A performance prediction method for nano-cerium dioxide UV shielding coatings based on machine learning optimization

Abstract

The present invention discloses a method for predicting the performance of nano-cerium dioxide ultraviolet shielding coatings based on machine learning optimization. This method uses experimental means to prepare and test the performance of the cerium dioxide ultraviolet shielding coating to obtain data, selects key characteristic parameters from the data, and constructs a database linking the key characteristic parameters with the performance of the ultraviolet shielding coating. A machine learning method is then used to construct a machine learning model, which is trained using the data in the database, and the optimal model is selected. During application, the cumulative particle size distribution of the nano-cerium dioxide that constitutes the coating to be tested, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating to nano-cerium dioxide, and the light transmittance of the cerium dioxide dispersion are input into the selected model. This method can predict the ultraviolet shielding performance and visible light transmittance of the applied ultraviolet shielding coating. This eliminates the need for extensive screening experiments, saving time and economic costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of nanomaterials, and in particular relates to a performance prediction method for nano-cerium dioxide ultraviolet shielding coatings based on machine learning optimization. Background Art

[0002] In recent years, with the depletion of the ozone layer, ultraviolet (UV) radiation has increased dramatically. Long-term exposure to UV radiation can have numerous adverse effects on human production and daily life. Therefore, the development of materials that can shield and reduce UV damage is essential. In particular, transparent UV shielding materials that block most UV rays while highly transmitting visible light can be added to the material being protected or coated on its surface. They achieve protection through multiple shielding effects, including absorption, reflection, and scattering of UV light.

[0003] Traditional organic UV absorbers are widely used, but they are prone to self-consumption, resulting in poor long-term stability. Therefore, inorganic metal oxides that can reflect, scatter, or absorb UV light while not absorbing visible light are suitable choices. Ceria with a wide bandgap is characterized by high physical and chemical stability, non-toxicity, and low price. At the same time, it has low photocatalytic activity and does not produce excessive reactive oxygen species after absorbing UV light, which would damage the substrate. Therefore, the choice of ceria as an inorganic UV shielding agent requires controlling the particle size to the nanometer level and modifying the particle surface to improve its compatibility with the substrate.

[0004] Nano-cerium dioxide ultraviolet shielding coating is a composite coating containing nano-cerium dioxide, organic solvent, and fluorocarbon coating. The composite coating is coated on the surface of the material and then subjected to heat treatment to obtain an ultraviolet shielding coating. The performance of the ultraviolet shielding coating (such as ultraviolet shielding performance and visible light transmittance) is closely related to the selection of nano-cerium dioxide, organic solvent, fluorocarbon coating, etc. in the composite coating. To obtain a composite coating with performance that meets the requirements or a coating that meets the performance requirements often requires a large number of screening experiments, which will consume too much manpower and material costs. The present invention intends to provide certain predictions and guidance for experiments through machine learning methods, thereby saving time and economic costs. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a performance prediction method for nano-cerium dioxide ultraviolet shielding coatings based on machine learning optimization.

[0006] The performance prediction method of nano-cerium dioxide ultraviolet shielding coating based on machine learning optimization provided by the present invention comprises the following steps:

[0007] S1, preparing a precursor solution for preparing nano-cerium dioxide particles, adding a precipitant to synthesize nano-cerium dioxide precipitates by a precipitation method, centrifuging and washing the precipitates to obtain nano-cerium dioxide particles; changing the preparation conditions to prepare multiple groups of different nano-cerium dioxide particles;

[0008] S2, adding a surface modifier to each group of nano-cerium dioxide and dispersing it in an organic solvent to obtain well-dispersed nano-cerium dioxide particles, mixing the organic solvent dispersion of nano-cerium dioxide with a fluorocarbon coating to obtain a composite coating, applying the composite coating and heat-treating to prepare multiple groups of transparent coatings containing nano-cerium dioxide, and testing the UV shielding properties and visible light transmittance of the coatings;

[0009] S3, establishing a database using the cumulative particle size distribution of nano-cerium dioxide, the surface modifier in step S2, the organic solvent, the light transmittance of the dispersion, the type of fluorocarbon coating, and the ratio of fluorocarbon coating to nano-cerium dioxide as characteristic values, and the UV shielding performance and visible light transmittance of the coating as target variables;

[0010] S4 builds machine learning models based on different machine learning methods, and uses the S3 database to train and cross-validate the machine learning models to obtain trained machine learning models; uses the scoring function to evaluate the machine learning models and selects the model with the best fitting effect.

[0011] S5. For the nano-cerium dioxide ultraviolet shielding coating that needs to be predicted for performance, the cumulative particle size distribution of the nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating and nano-cerium dioxide, and the transmittance of the cerium dioxide dispersion are taken as input and input into the model selected in S4, so that the ultraviolet shielding performance and visible light transmittance of the ultraviolet shielding coating applied thereon can be predicted.

[0012] Preferably, in S4, the machine learning method is one or more of linear regression, support vector machine, random forest regression, ridge regression, and extreme gradient boosting method.

[0013] Preferably, the cross-validation uses k-fold cross-validation, which divides the dataset into multiple subsets and uses different combinations of training sets and test sets to train and validate the model multiple times, thereby effectively evaluating the performance of the model on different data, reducing the risk of overfitting, and enhancing the generalization ability of the model.

[0014] Preferably, the score function is the coefficient of determination R 2The RMSE and the coefficient of determination indicate the model's ability to explain the target variable, and the value range is [0,1]. The closer it is to 1, the stronger the model's ability to explain the data. The RMSE measures the accuracy of the model's predictions, and the closer it is to 0, the smaller the prediction error.

[0015]

[0016] Where n is the amount of data, is the predicted value, y i The actual results of the test set.

[0017] Compared with the prior art, the present invention has found through research and analysis that the cumulative particle size distribution of nano-cerium dioxide and the transmittance of the cerium dioxide dispersion in the coating preparation process are key characteristics that affect the subsequent composite coating performance and coating performance (the cumulative particle size distribution characterizes the particle size distribution range and characteristics of the nanoparticles in the dispersion, and the transmittance is the transmittance of the organic solvent dispersion of nano-cerium dioxide, the purpose of which is to evaluate the optical properties and dispersion uniformity of the dispersion). By using these two characteristics of cerium dioxide to characterize cerium dioxide for database construction, the complexity of the database is greatly reduced, and it is unnecessary to consider the influence of other preparation parameters in the nano-cerium dioxide preparation process (such as the composition of the precursor solution, the synthesis temperature, the selection of the precipitant, the flushing liquid, etc.) on the coating or coating performance, thereby simplifying the difficulty of model training and making the method of the present invention applicable to commercially available cerium dioxide without being limited to its preparation process.

[0018] The present invention further selects UV shielding and visible light transmittance as key coating properties, which fully meets the application requirements of UV shielding coatings or coatings. Considering that the heat treatment process has little impact on coating performance after the components of the composite coating are determined, the heat treatment process is not used as an input feature, further reducing the data volume of the dataset. Finally, closely related performance characteristics such as the cumulative particle size distribution of nanoceria, organic solvent, type of fluorocarbon coating, ratio of fluorocarbon coating to nanoceria, and transmittance of the ceria dispersion are selected as input features.

[0019] The present invention uses multiple machine learning methods to construct a model, uses a database to train and cross-validate the machine learning model, and obtains a trained machine learning model; uses a scoring function to evaluate the machine learning model and selects the model with the best fitting effect. The obtained model can be used to predict the performance of nano-cerium dioxide ultraviolet shielding coatings. The method of the present invention does not require the preparation of composite coatings or the conduct of coating experiments. It only requires the characteristics of the composite coating to be prepared (the cumulative particle size distribution of nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating to nano-cerium dioxide, and the transmittance of the cerium dioxide dispersion) to obtain the performance prediction results of the composite coating and the coating after application, thereby saving time and economic costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the X-ray diffraction pattern of synthetic cerium dioxide;

[0021] Figure 2 This is a transmission electron microscope image of synthesized cerium dioxide;

[0022] Figure 3 is the UV-visible spectrum of synthesized cerium dioxide;

[0023] Figure 4 This is a comparison chart of the three models in terms of UV cutoff performance;

[0024] Figure 5 A graph summarizing the explanation of the SHAP method on the UV cutoff performance. DETAILED DESCRIPTION

[0025] The present invention will be further described and illustrated below in conjunction with specific embodiments. The embodiments are merely illustrative of the present disclosure and do not limit its scope. The technical features of the various embodiments of the present invention may be combined accordingly, provided that there is no conflict between them.

[0026] The present invention provides a performance prediction method for nano-cerium dioxide ultraviolet shielding coatings based on machine learning optimization. The method prepares and tests the performance of the cerium dioxide ultraviolet shielding coatings by experimental means to obtain data, selects key characteristic parameters from the data, and constructs a database between the key characteristic parameters and the performance of the ultraviolet shielding coatings; a machine learning method is used to construct a machine learning model, the model is trained using the data in the database, and the best model is selected. When applied, for the nano-cerium dioxide ultraviolet shielding coatings that need to be performance predicted, the cumulative particle size distribution of the nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating to nano-cerium dioxide, and the light transmittance of the cerium dioxide dispersion are used as input and input into the selected model, so that the ultraviolet shielding performance and visible light transmittance of the ultraviolet shielding coating applied thereon can be predicted.

[0027] The specific process of the inventive method is as follows:

[0028] S1, preparing a precursor solution for preparing nano-cerium dioxide particles, adding a precipitant to synthesize nano-cerium dioxide precipitates by a precipitation method, centrifuging and washing the precipitates to obtain nano-cerium dioxide particles; changing the preparation conditions to prepare multiple groups of different nano-cerium dioxide particles;

[0029] S2, adding a surface modifier to each group of nano-cerium dioxide and dispersing it in an organic solvent to obtain well-dispersed nano-cerium dioxide particles, mixing the organic solvent dispersion of nano-cerium dioxide with a fluorocarbon coating to obtain a composite coating, applying the composite coating and heat-treating to prepare multiple groups of transparent coatings containing nano-cerium dioxide, and testing the UV shielding properties and visible light transmittance of the coatings;

[0030] The surface modifier is one or more of phosphate, titanate and silane coupling agent, the phosphate is one or more of isooctyl phosphate, dodecyl phosphate, alkyl diphenyl phosphate, tetradecyl phosphate and lauryl alcohol polyoxyethylene ether, the titanate includes one or more of isopropyl tris(dodecylbenzenesulfonyl) titanate, bis(acetylacetonato) diisopropyl titanate, isopropyl tris(dioctyl pyrophosphate acyloxy) titanate, isopropyl tris(dioctyl phosphate acyloxy) titanate and isopropyl dioleyloxy(dioctyl phosphate acyloxy) titanate, and the silane coupling agent is one or more of vinyl trimethoxy silane, vinyl triethoxy silane, dodecyl trimethoxy silane, dodecyl triethoxy silane, hexadecyl trimethoxy silane, hexadecyl triethoxy silane and phenyl trimethoxy silane.

[0031] The organic solvent is one or more of methanol, ethanol, isopropanol, n-butanol, tert-butanol, octanol, propylene glycol, benzyl alcohol, acetone, butanone, propylene glycol methyl ether acetate, tetrahydrofuran, dimethyl sulfoxide, chloroform, ethyl acetate, butyl acetate, benzene, toluene, xylene, heptane, n-hexane, cyclohexane and petroleum ether, and the fluorocarbon coating is one or more of polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and chlorotrifluoroethylene-vinyl ether copolymer (FEVE).

[0032] The heat treatment process has little effect on the properties of the resulting coating. All coatings used in the present invention utilize the same heat treatment process to prepare the coating. Typically, but not limiting, the heat treatment involves first high-temperature curing at a temperature of 100-200 degrees Celsius, followed by low-temperature curing at a temperature of 40-80 degrees Celsius.

[0033] In the performance test of the present invention, the UV shielding performance test interval is 280-400 nanometers, characterized by UV cutoff rate; the visible light transmittance test interval is 400-800 nanometers, characterized by visible light transmittance;

[0034]

[0035] Where λ is the wavelength and T(λ) is the transmittance at that wavelength.

[0036] S3, establishing a database using the cumulative particle size distribution of nano-cerium dioxide, the surface modifier in step S2, the organic solvent, the light transmittance of the dispersion, the type of fluorocarbon coating, and the ratio of fluorocarbon coating to nano-cerium dioxide as characteristic values, and the UV shielding performance and visible light transmittance of the coating as target variables;

[0037] S4, constructs machine learning models based on different machine learning methods, and uses the database of S3 to train and cross-validate the machine learning models to obtain trained machine learning models; uses a scoring function to evaluate the machine learning models and selects the model with the best fitting effect. The machine learning method is one or more of linear regression, support vector machine, random forest regression, ridge regression, and extreme gradient boosting method. The cross-validation uses k-fold cross-validation, which divides the data set into multiple subsets and uses different training sets and test sets to train and validate the model multiple times, thereby effectively evaluating the performance of the model on different data, reducing the risk of overfitting, and enhancing the generalization ability of the model. The scoring function is the determination coefficient R 2 The RMSE and the coefficient of determination indicate the model's ability to explain the target variable, and the value range is [0,1]. The closer it is to 1, the stronger the model's ability to explain the data. The RMSE measures the accuracy of the model's predictions, and the closer it is to 0, the smaller the prediction error.

[0038]

[0039] Where n is the amount of data, is the predicted value, y i The actual results of the test set.

[0040] S5. For the nano-cerium dioxide ultraviolet shielding coating that needs to be predicted for performance, the cumulative particle size distribution of the nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating and nano-cerium dioxide, and the transmittance of the cerium dioxide dispersion are taken as input and input into the model selected in S4, so that the ultraviolet shielding performance and visible light transmittance of the ultraviolet shielding coating applied thereon can be predicted.

[0041] To ensure the practicality of the model, the database of the present invention needs to contain data on different nano-cerium dioxide. A certain amount or cumulative particle size distribution of nano-cerium dioxide can be obtained through commercial purchase. However, the types of cerium dioxide available commercially are generally limited and often insufficient to cover the range requirements of the training data. Therefore, the present invention further provides a method for preparing nano-cerium dioxide to obtain a richer range of nano-cerium dioxide. The preparation process of nano-cerium dioxide is as follows: a precursor solution for preparing nano-cerium dioxide particles is prepared, a precipitant is added to synthesize a nano-cerium dioxide precipitate by precipitation, and the precipitate is centrifuged and washed to obtain nano-cerium dioxide particles.

[0042] Typically, but not limiting, the precursor solution includes a cerium salt and a solvent, the cerium salt is one or more of cerium sulfate, cerium nitrate, cerium chloride, and cerium acetate, the solvent is one or more of water, methanol, ethanol, ethylene glycol, n-propanol, isopropanol, and propylene glycol, the precipitant is one or more of ammonia water, sodium hydroxide solution, potassium hydroxide solution, sodium bicarbonate solution, and sodium carbonate solution, the synthesis temperature is 40-90 degrees, the synthesis time is 2-20 hours, the centrifugal speed is 5000-11000 rpm, and the detergent is a solution of water and ethanol in different volume ratios, and the ratio of water to ethanol is 1:1-1:15.

[0043] The present invention uses two characteristics, the cumulative particle size distribution of nano-cerium dioxide and the transmittance of the cerium dioxide dispersion in the coating preparation process, to characterize cerium dioxide and construct a database. This eliminates the need to consider the impact of other preparation parameters in the nano-cerium dioxide preparation process on the coating or coating performance, and also makes the method of the present invention applicable to commercially available cerium dioxide without being limited to its preparation process.

[0044] Example 1

[0045] 21.7g of cerium nitrate was dissolved in 800mL of a solution with a volume ratio of water to ethylene glycol of 1:3, stirred for 0.5h, transferred to a reactor, heated to 60℃, and slowly added 36mL of ammonia water. The reaction was continued for 5 hours, and the mixture was allowed to stand for precipitation and then centrifuged. A mixture of water and ethanol with a volume ratio of 1:2 was used as a detergent to wash the precipitate. The centrifugal speed was 10000rpm. After washing 3 times, cerium dioxide was obtained, and its particle size was tested and the cumulative particle size distribution was calculated. The synthesized cerium dioxide was characterized, as shown in FIG. Figure 1 is the X-ray diffraction pattern of synthetic cerium dioxide, corresponding to the PDF card of cerium dioxide, such as Figure 2 This is a transmission electron microscope image of synthetic cerium dioxide, which has obvious lattice fringes and good crystallinity. Figure 3 This is the UV-visible spectrum of synthesized cerium dioxide, which has good absorption in the ultraviolet region and poor absorption in the visible light region.

[0046] Cerium dioxide and a surface modifier were dispersed in toluene, the surface modifier was isopropyl tri(dodecylbenzenesulfonyl) titanate, and the mass ratio of cerium dioxide: isopropyl tri(dodecylbenzenesulfonyl) titanate: toluene was 1:0.1:20. The transmittance was tested, and the toluene dispersion of cerium dioxide was mixed with a fluorocarbon coating to obtain a composite coating. The coating was obtained by scraping, and the coating was first dried at 100°C for 20 minutes and then at 60°C for 40 hours. The ultraviolet cutoff performance and visible light transmittance of the composite coating were tested.

[0047] Example 2

[0048] 26 g of cerium nitrate was dissolved in 900 mL of a solution containing 1:2 water and propylene glycol by volume, stirred for 0.5 h, transferred to a reactor, heated to 70°C, and slowly added dropwise 40 mL of ammonia water. The mixture was reacted for 6 hours, allowed to settle, and then centrifuged. The precipitate was washed three times with a mixture of 1:2 water and ethanol by volume at a centrifugal speed of 10,000 rpm to obtain cerium dioxide. The subsequent processing steps were consistent with those in Example 1. Finally, the UV cutoff performance and visible light transmittance of the composite coating were tested.

[0049] Example 3

[0050] Ceria with different cumulative particle size distributions is obtained by commercially available or continuously changing synthesis conditions. Ceria dispersions are obtained by selecting different surface modifiers and organic solvents, and the light transmittance of the dispersions is tested. A composite coating is prepared by selecting different types of fluorocarbon coatings and different ratios of fluorocarbon coatings to nano-ceria. The coatings are applied, and the resulting coatings are tested for UV cutoff performance and visible light transmittance. The cumulative particle size distribution of nano-ceria, the surface modifier in step S2, the organic solvent, the light transmittance of the dispersion, the type of fluorocarbon coating, and the ratio of fluorocarbon coating to nano-ceria are used as characteristic values, wherein the light transmittance represents the light transmittance of the organic solvent dispersion of nano-ceria. The UV cutoff performance and visible light transmittance are used as target variables to establish a database.

[0051] Ridge regression, random forest regression, and extreme gradient boosting methods were used to construct machine learning models respectively; the three models were trained and k-fold cross-validated using the data in the aforementioned database to obtain three trained models.

[0052] According to the coefficient of determination R 2 The three models were evaluated with the value of the root mean square error RMSE and the better model was selected, such as Figure 4 , we can get the performance of the three models. For the three models, the determination coefficient R of the model constructed by the extreme gradient boosting method is 2The maximum and the root mean square error RMSE is the smallest, so the model corresponding to the extreme gradient boosting method is selected, and then the SHAP analysis is performed to obtain the important features with greater influence, such as Figure 5 , a larger cumulative particle size distribution can be obtained, which has a greater impact on the results of UV cutoff performance and visible light transmittance performance.

[0053] For nano-cerium dioxide ultraviolet shielding materials that need to have their performance predicted, after determining their composition, there is no need to conduct coating compounding or coating experiments. The cumulative particle size distribution of the nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating to nano-cerium dioxide, and the transmittance of the cerium dioxide dispersion that constitutes it can be directly input into the selected model, and the ultraviolet shielding performance and visible light transmittance of the ultraviolet shielding coating applied thereto can be predicted, thereby saving time and economic costs.

[0054] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. Persons skilled in the art will readily appreciate that variations and modifications may be made without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. A performance prediction method for nano-cerium dioxide ultraviolet shielding coating based on machine learning optimization, characterized in that: The steps include: S1, preparing a precursor solution for preparing nano-cerium dioxide particles, adding a precipitant to synthesize nano-cerium dioxide precipitates by a precipitation method, centrifuging and washing the precipitates to obtain nano-cerium dioxide particles; changing the preparation conditions to prepare multiple groups of different nano-cerium dioxide particles; S2, adding a surface modifier to each group of nano-cerium dioxide and dispersing it in an organic solvent to obtain well-dispersed nano-cerium dioxide particles, mixing the organic solvent dispersion of nano-cerium dioxide with a fluorocarbon coating to obtain a composite coating, applying the composite coating and heat-treating to prepare multiple groups of transparent coatings containing nano-cerium dioxide, and testing the UV shielding properties and visible light transmittance of the coatings; S3, establishing a database using the cumulative particle size distribution of nano-cerium dioxide, the surface modifier in step S2, the organic solvent, the light transmittance of the dispersion, the type of fluorocarbon coating, and the ratio of fluorocarbon coating to nano-cerium dioxide as characteristic values, and the UV shielding performance and visible light transmittance of the coating as target variables; S4 builds machine learning models based on different machine learning methods, and uses the S3 database to train and cross-validate the machine learning models to obtain trained machine learning models; uses the scoring function to evaluate the machine learning models and selects the model with the best fitting effect. S5. For the nano-cerium dioxide ultraviolet shielding coating that needs to be predicted for performance, the cumulative particle size distribution of the nano-cerium dioxide, the surface modifier, the organic solvent, the type of fluorocarbon coating, the ratio of fluorocarbon coating and nano-cerium dioxide, and the transmittance of the cerium dioxide dispersion are taken as input and input into the model selected in S4, so that the ultraviolet shielding performance and visible light transmittance of the ultraviolet shielding coating applied thereon can be predicted.

2. The performance prediction method according to claim 1, characterized in that: The precursor solution includes a cerium salt and a solvent, wherein the cerium salt is one or more of cerium sulfate, cerium nitrate, cerium chloride, and cerium acetate, and the solvent is one or more of water, methanol, ethanol, ethylene glycol, n-propanol, isopropanol, and propylene glycol. The precipitant is one or more of ammonia water, sodium hydroxide solution, potassium hydroxide solution, sodium bicarbonate solution, and sodium carbonate solution; the synthesis temperature is 40-90 degrees, the synthesis time is 2-20 hours, the centrifugal speed is 5000-11000 revolutions per minute; and the detergent is a solution of water and ethanol in different volume ratios, with the ratio of water to ethanol being 1:1-1:

15.

3. The performance prediction method according to claim 1, characterized in that: The surface modifier described in S2 is one or more of phosphate, titanate and silane coupling agent, the phosphate is one or more of isooctyl phosphate, dodecyl phosphate, alkyl diphenyl phosphate, tetradecyl phosphate and lauryl polyoxyethylene ether, the titanate includes one or more of isopropyl tris(dodecylbenzenesulfonyl) titanate, bis(acetylacetonato) diisopropyl titanate, isopropyl tris(dioctyl pyrophosphate acyloxy) titanate, isopropyl tris(dioctyl phosphate acyloxy) titanate and isopropyl dioleyloxy(dioctyl phosphate acyloxy) titanate, the silane coupling agent is one or more of vinyltrimethoxysilane, vinyltriethoxysilane, dodecyltrimethoxysilane, dodecyltriethoxysilane, hexadecyltrimethoxysilane, hexadecyltriethoxysilane and phenyltrimethoxysilane.

4. The performance prediction method according to claim 1, characterized in that: The organic solvent described in S2 is one or more of methanol, ethanol, isopropanol, n-butanol, tert-butanol, octanol, propylene glycol, benzyl alcohol, acetone, butanone, propylene glycol methyl ether acetate, tetrahydrofuran, dimethyl sulfoxide, chloroform, ethyl acetate, butyl acetate, benzene, toluene, xylene, heptane, n-hexane, cyclohexane and petroleum ether, and the fluorocarbon coating is one or more of polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and trifluorochloroethylene-vinyl ether copolymer (FEVE).

5. The performance prediction method according to claim 1, characterized in that: The heat treatment in S2 is first high-temperature curing, the temperature is 100-200 degrees Celsius, and then low-temperature curing, the low-temperature temperature is 40-80 degrees Celsius.

6. The performance prediction method according to claim 1, characterized in that: In S4, the machine learning method is one or more of linear regression, support vector machine, random forest regression, ridge regression, and extreme gradient boosting method.

7. The performance prediction method according to claim 1, characterized in that: The cross-validation uses k-fold cross-validation, which divides the dataset into multiple subsets and uses different combinations of training sets and test sets to train and validate the model multiple times, thereby effectively evaluating the performance of the model on different data, reducing the risk of overfitting, and enhancing the model's generalization ability.

8. The performance prediction method according to claim 1, characterized in that: The score function is the coefficient of determination R 2 The RMSE and the coefficient of determination indicate the model's ability to explain the target variable, and the value range is [0,1]. The closer it is to 1, the stronger the model's ability to explain the data. The RMSE measures the accuracy of the model's predictions, and the closer it is to 0, the smaller the prediction error. Where n is the amount of data, is the predicted value, y i The actual results of the test set.

9. The performance prediction method according to claim 1, characterized in that: The UV shielding performance test range is 280-400 nanometers, characterized by UV cutoff rate; the visible light transmittance test range is 400-800 nanometers, characterized by visible light transmittance; Where λ is the wavelength and T(λ) is the transmittance at that wavelength.

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