Preparation method of metal oxide nanoparticles with controllable particle size

Abstract

The invention discloses a preparation method of metal oxide nano-particles with controllable particle sizes, and belongs to the field of nano-material preparation. According to two modes of existing literature data and experimental synthesis, a data set of process parameters and nano metal oxide particle size distribution in the preparation process is obtained; variables and product particle sizes in the database are processed through calculation and analysis methods such as one-heat coding, data normalization and model explanatory analysis, a prediction model is constructed, and the prediction model is embedded into a Bayesian optimization algorithm to construct a key process parameter reverse design platform; according to the target particle size required to be synthesized, synthesis process parameters can be obtained by utilizing a reverse design platform, so that the metal oxide is prepared, and the particle size controllable synthesis of the three nanoparticles of titanium oxide, zirconium oxide and cerium oxide is verified through experiments.

Description

Technical Field

[0001] The present invention relates to the technical field of nanomaterial preparation; more specifically, the present invention relates to a method for preparing metal oxide nanoparticles with controllable particle size, which prepares nano metal oxides with target particle size by solvent thermal synthesis under the guidance of a big data optimization algorithm. Background Art

[0002] The International Union of Pure and Applied Chemistry (IUPAC) defines nanoparticles (NPs) as particles with a size below 100 nanometers in one or more dimensions. Titanium oxide (TiO2) is a widely studied metal oxide with important applications in optoelectronics, thermoelectrics, medicine, and energy storage due to its unique electronic structure at different sizes. In addition, small-sized (about 15 nm) TiO2 NPs have irreplaceable advantages, such as special dielectric constant, low Rayleigh scattering, and high stability, making them the target of inorganic modified polymer materials with excellent optical properties. The precise control of size depends critically on the control of the synthesis process of TiO2 NPs. However, the synthesis of TiO2 NPs is jointly affected by multiple factors, such as temperature, reaction duration, pH value, reactants, and mixing efficiency. In particular, different reactants, such as dopants, can affect the nucleation and particle size of NPs, while the solvent in the reaction acts as a surface ligand to affect the monodisperse characteristics of NPs. These factors in the reaction system are interdependent and influence each other. Optimizing each reaction condition to achieve the target particle size consumes a lot of experimental time and resources. The interrelatedness of these factors makes the precise and controllable synthesis of TiO2 NPs challenging.

[0003] Complex TiO2 NPs composited with optical polymers have a variety of application scenarios, not limited to weather-resistant coatings for UV shielding, photocatalytically mediated reversible color conversion, ultrahigh diffraction efficiency volume holographic gratings, thermally regulated transparent coatings, etc. In these applications, the size of TiO2 has a great influence on the performance and stability of the nanocomposites. Many complex TiO2 NPs size control synthesis processes are based on manual grid search (orthogonal experiments) synthesis parameters. Each variable in the experimental process needs to be constantly debugged to determine the best formula, which not only consumes a lot of experimental time and resources, but also has a particularly low efficiency in obtaining the best formula. To thoroughly determine a set of suitable additive and dopant parameters to obtain the target particle size, hundreds of experiments and characterizations are required. For more than one number of admixtures, the required experimental time and resources will increase exponentially, which is almost impossible to complete with laboratory manpower. In the actual synthesis process, in addition to the effects of additives and dopants, more than 6 variables such as temperature, pH value, reaction time, etc. will affect the particle size distribution of synthesized NPs. The screening of reaction conditions consumes a lot of experimental time and resources.

[0004] The field of artificial intelligence (AI) is advancing by leaps and bounds. Machine learning (ML), as a branch of AI, plays a powerful role in data prediction and classification. When physical conditions are unknown, ML can break through physical limitations and obtain good results through established algorithms. Many papers show that ML has also been developed in the field of nanomaterial synthesis. For example, Zhang et al. applied ML to the synthesis process of Fe3O4 particles. They obtained the best model for predicting average particle size and crystallinity through multi-model comparison. Ling et al. established an ML model to study the purity, average particle size, and shape of Ag NPs mixtures by extinction spectroscopy. However, most ML studies only focus on the synthesis parameters of NPs with a specific average particle size under a single condition, while the synthesis of NPs with precise particle size distribution and complex composition still requires a lot of experiments. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for preparing metal oxide nanoparticles with controllable particle size.

[0006] The present invention improves the development efficiency of nano-metal oxides with different particle size distributions by machine learning methods, comprehensively considers the influence of different reaction variables on the particle size distribution of the product, thereby optimizing the screening of precursors and the design of reaction conditions, and the process conditions meet the effective boundary conditions for the preparation of nano-metal oxides and the wide range of performance changes. The present invention further develops a nano-metal oxide synthesis parameter reverse design platform, which integrates the metal oxide particle size distribution prediction model with the Bayesian optimization algorithm, and obtains a metal oxide synthesis parameter reverse recommendation platform with practical application value after hyperparameter optimization.

[0007] Based on this, the present invention mainly adopts the following technical solution, a method for preparing metal oxide nanoparticles with controllable particle size, which comprises the following steps:

[0008] 1) Based on the existing literature data on the solvothermal preparation of nano-metal oxides, the process parameters and the corresponding nano-metal oxide particle size distribution in the literature data are extracted to obtain a literature data set of process parameters-nano-metal oxide particle size distribution;

[0009] 2) preparing a precursor solution for preparing nano-metal oxides, synthesizing a nano-metal oxide wet agglomerate precipitate by a solvothermal method, centrifuging and washing the wet agglomerate precipitate, and obtaining the nano-metal oxide after post-treatment; changing the process parameters in the preparation process to obtain multiple groups of nano-metal oxides under different process parameters; performing a particle size distribution test on each of the prepared nano-metal oxides to obtain an experimental data set of process parameter-nano-metal oxide particle size distribution; and merging the experimental data set with the literature data set into a final data set;

[0010] 3) According to the final data set obtained in step 2), the outliers of the particle size distribution are calculated and analyzed by the IQR method and the corresponding data are excluded, the process parameters are further screened to obtain the key process parameters, and the non-key process parameters are removed to obtain the preprocessed data set; a metal oxide target particle size prediction model based on the GBDT regression model is constructed, and the optimal hyperparameters of the preprocessing are obtained by performing a ten-fold cross validation on the GBDT regression model through the preprocessed data set, and then the model is trained by the preprocessed data set to obtain a nano metal oxide target particle size prediction model that can be actually applied;

[0011] 4) The above-mentioned nano-metal oxide target particle size prediction model is embedded in the Bayesian optimization algorithm to construct a key process parameter reverse design platform; the target particle size distribution of the nano-metal oxide to be prepared, whether stirring is performed, and the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the precursor solution are input into the platform, and the remaining process parameters recommended by the Bayesian optimization algorithm are searched and output; a synthesis route is designed according to the output process parameters, and the nano-metal oxide is synthesized according to the synthesis route.

[0012] Among them, the process parameters of the present invention include the type of precursor, the molar ratio of the precursor to water, the type of metal salt, the molar ratio of the metal salt to the precursor, the type of additive, the molar ratio of the additive to the precursor, the type of organic solvent, the molar ratio of the organic solvent to the precursor, the type of hydrolysis inhibitor, the molar ratio of the hydrolysis inhibitor to water, pH value, reaction temperature, reaction time, and whether stirring is performed.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects:

[0014] 1) The present invention adopts a machine learning model to predict the particle size distribution of metal oxides. Through importance analysis and partial dependence analysis, it deeply explores the influencing conditions of nano-metal oxides in the synthesis process, and contributes to the subsequent optimization of synthesis formula and improvement of nano-metal oxide synthesis ideas.

[0015] 2) The method of the present invention builds a Bayesian optimization algorithm synthesis parameter reverse design platform, sets the target particle size area, and obtains recommended synthesis parameters through the Bayesian optimization method to guide the experiment, reducing the tedious formula optimization process to a few minutes and liberating productivity.

[0016] 3) Improve the development efficiency of nano-metal oxides with adjustable particle size through machine learning methods, comprehensively consider the impact of different reaction variables on the various properties of the products, and optimize the screening of precursors and the design of reaction conditions, which can save a lot of manpower and material resources and has inspiring significance for the design of other nano-functional materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for preparing metal oxide nanoparticles with controllable particle size according to an embodiment of the present invention;

[0018] Figure 2 This is the heat map of the correlation coefficients of the reaction variables in Example 1;

[0019] Figure 3 This is a ranking diagram of the importance of the reaction variables in Example 1;

[0020] Figure 4 It is the partial dependence analysis diagram of the reaction variables in Example 1;

[0021] Figure 5 is a graph showing the change in the relative error of the reverse recommendation platform in Example 1 with respect to the number of iterations;

[0022] Figure 6 Figure 2 is a dispersion of titanium oxide, zirconium oxide and cerium oxide obtained by optimizing the platform in the embodiment;

[0023] Figure 7 is a particle size distribution diagram of titanium oxide obtained by dynamic light scattering in Example 1;

[0024] Figure 8 is the X-ray diffraction pattern of titanium oxide in Example 1;

[0025] Fig. 9 is a high-resolution transmission electron micrograph of titanium oxide in Example 1;

[0026] Fig.10 is a particle size distribution diagram of zirconium oxide obtained by dynamic light scattering in Example 3;

[0027] Fig.11 is the X-ray diffraction pattern of zirconium oxide in Example 3;

[0028] Fig.12 is a high-resolution transmission electron microscopy image of zirconium oxide in Example 3;

[0029] Fig.13 is a particle size distribution diagram of cerium oxide obtained by dynamic light scattering in Example 4;

[0030] Fig.14 is the X-ray diffraction pattern of cerium oxide in Example 4;

[0031] Fig.15 This is a high-resolution transmission electron micrograph of cerium oxide in Example 4. DETAILED DESCRIPTION

[0032] The present invention is further described and illustrated below in conjunction with specific embodiments. The embodiments are merely exemplary of the present disclosure and do not define the scope of limitation. The technical features of each embodiment of the present invention may be combined accordingly without conflicting with each other.

[0033] In terms of the controlled synthesis of nano-metal oxide particle size, the present invention introduces a machine learning method to construct a prediction model based on a gradient boosting tree algorithm, trains the model with processed and screened preliminary experimental data, embeds the above-mentioned nano-metal oxide target particle size prediction model into a Bayesian optimization algorithm to construct a key process parameter reverse design platform, and uses the Bayesian optimization algorithm to reversely recommend synthesis parameter strategies to discover target products that meet the expected product performance requirements, thereby optimizing the preparation analysis direction of the material, greatly improving efficiency and the comprehensive grasp and understanding of the overall experimental variables.

[0034] In the application link of the present invention, the target particle size distribution of the nano metal oxide (titanium oxide, zirconium oxide or cerium oxide) to be prepared, whether to stir (stirring conditions) and the types of precursor, metal salt, hydrolysis inhibitor, additive and organic solvent in the precursor solution are input into the constructed reverse design platform, and the remaining key process parameters recommended by the Bayesian optimization algorithm search parameter output (i.e., the molar ratio of precursor to water, the molar ratio of metal salt to precursor, the molar ratio of additive to precursor, the molar ratio of organic solvent to precursor, the molar ratio of hydrolysis inhibitor to water, pH value, reaction temperature, reaction time) are output; according to the output key process parameters combined with the stirring conditions and the pre-selected types of precursor, metal salt, hydrolysis inhibitor, additive and organic solvent in the precursor solution, a statistical thermal synthesis route is designed, and nano metal oxides with the desired target particle size distribution are synthesized according to the synthesis route.

[0035] The present invention uses a platform to obtain process parameters and then synthesizes nano metal oxides by a solvothermal method. The solvothermal method is a common method for preparing nano metal oxides, and its synthesis route is: preparing a precursor solution for preparing nano metal oxides, synthesizing a wet agglomerate precipitate of nano metal oxides by a solvothermal method, centrifuging and washing the wet agglomerate precipitate, and obtaining the nano metal oxide after post-treatment. The solvothermal reaction temperature is 100-250°C, and the solvothermal reaction time is 1-72h; preferably, the solvothermal reaction temperature is 110-200°C, and the solvothermal reaction time is 5-48h; more preferably, the solvothermal reaction temperature is 150-200°C, and the solvothermal reaction time is 8-24h.

[0036] The precursor of the present invention is one of a titanium source, a zirconium source and a cerium source. The obtained nano metal oxide is titanium oxide, zirconium oxide or cerium oxide; the input target particle size distribution parameter of the nano metal oxide is one or more of D3, D10, D25, D50, D75, D90 and D99, preferably one or more of D3, D50 and D90, the input range of D3 is 1-100nm, the input range of D50 is 1-100nm, and the input range of D90 is 10-1000nm.

[0037] In the solvothermal method, the precursor solution includes a precursor, a metal salt, water, a hydrolysis inhibitor, an additive, and an organic solvent.

[0038] The titanium source includes one or more of tetrabutyl titanate, tetraethyl titanate, isopropyl titanate, titanium tetrachloride, titanyl sulfate and titanium sulfate; the zirconium source includes one or more of zirconium oxychloride octahydrate, zirconium sulfate, zirconium nitrate, basic zirconium carbonate, tetrabutyl zirconate and tetraisopropyl zirconate; the cerium source includes one or more of cerium nitrate, cerium chloride and cerium sulfate; the hydrolysis inhibitor includes one or more of octanoic acid, butyric acid, nitric acid, hydrochloric acid and sulfuric acid; the additive includes phenyltrimethoxysilane, γ-aminopropyl The organic solvent comprises one or more of triethoxysilane, γ-glycidyloxypropyltrimethoxysilane, 3-(methacryloyloxy)propyltrimethoxysilane, 3-(acryloyloxy)propyltrimethoxysilane, 3-mercaptopropyltrimethoxysilane, vinyltrimethoxysilane and vinyltriethoxysilane; the organic solvent comprises one or more of methanol, ethanol, isopropanol, n-butanol, tert-butanol, octanol, isomeric hexadecanol, propylene glycol, benzyl alcohol, acetone, butanone, propylene glycol methyl ether acetate and tetrahydrofuran.

[0039] The present invention provides an exemplary method for preparing metal oxide nanoparticles with controllable particle size, comprising the following steps:

[0040] 1) Based on the existing literature data on the solvothermal preparation of nano-metal oxides, the process parameters and the corresponding nano-metal oxide particle size distribution in the literature data are extracted to obtain a literature data set of process parameters-nano-metal oxide particle size distribution;

[0041] The process parameters include the type of precursor, the molar ratio of the precursor to water, the type of metal salt, the molar ratio of the metal salt to the precursor, the type of additive, the molar ratio of the additive to the precursor, the type of organic solvent, the molar ratio of the organic solvent to the precursor, the type of hydrolysis inhibitor, the molar ratio of the hydrolysis inhibitor to water, pH value, reaction temperature, reaction time, and whether stirring is performed.

[0042] In a specific embodiment of the present invention, a literature data automatic processing script can be used to obtain literature data, and optical-level image recognition technology can be used to search for relevant images about the size of nano-metal oxides in the literature data; a multivariate bag-of-words method is used to identify specific keywords contained in the images and exclude irrelevant images; the multivariate bag-of-words method is: first, images containing keywords such as "size", "diameter" or "dimension" are identified from the images as candidate images, and then images containing keywords such as "pore" are removed from the candidate images. The image recognition effect obtained through this constraint is the best.

[0043] The obtained pictures were digitized by Origin and the particle size distribution data were extracted; further, the process parameters for synthesizing the nano-metal oxide were obtained from the literature data, and a literature data set of process parameters-nano-metal oxide particle size distribution was obtained.

[0044] This example collects a large amount of nano-metal oxide synthesis data published in international journals through the method described above.

[0045] 2) preparing a precursor solution for preparing nano-metal oxides, synthesizing a nano-metal oxide wet agglomerate precipitate by a solvothermal method, centrifuging and washing the wet agglomerate precipitate, and obtaining the nano-metal oxide after post-treatment; changing the process parameters in the preparation process to obtain multiple groups of nano-metal oxides under different process parameters; and testing the particle size distribution of each of the prepared nano-metal oxides to obtain an experimental data set of process parameter-nano-metal oxide particle size distribution;

[0046] The precursor solution used in the preparation process of the solvothermal method is described in the above text. The post-treatment is drying, vacuum drying or freeze drying; the particle size distribution of the nano-metal oxide is measured by characterization methods such as dynamic light scattering and high-resolution transmission electron microscopy.

[0047] In this embodiment, in order to quantify the process parameters and particle size distribution data, the particle size distribution data obtained in step 1) and step 2) and the corresponding process parameters are quantified, and after quantification, the experimental data set and the literature data set are merged into the final data set.

[0048] The quantification steps include: firstly, one-hot encoding the process parameters such as "precursor type", "metal salt type", "additive type", "solvent type", "hydrolysis inhibitor type", and "whether stirring"; and then standardizing, scaling, and reducing the dimension of the particle size distribution data and its corresponding process parameter data.

[0049] In one embodiment of the present invention, one-hot encoding is as follows: a sparse matrix is ​​established according to these category variables, and the sparse matrix is ​​converted into {0, 1} related features as input.

[0050] Based on the above data processing, the data of different orders of magnitude are uniformly scaled to data with an expectation of 0 and a variance of 1 through normalization using the expectation and variance of the variable data.

[0051] Use heatmap to analyze the correlation between input features. Heatmap can intuitively display the correlation coefficients between various features. The heatmaps of experimental variables are as follows: Figure 2 As shown,

[0052] 3) If Figure 1 As shown, according to the final data set obtained in step 2), the outliers of the particle size distribution are calculated and analyzed by the IQR method and the corresponding data are excluded (the range after excluding the outliers here is consistent with the target particle size distribution input range of the reverse design platform constructed subsequently. Exemplarily, taking D3, D50, and D90 as examples, this step can exclude the particle size distribution of D3 outside 1-100nm, D50 outside 1-100nm, and D90 outside 10-1000nm as outliers), further screen the process parameters to obtain the key process parameters, and remove the non-key process parameters to obtain the preprocessed data set; construct a metal oxide target particle size prediction model based on the GBDT regression model, perform a ten-fold cross validation on the GBDT regression model through the preprocessed data set to obtain the optimal hyperparameters for preprocessing, and then train the model with the preprocessed data set to obtain a nano metal oxide target particle size prediction model that can be actually applied;

[0053] The optimal hyperparameters for preprocessing are obtained by performing a ten-fold cross validation on the GBDT regression model through the preprocessed data set, specifically including:

[0054] a) The preprocessed data set is randomly divided into a training set and a test set, the metal oxide target particle size prediction model is a gradient boosting tree model, and the model parameters of the gradient boosting tree model are adjusted by the training set, and the model parameters include optimization criteria, number of decision trees, and maximum depth of decision trees; the prediction results of the metal oxide target particle size prediction model after parameter adjustment are evaluated and verified by using the test set, and the evaluation indicators include mean square error RMSE and determination coefficient R 2 ;

[0055] b) Re-divide the training set and test set, and repeat step a) until the set number of cycles is reached.

[0056] 4) Embedding the above-mentioned nano-metal oxide target particle size prediction model into the Bayesian optimization algorithm to construct a key process parameter reverse design platform; inputting the target particle size distribution of the nano-metal oxide to be prepared into the platform, searching the parameters through the Bayesian optimization algorithm to output the recommended key process parameters; designing a synthesis route according to the output key process parameters, and synthesizing the nano-metal oxide according to the synthesis route.

[0057] The synthesis parameter reverse design platform described in step 4) is obtained by embedding the preprocessing database obtained in step 2) and the nano-metal oxide target particle size prediction model obtained in step 3) into the Bayesian optimization algorithm; through the original uncertainty search capability of the Bayesian algorithm, the nano-metal oxide target particle size prediction model obtained in step 3) is used as the nano-metal oxide characterization proxy model, and the key process parameters corresponding to the input target particle size are obtained by continuous iterative optimization.

[0058] In this embodiment, for step 2), a specific experimental scheme and operating instructions are given as follows:

[0059] Dissolve 0.75mL of deionized water and 0.13g of aluminum nitrate nonahydrate in 20mL of benzyl alcohol solution, stir thoroughly until dissolved to form a mixed solution, add 5.7mL of tetrabutyl titanate to the mixed solution, and form a uniform transparent precursor solution under high-speed magnetic stirring. Transfer the above precursor solution to a 50mL hydrothermal autoclave, the reaction temperature is 200℃, and the reaction time is 1h. Centrifuge the wet solid suspension at a speed of 4500rpm / min, wash with benzyl alcohol, and repeat the centrifugation 3 times.

[0060] The particle size distribution test of the nano titanium dioxide was performed.

[0061] The importance of the process parameters concerned in the present invention is analyzed, such as Figure 3 As shown, f1 represents the type of precursor, f2 represents the molar ratio of water to precursor, f3 represents the type of additive, f4 represents the molar ratio of additive to precursor, f5 represents pH value, f6 represents the type of dopant, f7 represents the molar ratio of dopant to precursor, f8 represents temperature, f9 represents reaction duration, f10 represents the type of solvent, f11 represents the molar ratio of solvent to precursor, f12 represents the presence of stirring, and f13 represents the absence of stirring. The experiment found that the type of dopant, precursor, and solvent have the strongest influence on the particle size distribution of nano-metal oxides. The partial dependence analysis technique was used to analyze the numerical variables, such as Figure 4 , solvents, dopants and other substances have a certain regional effect on the particle size distribution of nano metal oxides. When exceeding this region, the influence of these variables on the particle size distribution will become less obvious. Based on this, preferably, the process parameters can be screened according to importance according to the needs to obtain key process parameters, thereby reducing the amount of data and the training cost of subsequent models. When screening, the process parameters with low importance are preferentially removed, and their influence on the target particle size distribution is usually low. When the cost is not taken into account, more process parameters can be considered to be retained according to importance.

[0062] In one embodiment, the above model of the present invention is embedded in the Bayesian optimization algorithm to develop a reverse design platform for synthesis parameters. When applied, the target particle size distribution of the nano-metal oxide, the stirring conditions (whether to stir), and the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the precursor solution are set. The platform is guided by the particle size distribution of the nano-metal oxide and reversely recommends synthesis parameters through the platform, such as Figure 5 As shown in the figure, only 45 optimization iterations are needed to obtain better results.

[0063] Example 1

[0064] For titanium oxide nanoparticles, the D50 range is set to 8-15nm, stirring is selected, the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the required precursor solution are selected, and the synthesis parameter reverse design platform is input. The optimized synthesis process parameters are obtained using the platform. According to the process parameters, the solvothermal synthesis route is designed according to the solvothermal method described above as follows: 22mL of butanol, 0.39mL of butyric acid and 0.75mL of deionized water are mixed evenly with a high-speed magnetic stirrer, and 5.72mL of tetrabutyl titanate is added to the solution and mixed evenly. The solution is placed in a reactor and kept warm at 180°C for 20h. The turbid liquid after the reaction is taken out and centrifuged at 4500rpm for 5min to obtain a white wet cake at the bottom. An appropriate amount of acetone is added to wash and centrifuge, and repeated 2-3 times. It is prepared into a dispersion and subjected to dynamic light scattering ( Figure 7 )、XRD( Figure 8 )、TEM( Fig. 9 ) analysis. The D50 measured by dynamic light scattering is about 10nm, which is within the target particle size range. XRD analysis proves that the synthesized titanium oxide nanoparticles are anatase type. TEM analysis once again verifies that the measured particle size is within the range.

[0065] Example 2

[0066] Set the zirconium oxide D50 range to 8-15nm, choose not to stir, select the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the required precursor solution, and input them into the platform. Use the platform to obtain optimized synthesis process parameters and design the solvothermal synthesis route as follows: Mix 10g of basic zirconium carbonate, 10g of sodium hydroxide, and 100g of water evenly, and put the solution into a reactor and keep it at 110°C for 6h. Take out the turbid liquid after the reaction and centrifuge it at 4500rpm for 5min to obtain a white wet cake at the bottom. Add appropriate amount of water to wash and centrifuge, and repeat 2-3 times. Prepare it into a dispersion and perform dynamic light scattering ( Fig.10 )、TEM( Fig.11 )、XRD( Fig.12) analysis. The D50 measured by dynamic light scattering is about 9nm, which is within the target particle size range. XRD analysis proves that the synthesized zirconium oxide nanoparticles are tetragonal. TEM analysis once again verifies that the measured particle size is within the range.

[0067] Example 3

[0068] Set the D50 range of cerium oxide to 10-20nm, choose to use stirring, select the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the required precursor solution, and input them into the platform. Use the platform to obtain optimized synthesis process parameters and design the solvothermal synthesis route as follows: In a 250ml beaker, dissolve 2.52g of cerium nitrate hexahydrate in 100ml of a 1:1 water-ethylene glycol mixed solution, heat the mixed solution to 60°C, and add 16ml of 25% ammonia water under strong stirring, stir for three hours, cool to room temperature, wash with deionized water, and repeat 2-3 times. Prepare it into a dispersion and perform dynamic light scattering ( Fig.13 )、TEM( Fig.14 )、XRD( Fig.15 ) analysis. The D50 measured by dynamic light scattering is about 20nm, which is within the target particle size range. XRD analysis proves that the synthesized cerium oxide nanoparticles are cubic. TEM analysis once again verifies that the measured particle size is within the range.

[0069] The titanium oxide, zirconium oxide and cerium oxide dispersions obtained by the platform optimization and solvent thermal method in the above examples 1-3 are as follows: Figure 6 As shown above, it can be seen that the present invention adopts a machine learning model to predict the particle size distribution of metal oxides, and through importance analysis and partial dependence analysis, deeply explores the influencing conditions of nano-metal oxides in the synthesis process, and contributes to the subsequent optimization of synthesis formulas and improvement of nano-metal oxide synthesis ideas.

[0070] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for preparing metal oxide nanoparticles with controllable particle size, characterized in that: The steps include: 1) Based on the existing literature data on the solvothermal preparation of nano-metal oxides, the process parameters and the corresponding nano-metal oxide particle size distribution in the literature data are extracted to obtain a literature data set of process parameters-nano-metal oxide particle size distribution; 2) preparing a precursor solution for preparing nano-metal oxides, synthesizing a wet aggregate precipitate of the nano-metal oxides by a solvothermal method, centrifuging and washing the wet aggregate precipitate, and obtaining the nano-metal oxides after post-treatment; Changing the process parameters in the preparation process to obtain multiple groups of nano metal oxides under different process parameters; performing a particle size distribution test on each of the prepared nano metal oxides to obtain an experimental data set of process parameter-nano metal oxide particle size distribution; Merge the experimental dataset and the literature dataset into the final dataset; 3) According to the final data set obtained in step 2), the outliers of the particle size distribution are calculated and analyzed by the IQR method and the corresponding data are excluded, the process parameters are further screened to obtain the key process parameters, and the non-key process parameters are removed to obtain the preprocessed data set; a metal oxide target particle size prediction model based on the GBDT regression model is constructed, and the optimal hyperparameters of the preprocessing are obtained by performing a ten-fold cross validation on the GBDT regression model through the preprocessed data set, and then the model is trained by the preprocessed data set to obtain a nano metal oxide target particle size prediction model that can be actually applied; 4) Embedding the above-mentioned nano-metal oxide target particle size prediction model into the Bayesian optimization algorithm to construct a reverse design platform for key process parameters; The target particle size distribution of the nano-metal oxide to be prepared, whether to stir, and the types of precursors, metal salts, hydrolysis inhibitors, additives and organic solvents in the precursor solution are input into the platform, and the remaining process parameters recommended are output through the Bayesian optimization algorithm search parameter output; the synthesis route is designed according to the output process parameters, and the nano-metal oxide is synthesized according to the synthesis route.

2. The preparation method according to claim 1, characterized in that: In step 1), the process parameters and the corresponding nano-metal oxide particle size distribution extracted from the literature data include: Obtain literature data, and use image recognition technology to search for relevant images about the size of nano-metal oxides in the literature data; use the multivariate bag-of-words method to identify specific keywords contained in the images and exclude irrelevant images; use Origin to digitize the obtained images and extract particle size distribution data; further obtain the process parameters for synthesizing the nano-metal oxide from the literature data, and obtain a literature data set of process parameters-nano-metal oxide particle size distribution.

3. The preparation method according to claim 2, characterized in that : The multi-word bag method is used to identify specific keywords contained in the picture and exclude irrelevant pictures. Specifically, first, pictures containing the keyword "size", "diameter" or "dimension" are identified as candidate pictures, and then pictures containing the keyword "pore" are removed from the candidate pictures.

4. The preparation method according to claim 2, characterized in that : In step 1) and step 2), after obtaining the particle size distribution data and the corresponding process parameters, the method further includes the step of quantifying the particle size distribution and the process parameters, and obtaining the corresponding data set after quantification; The quantification step includes: firstly performing one-hot encoding on the process parameters such as "precursor type", "metal salt type", "additive type", "solvent type", "hydrolysis inhibitor type", and "whether stirring"; and then performing standardization, scaling, and dimension reduction on the particle size distribution data and its corresponding process parameter data.

5. The preparation method according to claim 1, characterized in that: The precursor solution in step 2) comprises a precursor, a metal salt, water, a hydrolysis inhibitor, an additive and an organic solvent; wherein the precursor is one of a titanium source, a zirconium source and a cerium source; The titanium source includes one or more of tetrabutyl titanate, tetraethyl titanate, isopropyl titanate, titanium tetrachloride, titanyl sulfate and titanium sulfate; The zirconium source includes one or more of zirconium oxychloride octahydrate, zirconium sulfate, zirconium nitrate, basic zirconium carbonate, tetrabutyl zirconate, and tetraisopropyl zirconate; The cerium source includes one or more of cerium nitrate, cerium chloride, and cerium sulfate; the hydrolysis inhibitor includes one or more of octanoic acid, butyric acid, nitric acid, hydrochloric acid, and sulfuric acid; The additive includes one or more of phenyltrimethoxysilane, γ-aminopropyltriethoxysilane, γ-glycidyloxypropyltrimethoxysilane, 3-(methacryloxy)propyltrimethoxysilane, 3-(acryloxy)propyltrimethoxysilane, 3-mercaptopropyltrimethoxysilane, vinyltrimethoxysilane, and vinyltriethoxysilane; The organic solvent includes one or more of methanol, ethanol, isopropanol, n-butanol, tert-butanol, octanol, isomeric hexadecanol, propylene glycol, benzyl alcohol, acetone, butanone, propylene glycol methyl ether acetate, and tetrahydrofuran.

6. The preparation method according to claim 1, characterized in that: The process parameters include the type of precursor, the molar ratio of the precursor to water, the type of metal salt, the molar ratio of the metal salt to the precursor, the type of additive, the molar ratio of the additive to the precursor, the type of organic solvent, the molar ratio of the organic solvent to the precursor, the type of hydrolysis inhibitor, the molar ratio of the hydrolysis inhibitor to water, the pH value, the reaction temperature, the reaction time, and whether to stir; the reaction temperature described in step 2) is 100-250°C, and the reaction time is 1-72h.

7. The preparation method according to claim 1, characterized in that : In step 2), the particle size distribution of the nano metal oxide is measured by dynamic light scattering and high-resolution transmission electron microscopy.

8. The preparation method according to claim 1, characterized in that : The optimal hyperparameters for preprocessing are obtained by performing a ten-fold cross validation on the GBDT regression model through the preprocessed data set, specifically including: a) The preprocessed data set is randomly divided into a training set and a test set, the metal oxide target particle size prediction model is a gradient boosting tree model, and the model parameters of the gradient boosting tree model are adjusted by the training set, and the model parameters include optimization criteria, number of decision trees, and maximum depth of decision trees; the prediction results of the metal oxide target particle size prediction model after parameter adjustment are evaluated and verified by using the test set, and the evaluation indicators include mean square error RMSE and determination coefficient R 2 ; b) Re-divide the training set and test set, and repeat step a) until the set number of cycles is reached.

9. The preparation method according to claim 1, characterized in that: The synthesis parameter reverse design platform described in step 4) is obtained by embedding the preprocessing database obtained in step 2) and the nano-metal oxide target particle size prediction model obtained in step 3) into the Bayesian optimization algorithm; through the original uncertainty search capability of the Bayesian algorithm, the nano-metal oxide target particle size prediction model obtained in step 3) is used as the nano-metal oxide characterization proxy model, and the key process parameters corresponding to the input target particle size are obtained by continuous iterative optimization.

10. The preparation method according to claim 1, characterized in that: The nano metal oxide is titanium oxide, zirconium oxide or cerium oxide; the target particle size distribution of the nano metal oxide input into the key process parameter reverse design platform in step 4) is one or more of D3, D10, D25, D50, D75, D90, and D99.

Images