Preparation method of environment-friendly flame-retardant aluminum nitride silica gel composite film

Abstract

The invention relates to a preparation method of an environment-friendly flame-retardant aluminum nitride silica gel composite film in the field of new materials, which comprises the following steps: selecting a silane coupling agent to carry out surface modification treatment on spherical aluminum nitride according to surface energy distribution characteristics, and introducing hydrophilic groups on the surface of the spherical aluminum nitride through chemical grafting reaction; a hydrophilic group is grafted on the surface of spherical aluminum nitride by adopting a silane modification method, and the hydrophilic group is hydroxyl or carboxyl, so that the surface activity and hydrophilicity are improved; mixing the silane-modified spherical aluminum nitride with a silica gel matrix to obtain dispersity data of the mixture, and judging whether the dispersity of the filler is uniform or not and the standard deviation of particle size distribution is less than 0.5 mu m; according to the dispersity data of the mixture, the filling amount of the spherical aluminum nitride is adjusted, and the heat conduction performance, thermal expansion performance and heat resistance data of the composite material under different filling amounts are obtained, so that the heat conduction coefficient of the composite material film is larger than 18 W / (m.K), the thermal expansion coefficient is smaller than 30 ppm / DEG C, and the heat resistance temperature is higher than 200 DEG C.

Description

Technical Field

[0001] The present invention relates to the field of new materials, in particular to an aluminum nitride silicone composite material soft film material, and in particular to a method for preparing an environmentally friendly flame-retardant aluminum nitride silicone composite material soft film having a thermal conductivity of up to 18 W / (m·K) obtained after being treated with silane. Background Art

[0002] In the process of preparing environmentally friendly flame-retardant composite spherical aluminum nitride films, spherical aluminum nitride is used as an inorganic filler, and its surface hydrophilicity and compatibility with the silica gel matrix directly affect the performance of the composite material. As an inorganic material with a crystal structure, spherical aluminum nitride has excellent heat resistance, thermal conductivity and low thermal expansion coefficient, but its surface energy is high and the interfacial force with the organic silica gel matrix is ​​weak. How to improve the surface hydrophilicity of spherical aluminum nitride and enhance the interfacial bonding with the silica gel matrix while maintaining the excellent performance of spherical aluminum nitride is a key technical problem faced in the preparation of high-performance environmentally friendly flame-retardant composite spherical aluminum nitride films. The insufficient surface hydrophilicity of spherical aluminum nitride leads to weak interfacial force with the silica gel matrix, and it is easy to agglomerate and settle during the preparation of the composite material, affecting the uniformity and stability of the material. At the same time, the insufficient interfacial bonding force also limits the filling amount of spherical aluminum nitride filler in the composite material, making it difficult to give full play to its excellent performance. Therefore, how to improve the surface hydrophilicity of spherical aluminum nitride and enhance the interfacial interaction force with the silica gel matrix by surface modification or introduction of hydrophilic groups without affecting the original performance of spherical aluminum nitride is a technical problem that needs to be solved urgently in the preparation of high-performance, environmentally friendly, flame-retardant composite spherical aluminum nitride films. Summary of the invention

[0003] The present invention provides a method for preparing an environmentally friendly flame-retardant aluminum nitride silica gel composite film, the method comprising the following steps:

[0004] Step S101, obtaining surface energy data of spherical aluminum nitride, analyzing the specific manifestations of insufficient surface hydrophilicity, including a contact angle greater than 90 degrees and a surface free energy lower than 72 mN / m, and determining surface energy distribution characteristics;

[0005] Step S102, selecting a silane coupling agent to perform surface modification treatment on the spherical aluminum nitride according to the surface energy distribution characteristics, and introducing hydrophilic groups on its surface through a chemical grafting reaction;

[0006] Step S103, using a silane modification method to graft hydrophilic groups on the surface of the spherical aluminum nitride, the hydrophilic groups being hydroxyl groups or carboxyl groups, to improve its surface activity and hydrophilicity;

[0007] Step S1014, obtaining the surface energy data of the spherical aluminum nitride after silane modification, judging whether its surface hydrophilicity reaches a preset threshold, the contact angle is less than 60 degrees and the surface free energy is higher than 50 mN / m, if not, adjusting the silane modification process conditions;

[0008] Step S105, mixing the silane-modified spherical aluminum nitride with the silica gel matrix, obtaining dispersion data of the mixture, and judging whether the dispersion of the filler is uniform and the standard deviation of the particle size distribution is less than 0.5 μm;

[0009] Step S106, adjusting the filling amount of the spherical aluminum nitride according to the dispersion data of the mixture, and obtaining the thermal conductivity, thermal expansion and heat resistance data of the composite material at different filling amounts;

[0010] Step S107, analyzing the performance data of the composite material under different filling amounts, determining the optimal filling amount, so that the thermal conductivity of the composite film is greater than 18W / (m·K), the thermal expansion coefficient is less than 30ppm / °C, and the heat-resistant temperature is higher than 200°C;

[0011] Step S108, preparing a silane-modified spherical aluminum nitride / silica gel composite film sample with an optimal filling amount, obtaining composite material interface bonding force data, and determining whether the interface shear strength is greater than 10 MPa. If not, re-optimizing the silane modification process;

[0012] Step S109, determining the final silane modification scheme and composite material formula through the interface bonding force data, and preparing an environmentally friendly flame-retardant spherical aluminum nitride / silicone composite material film with high thermal conductivity, low thermal expansion, and high heat resistance.

[0013] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0014] The invention discloses a method for preparing an environmentally friendly flame-retardant aluminum nitride silica gel composite film. Aiming at the problem of insufficient hydrophilicity of the surface of spherical aluminum nitride, a silane coupling agent is used for surface modification, and a hydrophilic group is introduced through a chemical grafting reaction to improve its surface activity. By adjusting the silane modification process conditions, the surface energy of the modified spherical aluminum nitride can reach a preset threshold. The modified spherical aluminum nitride is mixed with a silica gel matrix, and the filling amount is optimized to obtain the best performance, thereby obtaining an aluminum nitride / silicone composite film. The invention solves the problem of insufficient interface bonding between spherical aluminum nitride and silica gel matrix through surface modification and formula optimization, and prepares an environmentally friendly flame-retardant composite film with high thermal conductivity, low thermal expansion and high heat resistance. The method can effectively improve the comprehensive performance of the composite material and provide a new material solution for the field of electronic packaging. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below in conjunction with specific embodiments.

[0016] The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film of this embodiment comprises the following steps:

[0017] Step S101, obtaining surface energy data of spherical aluminum nitride, analyzing the specific manifestations of insufficient surface hydrophilicity, including a contact angle greater than 90 degrees and a surface free energy lower than 72 mN / m, and determining surface energy distribution characteristics.

[0018] Step S101 includes: obtaining a spherical aluminum nitride sample, measuring the contact angle between the surface of the spherical aluminum nitride sample and water using a contact angle meter, and if the contact angle is greater than 90 degrees, determining that the surface hydrophilicity of the spherical aluminum nitride is insufficient; measuring the surface free energy of the spherical aluminum nitride using a surface tension meter, and if the surface free energy is lower than 72mN / m, further determining that the surface hydrophilicity of the spherical aluminum nitride is poor; drawing a surface energy distribution diagram based on the contact angle and the surface free energy data at different surface positions of the spherical aluminum nitride to obtain the distribution characteristics of the surface energy; using a support vector machine algorithm, with the contact angle and the surface free energy as the characteristics, establishing a prediction model for the surface hydrophilicity of the spherical aluminum nitride; using the prediction model, predicting the surface hydrophilicity of other batches of spherical aluminum nitride samples, and determining the stability of the surface treatment process; comprehensively analyzing the contact angle, the surface free energy surface energy data and the surface energy distribution characteristics, determining the reason for the insufficient surface hydrophilicity of the spherical aluminum nitride, and providing a basis for optimizing the surface treatment process.

[0019] Specifically, the acquisition of surface energy data of spherical aluminum nitride samples is a key step in evaluating their hydrophilicity. The contact angle meter determines the contact angle by observing the morphology of water droplets on the sample surface. For example, if the measured contact angle is 120 degrees, which is significantly greater than the 90-degree threshold, it can be preliminarily judged that the surface hydrophilicity of the sample is insufficient. The surface tension meter calculates the surface free energy by measuring the degree of spreading of the droplet on the solid surface. Assuming that the surface free energy of a sample is measured to be 65mN / m, which is lower than the standard value of 72mN / m, it further confirms that its surface hydrophilicity is poor. In order to fully understand the distribution characteristics of the surface energy of spherical aluminum nitride, multi-point measurements are required at different locations of the sample. Taking a spherical sample with a diameter of 10mm as an example, 20 measurement points can be evenly selected on its surface, and the contact angle and surface free energy data of each point can be recorded. Plotting these data into a heat map can intuitively display the distribution of surface energy. For example, the heat map may show that the central area has better hydrophilicity (smaller contact angle, higher surface free energy), while the edge area has poor hydrophilicity. The application of the support vector machine algorithm enables the establishment of a prediction model for the hydrophilicity of the surface of spherical aluminum nitride. A classification model is trained with contact angle and surface free energy as feature inputs and hydrophilicity rating as output. For example, hydrophilicity can be divided into four levels: "excellent", "good", "average" and "poor". By learning from a large amount of sample data, the model can accurately predict the hydrophilicity level of new samples. The establishment of such a prediction model helps to quickly evaluate the surface treatment effects of different batches of samples, thereby judging the stability of the process. In practical applications, assuming that the model is used to predict 10 batches of spherical aluminum nitride samples, it is found that the hydrophilicity level of 8 batches is "good", 1 batch is "excellent", and 1 batch is "average". This result shows that the current surface treatment process has good stability, but there is still room for optimization. Comprehensive analysis of surface energy data and distribution characteristics can deeply explore the reasons for the insufficient hydrophilicity of the spherical aluminum nitride surface. For example, if a local high hydrophobic area is found on the sample surface, it may be caused by uneven surface treatment. For example, if the overall surface free energy is low, it may be due to insufficient treatment time or insufficient concentration of the treatment agent. These analysis results provide clear directions for optimizing the surface treatment process, such as adjusting the treatment time, improving the treatment agent formulation or optimizing the treatment equipment. Through continuous optimization and verification, the hydrophilicity of the surface of spherical aluminum nitride samples can be significantly improved and stably controlled.

[0020] Step S102, selecting a silane coupling agent to perform surface modification treatment on the spherical aluminum nitride according to the surface energy distribution characteristics, and introducing hydrophilic groups on the surface thereof through a chemical grafting reaction.

[0021] Step S102 includes: obtaining surface energy distribution data of spherical aluminum nitride powder, and determining high surface energy areas and low surface energy areas on the surface of the spherical aluminum nitride powder according to the surface energy distribution data; selecting a silane coupling agent that matches the surface energy characteristics of the high surface energy area from a preset silane coupling agent database according to the surface energy characteristics of the high surface energy area; mixing the spherical aluminum nitride powder with the selected silane coupling agent, and making the two fully contact by mechanical stirring; promoting a chemical grafting reaction between the silane coupling agent and the surface of the spherical aluminum nitride powder under preset temperature and preset pH conditions, A spherical aluminum nitride powder with a silane coupling agent grafted on the surface is obtained; the spherical aluminum nitride powder with a silane coupling agent grafted on the surface is analyzed by an infrared spectroscopy analysis method to determine whether the silane coupling agent is successfully grafted to the surface of the spherical aluminum nitride; if so, a hydrophilic group is introduced onto the surface of the spherical aluminum nitride by chemically reacting the terminal group of the silane coupling agent grafted to the surface of the spherical aluminum nitride with a preset hydrophilic group to obtain a spherical aluminum nitride powder with enhanced surface hydrophilicity; and the spherical aluminum nitride powder with enhanced surface hydrophilicity is washed and dried to obtain a modified spherical aluminum nitride powder.

[0022] Specifically, the surface energy distribution data of spherical aluminum nitride powder is crucial for optimizing its surface treatment process. Through high-resolution surface analysis techniques such as atomic force microscopy or scanning tunneling microscopy, a nanoscale surface energy distribution map can be obtained. For example, in a batch of spherical aluminum nitride powder samples, it may be observed that the surface energy is distributed between 20-60mN / m, where the high surface energy area (50-60mN / m) is mainly concentrated at the edges and defects of the particles, while the low surface energy area (20-30mN / m) is distributed on the smooth surface. According to this surface energy distribution feature, an appropriate silane coupling agent can be selected for surface modification. For the above samples, γ-aminopropyltriethoxysilane (APTES) can be considered as a coupling agent. The amino group in the APTES molecule has a strong polarity and can form a stable chemical bond with the high surface energy area, while its silane group can undergo a condensation reaction with the hydroxyl group on the aluminum nitride surface. In actual operation, spherical aluminum nitride powder can be mixed with APTES in a mass ratio of 1:0.01 and stirred at 200 rpm in a mechanical stirrer for 2 hours to ensure full contact. Subsequently, the mixture is placed in a weakly acidic environment with a pH value of 4.5-5.5 and reacted at 60-80°C for 4-6 hours to promote the chemical grafting of the silane coupling agent to the aluminum nitride surface. Infrared spectroscopy analysis is an effective method to confirm the successful grafting of the silane coupling agent. By comparing the infrared spectra before and after modification, it can be observed that a new NH stretching vibration peak appears at 3300-3500 cm-1, and the characteristic absorption peaks of SiO2 and SiO-Al appear at 1000-1100 cm-1, which are all evidence of the successful grafting of APTES. Next, more hydrophilic groups can be introduced by using the amino groups at the ends of the grafted APTES molecules. For example, a large number of hydrophilic PEG chains can be introduced on the surface of spherical aluminum nitride by a Michael addition reaction with polyethylene glycol (PEG). The specific operation is to mix the modified powder with acrylate-terminated PEG (molecular weight of about 2000) in an aqueous solution and react at room temperature for 24 hours. Finally, the modified spherical aluminum nitride powder is washed and dried. Deionized water and ethanol can be used for alternating washing 3-5 times to remove unreacted reagents and by-products. Then vacuum dry at 50°C for 12 hours to obtain spherical aluminum nitride powder with significantly enhanced surface hydrophilicity. The advantage of this surface modification method is that it makes full use of the energy distribution characteristics of the spherical aluminum nitride surface and introduces hydrophilic groups in high surface energy areas in a targeted manner. This not only improves the modification efficiency, but also ensures the uniformity of the surface properties of the modified powder. The spherical aluminum nitride powder treated in this way has significantly improved surface hydrophilicity and can be better dispersed in aqueous media, thereby improving its performance in applications such as thermal conductive materials and ceramic-based composites.

[0023] Step S103, using a silane modification method to graft hydrophilic groups on the surface of the spherical aluminum nitride, the hydrophilic groups being hydroxyl groups or carboxyl groups, to improve its surface activity and hydrophilicity.

[0024] Step S103 includes: obtaining spherical aluminum nitride powder, wherein the spherical aluminum nitride powder has surface properties; preparing a silane solution containing hydroxyl and carboxyl groups according to the surface properties, wherein the silane solution is prepared by a sol-gel method and contains hydrophilic groups; dispersing the spherical aluminum nitride powder in the silane solution, stirring the reaction at a preset temperature, so that the silane molecules are grafted onto the surface of the spherical aluminum nitride powder through a condensation reaction; filtering, washing and drying the grafted spherical aluminum nitride powder to obtain silane-modified aluminum nitride powder with hydrophilic groups grafted onto the surface. body; using infrared spectroscopy analysis and contact angle test to characterize the changes in the surface chemical composition and wettability of the silane-modified aluminum nitride powder before and after modification, and determine the optimal process parameters for silane modification; dispersing the silane-modified aluminum nitride powder in water, testing its dispersion stability and suspension rate, and evaluating the improvement of the hydrophilicity by the modification effect; according to the application requirements of the silane-modified aluminum nitride powder, using at least two silane coupling agents to compound, or introducing other hydrophilic polymers, the silane modification method is optimized to further improve the surface activity and hydrophilicity of the silane-modified aluminum nitride powder.

[0025] Specifically, the surface properties of spherical aluminum nitride determine the choice of silane coupling agent. For example, for aluminum nitride with hydroxyl groups on the surface, silane coupling agents containing methoxy or ethoxy groups, such as γ-aminopropyltriethoxysilane (APTES), can be used. This type of silane can undergo condensation reaction with surface hydroxyl groups to form stable SiOA l bonds. The sol-gel method is a key step in preparing silane solutions containing hydrophilic groups. Taking APTES as an example, it is dissolved in an ethanol aqueous solution and a sol containing a large number of silanol groups (Si-OH) is generated through a hydrolysis reaction. Subsequently, a polycondensation reaction occurs to form a gel with a network structure. In this process, the structure and properties of the gel can be controlled by adjusting the pH value, temperature and reaction time. The spherical aluminum nitride powder is dispersed in a silane solution for grafting reaction. The reaction temperature is usually in the range of 60-80°C, and the stirring time is 2-4 hours. In this process, the silanol groups of the silane molecules undergo condensation reaction with the hydroxyl groups on the surface of aluminum nitride, and cross-linking also occurs between the silane molecules. This dual effect allows silane to form a network-like modified layer on the surface of aluminum nitride. The filtering, washing and drying steps after the reaction are crucial to remove unreacted silane and by-products. Centrifugation or vacuum filtration is usually used for solid-liquid separation, followed by repeated washing with ethanol and deionized water. Drying can be carried out at 60-80°C for 4-6 hours. Infrared spectroscopy is a powerful tool for characterizing the modification effect. By comparing the infrared spectra before and after modification, new characteristic peaks such as SiO-i and SiO-Al can be observed, confirming the successful grafting of silane. The contact angle test intuitively reflects the change in surface wettability. For example, the water contact angle of unmodified aluminum nitride may be as high as 120°, while it can be reduced to 30-40° after modification, indicating that the hydrophilicity is significantly improved. Dispersion stability and suspension rate tests can evaluate the modification effect. The modified aluminum nitride powder is dispersed in water and the sedimentation is observed after 24 hours. A good modification effect can increase the suspension rate from less than 20% to more than 80%. In addition, the dispersion stability of the powder in water can be evaluated by Zeta potential test. In order to further optimize the modification effect, a variety of silane coupling agents can be used. For example, APTES and γ-glycidyloxypropyltrimethoxysilane (GPTMS) are mixed in a certain proportion to provide hydrophilicity and enhance compatibility with the matrix. Another method is to introduce hydrophilic polymers such as polyethylene glycol (PEG). PEG can be pre-reacted with a silane coupling agent to form PEGylated silane, which is then used for aluminum nitride surface modification. This method can not only significantly improve hydrophilicity, but also give the powder good dispersibility and biocompatibility.

[0026] Step S104, obtaining the surface energy data of the spherical aluminum nitride after silane modification, and judging whether its surface hydrophilicity reaches a preset threshold, the contact angle is less than 60 degrees and the surface free energy is higher than 50mN / m. If not, adjusting the silane modification process conditions.

[0027] Step S104 includes: obtaining a silane-modified spherical aluminum nitride sample, and measuring the surface contact angle data of the spherical aluminum nitride sample using a contact angle meter; judging whether the surface contact angle of the spherical aluminum nitride is less than a preset threshold of 60 degrees according to the contact angle data, and if it is greater than the threshold, determining that the surface hydrophilicity of the spherical aluminum nitride does not meet the standard. Obtaining the silane-modified spherical aluminum nitride sample, and testing the surface free energy data of the spherical aluminum nitride sample using a surface tension meter; judging whether the surface free energy of the spherical aluminum nitride is higher than a preset threshold of 50mN / m according to the surface free energy data, and if it is lower than the threshold, determining that the surface hydrophilicity of the spherical aluminum nitride does not meet the standard. If the surface contact angle of the spherical aluminum nitride is greater than 60 degrees or the surface free energy is lower than 50mN / m, the silane modification process parameters are adjusted, and the silane modification process parameters include silane concentration, reaction temperature and reaction time; the spherical aluminum nitride is surface treated with the adjusted silane modification process, and the surface hydrophilicity of the spherical aluminum nitride is improved through the chemical reaction between the silane molecules and the hydroxyl groups on the surface of the spherical aluminum nitride. The contact angle and surface free energy of the spherical aluminum nitride sample treated with the adjusted silane modification process are tested again until the surface contact angle of the spherical aluminum nitride sample is less than 60 degrees and the surface free energy is higher than 50mN / m, and it is determined that the surface hydrophilicity of the spherical aluminum nitride meets the preset application requirements.

[0028] Specifically, the evaluation of the surface properties of spherical aluminum nitride after silane modification is a key step to ensure the modification effect. Contact angle measurement is an important method to evaluate the hydrophilicity of the surface. The wettability of the surface is determined by measuring the contact angle between the droplet and the solid surface. For the modified aluminum nitride, if the contact angle is less than 60 degrees, it indicates that the surface has good hydrophilicity. For example, a sample measured a contact angle of 45 degrees, indicating that its surface hydrophilicity meets the standard. The surface free energy test reflects the energy state of the material surface and is closely related to its hydrophilicity. High surface free energy usually means better hydrophilicity. Taking 50mN / m as the threshold, if the surface free energy of the sample is measured to be 55mN / m, it means that its hydrophilicity meets the requirements. These two indicators complement each other and jointly evaluate the surface modification effect. If the test results do not meet the standards, the silane modification process needs to be optimized. Adjusting the silane concentration can affect the surface coverage. For example, increasing the concentration from 1% to 2% may increase the density of hydrophilic groups. Adjusting the reaction temperature can affect the reaction rate and extent. For example, increasing the temperature from 60°C to 80°C may promote the binding of silane molecules to the surface. Prolonging the reaction time, such as from 2 hours to 4 hours, may allow more silane molecules to react with the surface. The optimized process aims to increase the surface hydrophilic groups through the chemical reaction of silane molecules with the hydroxyl groups on the surface of aluminum nitride. This process involves the condensation reaction of the silanol groups in the silane molecules with the surface hydroxyl groups to form Si-O-Al bonds and introduce hydrophilic functional groups on the surface. For example, modification with γ-aminopropyltriethoxysilane (APTES) can introduce amino groups on the surface and significantly improve the hydrophilicity. The modified samples need to be tested for contact angle and surface free energy again to verify the optimization effect. Assuming that the contact angle of the optimized sample is reduced to 35 degrees and the surface free energy is increased to 58mN / m, both reaching the preset threshold, it means that the modification process successfully improves the hydrophilicity of the aluminum nitride surface. This cyclic optimization process ensures that the final product meets the application requirements. Through this systematic surface modification and evaluation method, the surface properties of spherical aluminum nitride can be precisely controlled. This not only improves the hydrophilicity and dispersibility of the material, but also lays the foundation for its application in thermal interface materials, composite fillers and other fields. For example, the use of modified aluminum nitride in thermal conductive silicone grease can significantly improve the dispersion uniformity of the filler, thereby improving the thermal conductivity of the composite material. At the same time, the improved hydrophilicity also helps to enhance the compatibility of aluminum nitride with water-based coolants, expanding its application prospects in liquid cooling systems.

[0029] Step S105, mixing the silane-modified spherical aluminum nitride with the silica gel matrix, obtaining dispersion data of the mixture, and judging whether the dispersion of the filler is uniform and whether the standard deviation of the particle size distribution is less than 0.5 μm.

[0030] Step S105 includes: mixing the silane-modified spherical aluminum nitride filler and the silica gel matrix according to a preset ratio, using a high-speed disperser for mixing, the mixing time is 30 minutes, the mixing speed is 1000 rpm. Randomly sample from the mixed composite material, the volume of each sample is 1cm 3 , take 5 samples in total, use a laser particle size analyzer to test the particle size distribution of each sample, and obtain the particle size distribution data of the sample. Perform statistical analysis on the particle size distribution data of each sample, calculate the arithmetic mean and standard deviation of the particle size distribution of each sample, and obtain the average standard deviation of the overall particle size distribution of the mixture by arithmetically averaging the standard deviations of the 5 samples. Compare the average standard deviation of the overall particle size distribution of the mixture with the preset threshold value of 5μm. If the average standard deviation is less than or equal to 5μm, it is judged that the filler is evenly dispersed in the matrix and the mixing process parameters are reasonably set; if the average standard deviation is greater than 5μm, it is judged that the filler is unevenly dispersed in the matrix. According to the judgment result, if the filler is evenly dispersed, the batch of mixture samples is marked as qualified, and the mixing ratio and process parameters of the batch of mixtures are recorded; if the filler is unevenly dispersed, the batch of mixture samples is marked as unqualified, and rework and remix. Obtain existing mixture sample data, use machine learning algorithms to train the sample data, and establish a mathematical model between the filler mixing ratio, the mixing process parameters, and the standard deviation of the mixture particle size distribution. The mathematical model is embedded in the control system of the automated mixing equipment, and the mixing process parameters are adjusted in real time according to the model prediction results to achieve intelligent control of the mixing process.

[0031] Specifically, the mixing of silane-modified spherical aluminum nitride fillers with silica matrix is ​​a key step that directly affects the performance of the composite material. The use of a high-speed disperser can provide sufficient shear force to promote the uniform dispersion of the filler in the matrix. The selection of mixing time and speed requires a trade-off: too short a time may lead to insufficient dispersion, while too long a time may cause filler agglomeration; too low a speed is difficult to break the filler agglomeration, and too high a speed may introduce bubbles or cause filler breakage. The application of laser particle size analyzer reflects the importance of modern material characterization technology in the preparation of composite materials. By testing multiple samples, the uniformity of the mixture can be effectively evaluated. The arithmetic mean of the particle size distribution reflects the overall size characteristics of the filler, while the standard deviation quantifies the degree of dispersion. For example, if the mean particle size of a sample is 50μm and the standard deviation is 3μm, it means that most of the filler particles are in the range of 47-53μm and are relatively uniformly dispersed. Setting 5μm as the threshold of the standard deviation is based on practical experience and material performance requirements. A value lower than this means that the filler distribution is more concentrated, which is conducive to the uniformity of the composite material performance. For example, in thermal conductivity applications, evenly dispersed fillers can form a more continuous heat conduction network and improve the overall thermal conductivity of composite materials. The introduction of machine learning algorithms represents the trend of composite material preparation towards intelligence and precision. By establishing a mathematical model between filler mixing ratio, process parameters and particle size distribution standard deviation, the mixing effect under different conditions can be predicted. For example, if the model predicts that under the current formula, extending the mixing time from 30 minutes to 35 minutes can reduce the standard deviation from 5.5μm to 4.8μm, the process parameters can be adjusted accordingly. Embedding the mathematical model into the control system of the automated mixing equipment realizes closed-loop control of the production process. The system can automatically adjust the mixing speed or time according to the real-time monitored particle size distribution data to maintain the optimal dispersion state. This not only improves production efficiency, but also ensures the consistency of product quality. For example, when a slight change in the surface properties of a batch of raw materials is detected, the system may automatically extend the mixing time by 5% to ensure the uniformity of the final product. The application of this intelligent control method not only optimizes the current production process, but also provides valuable data support for future product development. By analyzing a large amount of historical data, R&D personnel can determine the optimal processing parameters for new formulas more quickly and shorten the product development cycle. At the same time, this method also opens up new ways to further improve the performance of composite materials, such as achieving anisotropic design of material properties by precisely controlling the spatial distribution of fillers to meet more complex application requirements.

[0032] Step S106, adjusting the filling amount of the spherical aluminum nitride according to the dispersion data of the mixture, and obtaining the thermal conductivity, thermal expansion and heat resistance data of the composite material at different filling amounts.

[0033] Step S106 includes: obtaining the dispersion data of the mixture, using a machine learning algorithm to establish a relationship model between the dispersion and the filling amount of spherical aluminum nitride, and obtaining the optimal filling amount range of spherical aluminum nitride; according to the optimal filling amount range, setting multiple filling amount gradients, and preparing a series of composite material samples with different filling amounts; using a thermal conductivity meter to test the thermal conductivity of the composite material samples with different filling amounts, and obtaining a relationship curve between the filling amount and thermal conductivity performance; using a thermal mechanical analyzer to test the thermal expansion coefficient of the composite material samples with different filling amounts, and obtaining a relationship curve between the filling amount and thermal expansion performance; using a thermogravimetric analyzer to test the weightlessness temperature of the composite material samples with different filling amounts, and obtaining a relationship curve between the filling amount and heat resistance performance; according to the relationship curve between the filling amount and thermal conductivity, thermal expansion performance and heat resistance performance, using a multi-objective optimization algorithm to obtain the optimal filling amount that takes into account multiple performance indicators; according to the optimal filling amount ratio, a spherical aluminum nitride composite material with optimized performance is prepared, and its performance is verified.

[0034] Specifically, machine learning algorithms play an important role in establishing a model of the relationship between dispersion and spherical aluminum nitride filling amount. Commonly used algorithms include support vector regression, random forest, and neural network. These algorithms can predict dispersion at different filling amounts by learning existing experimental data, thereby determining the optimal filling amount range. For example, using the support vector regression algorithm, the input feature is the filling amount, and the output is the dispersion index. The model obtained through training can predict the dispersion at any filling amount. Within the determined optimal filling amount range, setting multiple filling amount gradients to prepare samples is to comprehensively evaluate the effect of filling amount on the performance of composite materials. Usually at least five filling amount gradients can be selected, such as when the optimal range is 20% to 40%, 20%, 25%, 30%, 35% and 45 gradient points can be selected. Thermal conductivity testing is the key to evaluating the thermal performance of composite materials. The thermal conductivity meter calculates the thermal conductivity by measuring the temperature difference and heat flow at both ends of the material. Increasing the filling amount usually increases the thermal conductivity, but may affect other properties. For example, a 30 percent filler may give a thermal conductivity of 2.5 watts per meter Kelvin, while a 40 percent filler may give a thermal conductivity of 3 watts per meter Kelvin. Thermal expansion coefficient testing reflects the dimensional stability of a material under temperature changes. Thermomechanical analyzers calculate thermal expansion coefficients by measuring the dimensional changes of a sample during heating. Increasing the filler generally reduces the thermal expansion coefficient and improves dimensional stability. For example, the thermal expansion coefficient of pure silicone may be 300 microns per meter Kelvin, while adding 30 percent spherical aluminum nitride may reduce it to 150 microns per meter Kelvin. Heat resistance is evaluated by measuring the weight loss temperature using a thermogravimetric analyzer. The weight loss temperature reflects the temperature point at which a material begins to decompose and is an important indicator of heat resistance. Increasing the filler generally increases the weight loss temperature and enhances heat resistance. For example, the weight loss temperature of pure silicone may be 350 degrees Celsius, while adding 30 percent spherical aluminum nitride may increase it to 400 degrees Celsius. Multi-objective optimization algorithms are used to balance thermal conductivity, thermal expansion, and heat resistance to find the optimal filler. Commonly used algorithms include genetic algorithms, particle swarm optimization algorithms, etc. These algorithms find the best balance between multiple goals by setting weights for different performance indicators. For example, it may be found that when the filling amount is 35%, a higher thermal conductivity (2.8 watts per meter Kelvin) can be obtained while maintaining a lower thermal expansion coefficient (120 microns per meter Kelvin) and a higher weightlessness temperature (380 degrees Celsius). Finally, a composite material is prepared according to the optimal filling amount obtained by optimization, and the performance is verified. The verification process includes repeating the above tests to ensure that the actual performance is consistent with expectations. At the same time, other related tests, such as mechanical strength, dielectric properties, etc., should also be carried out to comprehensively evaluate the application performance of the material. This series of optimization and verification processes ensures that the final spherical aluminum nitride / silicone composite material can meet the multi-faceted requirements of specific applications, such as heat dissipation, dimensional stability, and heat resistance.

[0035] Step S107, analyzing the performance data of the composite material at different filling amounts, determining the optimal filling amount, so that the thermal conductivity of the composite film is greater than 18W / (m·K), the thermal expansion coefficient is less than 30ppm / °C, and the heat resistance temperature is higher than 200°C.

[0036] Step S107 includes: obtaining performance data of composite film under different filling amounts, including parameters of thermal conductivity, thermal expansion coefficient and heat resistant temperature. Preprocessing the obtained performance data, eliminating abnormal values ​​and noise data, and ensuring the accuracy and reliability of the data. Using a multivariate linear regression algorithm, a mathematical model between filling amount and thermal conductivity, thermal expansion coefficient and heat resistant temperature is established to quantify the influence of filling amount on the performance of the composite material. According to the established mathematical model, the thermal conductivity, thermal expansion coefficient and heat resistant temperature of the composite film under different filling amounts are predicted to obtain predicted performance data. The predicted performance data is compared with the target performance requirements to screen out the filling amount interval that satisfies the thermal conductivity greater than 18W / (m·K), the thermal expansion coefficient less than 30ppm / ℃, and the heat resistant temperature higher than 200℃. In the filling amount interval that meets the performance requirements, a particle swarm optimization algorithm is used to search for the optimal filling amount with the optimal comprehensive performance as the objective function to obtain the optimal filling amount that makes the composite film perform optimally. Based on the determined optimal filling amount, prepare composite film samples, test their thermal conductivity, thermal expansion coefficient and heat resistance temperature to verify whether the performance meets the requirements, and fine-tune the filling amount until the optimal composite film that meets the performance requirements is obtained.

[0037] Specifically, obtaining the performance data of composite film at different filling amounts is a key step in optimizing material performance. Taking spherical aluminum nitride / epoxy resin composites as an example, thermal conductivity can be measured by thermal diffusion instrument, thermal expansion coefficient can be measured by thermomechanical analyzer, and heat resistance temperature can be measured by thermogravimetric analyzer. During the test, it is necessary to pay attention to the influence of factors such as ambient temperature and humidity on the measurement results, and repeat the test many times to improve the data reliability. Data preprocessing is essential to ensure the accuracy of the model. The box plot method can be used to identify outliers, such as data points where the thermal conductivity suddenly decreases or the thermal expansion coefficient increases abnormally. For noisy data, the moving average method can be used for smoothing to reduce the impact of random fluctuations on the model. The multivariate linear regression model can quantify the relationship between filling amount and material performance. For example, the following model can be established: thermal conductivity = a × filling amount + b, where a and b are regression coefficients. By determining these coefficients by the least squares method, the quantitative relationship between filling amount and thermal conductivity can be obtained. Similarly, a model of filling amount, thermal expansion coefficient, and heat resistance temperature can be established. Using the established mathematical model, the performance of composite materials at different filling amounts can be predicted. Assume that the model predicts that at 40% filling, the thermal conductivity is 20W / (m·K), the thermal expansion coefficient is 25ppm / ℃, and the heat resistance temperature is 220℃. These prediction results can be used to guide the optimization of material formulations, avoid unnecessary experiments, and thus save time and cost. By comparing the predicted performance with the target requirements, the filling range that meets the performance indicators can be screened out. For example, if the thermal conductivity is required to be greater than 18W / (m·K), the thermal expansion coefficient is less than 30ppm / ℃, and the heat resistance temperature is higher than 200℃, then a filling range of 35% to 45% may be obtained to meet the requirements. This step helps to narrow the search range for the optimal filling amount. Within the determined filling amount range, the particle swarm optimization algorithm is used to search for the optimal filling amount. The algorithm simulates swarm intelligence and finds the filling amount that optimizes the overall performance through iterative optimization. For example, the objective function can be set as: f = w1×thermal conductivity + w2 / thermal expansion coefficient + w3×heat resistance temperature, where w1, w2, and w3 are weight coefficients. By adjusting these weights, the importance of different performance indicators can be balanced. Finally, samples are prepared based on the optimal filling amount obtained through optimization and the performance is verified. If it is found that the actual performance deviates from the expected value, the filling amount may need to be fine-tuned. For example, if the thermal conductivity is slightly lower than the target value, the filling amount can be appropriately increased; if the thermal expansion coefficient is slightly higher, the filling amount can be slightly reduced. Through this iterative optimization process, the optimal composite film that meets the performance requirements is finally obtained.

[0038] Step S108, prepare a silane-modified spherical aluminum nitride / silicone composite film sample with an optimal filling amount, obtain the composite material interface bonding force data, and determine whether the interface shear strength is greater than 10MPa. If not, re-optimize the silane modification process.

[0039] Step S108 includes: obtaining a series of composite material interface bonding force data with different silane-modified spherical aluminum nitride filling amounts as training samples; using a machine learning algorithm to train the training samples to obtain a prediction model between the silane-modified spherical aluminum nitride filling amount and the composite material interface bonding force; determining the optimal filling amount range of silane-modified spherical aluminum nitride according to the prediction model. Within the optimal filling amount range, preparing a series of silane-modified spherical aluminum nitride / silicone composite material film samples with different filling amounts; using a tensile shear test method to obtain the interface shear strength data of each of the samples; judging whether the interface shear strength of all samples is greater than a preset threshold; if not, obtaining the relationship data between the interface shear strength and the silane modification process parameters as training samples; using a machine learning algorithm to train the training samples to obtain a relationship model between the interface shear strength and the silane modification process parameters; optimizing the silane modification process parameters according to the relationship model. Obtain data on the interfacial bonding strength of composite materials under a series of different silane modification process parameter combinations as training samples; use support vector machine algorithm to train the training samples to obtain a prediction model between silane modification process parameters and interfacial bonding strength of composite materials; determine the optimal silane modification process parameter combination according to the prediction model. Under the optimal silane modification process parameter combination, prepare silane-modified spherical aluminum nitride / silicone composite film samples; use a tensile shear test method to test the interfacial shear strength of the samples, compare the test results with the interfacial shear strength of spherical aluminum nitride / silicone composite materials that have not been silane-modified, and evaluate the effect of silane modification on improving the interfacial bonding strength of composite materials. Characterize and analyze the microstructure of silane-modified spherical aluminum nitride / silicone composite film samples; use a scanning electron microscope to observe the bonding state of the composite interface; combine the energy spectrum analysis method to study the mechanism of action of silane coupling agent at the interface between aluminum nitride and silica gel, and provide theoretical guidance for further optimization of silane modification process.

[0040] Specifically, the relationship between the filling amount of silane-modified spherical aluminum nitride and the interfacial bonding strength of the composite material is a complex nonlinear problem. The use of machine learning algorithms to establish a prediction model can effectively capture this complex relationship. For example, the random forest algorithm can be used, which can handle nonlinear relationships and has good generalization ability. When training the model, the filling amount can be used as an input feature and the interfacial bonding strength can be used as an output label. Through a large amount of experimental data, a model that can accurately predict the interfacial bonding strength under different filling amounts is obtained. After determining the optimal filling amount range, preparing a series of samples and conducting tensile shear tests are key steps to verify the model prediction results. The tensile shear test can directly reflect the interfacial bonding strength of the composite material. The test method usually uses a double shear fixture to fix the sample between two metal blocks, and then applies a tensile force until the sample is sheared. By recording the maximum load at the time of failure, the interfacial shear strength can be calculated. For the statistical analysis of the interfacial shear strength data, visualization methods such as box plots can be used to intuitively display the data distribution and outliers. If it is found that the interfacial shear strength of some samples is lower than 10MPa, the silane modification process needs to be further optimized. At this time, a gradient boosting decision tree algorithm, such as XGBoost, can be used to establish a relationship model between silane modification process parameters (such as silane concentration, reaction time, temperature, etc.) and interface shear strength, so as to find out the key factors affecting interface bonding. The support vector machine (SVM) algorithm has unique advantages in establishing a prediction model for silane modification process parameters and composite interface bonding strength. SVM can process high-dimensional feature spaces and can effectively capture nonlinear relationships through kernel function techniques. In practical applications, a radial basis function (RBF) kernel can be selected, and the kernel parameters and regularization parameters can be optimized through cross-validation to obtain the best prediction performance. The effect of silane modification on improving the interface bonding strength of composite materials can be evaluated by comparative experiments. For example, a batch of silane-modified samples and unmodified samples can be prepared and their interface shear strength can be tested separately. If the average interface shear strength of the silane-modified sample is significantly higher than that of the unmodified sample, and the standard deviation is small, it means that the silane modification effectively improves the interface bonding strength and improves the consistency of the material. Microstructural characterization is an important means to understand the mechanism of silane modification. The interface morphology between aluminum nitride particles and silica gel matrix can be observed by field emission scanning electron microscopy (FESEM). If the interface is well bonded, no obvious interface separation should be observed. Energy dispersive spectroscopy (EDS) can detect the element distribution in the interface area. If silicon enrichment is detected at the interface, it means that the silane coupling agent has successfully formed a chemical bond on the aluminum nitride surface. In addition, Fourier transform infrared spectroscopy (FT IR) analysis can provide direct evidence of the reaction between the silane coupling agent and the functional groups on the aluminum nitride surface. By observing the changes in the characteristic peaks, the type and degree of chemical bonding can be inferred.These microscopic analysis results can not only verify the effectiveness of silane modification, but also provide a theoretical basis for further optimizing the modification process, thereby achieving continuous improvement in the interfacial bonding strength of composite materials.

[0041] Step S109, determining the final silane modification scheme and composite material formula through the interface bonding force data, and preparing an environmentally friendly flame-retardant spherical aluminum nitride / silicone composite material film with high thermal conductivity, low thermal expansion, and high heat resistance.

[0042] Step S109 includes: obtaining interface bonding force data, using support vector machine or artificial neural network algorithm to establish a quantitative relationship model between the interface bonding force and silane modification process parameters; determining the best silane modification scheme through optimization algorithm to obtain modified spherical aluminum nitride powder. Using orthogonal experimental design method, taking the modified spherical aluminum nitride filler content, silica gel matrix type and ratio as factors, preparing multiple composite material samples; testing the thermal conductivity, thermal expansion coefficient and heat resistance of the composite material samples, and establishing a response surface model between the material formula and performance of the composite material samples. By solving multi-objective optimization problems, while ensuring the high thermal conductivity, low thermal expansion and high heat resistance of the composite material samples, the amount of environmentally friendly flame retardant is optimized to obtain the best composite material formula that takes into account both performance and environmental protection. According to the best composite material formula, a spherical aluminum nitride / silicone composite material slurry is prepared by high-speed mixing and vacuum degassing process; and a composite material film blank is prepared by tape casting process. The composite material film blank is cured, and the curing temperature and time are optimized to obtain a composite material film finished product with stable performance. A thermal conductivity meter, a thermomechanical analyzer and a vertical combustion tester are used to characterize the thermal conductivity, thermal expansion coefficient, heat resistance and flame retardancy of the finished composite film; and to determine whether the finished composite film meets the requirements of high thermal conductivity, low thermal expansion, high heat resistance and environmental protection and flame retardancy.

[0043] Specifically, the establishment of a quantitative relationship model between the interface bonding force and the silane modification process parameters is the key to optimizing the performance of composite materials. Taking the support vector machine algorithm as an example, the silane concentration, modification temperature and time can be selected as input variables, and the interface bonding force can be selected as the output variable. A series of data points are obtained through experiments, and the data are mapped to a high-dimensional space using a kernel function to find the optimal separation hyperplane, thereby establishing a prediction model. For example, experimental data show that the interface bonding force is optimal when the silane concentration is 3-5%, the modification temperature is 60-80°C, and the time is in the range of 2-4 hours. The orthogonal experimental design method can effectively reduce the number of experiments. Taking the L9 (34) orthogonal table as an example, the modified spherical aluminum nitride filler content (30%, 40%, 50%), the silica gel matrix type (methyl, vinyl, phenyl) and the ratio (1:1, 1:2, 2:1) are selected as three factors, and each factor is set at three levels. Through 9 groups of experiments, the influence of each factor on the performance of the composite material can be obtained. For example, the experimental results may show that the composite material has the best comprehensive performance when the filler content is 40%, the vinyl silicone ratio is 1:2. The establishment of the response surface model helps to deeply understand the relationship between material formulation and performance. Taking the central composite design as an example, the filler content and the silicone ratio are selected as independent variables, and the thermal conductivity is selected as the dependent variable. The data points are obtained through experiments and the quadratic polynomial model is fitted. The model may show that as the filler content increases, the thermal conductivity shows a trend of first rising rapidly and then slowly increasing; while the effect of the silicone ratio on the thermal conductivity presents a parabolic shape. The solution of multi-objective optimization problems requires weighing multiple performance indicators. The Pareto optimization method is adopted, with the objective functions of maximizing thermal conductivity, minimizing thermal expansion coefficient and minimizing the amount of environmentally friendly flame retardant. A series of non-dominated solutions are generated by genetic algorithms to form a Pareto frontier. Decision makers can select the best solution from the Pareto solution set according to specific needs. For example, a set of optimized formulas may be obtained: 45% modified spherical aluminum nitride filler, 54% vinyl silicone matrix, and 1% environmentally friendly flame retardant. High-speed mixing and vacuum degassing processes are essential for improving the uniformity of composite material slurry and removing bubbles. A planetary mixer can be used to stir at 500 rpm for 5 minutes, then at 1000 rpm for 10 minutes, and finally degas for 15 minutes at a vacuum of 0.1 Pa. The tape casting process can use the scraper method to control the scraper height and movement speed to prepare a soft film blank with uniform thickness. The optimization of the curing process needs to consider the cross-linking reaction kinetics of the silicone matrix. Taking vinyl silicone as an example, a step-by-step temperature curing method can be used: 80°C for 1 hour, 120°C for 2 hours, and 150°C for 4 hours. This method can fully promote the cross-linking reaction while avoiding the accumulation of internal stress caused by too fast a temperature rise. The performance characterization of composite film is an important part of evaluating product quality.Thermal conductivity can be tested by transient plane heat source method, thermal expansion coefficient can be measured by thermomechanical analyzer in the temperature range of -50℃ to 150℃, heat resistance can be measured by thermogravimetric analyzer to measure the decomposition temperature of the material, and flame retardancy can be evaluated by vertical burning test method. If the test results show that thermal conductivity is greater than 5W / (m·K), thermal expansion coefficient is less than 30ppm / ℃, decomposition temperature is higher than 300℃ and has V-0 flame retardant performance, it can be considered that the composite film meets the requirements of high thermal conductivity, low thermal expansion, high heat resistance and environmentally friendly flame retardancy.

[0044] Based on the above embodiments of the present invention, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for preparing an environmentally friendly flame-retardant aluminum nitride silica gel composite film, characterized in that: The method comprises: Step S101, obtaining surface energy data of spherical aluminum nitride, analyzing the specific manifestations of insufficient surface hydrophilicity, including a contact angle greater than 90 degrees and a surface free energy lower than 72 mN / m, and determining surface energy distribution characteristics; Step S102, selecting a silane coupling agent to perform surface modification treatment on the spherical aluminum nitride according to the surface energy distribution characteristics, and introducing hydrophilic groups on its surface through a chemical grafting reaction; Step S103, using a silane modification method to graft hydrophilic groups on the surface of the spherical aluminum nitride, the hydrophilic groups being hydroxyl groups or carboxyl groups, to improve its surface activity and hydrophilicity; Step S104, obtaining the surface energy data of the spherical aluminum nitride after silane modification, judging whether its surface hydrophilicity reaches a preset threshold, the contact angle is less than 60 degrees and the surface free energy is higher than 50mN / m, and if not, adjusting the silane modification process conditions; Step S105, mixing the silane-modified spherical aluminum nitride with the silica gel matrix, obtaining dispersion data of the mixture, and judging whether the dispersion of the filler is uniform and the standard deviation of the particle size distribution is less than 0.5 μm; Step S106, adjusting the filling amount of the spherical aluminum nitride according to the dispersion data of the mixture, and obtaining the thermal conductivity, thermal expansion and heat resistance data of the composite material at different filling amounts; Step S107, analyzing the performance data of the composite material under different filling amounts, determining the optimal filling amount, so that the thermal conductivity of the composite film is greater than 18W / (m·K), the thermal expansion coefficient is less than 30ppm / °C, and the heat-resistant temperature is higher than 200°C; Step S108, preparing a silane-modified spherical aluminum nitride / silica gel composite film sample with an optimal filling amount, obtaining composite material interface bonding force data, and determining whether the interface shear strength is greater than 10 MPa. If not, re-optimizing the silane modification process; Step S109, determining the final silane modification scheme and composite material formula through the interface bonding force data, and preparing an environmentally friendly flame-retardant spherical aluminum nitride / silicone composite material film with high thermal conductivity, low thermal expansion, and high heat resistance.

2. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to claim 1, characterized in that: The step S101 comprises: Obtain a spherical aluminum nitride sample, and measure the contact angle between the surface of the spherical aluminum nitride sample and water using a contact angle meter. If the contact angle is greater than 90 degrees, it is determined that the surface of the spherical aluminum nitride is insufficiently hydrophilic; The surface free energy of the spherical aluminum nitride is measured by a surface tension meter. If the surface free energy is lower than 72 mN / m, it is further determined that the surface hydrophilicity of the spherical aluminum nitride is poor. According to the contact angle and the surface free energy data at different surface positions of the spherical aluminum nitride, a surface energy distribution diagram is drawn to obtain the distribution characteristics of the surface energy; Using a support vector machine algorithm, taking the contact angle and the surface free energy as characteristics, a prediction model for the hydrophilicity of the spherical aluminum nitride surface is established; Using the prediction model, the surface hydrophilicity of other batches of spherical aluminum nitride samples is predicted to determine the stability of the surface treatment process; The contact angle, the surface free energy, the surface energy data and the surface energy distribution characteristics are comprehensively analyzed to determine the reason why the hydrophilicity of the spherical aluminum nitride surface is insufficient, thereby providing a basis for optimizing the surface treatment process.

3. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to claim 1, characterized in that: The step S102 comprises: Acquiring surface energy distribution data of spherical aluminum nitride powder, and determining high surface energy areas and low surface energy areas on the surface of the spherical aluminum nitride powder according to the surface energy distribution data; According to the surface energy characteristics of the high surface energy region, selecting a silane coupling agent that matches the surface energy characteristics of the high surface energy region from a preset silane coupling agent database; The spherical aluminum nitride powder is mixed with a selected silane coupling agent, and the two are fully contacted by mechanical stirring; Under preset temperature and preset pH conditions, promoting a chemical grafting reaction between the silane coupling agent and the surface of the spherical aluminum nitride powder to obtain a spherical aluminum nitride powder with the silane coupling agent grafted on the surface; The spherical aluminum nitride powder with the silane coupling agent grafted on the surface is analyzed by infrared spectroscopy to determine whether the silane coupling agent is successfully grafted onto the surface of the spherical aluminum nitride; If so, the terminal groups of the silane coupling agent grafted to the surface of the spherical aluminum nitride are chemically reacted with the preset hydrophilic groups to introduce hydrophilic groups onto the surface of the spherical aluminum nitride, thereby obtaining spherical aluminum nitride powder with enhanced surface hydrophilicity; The spherical aluminum nitride powder with enhanced surface hydrophilicity is washed and dried to obtain modified spherical aluminum nitride powder.

4. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to claim 1, characterized in that: The step S103 comprises: Obtaining spherical aluminum nitride powder, wherein the spherical aluminum nitride powder has surface properties; According to the surface properties, preparing a silane solution containing hydroxyl groups and carboxyl groups, wherein the silane solution is prepared by a sol-gel method and contains a hydrophilic group; Dispersing the spherical aluminum nitride powder in the silane solution, stirring and reacting at a preset temperature, so that the silane molecules are grafted onto the surface of the spherical aluminum nitride powder through a condensation reaction; The grafted spherical aluminum nitride powder is filtered, washed and dried to obtain silane-modified aluminum nitride powder with hydrophilic groups grafted on the surface; Using infrared spectroscopy analysis and contact angle test to characterize the changes in the surface chemical composition and wettability of the silane-modified aluminum nitride powder before and after modification, and determine the optimal process parameters for silane modification; Dispersing the silane-modified aluminum nitride powder in water, testing its dispersion stability and suspension rate, and evaluating the improvement of the hydrophilicity by the modification effect; According to the application requirements of the silane-modified aluminum nitride powder, at least two silane coupling agents are compounded, or other hydrophilic polymers are introduced to optimize the silane modification method, so as to further improve the surface activity and hydrophilicity of the silane-modified aluminum nitride powder.

5. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to any one of claims 1 to 4, characterized in that: The step S104 comprises: Obtaining a silane-modified spherical aluminum nitride sample, and measuring surface contact angle data of the spherical aluminum nitride sample using a contact angle meter; According to the contact angle data, it is determined whether the contact angle of the spherical aluminum nitride surface is less than a preset threshold of 60 degrees; if it is greater than the threshold, it is determined that the hydrophilicity of the spherical aluminum nitride surface does not meet the standard; Obtaining the silane-modified spherical aluminum nitride sample, and testing the surface free energy data of the spherical aluminum nitride sample using a surface tension meter; According to the surface free energy data, determining whether the surface free energy of the spherical aluminum nitride is higher than a preset threshold value of 50 mN / m; if lower than the threshold value, determining that the surface hydrophilicity of the spherical aluminum nitride does not meet the standard; If the contact angle of the spherical aluminum nitride surface is greater than 60 degrees or the surface free energy is less than 50 mN / m, adjusting the silane modification process parameters, wherein the silane modification process parameters include silane concentration, reaction temperature and reaction time; The surface of the spherical aluminum nitride is treated by using the adjusted silane modification process, and the hydrophilicity of the surface of the spherical aluminum nitride is improved by chemical reaction between silane molecules and hydroxyl groups on the surface of the spherical aluminum nitride; The contact angle and surface free energy of the spherical aluminum nitride sample treated with the adjusted silane modification process were tested again until the surface contact angle of the spherical aluminum nitride sample was less than 60 degrees and the surface free energy was higher than 50 mN / m, to determine that the hydrophilicity of the spherical aluminum nitride surface met the preset application requirements.

6. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to any one of claims 1 to 4, characterized in that: The step S106 comprises: Obtaining dispersibility data of the mixture, using a machine learning algorithm to establish a relationship model between the dispersibility and the filling amount of spherical aluminum nitride, and obtaining an optimal filling amount range of spherical aluminum nitride; According to the optimal filling amount range, multiple filling amount gradients are set to prepare a series of composite material samples with different filling amounts; The thermal conductivity of the composite material samples with different filling amounts is tested by a thermal conductivity meter to obtain a relationship curve between the filling amount and the thermal conductivity; The thermal expansion coefficients of the composite material samples with different filling amounts are tested by a thermal mechanical analyzer to obtain a relationship curve between the filling amount and the thermal expansion performance; The weight loss temperature of the composite material samples with different filling amounts is tested by a thermogravimetric analyzer to obtain a relationship curve between the filling amount and the heat resistance; According to the relationship curve between the filling amount and thermal conductivity, thermal expansion performance and heat resistance, a multi-objective optimization algorithm is used to obtain the optimal filling amount taking into account multiple performance indicators; According to the optimal filling ratio, a spherical aluminum nitride composite material with optimized performance is prepared, and its performance is verified.

7. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to any one of claims 1 to 4, characterized in that: The step S107 comprises: Obtain performance data of composite film at different filling contents, including parameters of thermal conductivity, thermal expansion coefficient and heat resistance temperature; Pre-process the acquired performance data to remove outliers and noise data to ensure the accuracy and reliability of the data; A multivariate linear regression algorithm was used to establish a mathematical model between the filling amount and the thermal conductivity, thermal expansion coefficient and heat resistance temperature, and to quantify the influence of the filling amount on the performance of the composite material. According to the established mathematical model, the thermal conductivity, thermal expansion coefficient and heat resistance temperature of the composite soft film under different filling amounts are predicted to obtain the predicted performance data; Compare the predicted performance data with the target performance requirements to select the filling amount range that satisfies the thermal conductivity greater than 18W / (m·K), the thermal expansion coefficient less than 30ppm / ℃ and the heat resistance temperature higher than 200℃; In the filling amount range that meets the performance requirements, the particle swarm optimization algorithm is used to search for the optimal filling amount with the optimal comprehensive performance as the objective function, and the optimal filling amount that makes the composite film perform best is obtained; Based on the determined optimal filling amount, prepare composite film samples, test their thermal conductivity, thermal expansion coefficient and heat resistance temperature to verify whether the performance meets the requirements, and fine-tune the filling amount until the optimal composite film that meets the performance requirements is obtained.

8. The method for preparing the environmentally friendly flame-retardant aluminum nitride silica gel composite film according to any one of claims 1 to 4, characterized in that: The step S109 comprises: Acquiring interface bonding force data, and using a support vector machine or an artificial neural network algorithm to establish a quantitative relationship model between the interface bonding force and silane modification process parameters; The best silane modification scheme is determined by an optimization algorithm to obtain modified spherical aluminum nitride powder; An orthogonal experimental design method is used to prepare a plurality of composite material samples with the content of the modified spherical aluminum nitride filler, the type and ratio of the silica gel matrix as factors; Testing the thermal conductivity, thermal expansion coefficient and heat resistance of the composite material sample, and establishing a response surface model between the material formula and performance of the composite material sample; By solving multi-objective optimization problems, while ensuring high thermal conductivity, low thermal expansion and high heat resistance of the composite material sample, the amount of environmentally friendly flame retardant is optimized to obtain the best composite material formula that takes into account both performance and environmental protection; According to the optimal composite material formula, a spherical aluminum nitride / silica gel composite material slurry is prepared by adopting a high-speed mixing and vacuum degassing process; Prepare composite soft sheet green body by tape casting process; Performing a curing treatment on the composite material soft film blank, optimizing the curing temperature and time, and obtaining a composite material soft film finished product with stable performance; The thermal conductivity, thermal expansion coefficient, heat resistance and flame retardancy of the finished composite film are characterized by using a thermal conductivity meter, a thermomechanical analyzer and a vertical combustion tester; Determine whether the finished composite film meets the requirements of high thermal conductivity, low thermal expansion, high heat resistance and environmental protection and flame retardancy.