution. Analyze the influence of the silanization treatment on the interfacial bonding force between the hexagonal boron nitride particles and the silica gel matrix.

[0025] Obtain the particle size distribution data of silanized hexagonal boron nitride particles, classify the particle size distribution data using a clustering algorithm to obtain the distribution characteristics of particles with different particle sizes; according to the distribution characteristics of the particles with different particle sizes, construct a geometric model of the particle arrangement in the silica gel to generate the initial conditions for finite element simulation; use the finite element method to simulate the heat conduction path of the particle arrangement model, analyze the distribution law of the heat flow direction to obtain the structural characteristics of the heat conduction path; according to the structural characteristics of the heat conduction path, calculate the spatial distribution of the thermal resistance, and determine the high-density area and low-density area of the heat flux density; evaluate the influence of the silanization treatment on the interfacial bonding force between the particles and the silica gel to obtain a quantitative index of the interfacial bonding force; according to the quantitative index of the interfacial bonding force, judge the effect of the silanization treatment on optimizing the heat conduction path, and determine the improvement direction of the thermal resistance and the heat flux density; use the finite element method to optimize the particle arrangement model, adjust the distribution of the heat flow direction and the heat flux density, and generate an optimized structural model of the heat conduction path.

[0026] Specifically, the clustering algorithm is used to divide the particle size categories of silanized hexagonal boron nitride particles, and K-means or hierarchical clustering methods can be adopted. Taking K-means as an example, a suitable K value (such as 3 or 5) is selected to cluster the particle size data, and the distribution characteristics of different particle size ranges are obtained. This helps to understand the arrangement of particles in the silica colloid and lays a foundation for constructing a geometric model. When constructing the geometric model, the spatial distribution of particles with different particle sizes can be considered. For example, large particle size particles (such as 50-100 μm) may show a relatively dispersed distribution, while small particle size particles (such as 1-10 μm) may fill between the large particles. This hierarchical structure can be realized through 3D modeling software to generate a microscopic structure model of the composite material. When simulating the heat conduction path by finite element method, the thermal physical properties of the material need to be set. Assume that the thermal conductivity of hexagonal boron nitride is 300 W / (m·K) and that of the silica colloid is 0.15 W / (m·K). During the simulation, the heat transfer path between high thermal conductivity particles can be observed and the heat flux density distribution can be analyzed. The results may show that heat mainly transfers along the shortest path between adjacent boron nitride particles, forming a "thermal bridge" phenomenon. The calculation of the thermal resistance distribution is based on the simulation results and can be represented by the reciprocal of the heat flux density. High heat flux density regions (such as particle contact areas) correspond to low thermal resistance, while low heat flux density regions (such as pure silica colloid regions) show high thermal resistance. This analysis helps to identify the heat conduction bottlenecks in the composite material and provides a basis for optimizing the design. The silanization treatment affects the interfacial bonding force between the particles and the silica colloid. It can be quantified by measuring the contact angle or conducting a peeling test. For example, the interfacial bonding strength between untreated boron nitride and the silica colloid may be 2 MPa, while it can be increased to 5 MPa after silanization treatment. This improvement is beneficial to reducing the interfacial thermal resistance and optimizing the heat conduction path. When evaluating the effect of the silanization treatment, the effective thermal conductivity before and after the treatment can be compared. Assume that the effective thermal conductivity of the composite material before treatment is 10 W / (m·K), and it can be increased to 12 W / (m·K) after treatment. This increase is due to the reduction of the interfacial thermal resistance and the optimization of the heat conduction network, reflecting the positive effect of the silanization treatment. When finally optimizing the particle arrangement, the proportion and distribution of particles with different particle sizes can be considered. For example, increasing the proportion of medium particle size particles (such as 20-50 μm) may help to form a more continuous heat conduction network. Through iterative optimization, a composite material structure model with higher thermal conductivity and more uniform thermal resistance distribution can be obtained, providing theoretical guidance for actual preparation.

[0027] S4. According to the simulation results of the heat conduction path structure, adopt the genetic algorithm in the optimization algorithm to optimize the arrangement of silanized hexagonal boron nitride particles to obtain the optimal arrangement plan. The optimal arrangement plan should make full use of the high thermal conductivity characteristics of silanized hexagonal boron nitride particles to form an efficient heat conduction path.

[0028] Obtain the simulation result data of the heat conduction path structure, and extract the heat conduction performance parameters; establish a heat conduction characteristic model of silanized hexagonal boron nitride particles according to the heat conduction performance parameters; initialize the particle arrangement population by using the genetic algorithm, and use the heat conduction path efficiency as the fitness function; optimize the particle arrangement method through the selection, crossover and mutation operations of the genetic algorithm; judge whether the value of the fitness function reaches the preset threshold, and if not, continue to execute the optimization operation; obtain the optimal particle arrangement scheme, generate the structure data of the high-efficiency heat conduction path; output the heat conduction performance optimization result of the silanized hexagonal boron nitride particles according to the optimal arrangement scheme.

[0029] Specifically, the simulation result data of the heat conduction path structure is the key basis for optimizing the heat conduction performance of silanized hexagonal boron nitride particles. These data contain important information such as the heat flow direction, heat resistance distribution, and heat flux density distribution. By analyzing these data, key heat conduction performance parameters such as effective thermal conductivity, interfacial thermal resistance, and heat flow channel efficiency can be extracted. For example, for a typical silica-based composite material sample, the effective thermal conductivity can be obtained as 12.5 W / (m·K), and the interfacial thermal resistance is 1×10^-8 m 2W / (m·K). Establishing a thermal conductivity model of silanized hexagonal boron nitride particles is an important means to understand and predict their heat conduction behavior. This model needs to consider factors such as particle size, shape, degree of surface treatment, and distribution in the matrix. A simplified model may be expressed as: λeff = λm(1 + αφ), where λeff is the effective thermal conductivity of the composite material, λm is the thermal conductivity of the matrix material, φ is the filler volume fraction, and α is a coefficient related to particle shape and arrangement. The genetic algorithm is a powerful optimization tool suitable for solving complex particle arrangement optimization problems. When initializing the particle arrangement population, a series of different particle distribution schemes can be randomly generated. Each scheme can be represented by a coding sequence. For example, in a 100×100×100 three-dimensional grid, each grid point can be represented by 0 or 1 to indicate whether there is a particle. The fitness function can be defined as the efficiency of the heat conduction path and can be quantified by calculating the effective thermal conductivity or heat flux density. The selection operation of the genetic algorithm simulates the natural selection process. The roulette wheel selection method can be used to give individuals with higher fitness a greater chance of being selected. The crossover operation can adopt single-point crossover or multi-point crossover. For example, one or more crossover points are randomly selected between two parent solutions, and the gene sequences between these points are exchanged. The mutation operation can randomly change the values of some genes to increase the diversity of the population. During the optimization process, it is necessary to continuously evaluate the fitness of individuals in each generation of the population. Suppose the preset threshold is a 50% increase in thermal conductivity. Then, only when the thermal conductivity of the optimal individual reaches 1.5 times the original value is the optimization goal considered achieved. If the threshold is not reached, it is necessary to continue with the selection, crossover, and mutation operations to generate a new generation of the population. The finally obtained optimal particle arrangement scheme will present an efficient heat conduction path. This arrangement may show that the particles form a continuous chain structure in the heat flow direction or a sheet-like distribution perpendicular to the heat flow direction. Through this optimization, the thermal conductivity of the composite material may be increased from the original 12.5 W / (m·K) to 18.75 W / (m·K) or higher. This optimization result not only improves the thermal conductivity of the material but also provides important guidance for the design and manufacture of high-performance thermal conductive composite materials. By adjusting the silanization treatment process, controlling the particle size distribution, and optimizing the forming process, the performance improvement predicted theoretically can be achieved in actual production. This has important application value in fields such as electronic packaging and radiator design.

[0030] S5. Based on the optimal arrangement scheme, verify the influence of different material formulations on the thermal conductivity through experiments and obtain the thermal conductivity data under different formulations. The material formulations include the content of silanized hexagonal boron nitride, the type and proportion of the silica gel matrix, and the type and content of other additives. While ensuring the mechanical properties, the filling amount of silanized hexagonal boron nitride should be maximized to obtain a high thermal conductivity.

[0031] Obtain the candidate material information of silanized hexagonal boron nitride, silica gel matrix and other additives, where the candidate material information includes the material types and proportion ranges; according to the candidate material information, use the orthogonal experimental design method to generate multiple groups of material formulation combinations, and each group of material formulation combinations includes the content of silanized hexagonal boron nitride, the type and proportion of silica gel matrix, and the type and content of other additives; for the multiple groups of material formulation combinations, use the experimental verification method to obtain the thermal conductivity data corresponding to each group of material formulation combinations and generate a thermal conductivity data set; for the thermal conductivity data set, use the multiple regression analysis method to establish a quantitative relationship model between the thermal conductivity and the content of silanized hexagonal boron nitride, the type and proportion of silica gel matrix, and the type and content of other additives; obtain the material mechanical property indexes, and according to the quantitative relationship model and the material mechanical property indexes, use the nonlinear programming algorithm to calculate the optimal filling amount of silanized hexagonal boron nitride under the mechanical property constraint conditions and generate the optimal filling amount value; for the multiple groups of material formulation combinations, adjust the content of silanized hexagonal boron nitride to the optimal filling amount value to generate multiple groups of optimized material formulation combinations; for the multiple groups of optimized material formulation combinations, use the experimental verification method to obtain the thermal conductivity data corresponding to each group of optimized material formulation combinations and generate an optimized thermal conductivity data set; according to the optimized thermal conductivity data set, use the quicksort algorithm to sort the multiple groups of optimized material formulation combinations in descending order of thermal conductivity and generate a sorted material formulation list; take the material formulation combination with the highest thermal conductivity in the sorted material formulation list as the final material formulation plan.

[0032] Specifically, after obtaining the optimal arrangement plan, a list of material formula combinations needs to be generated. This list includes the content of silanized hexagonal boron nitride, the types and proportions of silica gel matrices, and the types and contents of other additives. For example, a series of formulas can be designed where the content of silanized hexagonal boron nitride varies from 5% to 30%, the silica gel matrix selects two types, namely methyl vinyl silicone rubber and phenyl silicone rubber, and the content of other additives such as zinc oxide or alumina varies between 0.5% and 3%. According to these formula combinations, experimental verification is carried out to measure the thermal conductivity of each formula. The measurement method can adopt the transient plane heat source method or the laser flash method. Through these experiments, a data set containing different formulas and their corresponding thermal conductivities can be obtained. Next, regression analysis is performed on this data set to establish a relationship model between the thermal conductivity and the contents of each component. Multivariate linear regression or non-linear regression methods can be used, such as polynomial regression or support vector regression. This model will help understand the influence degree of each component on the thermal performance. With this relationship model, optimization calculations can be carried out to solve for the maximum filling amount of silanized hexagonal boron nitride on the premise of ensuring that the material has sufficient mechanical properties. Optimization methods such as the gradient descent method or the genetic algorithm can be used here. Suppose through calculation, the optimal filling amount is obtained as 25%. Based on this optimal filling amount, the previous material formula is adjusted to generate a new list of optimized formulas. For example, the following formulas may be obtained: 25% silanized hexagonal boron nitride, 73% methyl vinyl silicone rubber, and 2% zinc oxide. For these optimized formulas, experimental verification is carried out again to measure their thermal conductivities. The purpose of this step is to confirm the optimization effect and provide a basis for the selection of the final plan. Finally, these optimized formulas are sorted in descending order of thermal conductivity. Sorting can use simple comparison sorting algorithms such as quicksort or mergesort. The sorting result will intuitively show which formulas have the best thermal performance. Through this series of steps, not only the thermal performance of the material is optimized, but also a quantitative relationship between the component content and the performance is established. The advantage of this method is that it combines a theoretical model and experimental verification, which can improve the development efficiency while ensuring reliability. In addition, this method also provides reliable data support and a theoretical basis for further optimizing and improving the material performance.

[0033] S6. According to the thermal conductivity data, adopt the regression analysis method to establish a mathematical model between the thermal conductivity and the material formula, and determine the optimal material formula. Considering comprehensively the influence of the content of silanized hexagonal boron nitride on the thermal conductivity and mechanical properties, optimize the material formula.

[0034] Obtain multiple sets of material formulation data, extract the thermal conductivity and mechanical property indexes from the material formulation data, and construct an initial training data set; use the multiple regression algorithm to establish a mathematical relationship model between the material formulation and the thermal conductivity based on the initial training data set, and obtain a regression equation; in the mathematical relationship model, take the content of silanized hexagonal boron nitride as the independent variable and analyze the influence degree of the content of silanized hexagonal boron nitride on the thermal conductivity; optimize the regression parameters of the mathematical relationship model by the least squares method to determine the quantitative relationship between the material formulation and the thermal conductivity; according to the mechanical property test data, use the linear regression algorithm to analyze the correlation between the content of silanized hexagonal boron nitride and the mechanical properties of the material; construct a multi-objective optimization model, take the thermal conductivity and mechanical properties as optimization objectives, and combine the thermal conductivity and mechanical properties of the material; if there is an optimization conflict between the thermal conductivity and the mechanical properties, use the weighted average method to balance multiple optimization objectives to determine the optimal addition amount of silanized hexagonal boron nitride and obtain the optimized material formulation.

[0035] Specifically, obtaining material formulation data is the basis for optimizing thermal conductivity. For example, for a thermal conductive silicone material containing silanized hexagonal boron nitride, the thermal conductivity and mechanical property data under different formulations can be collected. Suppose there are 10 different formulations, each containing different proportions of silanized hexagonal boron nitride (such as 10%, 20%, 30%, etc.), a silicone matrix, and other additives. Multiple regression analysis is an effective method for establishing the relationship between thermal conductivity and material formulation. Through this method, a mathematical model can be obtained to describe how the thermal conductivity changes with the content of each component. For example, the model may indicate that the thermal conductivity is positively correlated with the content of silanized hexagonal boron nitride, but this relationship may be non-linear. In the regression model, special attention is paid to the variable of the content of silanized hexagonal boron nitride. By analyzing the magnitude and significance of its coefficient, the degree of its influence on thermal performance can be quantified. For example, it may be found that when the content of silanized hexagonal boron nitride increases from 20% to 30%, the thermal conductivity increases by 50%. The least squares method is a commonly used method for optimizing the parameters of the regression model. By minimizing the sum of squares between the predicted values and the actual observed values, the optimal fitting model parameters can be obtained. This enables more accurate prediction of the thermal conductivity under different formulations. Mechanical properties are equally important, and it is necessary to analyze the influence of the content of silanized hexagonal boron nitride on material strength, elastic modulus and other indicators. Linear regression analysis may reveal that as the content of silanized hexagonal boron nitride increases, the tensile strength of the material may decrease, but the hardness may increase. The multi-objective optimization model allows simultaneous consideration of thermal performance and mechanical properties. For example, the objective function can be set as the maximization of thermal conductivity, while the constraint conditions are to maintain a certain tensile strength and elastic modulus. In this way, the optimal content of silanized hexagonal boron nitride can be found without sacrificing mechanical properties. In actual situations, there may be a trade-off between thermal performance and mechanical properties. For example, increasing the content of silanized hexagonal boron nitride may increase the thermal conductivity, but at the same time reduce the flexibility of the material. In this case, the weighted average method can be adopted. Suppose it is considered that thermal performance and mechanical properties are equally important, a weight of 0.5 can be assigned to each of them, and then the formulation that maximizes the weighted performance index is searched. Through this series of analysis and optimization steps, the material formulation space can be systematically explored to find the formulation that achieves the best balance between thermal performance and mechanical properties. This method is not only applicable to thermal conductive silicone materials, but can also be extended to the optimization design of other composite materials, providing a scientific basis for the precise regulation of material properties.

[0036] S7. Analyze the relationship between the optimal material formulation and process parameters, and determine the key process parameters, including silanization reaction conditions, mixing time, mixing temperature, molding pressure, molding temperature, and curing time. The silanization reaction conditions will affect the surface properties of hexagonal boron nitride, and thus affect its compatibility and interfacial bonding force with the silicone matrix.

[0037] Obtain the data of the silanization reaction conditions, judge the influence degree of the reaction conditions on the surface properties of hexagonal boron, and obtain the surface property change parameters; according to the surface property change parameters, use the correlation analysis method to analyze the correlation between hexagonal boron and silica gel matrix compatibility, and obtain the compatibility index; if the compatibility index is greater than the preset threshold, judge that the binding force between hexagonal boron and silica gel matrix is strong, and obtain the binding force evaluation result; according to the binding force evaluation result, analyze the influence of mixing time and mixing temperature on the material formula, and obtain the optimized mixing process parameters; use the optimized mixing process parameters to study the effect of molding pressure and molding temperature on the molding quality of the material, and determine the molding process range; according to the molding process range, analyze the influence of curing time on the stability of the material properties, and obtain the optimized curing process value; comprehensively consider the optimized curing process value, use the data correlation modeling method to generate the correlation model between the material formula and process parameters, and determine the optimal process parameter combination.

[0038] Specifically, the silanization reaction conditions are crucial for the surface properties of hexagonal boron nitride. By regulating factors such as temperature, time, and silanizing agent concentration, the polarity and activity of the hexagonal boron nitride surface can be changed. For example, silanization treatment at a higher temperature may increase the number of surface hydroxyl groups, thereby improving its compatibility with the silica matrix. Conversely, a lower temperature may result in insufficient silanization, affecting subsequent properties. The surface property change parameters are closely related to the compatibility between hexagonal boron nitride and the silica matrix. By measuring indicators such as contact angle and surface energy, the degree of compatibility can be quantified. Suppose that after silanization treatment, the contact angle of hexagonal boron nitride decreases from the original 120° to 80°, which indicates an increase in its surface hydrophilicity and easier compatibility with the silica matrix. The compatibility index directly affects the bonding strength between hexagonal boron nitride and the silica matrix. Through tensile testing or shear strength testing, the interfacial bonding strength between the two can be evaluated. If the measured interfacial bonding strength exceeds 10 MPa, it can be considered that the bonding force is strong, which is beneficial to the improvement of the overall performance of the composite material. The mixing process has a significant impact on the material formulation. Appropriate mixing time and temperature can ensure the uniform dispersion of hexagonal boron nitride in the silica matrix. For example, using high-speed shear mixing and stirring at 60°C for 30 minutes may be more conducive to dispersion than conventional stirring. By microscopic observation or particle size analysis, the mixing parameters can be optimized to achieve the best dispersion effect. The molding process is crucial for the material quality. The molding pressure and temperature need to be adjusted according to the rheological properties of the material. For example, for composites with a high filling amount, a higher molding pressure (such as 20 MPa) and temperature (such as 150°C) may be required to ensure the molding quality. By testing different combinations of pressure and temperature, the optimal molding process range can be determined. The curing process has an important impact on the stability of the material properties. Appropriate curing time and temperature can ensure the full cross-linking of the silica matrix while avoiding embrittlement caused by over-curing. For example, using a stepwise temperature increase curing method, curing at 80°C for 2 hours first and then raising the temperature to 120°C for 4 hours may be more conducive to the balanced development of properties than constant temperature curing. By comprehensively analyzing each of the above links, a correlation model between the material formulation and process parameters can be established. This model may include multiple variables such as silanization conditions, mixing parameters, molding pressure and temperature, and curing regime. Through orthogonal experiments or response surface methods, these parameters can be optimized to finally determine a set of optimal process parameter combinations. For example, it may be concluded that silanization treatment at 120°C for 2 hours, high-speed shear mixing at 70°C for 40 minutes, molding at 160°C under a pressure of 25 MPa, and then stepwise curing at 90°C for 3 hours and 130°C for 5 hours can obtain the best comprehensive performance.

[0039] S8. Verify the influence of key process parameters on thermal conductivity, mechanical strength, and flame retardancy through experiments, and obtain performance data under different process parameters. Focus on the influence rules of silanization treatment conditions on the surface properties of hexagonal boron nitride and the properties of the composite material.

[0040] Obtain experimental data on the surface modification effect of silanization treatment conditions on hexagonal boron nitride at different temperatures, times, and concentrations, and extract characteristic information characterizing the modification effect; obtain data on the thermal conductivity, mechanical strength, and flame retardancy of the composite material under different process parameters, and establish a composite material performance dataset; use a clustering algorithm to classify the composite material performance dataset to obtain the influence law of the composite material performance under different silanization treatment conditions; if there is a significant correlation between the modification effect characteristics and the thermal conductivity data, then extract the key influencing factors of the thermal conductivity; according to the key influencing factors, establish a thermal conductivity prediction model, use a regression algorithm to optimize the parameters of the thermal conductivity prediction model, and obtain an optimized thermal conductivity prediction model; input the modification effect characteristics under different silanization treatment conditions into the optimized thermal conductivity prediction model to judge the influence degree of the silanization treatment conditions on the surface properties of hexagonal boron nitride; according to the influence degree, determine the optimal parameter range of the silanization treatment conditions to obtain the best process parameter combination of the composite material performance.

[0041] Specifically, silanization is an important method to improve the surface properties of hexagonal boron nitride. By adjusting parameters such as temperature, time, and concentration, different degrees of surface modification effects can be obtained. For example, silanization treatment at 80 °C for 2 hours with a 3% concentration of silane coupling agent may significantly increase the polarity of the surface of hexagonal boron nitride, which is beneficial to its compatibility with the silica matrix. The characteristics of the modification effect are closely related to the properties of the composite material. Taking thermal conductivity as an example, hexagonal boron nitride after silanization treatment may exhibit better dispersion, thereby increasing the thermal conductivity of the composite material. Assuming that at a 60% filling amount, the thermal conductivity of the untreated composite material is 12.5 W / (m·K), while after optimized silanization treatment, the thermal conductivity may increase to 162 W / (m·K). Cluster analysis helps to reveal the relationship between the silanization treatment conditions and the properties of the composite material. Through the K-means clustering algorithm, it may be found that the effects of two modes of high-temperature short-time treatment and low-temperature long-time treatment on thermal conductivity are similar, but there are differences in their effects on mechanical strength. This discovery helps to select appropriate treatment conditions according to different performance requirements in actual production. If it is found that there is a significant correlation between the characteristics of the modification effect and thermal conductivity, the key influencing factors can be further analyzed. For example, the increase in surface polarity may be the main reason for improving thermal conductivity. Based on this discovery, a linear regression model with surface polarity as the independent variable and thermal conductivity as the dependent variable can be established. By optimizing the model parameters using the least squares method, a mathematical expression for predicting thermal conductivity can be obtained. Using the optimized model, the influence of silanization treatment conditions on the surface properties of hexagonal boron nitride can be evaluated more accurately. Assuming that the model predicts that treatment at 90 °C, 4 hours, and 5% concentration can make the thermal conductivity of the composite material reach 13.5 W / (m·K), this combination of conditions may be considered the optimal parameter range. Finally, by comprehensively considering thermal conductivity, mechanical strength, and flame retardancy, the optimal process parameter combination is determined. For example, the silanization treatment conditions are 85 °C, 3.5 hours, and 4.5% concentration, the mixing time of the composite material is 2 hours, the mixing temperature is 40 °C, the molding pressure is 50 MPa, the molding temperature is 150 °C, and the curing time is 4 hours. This set of parameters not only ensures thermal conductivity but also takes into account the balance of mechanical strength and flame retardancy, reflecting the idea of multi-performance optimization.

[0042] S9. According to the thermal conductivity, mechanical strength, and flame retardancy data, the particle swarm optimization algorithm in the multi-objective optimization algorithm is used to optimize the process parameters to obtain the optimal process parameter combination.

[0043] Obtain the original experimental data of thermal conductivity, mechanical strength and flame retardancy, and establish a performance value matrix; according to the performance value matrix, determine the weight coefficients of the three objectives of thermal conductivity, mechanical strength and flame retardancy; use the particle swarm optimization algorithm to set the particle swarm size, the number of iterations and the learning factor parameters; construct a multi-objective optimization function, and use the performance values of thermal conductivity, mechanical strength and flame retardancy as input variables; in each iteration, update the position and velocity of the particle swarm, and calculate the fitness value of each particle; if the fitness value of the current particle is better than the historical best value, update the best value and the corresponding best parameter combination; when the preset number of iterations is reached, output the final best process parameter combination.

[0044] Specifically, in the process of optimizing the properties of composite materials, obtaining the original experimental data of thermal conductivity, mechanical strength, and flame retardancy is the key starting point. Taking a certain polymer-based composite material as an example, the measured thermal conductivity range in the experiment is 0.5 - 2.0 W / (m·K), the tensile strength is 50 - 150 MPa, and the limiting oxygen index is 28 - 35%. Organizing these data into a performance value matrix can intuitively reflect the distribution of each performance index. When determining the weight coefficients, the application scenario and performance requirements of the composite material need to be considered. Suppose this material is mainly used for electronic packaging, and the thermal conductivity is particularly important, a relatively high weight such as 0.5 can be assigned, while the mechanical strength and flame retardancy account for 0.3 and 0.2 respectively. This weight distribution reflects the performance priority and helps to make reasonable trade-offs in multi-objective optimization. The particle swarm optimization algorithm is an effective tool for solving such multi-objective optimization problems. During initialization, the particle swarm size can be set to 50, the number of iterations to 100, and the learning factors c1 = c2 = 2. These parameter settings can achieve a balance between computational efficiency and optimization effect. The particles represent possible combinations of process parameters, such as silanization temperature, time, and concentration, etc. When constructing the multi-objective optimization function, the three performance indicators are normalized to eliminate the influence of dimensions. For example, the thermal conductivity can be divided by 2.0 W / (m·K), the tensile strength by 150 MPa, and the limiting oxygen index by 35%. After such processing, the three indicators are all mapped to the 0 - 1 interval, which is convenient for comprehensive evaluation. During the iteration process, the positions and velocities of the particles are continuously updated. The position represents the specific process parameter values, such as a silanization temperature of 80°C, a time of 2 hours, and a concentration of 3%, etc. The velocity determines the change direction and amplitude of the parameters. After each iteration, the performance indicators corresponding to each particle are calculated, and the comprehensive fitness value is obtained according to the weight coefficients. The update of the best value reflects the progress of the optimization process. For example, the initial best combination may be a thermal conductivity of 1.5 W / (m·K), a tensile strength of 100 MPa, and a limiting oxygen index of 30%. As the iteration progresses, it may be found that a new parameter combination can increase the thermal conductivity to 1.8 W / (m·K) while keeping other properties unchanged, and at this time, the best value and the best parameter combination will be updated. After the preset number of iterations, the algorithm outputs the final best combination of process parameters. This set of parameters can achieve the optimal balance of performance under the given weights, such as a silanization temperature of 90°C, a time of 2.5 hours, and a concentration of 3.5%. This result provides important guidance for the actual production of composite materials and helps to improve product performance and production efficiency.

[0045] S10. Prepare a silanized hexagonal boron nitride / silica gel composite soft sheet according to the best combination of process parameters, verify its comprehensive properties, obtain the final data of thermal conductivity, mechanical strength, and flame retardancy, and determine the final preparation plan for the composite soft sheet. Improve the compatibility between hexagonal boron nitride and the silica gel matrix through silanization treatment, and combine the optimized material formula and process parameters to prepare a solid hexagonal boron nitride soft sheet with high thermal conductivity.

[0046] Obtain the compatibility data of silanized hexagonal boron nitride and silica gel matrix; according to the compatibility data, combine the preset formula values and process values to generate composite material preparation parameters; according to the composite material preparation parameters, use a hot pressing process to prepare a solid hexagonal boron nitride soft sheet to obtain the soft sheet; for the soft sheet, obtain thermal conductivity data through a thermal conductivity tester, and judge whether the thermal conductivity data meets the preset thermal conductivity threshold; if the thermal conductivity data meets the thermal conductivity threshold, use a tensile testing machine to obtain mechanical property data, and judge whether the mechanical property data meets the preset mechanical property threshold; if the mechanical property data meets the mechanical property threshold, obtain flame retardancy data through a vertical burning test, and judge whether the flame retardancy data meets the preset flame retardancy threshold; if the thermal conductivity data, the mechanical property data, and the flame retardancy data all meet the corresponding performance thresholds, determine the preparation scheme value of the composite soft sheet; according to the preparation scheme value, generate a composite soft sheet preparation process document.

[0047] Specifically, the silanization treatment is a key step in improving the compatibility between hexagonal boron nitride and the silica gel matrix. By introducing organosilane groups on the surface of boron nitride, its chemical affinity with the silica gel matrix can be enhanced. For example, using γ-aminopropyltriethoxysilane for surface treatment can form an organosilane layer containing amino groups on the surface of boron nitride. This treatment not only increases the interfacial bonding strength between boron nitride and silica gel but also improves the dispersibility and uniformity of the composite material. When determining the preparation parameters of the composite material, factors such as filler content, curing agent dosage, and curing temperature need to be comprehensively considered. For example, for a silica gel composite containing 30 wt% silanized hexagonal boron nitride, 1.5 wt% dicumyl peroxide can be selected as the curing agent, and hot pressing is carried out at 170 °C for 2 hours. The selection of these parameters directly affects the final properties of the composite material. The hot pressing process is an effective method for preparing solid hexagonal boron nitride soft sheets. By controlling the pressure, temperature, and time, a composite soft sheet with high density and uniform thickness can be obtained. Taking the above formula as an example, a process condition of 10 MPa pressure, 170 °C temperature, and holding pressure for 2 hours can be adopted. Such process conditions are conducive to the full curing of the material and the release of internal stress, thereby obtaining a soft sheet with stable performance. The thermal conductivity test is the primary step in evaluating the performance of the composite soft sheet. The steady-state hot plate method is used to measure the thermal conductivity. If the obtained thermal conductivity reaches above 2 W / (m·K), it can be considered to meet the preset threshold. This high thermal conductivity performance stems from the formation of an effective heat conduction network by the silanized hexagonal boron nitride in the silica gel matrix. The mechanical property test mainly focuses on the tensile strength and elongation at break of the composite soft sheet. Through the tensile test, if the tensile strength of the soft sheet exceeds 3 MPa and the elongation at break is greater than 100%, its mechanical properties can be determined to meet the standard. This excellent mechanical property benefits from the good interfacial bonding between the filler and the matrix. The flame retardancy test uses the vertical burning method to evaluate the self-extinguishing time and burning rate of the material. If the composite soft sheet can self-extinguish within 10 seconds in the vertical burning test and the char length is less than 25 mm, its flame retardancy can be considered to meet the requirements. The improvement of this flame retardancy is mainly attributed to the barrier effect and thermal stability of hexagonal boron nitride. When all the performance indicators of the composite soft sheet reach the preset threshold, the final preparation plan can be determined. This plan should include not only the raw material ratio and process parameters but also the detailed operation process and quality control measures. For example, for the composite soft sheet with all the above-mentioned performance indicators meeting the standards, its preparation process document should record in detail the whole process from raw material pretreatment to finished product inspection, including silanization treatment conditions, mixing ratio, mixing method, molding process parameters, etc. Through this series of strict experimental designs and performance evaluations, a hexagonal boron nitride / silica gel composite soft sheet with high thermal conductivity, good mechanical properties, and excellent flame retardancy can be obtained. This material has broad application prospects in the fields of electronic packaging, heat dissipation interfaces, etc.

[0048] As described above, this is only the specific implementation manner of this specification. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein. It should be understood that the protection scope of this specification is not limited thereto. Any person skilled in the art within the technical scope disclosed in this specification can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of this specification.

Claims

1. A method for preparing a high thermal conductivity hexagonal boron nitride film, characterized in that: The method comprises the following steps: S1. Obtaining the particle size distribution data of hexagonal boron nitride powder treated with silane, measuring the distribution of hexagonal boron nitride particles of different particle sizes by a particle size analyzer, and obtaining a particle size distribution curve; S2. According to the particle size distribution curve, the cluster analysis method in the machine learning algorithm is used to classify the silanized hexagonal boron nitride particles into multiple categories according to the particle size, and the particle size range of each category is determined. The silanization treatment improves the surface properties of hexagonal boron nitride particles of different particle sizes, which is beneficial to the subsequent preparation of composite materials; S3. For the silanized hexagonal boron nitride particles of different particle sizes obtained by cluster analysis, the finite element analysis method was used to simulate their arrangement in the silica gel matrix, and the heat conduction path structure under different arrangements was obtained, including the heat flow direction, thermal resistance distribution and heat flux density distribution, and the effect of silanization treatment on the interface bonding strength between the hexagonal boron nitride particles and the silica gel matrix was analyzed; S4. According to the simulation results of the heat conduction path structure, the genetic algorithm in the optimization algorithm is used to optimize the arrangement of the silanized hexagonal boron nitride particles to obtain the optimal arrangement scheme. The optimal arrangement scheme should make full use of the high thermal conductivity of the silanized hexagonal boron nitride particles to form an efficient heat conduction path; S5. Based on the optimal arrangement scheme, the influence of different material formulas on thermal conductivity is verified through experiments to obtain thermal conductivity data under different formulas. The material formula includes the content of silanized hexagonal boron nitride, the type and proportion of the silica gel matrix, and the type and content of other additives. It is necessary to maximize the filling amount of silanized hexagonal boron nitride while ensuring the mechanical properties to obtain a high thermal conductivity. S6. Based on the thermal conductivity data, a regression analysis method is used to establish a mathematical model between thermal conductivity and material formula, determine the best material formula, comprehensively consider the effect of silanized hexagonal boron nitride content on thermal conductivity and mechanical properties, and optimize the material formula; S7. Analyze the relationship between the optimal material formula and process parameters, and determine the key process parameters, including silanization reaction conditions, mixing time, mixing temperature, molding pressure, molding temperature and curing time. The silanization reaction conditions will affect the surface properties of hexagonal boron nitride, and thus affect its compatibility and interfacial bonding with the silica gel matrix; S8. Verify the influence of key process parameters on thermal conductivity, mechanical strength and flame retardancy through experiments, obtain performance data under different process parameters, and focus on the influence of silanization treatment conditions on the surface properties of hexagonal boron nitride and the performance of composite materials; S9. According to the thermal conductivity, mechanical strength and flame retardant performance data, the particle swarm optimization algorithm in the multi-objective optimization algorithm is used to optimize the process parameters and obtain the best process parameter combination; S10. Prepare silanized hexagonal boron nitride / silicone silica gel composite film according to the optimal process parameter combination, verify its comprehensive performance, obtain the final thermal conductivity, mechanical strength and flame retardant performance data, determine the final composite film preparation plan, improve the compatibility of hexagonal boron nitride and silica gel matrix through silanization treatment, and combine the optimized material formula and process parameters to prepare a solid hexagonal boron nitride film with high thermal conductivity.

2. The method for preparing a high thermal conductivity hexagonal boron nitride film according to claim 1, characterized in that: The S1 includes: Obtain particle size data of hexagonal boron nitride powder treated with silane; According to the particle size data, the number of particles in different particle size ranges is calculated to obtain particle size distribution statistics; Inputting the particle size distribution statistical results into a preset curve fitting algorithm to generate a particle size distribution curve; If the particle size distribution curve presents a multi-peak distribution, a Gaussian mixture model is used to decompose the particle size distribution curve to determine the particle size range corresponding to each peak; According to the decomposition result of the Gaussian mixture model, the proportion of each particle size range is calculated to obtain the proportion data of particles of different particle sizes; Comparing the particle size ratio data with a preset powder property database to determine the degree of influence of the silane treatment on the powder particle size distribution; According to the comparison results, an analysis report on the specific impact of silane treatment on the particle size distribution of hexagonal boron nitride powder is output.

3. The method for preparing a high thermal conductivity hexagonal boron nitride film according to claim 1, characterized in that: The S2 comprises: Acquiring particle size distribution data of silanized hexagonal boron nitride particles, and generating a particle size distribution curve according to the particle size distribution data; Performing cluster analysis on the particle size distribution data using a K-means clustering algorithm, and determining an optimal number of clusters through cluster analysis; According to the clustering results, the silanized hexagonal boron nitride particles were classified to obtain particle categories with different particle size ranges; Extracting surface property data corresponding to each particle size category and establishing a particle size-surface property correlation model; According to the particle types in different particle size ranges, set the corresponding composite material preparation process parameters; The principal component analysis method was used to evaluate the effect of different particle size categories on the properties of the composites; According to the particle size distribution data and surface characteristic data, the silanization treatment process parameters are optimized.

4. The method for preparing a high thermal conductivity hexagonal boron nitride film according to claim 1, characterized in that: The S3 includes: Obtaining particle size distribution data of silanized hexagonal boron nitride particles, and classifying the particle size distribution data using a clustering algorithm to obtain distribution characteristics of particles with different particle sizes; According to the distribution characteristics of the particles with different particle sizes, a geometric model of the arrangement of particles in the colloidal silica is constructed to generate initial conditions for finite element simulation; The finite element method is used to simulate the heat conduction path of the particle arrangement model, the distribution law of the heat flow direction is analyzed, and the structural characteristics of the heat conduction path are obtained; According to the structural characteristics of the heat conduction path, the spatial distribution of thermal resistance is calculated to determine the high-density area and the low-density area of ​​heat flux density; Evaluate the effect of silanization treatment on the interfacial bonding strength between particles and silica gel, and obtain a quantitative index of interfacial bonding strength; According to the quantitative index of the interface bonding force, the effect of silanization treatment on optimizing the heat conduction path is judged, and the improvement direction of thermal resistance and heat flux density is determined; The particle arrangement model is optimized by using a finite element method, the heat flow direction and the distribution of heat flux density are adjusted, and an optimized heat conduction path structure model is generated.

5. A method for preparing a high thermal conductivity hexagonal boron nitride film according to any one of claims 1 to 4, characterized in that: The S4 includes: Obtain simulation result data of heat conduction path structure and extract heat conduction performance parameters; According to the thermal conductivity performance parameters, a thermal conductivity characteristic model of silanized hexagonal boron nitride particles is established; A genetic algorithm is used to initialize the particle arrangement population, and the efficiency of the heat conduction path is used as the fitness function; Optimizing the arrangement of particles through the selection, crossover and mutation operations of the genetic algorithm; Determine whether the fitness function value reaches a preset threshold, and if not, continue to perform the optimization operation; Obtain the optimal particle arrangement solution and generate structural data for efficient heat conduction pathways; According to the optimal arrangement scheme, the thermal conductivity optimization result of the silanized hexagonal boron nitride particles is output.

6. A method for preparing a high thermal conductivity hexagonal boron nitride film according to any one of claims 1 to 4, characterized in that: The S6 comprises: Acquire multiple sets of material formula data, extract thermal conductivity and mechanical performance indicators from the material formula data, and construct an initial training data set; Using a multivariate regression algorithm, based on the initial training data set, a mathematical relationship model between material formula and thermal conductivity is established to obtain a regression equation; In the mathematical relationship model, the content of silanized hexagonal boron nitride is used as an independent variable to analyze the influence of the content of silanized hexagonal boron nitride on the thermal conductivity; By using the least square method, the regression parameters of the mathematical relationship model are optimized to determine the quantitative relationship between the material formula and the thermal conductivity; According to the mechanical properties test data, the linear regression algorithm is used to analyze the correlation between the silanized hexagonal boron nitride content and the mechanical properties of the material; Construct a multi-objective optimization model, taking thermal conductivity and mechanical properties as optimization targets, and combining the thermal conductivity and mechanical properties of the material; If there is an optimization conflict between the thermal conductivity and the mechanical properties, a weighted average method is used to balance multiple optimization objectives, determine the optimal amount of silanized hexagonal boron nitride added, and obtain an optimized material formula.

7. A method for preparing a high thermal conductivity hexagonal boron nitride film according to any one of claims 1 to 4, characterized in that: The S7 comprises: Acquire silanization reaction condition data, determine the influence of the reaction conditions on the surface properties of hexagonal boron, and obtain surface property change parameters; According to the surface property change parameters, a correlation analysis method is used to analyze the correlation between the compatibility of hexagonal boron and silica gel, and a compatibility index is obtained; If the compatibility index is greater than a preset threshold, it is determined that the binding force between the hexagonal boron and the silica gel base is strong, and a binding force evaluation result is obtained; According to the bonding strength evaluation results, the influence of mixing time and mixing temperature on the material formula is analyzed to obtain the mixing process optimization parameters; The mixing process optimization parameters are adopted to study the effects of molding pressure and molding temperature on the molding quality of the material and determine the molding process range; According to the molding process range, the influence of the curing time on the stability of the material properties is analyzed to obtain the curing process optimization value; Based on the optimization value of the curing process, a data association modeling method is used to generate an association model between material formula and process parameters to determine the best combination of process parameters.

8. A method for preparing a high thermal conductivity hexagonal boron nitride film according to any one of claims 1 to 4, characterized in that: The S8 includes: Obtain experimental data on the surface modification effects of hexagonal boron nitride under silanization treatment conditions at different temperatures, times and concentrations, and extract characteristic information that characterizes the modification effects; Obtain the thermal conductivity, mechanical strength and flame retardant performance data of composite materials under different process parameters, and establish a composite material performance data set; A clustering algorithm is used to classify the composite material performance data set to obtain the influence rules of the composite material performance under different silanization treatment conditions; If there is a significant correlation between the modification effect characteristics and the thermal conductivity data, then extract the key influencing factors of thermal conductivity; According to the key influencing factors, a thermal conductivity prediction model is established, and a regression algorithm is used to optimize the parameters of the thermal conductivity prediction model to obtain an optimized thermal conductivity prediction model; Inputting the modification effect characteristics under different silanization treatment conditions into the optimized thermal conductivity prediction model to determine the influence of the silanization treatment conditions on the surface properties of hexagonal boron nitride; According to the degree of influence, the optimal parameter range of the silanization treatment conditions is determined to obtain the best process parameter combination for the performance of the composite material.

9. A method for preparing a high thermal conductivity hexagonal boron nitride film according to any one of claims 1 to 4, characterized in that: The S10 includes: Obtain the compatibility data of hexagonal boron nitride and silica gel matrix after silanization treatment; Generating composite material preparation parameters according to the compatibility data in combination with preset formula values ​​and process values; According to the composite material preparation parameters, a solid hexagonal boron nitride film is prepared by a hot pressing process to obtain a film; For the film, obtaining thermal conductivity data through a thermal conductivity tester, and determining whether the thermal conductivity data meets a preset thermal conductivity performance threshold; If the thermal conductivity data meets the thermal conductivity performance threshold, a tensile testing machine is used to obtain mechanical data to determine whether the mechanical data meets the preset mechanical performance threshold; If the mechanical data meets the mechanical property threshold, flame retardancy data is obtained through a vertical burning test to determine whether the flame retardancy data meets the preset flame retardancy threshold; If the thermal conductivity data, the mechanical data and the flame retardancy data all meet the corresponding performance thresholds, then determining the preparation solution value of the composite film; According to the preparation scheme value, a composite film preparation process file is generated.