Method for testing and analyzing the performance of surface modification of inorganic materials

Abstract

The application discloses a kind of inorganic material surface modification performance test analysis methods, comprising: preparation inorganic material base material, surface modification is carried out, obtains several modified samples;Test the total surface energy and mechanical property of each modified sample;The durability test environment of modified sample is constructed, and the performance attenuation rate of modified sample after durability test is calculated;Random forest network model is constructed, and random forest network model is trained using input data, output data, and output convergent random forest network model;Predict the performance parameters of the material under new modification process parameters, surface modification after natural environment parameters, and evaluate whether the surface modified inorganic material prepared by new modification process meets the use requirements.The application realizes the comprehensive, accurate and rapid prediction and evaluation of the performance of inorganic material after surface modification, and provides strong technical support for subsequent process research and modification effect test evaluation of modified samples.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of inorganic material surface modification, and particularly relates to a performance test analysis method for inorganic material surface modification. BACKGROUND

[0002] Inorganic materials are widely used in composite materials, electronic devices, building materials and other fields due to their high strength, high temperature resistance, corrosion resistance and other excellent characteristics. However, the surface of inorganic materials usually has defects such as strong hydrophilicity, poor interfacial compatibility and insufficient dispersibility, which need to be optimized through surface modification (such as coupling agent modification, plasma modification, coating modification, etc.).

[0003] In the process of surface modification of inorganic materials, different modification processes have a great influence on the performance of inorganic materials after surface modification, and whether different modifications are suitable for inorganic materials and the corresponding natural environment need to test the performance after surface modification.

[0004] The existing performance test analysis method for inorganic material surface modification has the following core defects:

[0005] Single test dimension: the traditional method only tests a single performance (such as surface energy, tensile strength, dispersibility), ignores the coupling correlation of multi-dimensional indicators such as mechanical properties, surface chemical properties and durability, resulting in one-sided evaluation of modification effect;

[0006] Insufficient prediction accuracy: long-term performance prediction mostly uses a single empirical formula, without considering the dynamic coupling influence of environmental factors such as temperature and humidity, and without integrating data-driven methods, resulting in large prediction error;

[0007] Long test cycle: long-term durability test needs to take several months or even years, which is low in efficiency and cannot meet the rapid detection needs of industrial production. SUMMARY

[0008] In view of the above deficiencies of the prior art, the present application provides a performance test analysis method for inorganic material surface modification, which is suitable for performance prediction and analysis of surface modification of thin film type inorganic materials.

[0009] To achieve the above application purposes, the technical solutions adopted by the present application are as follows:

[0010] A performance test analysis method for inorganic material surface modification is provided, which comprises:

[0011] Step S1: preparing an inorganic material substrate, preparing the inorganic material substrate into a thin film material with a target thickness, surface modifying the inorganic material substrate according to different surface modification processes, obtaining a plurality of modified samples, and obtaining the modification process parameters of each modified sample;

[0012] Step S2: Test the surface contact angle of the modified sample using different probe liquids, and calculate the total surface energy of each modified sample considering the coupling effect of the dispersive component, polar component and hydrogen bond component; test the mechanical properties of each modified sample, and calculate the fracture load, average shear strength, critical anchorage length and stress attenuation coefficient of the modified sample.

[0013] Step S3: Construct a durability test environment for the modified sample. Based on the target natural environment parameters and the durability environment parameters set for the durability test, calculate the time for accelerated durability testing, conduct durability testing on the modified sample, and calculate the performance degradation rate of the modified sample after the durability test.

[0014] Step S4: Obtain the performance parameter data of modified samples under different modification processes and natural environmental conditions as output data, and use the modification process parameters and natural environmental parameters as input data to construct a random forest network model. Use the input data and output data to train the random forest network model and output a converged random forest network model.

[0015] Step S5: Input the new modification process parameters and the natural environment parameters for the use of the surface-modified inorganic material into the converged random forest network model to predict the performance parameters under the new modification process parameters and the natural environment parameters for the use of the surface-modified material, and evaluate whether the surface-modified inorganic material prepared by the new modification process meets the usage requirements.

[0016] Furthermore, in step S2, the contact angle of the modified sample surface is tested using different probe liquids, and the total surface energy of each modified sample is calculated considering the coupling effect of the dispersive component, polar component, and hydrogen bonding component. This specifically includes the following steps:

[0017] Step S21: Select three probe liquids of different polarities—deionized water, ethylene glycol, and diiodomethane—and use the seated drop method to test the surface contact angle of each modified sample. A uniformly spaced probe is placed on each modified sample. N Test points, and take N The average surface contact angle at each test point is used to obtain the surface contact angle of the modified sample tested for each probe liquid.

[0018] ;

[0019] in, u The type of probe liquid, For the first u The surface contact angle measured by a probe liquid. For the modified sample u The probe liquid in the first n The surface contact angle measured at each test point n This refers to the test point number;

[0020] Step S22: Using the surface contact angles measured by each probe liquid, and considering the coupling effect of the dispersive component, polar component and hydrogen bonding component, construct a coupled model to calculate the total surface energy of the modified sample.

[0021] ;

[0022] in, The first i The total dispersive, polar, and hydrogen bonding components of the three probe liquids corresponding to each modified sample. The first u The dispersive component, polar component, and hydrogen bonding component corresponding to the probe liquid. For the first u The total surface energy of the probe liquid. For the first i The total surface energy of the modified sample. i This is the number of the modified sample;

[0023] Step S23: Input the surface contact angles measured by the three probe liquids, as well as the dispersion components, polar components, hydrogen bonding components, and total surface energies corresponding to the three probe liquids, into the coupling model to construct a system of three linear equations in the coupling model, and calculate the... i The total dispersive components of the three probe liquids corresponding to each modified sample polar components and hydrogen bond components And then calculate the first i Total surface energy of the modified sample .

[0024] Further, in step S2, the mechanical properties of each modified sample are tested, and the fracture load, average shear strength, critical anchorage length, and stress attenuation coefficient of the modified sample are calculated. This specifically includes the following steps:

[0025] Step S24: Perform nanoindentation testing on each modified sample to obtain the elastic modulus of the modified sample. E and hardness H Furthermore, micro-tensile tests were performed on each modified sample to obtain the fracture load of the modified sample. F ;

[0026] Step S25: Based on the fracture load F Elastic modulus E Calculate the first i Average shear strength of each modified sample and according to the first i Average shear strength of each modified sample and the iMaximum shear strength of the edge of the modified sample Stress decay coefficient of the modified sample i

[0027]

[0028]

[0029] wherein, A is the bonding area during the test process, is the elastic modulus of the inorganic material substrate, is the bonding length of the modified sample during the test process; i Step S26: according to the hardness

[0030] and the average shear strength of the modified sample H , the critical anchoring length of the modified sample is calculated i i

[0031]

[0032] wherein, is the thickness of the modified sample. i

[0033] Further, step S3 comprises:

[0034] Step S31: a durability test environment of the modified sample is constructed, and based on the target natural environment parameters and the durability environment parameters set by the durability test, the time of the durability accelerated test is calculated, and the modified sample is subjected to the durability accelerated test in the durability test environment;

[0035] Step S32: after the durability accelerated test is completed, the total surface energy i , the breaking load , the average shear strength and the stress decay coefficient of the modified sample after the durability accelerated test are calculated by repeating step S2; the performance decay rate of the modified sample after the durability test is calculated;

[0036]

[0037] wherein, respectively, the total surface energy, the breaking load, the average shear strength and the stress decay coefficient are allowed to fluctuate.

[0038] Further, the durability environment parameters include environmental temperature​​​​​​​​​​​ , concentration of the erosion medium , and relative humidity

[0039] The target natural environment parameters include target temperature , concentration of the erosion medium , and humidity

[0040] The method for calculating the time of the durability accelerated test is:

[0041]

[0042] wherein, is the target equivalent time of the natural environment of the durability test, are the temperature conversion coefficient, the concentration conversion coefficient, and the humidity conversion coefficient, respectively.

[0043] Further, the calculation method of the temperature conversion coefficient is:

[0044]

[0045] wherein, is the temperature reference correction coefficient, is the activation energy of the erosion reaction, is the basic activation energy of the inorganic material substrate, R is the gas constant of the test environment;

[0046] The calculation method of the concentration conversion coefficient is:

[0047]

[0048] wherein, is the concentration of the erosion medium reference correction coefficient, p is the porosity of the modified sample;

[0049] The calculation method of the humidity conversion coefficient is:

[0050]

[0051] wherein, is the humidity reference correction coefficient, is the critical relative humidity of the durability environment, is the critical humidity reference.

[0052] Further, the step S4 comprises:

[0053] ​​​​​​​Step S41: obtaining performance parameter data of the first modified sample under different modification processes and natural environment conditions i The performance parameter data is taken as output data, and the modification process parameters of the first modified sample i The natural environment parameters used by the first modified sample are taken as input data i The types of the modification process parameters v The types of the natural environment parameters w

[0054] Step S42: constructing a random forest network model, the random forest network model comprising two decision trees, and the two decision trees respectively predicting the performance parameters of the modified sample by using the modification process parameters and the natural environment parameters used by the modified sample

[0055] The prediction model of the two decision trees is as follows:

[0056] Wherein, m represents the number of modification process parameters, n represents the number of natural environment parameters, and p represents the type of performance parameter V W z The first performance parameter predicted by the two decision trees z The weight of the modification process parameter and the weight coefficient of the natural environment parameter are respectively The performance parameters predicted by the two decision trees are averaged, and the average value is taken as the output of the final random forest network model

[0057] Y

[0058]

[0059] Step S43: taking the modification process parameters of each modified sample and the target natural environment parameters of the durability acceleration test as input data, and inputting the performance parameter data calculated in the test process into the random forest network model to train the prediction model of the two decision trees, and adopting the least square method to fit the weight coefficient, and outputting the converged random forest network model

[0060] The present application has the following advantages:

[0061] ​​​​​​​​​​​This invention addresses the shortcomings of existing performance evaluation and analysis methods for inorganic material surface modification, such as limited scope, inaccurate predictions, and lengthy processes. It achieves comprehensive, accurate, and rapid prediction and evaluation of the performance of modified inorganic materials. By conducting multi-dimensional performance tests on different modified samples and training a predictive model capable of predicting performance data, it provides strong technical support for subsequent process research and evaluation of modification effects. Attached Figure Description

[0062] Figure 1 A flowchart for performance testing and analysis methods of inorganic material surface modification. Detailed Implementation

[0063] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0064] like Figure 1 As shown, a performance testing and analysis method for inorganic material surface modification includes:

[0065] Step S1: Prepare an inorganic material substrate. Prepare the inorganic material substrate into a thin film material of the target thickness. Perform surface modification on the inorganic material substrate according to different surface modification processes to obtain several modified samples and obtain the modification process parameters for each modified sample.

[0066] Step S2: Test the surface contact angle of the modified sample using different probe liquids, and calculate the total surface energy of each modified sample considering the coupling effect of the dispersive component, polar component and hydrogen bond component; test the mechanical properties of each modified sample, and calculate the fracture load, average shear strength, critical anchorage length and stress attenuation coefficient of the modified sample.

[0067] In step S2, the contact angle of the modified sample surface is tested using different probe liquids, and the total surface energy of each modified sample is calculated considering the coupling effect of the dispersive component, polar component, and hydrogen bonding component. Specifically, this includes the following steps:

[0068] Step S21: Select three probe liquids of different polarities—deionized water, ethylene glycol, and diiodomethane—and use the seated drop method to test the surface contact angle of each modified sample. A uniformly spaced probe is placed on each modified sample. N Test points, and take N The average surface contact angle at each test point is used to obtain the surface contact angle of the modified sample tested for each probe liquid.

[0069] ;

[0070] wherein, u is the type of probe liquid, is the surface contact angle of the first u probe liquid tested on the modified sample, is the surface contact angle of the first u probe liquid tested on the modified sample at the first n test point, n is the number of the test point;

[0071] Step S22: using the surface contact angles of each probe liquid tested, and considering the coupling effect of the dispersion component, the polar component and the hydrogen bond component, a coupling model for calculating the total surface energy of the modified sample is constructed;

[0072] ;

[0073] wherein, are the total dispersion component, the polar component and the hydrogen bond component of the three probe liquids corresponding to the first i modified sample, respectively, are the dispersion component, the polar component and the hydrogen bond component corresponding to the first u probe liquid, respectively, is the total surface energy of the first u probe liquid, is the total surface energy of the first i modified sample, i is the number of the modified sample;

[0074] In this embodiment, the dispersion component, the polar component and the hydrogen bond component of each probe liquid can be obtained by consulting materials, and the total surface energy of each probe liquid can also be obtained by consulting materials. The dispersion component, the polar component and the hydrogen bond component of each probe liquid, and the total surface energy are all known quantities. For example, the dispersion component, the polar component and the hydrogen bond component of deionized water are 21.8 mJ / m², 51.0 mJ / m² and 0 mJ / m², respectively. The dispersion component represents the non-polar interaction energy of the probe liquid, the polar component represents the dipole-dipole interaction energy, and the hydrogen bond component represents the hydrogen bond interaction energy.

[0075] Step S23: the surface contact angles of the three probe liquids tested, and the dispersion component, the polar component, the hydrogen bond component and the total surface energy corresponding to the three probe liquids are respectively input into the coupling model, a three-order linear equation group about the coupling model is constructed, the total dispersion component , the polar component and the hydrogen bond component of the three probe liquids corresponding to the first i modified sample are calculated, and then the total surface energy of the first modified sample is calculated.total surface energy of the modified sample The mechanical properties of each modified sample are tested in step S2, and the breaking load, average shear strength, critical anchorage length and stress decay coefficient of the modified sample are calculated, specifically including the following steps:

[0076] Step S24: Nanoindentation test is performed on each modified sample to obtain the elastic modulus E and hardness H of the modified sample, and microtension test is performed on each modified sample to obtain the breaking load F of the modified sample.

[0077] Step S25: The average shear strength F of the first modified sample is calculated according to the breaking load E , and the stress decay coefficient of the first modified sample is calculated according to the average shear strength i of the first modified sample and the maximum shear strength of the edge of the first modified sample. i i i ;

[0078] ;

[0079] ;

[0080] wherein, A is the bonding area in the test process, is the elastic modulus of the inorganic material substrate, is the bonding length in the test process of the first modified sample. i

[0081] Step S26: The critical anchorage length H of the first modified sample is calculated according to the hardness i and the average shear strength of the first modified sample. i

[0082] ;

[0083] wherein, is the thickness of the first modified sample. i

[0084] ​​​​​​​​​Step S3: constructing a durability test environment of the modified sample, calculating a time of the durability accelerated test based on the target natural environment parameters and the durability environment parameters set by the durability test, performing the durability test on the modified sample, and calculating a performance attenuation rate of the modified sample after the durability test.

[0085] Step S3 specifically comprises the following steps:

[0086] Step S31: constructing a durability test environment of the modified sample, and calculating a time of the durability accelerated test based on the target natural environment parameters and the durability environment parameters set by the durability test, performing the durability accelerated test on the modified sample in the durability test environment;

[0087] The durability environment parameters include an environment temperature , an erosion medium concentration , and a relative humidity ;

[0088] The target natural environment parameters include a target temperature , an erosion medium concentration , and a humidity of the natural environment;

[0089] The method for calculating the time of the durability accelerated test is as follows:

[0090] ;

[0091] wherein, is a target equivalent time of the natural environment for the durability test, are a temperature conversion coefficient, a concentration conversion coefficient, and a humidity conversion coefficient, respectively;

[0092] The present embodiment equivalently converts the test conditions into the natural environment conditions by setting reasonable conversion coefficients, so that the durability test effect of the test process reaches the durability characterization of the natural environment use process.

[0093] The calculation method of the temperature conversion coefficient is as follows:

[0094] ;

[0095] wherein, is a temperature reference correction coefficient, and the temperature reference correction coefficient is fitted by performing a temperature gradient test on the inorganic material substrate, and is generally 0.9≤ ≤1.1; is an erosion reaction activation energy, is a basic activation energy of the inorganic material substrate, R is a gas constant of the test environment; ​

[0096] Index Term This reflects the fundamental effect of temperature on the reaction rate; the higher the temperature, the larger this value. (Temperature conversion factor) The larger the value, the more significant the acceleration effect.

[0097] Concentration conversion factor The calculation method is as follows:

[0098] ;

[0099] in, This is a correction factor based on the concentration of the corrosive medium. p Porosity of the modified sample; reference correction factor Generally, 0.9 ≤ ≤1.1, porosity is obtained by testing the modified surface of the modified sample.

[0100] The concentration of corrosive media (such as chloride ions and sulfates) affects the durability degradation of modified samples by influencing the diffusion driving force (concentration gradient). However, the "concentration threshold effect" of modified samples must be considered (when the concentration is lower than the concentration of corrosive media in the natural environment, the diffusion rate increases linearly with the concentration; when the concentration is higher than the concentration of corrosive media in the natural environment, the diffusion rate tends to level off due to pore channel saturation).

[0101] Logarithmic terms reflect the concentration threshold effect: when the concentration of the corrosive medium... When, the logarithmic term grows approximately linearly; when At the same time, several factors slowed down to avoid overestimating the accelerating effect of high concentrations; porosity correction term Porosity reflects the effect of density p The larger the pore size, the more developed the pore channels, the easier the concentration diffusion, and the higher the concentration conversion factor. Slightly increased.

[0102] Humidity conversion factor The calculation method is as follows:

[0103] ;

[0104] in, This is the humidity reference correction factor. The critical relative humidity for durable environments. The critical humidity reference is used; in this embodiment, the humidity reference correction factor is... Generally, 0.9 ≤ ≤1.1.

[0105] Relative humidity affects the transport of corrosive media by influencing the pore water saturation on the modified sample (pore water is discontinuous when dry and continuous when wet). The "critical humidity effect" of the modified sample needs to be considered (when the humidity is below the critical value, the transport rate drops sharply; when it is above the critical value, the transport rate tends to stabilize).

[0106] when At this time, the numerator is negative (which has no practical significance). (Values ​​below 0.1 are considered to have no accelerating effect). At that time, the denser the pores, the higher the humidity sensitivity, and the larger the humidity conversion factor; the humidity of the natural environment These are generally higher than the critical relative humidity for durability environments. .

[0107] formula mesoporous p The smaller the relative humidity (the denser the material), the lower the humidity required for continuous pore water flow; the critical relative humidity... Reduce; Critical Humidity Reference Porosity p=0.2 The critical humidity is 55%.

[0108] Step S32: After the durability accelerated test is completed, repeat step S2 to calculate the number of seconds after the durability accelerated test. i Total surface energy of the modified sample Fracture load Average shear strength and stress attenuation coefficient ; Calculate the performance degradation rate of the modified sample after durability testing;

[0109] ;

[0110] in, These represent the allowable fluctuations in total surface energy, fracture load, average shear strength, and stress attenuation coefficient, respectively.

[0111] Step S4: Obtain the performance parameter data of modified samples under different modification processes and natural environmental conditions as output data, and use the modification process parameters and natural environmental parameters as input data to construct a random forest network model. Use the input data and output data to train the random forest network model and output a converged random forest network model.

[0112] Step S4 specifically includes the following steps:

[0113] Step S41: Obtain the first [condition] under different modification processes and natural environmental conditions. i Performance parameter data of each modified sample The performance parameter data is used as the output data, and the first...i Modification process parameters of the modified sample The first i Natural environment parameters used by the modified sample As input data, v The type of modification process parameters, w The type of natural environment parameters;

[0114] Step S42: Construct a random forest network model, which contains two decision trees, and the two decision trees respectively use the modification process parameters and the natural environment parameters used by the modified sample to predict the performance parameters of the modified sample.

[0115] The prediction model of the two decision trees is: ;

[0116] Wherein, V The number of modification process parameters, W The number of natural environment parameters, z The type of performance parameters, The first z Performance parameter predicted by the two decision trees, Respectively, the weight of the modification process parameter, the weight coefficient of the natural environment parameter;

[0117] The performance parameters predicted by the two decision trees are averaged as the output of the final random forest network model prediction Y ;

[0118] ;

[0119] The present application uses the two decision trees of the random forest network model to respectively predict the influence of process conditions and use natural environment conditions on the performance of surface modified materials, and comprehensively outputs the final performance parameters of surface modified materials by comprehensively outputting the hardness of output performance parameters under different modification process conditions. The performance of the surface modified inorganic material under different modification processes meets the requirements.

[0120] Step S43: The modification process parameters of each modified sample, the target natural environment parameters of the durability acceleration test, and the performance parameter data calculated in the test process are input into the random forest network model, and the prediction model of the two decision trees is trained. The least square method is used to fit the weight coefficient, and the converged random forest network model is output.

[0121] ​​​​​​​​​​​​Step S5: input the new modified process parameters and the use natural environment parameters of the surface modified inorganic material into the converged random forest network model, predict the performance parameters under the new modified process parameters and the use natural environment parameters of the surface modified material, and evaluate whether the surface modified inorganic material prepared by the new modified process meets the use requirements.

[0122] According to the predicted different performance parameters, the performance parameters are compared with the optimal values of the performance parameters.

[0123] The present application realizes comprehensive, accurate and rapid prediction and evaluation of the performance of the surface modified inorganic material. Through multi-dimensional performance testing of different modified samples and training of a prediction model capable of predicting performance data, strong technical support is provided for subsequent process research and modification effect testing and evaluation of the modified samples.

Claims

1. A method for performance testing and analysis of inorganic material surface modification, characterized in that, include: Step S1: Prepare an inorganic material substrate. Prepare the inorganic material substrate into a thin film material of the target thickness. Perform surface modification on the inorganic material substrate according to different surface modification processes to obtain several modified samples and obtain the modification process parameters for each modified sample. Step S2: Use different probe liquids to test the surface contact angle of the modified sample, and calculate the total surface energy of each modified sample considering the coupling effect of the dispersive component, polar component and hydrogen bond component. The mechanical properties of each modified sample were tested, and the fracture load, average shear strength, critical anchorage length, and stress attenuation coefficient of the modified sample were calculated. Step S3: Construct a durability test environment for the modified sample. Based on the target natural environment parameters and the durability environment parameters set for the durability test, calculate the time for accelerated durability testing, conduct durability testing on the modified sample, and calculate the performance degradation rate of the modified sample after the durability test. Step S4: Obtain the performance parameter data of modified samples under different modification processes and natural environmental conditions as output data, and use the modification process parameters and natural environmental parameters as input data to construct a random forest network model. Use the input data and output data to train the random forest network model and output a converged random forest network model. Step S5: Input the new modification process parameters and the natural environment parameters of the surface-modified inorganic material into the converged random forest network model to predict the performance parameters under the new modification process parameters and the natural environment parameters of the surface-modified material, and evaluate whether the surface-modified inorganic material prepared by the new modification process meets the application requirements. Step S3 includes: Step S31: Construct a durability test environment for the modified sample, and calculate the time for accelerated durability testing based on the target natural environment parameters and the durability environment parameters set for the durability test. Perform accelerated durability testing on the modified sample in the durability test environment. Step S32: After the accelerated durability test is completed, repeat step S2 to calculate the total surface energy of the modified sample after the accelerated durability test. Fracture load Average shear strength and stress attenuation coefficient ; Calculate the performance degradation rate of the modified sample after durability testing; ； in, These represent the allowable fluctuations in total surface energy, fracture load, average shear strength, and stress attenuation coefficient, respectively. For the first i The total surface energy of the modified sample. i This is the number of the modified sample. F The fracture load of the modified sample. For the first i The average shear strength of the modified samples. For the first i The stress attenuation coefficient of each modified sample.

2. The performance testing and analysis method for inorganic material surface modification according to claim 1, characterized in that, In step S2, the contact angle of the modified sample surface is tested using different probe liquids, and the total surface energy of each modified sample is calculated considering the coupling effect of the dispersive component, polar component, and hydrogen bonding component. Specifically, this includes the following steps: Step S21: Select three probe liquids of different polarities—deionized water, ethylene glycol, and diiodomethane—and use the seated drop method to test the surface contact angle of each modified sample. A uniformly spaced probe is placed on each modified sample. N Test points, and take N The average surface contact angle at each test point is used to obtain the surface contact angle of the modified sample tested for each probe liquid. ； in, u The type of probe liquid, For the first u The surface contact angle measured by a probe liquid. For the modified sample u The probe liquid in the first n The surface contact angle measured at each test point n This refers to the test point number; Step S22: Using the surface contact angles measured by each probe liquid, and considering the coupling effect of the dispersive component, polar component and hydrogen bonding component, construct a coupled model to calculate the total surface energy of the modified sample. ； in, The first i The total dispersive, polar, and hydrogen bonding components of the three probe liquids corresponding to each modified sample. The first u The dispersive component, polar component, and hydrogen bonding component corresponding to the probe liquid. For the first u The total surface energy of the probe liquid; Step S23: Input the surface contact angles measured by the three probe liquids, as well as the dispersion components, polar components, hydrogen bonding components, and total surface energies corresponding to the three probe liquids, into the coupling model to construct a system of three linear equations in the coupling model, and calculate the... i The total dispersive components of the three probe liquids corresponding to each modified sample polar components and hydrogen bond components And then calculate the first i Total surface energy of the modified sample .

3. The performance testing and analysis method for inorganic material surface modification according to claim 2, characterized in that, Step S2 involves testing the mechanical properties of each modified sample and calculating its fracture load, average shear strength, critical anchorage length, and stress attenuation coefficient. This specifically includes the following steps: Step S24: Perform nanoindentation testing on each modified sample to obtain the elastic modulus of the modified sample. E and hardness H Furthermore, micro-tensile tests were performed on each modified sample to obtain the fracture load of the modified sample. F ; Step S25: Based on the fracture load F Elastic modulus E Calculate the first i Average shear strength of each modified sample and according to the first i Average shear strength of each modified sample and the i Maximum shear strength at the edge of each modified sample Calculate the first i Stress attenuation coefficient of each modified sample ; ； ； in, A This represents the bonding area during the testing process. The elastic modulus of an inorganic material substrate. For the testing process i The bonding length of each modified sample; Step S26: Based on hardness H and the i Average shear strength of each modified sample Calculate the first i Critical anchorage length of a modified sample ; ； in, For the first i The thickness of each modified sample.

4. The performance testing and analysis method for inorganic material surface modification according to claim 3, characterized in that, The durability environmental parameters include ambient temperature. Concentration of corrosive medium and relative humidity ; The target natural environment parameters include the target temperature of the natural environment. Concentration of corrosive medium and humidity ; Calculate the time for accelerated durability testing The method is as follows: ； in, The equivalent time for the natural environment target in the durability test. These are the temperature conversion factor, concentration conversion factor, and humidity conversion factor, respectively.

5. The performance testing and analysis method for inorganic material surface modification according to claim 4, characterized in that, The temperature conversion factor The calculation method is as follows: ； in, This is the temperature reference correction factor. The activation energy for the erosion reaction, The basic activation energy for inorganic material substrates. R The gas constant of the test environment; The concentration conversion factor The calculation method is as follows: ； in, This is a correction factor based on the concentration of the corrosive medium. p Porosity of the modified sample; The humidity conversion factor The calculation method is as follows: ； in, This is the humidity reference correction factor. The critical relative humidity for durable environments. This serves as the critical humidity benchmark.

6. The performance testing and analysis method for inorganic material surface modification according to claim 5, characterized in that, Step S4 includes: Step S41: Obtain the first [type of modification] under different modification processes and natural environmental conditions. i Performance parameter data of each modified sample The performance parameter data is used as the output data, and the first... i Modification process parameters of one modified sample , No. i Natural environmental parameters used for each modified sample As input data v The types of modification process parameters, w Types of natural environmental parameters; Step S42: Construct a random forest network model. The random forest network model contains two decision trees. The two decision trees predict the performance parameters of the modified sample using the modification process parameters and the natural environment parameters used by the modified sample, respectively. The prediction model using two decision trees is as follows: ; in, V The number of modified process parameters, W The number of natural environmental parameters, z For the types of performance parameters, The prediction of the first decision tree for two decision trees z One performance parameter, These are the weights of the modification process parameters and the weight coefficients of the natural environment parameters, respectively. The average performance parameters predicted by the two decision trees are used as the output of the final random forest network model prediction. Y ; ； Step S43: Use the modification process parameters and target natural environment parameters of the durability acceleration test for each modified sample as input data. Input the performance parameter data calculated during the test into the random forest network model, train the prediction model of the two decision trees, fit the weight coefficients using the least squares method, and output the converged random forest network model.

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