Water-based paint and testing method thereof

Abstract

The invention relates to the technical field of coating testing, in particular to a water-based coating and a testing method thereof. The method comprises the following steps: carrying out coating sample pretreatment on a to-be-tested coating to obtain a pretreated coating sample; performing coating thermogravimetric analysis on the pretreated coating sample to obtain a thermogravimetric curve; performing initial division of weight loss intervals according to the thermogravimetric curve to obtain an initial weight loss interval list; performing weightlessness interval division correction on the initial weightlessness interval list to obtain weightlessness interval division correction data; performing weightlessness interval visualization on the thermogravimetric curve according to the weightlessness interval division correction data to obtain weightlessness interval division result data; and carrying out sample thermogravimetric-infrared testing on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence. Through fine analysis of the thermal stability of the coating, the relevance between the thermal decomposition mechanism and the performance of the coating is disclosed, and an important technical support is provided for research, development, production and application of the coating.

Description

Technical Field

[0001] The present invention relates to the technical field of coating testing, in particular to a water-based coating and a testing method thereof. Background Art

[0002] Water-based paint is a water-soluble paint. The thermal stability of the paint is an important performance indicator, which directly affects the durability, safety and service life of the coating. Traditional methods for analyzing the thermal stability of paints mainly rely on thermogravimetric analysis (TGA), which evaluates the thermal stability of the paint by measuring the weight loss of the paint during a programmed temperature increase. However, TGA can only provide macroscopic weight loss information and cannot determine the specific composition and content of the escaping gas, making it difficult to gain an in-depth understanding of the thermal decomposition mechanism of the paint. FTIR is an analytical technique that can identify the molecular structure and functional groups of substances and can be used to analyze the composition and chemical structure of paints. Combining FTIR with TGA (TG-FTIR) can monitor the changes in the composition and content of the escaping gas during the thermal decomposition process of the paint in real time, providing important information for studying the thermal decomposition mechanism of the paint. Traditional infrared spectroscopy testing methods for coatings lack a detailed analysis of the thermal stability of coatings. They usually only focus on the qualitative analysis of the main escaping gases, lack a quantitative assessment of the thermal stability of coatings, and are unable to meet the needs of actual applications. They also lack an analysis of the correlation between the thermal decomposition mechanism and performance of coatings, mainly focusing on the composition analysis of escaping gases, and lack the ability to correlate the escaping gas composition with coating performance, making it difficult to reveal the intrinsic connection between the thermal decomposition mechanism and performance of coatings. Summary of the Invention

[0003] Based on this, it is necessary to provide a water-based paint and a testing method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for testing water-based coatings comprises the following steps:

[0005] Step S1: pre-treating the coating sample to be tested to obtain a pre-treated coating sample;

[0006] Step S2: performing thermogravimetric analysis on the pretreated coating sample to obtain a thermogravimetric curve;

[0007] Step S3: performing an initial weight loss interval division according to the thermogravimetric curve to obtain an initial weight loss interval list; performing weight loss interval division correction on the initial weight loss interval list to obtain weight loss interval division correction data; visualizing the weight loss intervals on the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data;

[0008] Step S4: performing a thermogravimetric-infrared test on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence; performing a weight loss interval correspondence on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain an infrared spectrum corresponding to the weight loss interval; performing infrared spectrum extraction on the infrared spectrum corresponding to the weight loss interval to obtain an infrared spectrum;

[0009] Step S5: performing multi-component spectrum analysis on the infrared spectrum to obtain characteristic peak intensity separation data; performing escaping gas component analysis based on the characteristic peak intensity data to obtain escaping gas component analysis result data;

[0010] Step S6: Perform a correlation analysis between the escaping gas composition and the coating performance based on the weight loss interval division result data and the escaping gas composition analysis result data to obtain an escaping gas composition-coating performance correlation analysis matrix; perform a thermal decomposition kinetics analysis based on the escaping gas composition-coating performance correlation analysis matrix to obtain thermal decomposition mechanism description data; perform a coating thermal stability and performance correlation analysis based on the thermal decomposition mechanism description data to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum testing operation.

[0011] The present invention can remove impurity interference and extract target components through sample pretreatment and material analysis, providing high-quality samples and basic data for subsequent analysis. Through thermogravimetric analysis, the thermal decomposition curve of the coating can be obtained, which intuitively reflects its weight loss process and provides original data for subsequent analysis of weight loss intervals and thermal decomposition behavior. By dividing the weight loss intervals of the thermogravimetric curve, the complex thermal decomposition process can be decomposed into multiple stages, and the temperature range and weightless substances of each stage can be preliminarily determined, providing a reference for subsequent analysis of the composition of the escaped gas and the thermal decomposition mechanism. Through thermogravimetric-infrared combined testing, the thermal decomposition information of the coating and the infrared spectrum information of the escaped gas can be obtained in real time and synchronously, and the two can be correlated, providing a direct basis for analyzing the composition and evolution of the escaped gas. By performing multi-component spectral analysis and quantitative analysis on the infrared spectrum, the various escaped gases generated during the thermal decomposition process of the coating can be accurately identified and quantified, providing data support for the subsequent analysis of their source, contribution and relationship with the coating performance. By integrating thermogravimetric analysis, infrared spectroscopy and coating performance test data, the correlation between the evolved gas composition and the coating performance can be established, the coating thermal decomposition mechanism can be revealed, the coating thermal stability can be evaluated, and the relationship between it and its performance can be analyzed, which provides an important theoretical basis and technical support for optimizing coating formulations, improving coating performance, and broadening the scope of coating applications. Therefore, the present invention provides a testing method for water-based coatings, which effectively solves the defects of traditional coating infrared spectroscopy testing methods by introducing weight loss interval division, multi-component spectral analysis, evolved gas composition-coating performance correlation analysis and thermal decomposition kinetics analysis. It achieves a refined analysis of coating thermal stability, reveals the correlation between the coating thermal decomposition mechanism and performance, and provides important technical support for coating research and development, production and application.

[0012] Preferably, step S1 includes the following steps:

[0013] Step S11: Grind the coating to be tested to obtain sample powder data;

[0014] Step S12: Selecting a solvent based on the sample powder data to obtain solvent data;

[0015] Step S13: performing ultrasonic-assisted extraction according to the sample powder data and the solvent data, and performing cyclic washing to obtain a washed precipitate;

[0016] Step S14: vacuum drying the washed precipitate to obtain a pretreated coating sample.

[0017] The present invention can effectively reduce the particle size of the coating sample and increase its specific surface area by grinding the sample, thereby improving the efficiency of subsequent solvent extraction, making the target component easier to extract, and improving the accuracy of the analysis results. Selecting a suitable solvent based on the sample powder data can dissolve the target component to the greatest extent while avoiding dissolving other unnecessary components, thereby improving the extraction efficiency and the purity of the target component. Ultrasonic-assisted extraction can utilize the cavitation effect of ultrasound to accelerate the contact between solvent molecules and target components, thereby improving extraction efficiency. Circular washing can effectively remove residual solvents and impurities, further improving the purity of the target component. Vacuum drying can quickly and effectively remove solvents from the sample, avoiding the impact of solvent residues on subsequent analytical tests.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: weighing the pretreated coating sample to obtain initial sample mass data;

[0020] Step S22: performing an empty crucible thermogravimetric test on the crucible to obtain an empty crucible thermogravimetric curve;

[0021] Step S23: transferring the pretreated coating sample to a crucible, setting the heating program parameters, and obtaining the heating program parameters;

[0022] Step S24: using a thermogravimetric analyzer to perform a thermogravimetric test on the pretreated coating sample in the crucible according to the temperature ramp parameters to obtain a thermogravimetric curve of the sample;

[0023] Step S25: performing thermogravimetric curve correction on the sample thermogravimetric curve and the empty crucible thermogravimetric curve to obtain a thermogravimetric curve.

[0024] The present invention ensures the accuracy of subsequent thermogravimetric analysis results by accurately weighing pre-treated coating samples, and provides reliable initial mass data for calculating weight loss rate. By performing an empty crucible thermogravimetric test, the mass change law of the crucible itself during the heating process can be obtained, which provides benchmark data for subsequent sample thermogravimetric curve correction and eliminates the interference of crucible factors on the sample thermogravimetric analysis results. The pre-treated coating sample is transferred to a crucible and the heating program parameters are set. The appropriate heating rate and atmosphere can be selected according to the characteristics of the coating to be tested, ensuring that the thermogravimetric analysis process is stable and controllable, and more accurate thermal decomposition information is obtained. By using a thermogravimetric analyzer to perform a thermogravimetric test, the mass change of the sample during the heating process can be recorded in real time, and a sample thermogravimetric curve can be obtained, which intuitively reflects the thermal decomposition process of the coating and provides raw data for subsequent analysis. Performing an empty crucible thermogravimetric curve correction on the sample thermogravimetric curve can eliminate the influence of the mass change of the crucible itself on the sample thermogravimetric analysis result, obtain a thermogravimetric curve that more truly reflects the thermal decomposition behavior of the sample itself, and improve the accuracy of data analysis.

[0025] Preferably, step S3 includes the following steps:

[0026] Step S31: performing data smoothing processing on the thermogravimetric curve to obtain a thermogravimetric smooth curve; calculating the weight loss rate of the curve based on the thermogravimetric smooth curve to obtain a weight loss rate curve;

[0027] Step S32: performing peak identification on the weight loss rate curve to obtain a weight loss peak temperature list; performing peak shoulder identification on the weight loss rate curve to obtain a peak shoulder temperature range list;

[0028] Step S33: performing initial weight loss interval division on the thermogravimetric curve according to the weight loss peak temperature list and the peak shoulder temperature interval list to obtain an initial weight loss interval list;

[0029] Step S34: performing peak shape fitting on each interval in the initial weight loss interval list to obtain a weight loss interval fitting list; calculating the interval weight loss rate based on the weight loss interval fitting list to obtain a weight loss interval weight loss rate list;

[0030] Step S35: performing a database comparison on the weight loss rate list of the weight loss interval and the weight loss interval fitting list according to a preset material thermogravimetric database to obtain initial material type inference data;

[0031] Step S36: performing interval division based on expert experience correction based on the initial material type inference data to obtain weight loss interval division correction data;

[0032] Step S37: Visualizing the weight loss intervals of the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data.

[0033] The present invention can effectively reduce the data fluctuations caused by instrument noise and experimental errors by performing data smoothing processing on the thermogravimetric curve, making the curve smoother and more conducive to subsequent analysis. Calculating the weight loss rate curve can more intuitively reflect the weight loss of the sample at different temperatures, providing a basis for the division of the weight loss interval. Peak recognition of the weight loss rate curve can accurately determine the peak temperature of each weight loss stage, that is, the temperature point with the maximum weight loss rate. Peak shoulder recognition can determine the starting and ending temperatures of each peak, providing a more accurate boundary for the division of the weight loss interval. The thermogravimetric curve is initially divided according to the weight loss peak temperature and peak shoulder temperature interval, and the temperature range of each weight loss stage can be preliminarily determined, providing a basis for subsequent more precise weight loss interval division. Peak shape fitting of the initial weight loss interval can more accurately describe the weight loss behavior of each weight loss stage, such as distinguishing whether it is a single reaction or the superposition of multiple reactions. Calculating the interval weight loss rate based on the fitting curve can more accurately quantify the degree of weight loss in each weight loss stage. Comparing the weight loss rate and fitted curve within the weight loss interval with a thermogravimetric database allows for preliminary inference of the substance types corresponding to each weight loss stage, providing a reference for subsequent analysis of the escaping gas composition. Correcting the weight loss interval divisions based on the initial substance type inference data and expert experience can compensate for the shortcomings of the database comparison, improve the accuracy and reliability of the weight loss interval division results, and avoid misjudgments caused by a single data source. Visualizing the weight loss intervals of the thermogravimetric curve can more intuitively display the temperature range, weight loss rate, and corresponding substance types for each weight loss stage, making the analysis results clearer and easier to understand, facilitating subsequent research.

[0034] Preferably, step S4 includes the following steps:

[0035] Step S41: connecting a thermogravimetric analyzer and an infrared spectrometer, and preheating the coupled instrument to obtain a preheated coupled instrument; optimizing the transmission line temperature of the preheated coupled instrument according to the temperature range data of the weight loss interval division result data to obtain transmission line temperature data;

[0036] Step S42: collecting a background spectrum based on the preheated coupled instrument and the transmission line temperature data to obtain a background infrared spectrum;

[0037] Step S43: performing a thermogravimetric-infrared test on the pretreated coating sample using a preheated coupled instrument according to the background infrared spectrum to obtain a time-resolved infrared spectrum sequence;

[0038] Step S44: performing weight loss interval mapping on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain infrared spectra corresponding to the weight loss intervals;

[0039] Step S45: performing spectral data preprocessing on the infrared spectrum corresponding to the weightlessness interval to obtain a preprocessed infrared spectrum; performing characteristic peak identification on the preprocessed infrared spectrum to obtain a characteristic peak position list; performing peak intensity normalization on the characteristic peak position list to obtain a normalized characteristic peak intensity list;

[0040] Step S46: constructing a three-dimensional spectrum according to the normalized characteristic peak intensity list to obtain a three-dimensional infrared spectrum; and extracting a two-dimensional spectrum from the three-dimensional infrared spectrum to obtain an infrared spectrum.

[0041] The present invention connects and preheats a thermogravimetric analyzer and an infrared spectrometer, thereby ensuring that the two instruments are in a stable working state during the combined use process and reducing the errors caused by the instruments themselves. Optimizing the transmission line temperature according to the weight loss interval division results can effectively prevent the condensation of the escaped gas during the transmission process, ensure that all the escaped gas can be detected by the infrared spectrometer, and improve the accuracy of the analysis results. Collecting the background infrared spectrum at the optimized transmission line temperature can eliminate the interference of ambient gas and the instrument itself on the infrared spectrum, and improve the signal-to-noise ratio and analysis accuracy of the subsequent sample infrared spectrum. Using the preheated combined instrument to perform a thermogravimetric-infrared test on the sample, the mass change of the sample and the infrared spectrum of the escaped gas can be recorded in real time and synchronously, and the thermal decomposition process and the change in the composition of the escaped gas can be associated, providing direct evidence for analyzing the thermal decomposition mechanism of the coating. By matching the time-resolved infrared spectrum sequence according to the weight loss interval division results, the escaped gas composition information of each weight loss stage can be extracted, providing a basis for the subsequent analysis of the source and evolution law of each escaped gas component. Preprocessing the infrared spectrum corresponding to the weight loss interval can eliminate noise and background interference and improve the spectral quality. Characteristic peak identification can identify the characteristic absorption peaks of each escaping gas, providing a basis for subsequent qualitative and quantitative analysis. Peak intensity normalization eliminates the effects of sample size and test conditions on peak intensity, facilitating comparison of infrared spectra from different samples or at different temperatures. Constructing a three-dimensional infrared spectrum can more intuitively demonstrate the changing trends of escaping gas composition at different temperatures or times, providing richer visual information for analyzing thermal decomposition processes. Extracting a two-dimensional infrared spectrum allows for easier visualization of escaping gas composition at specific temperatures or time points, providing foundational data for subsequent qualitative and quantitative analysis.

[0042] Preferably, step S5 includes the following steps:

[0043] Step S51: performing original spectrum correction on the infrared spectrum to obtain a corrected infrared spectrum;

[0044] Step S52: performing characteristic peak comparison on the corrected infrared spectrum using a preset standard infrared spectrum database to obtain characteristic peak comparison data;

[0045] Step S53: performing intensity analysis on the characteristic peak comparison data to obtain characteristic peak intensity data; performing multi-component spectrum analysis on the characteristic peak intensity data to obtain characteristic peak intensity separation data;

[0046] Step S54: performing quantitative correction of the escaped gas based on the characteristic peak intensity separation data to obtain a quantitative calibration curve;

[0047] Step S55: Analyze the components of the evolved gas using the quantitative calibration curve and the characteristic peak intensity data to obtain the evolved gas component analysis result data.

[0048] The present invention can eliminate the interference of water vapor and carbon dioxide in the environment on the infrared spectrum by performing original spectrum correction, such as atmospheric correction, on the infrared spectrum, improve the accuracy and reliability of the spectrum, and lay the foundation for subsequent characteristic peak analysis. By using a preset standard infrared spectrum database and a corrected infrared spectrum to compare characteristic peaks, the types of escaping gases present in the sample can be quickly identified, providing target component information for subsequent quantitative analysis. By performing intensity analysis on the characteristic peak comparison data, the intensity information of each characteristic peak can be extracted, providing a data basis for subsequent quantitative analysis. Multi-component spectral analysis can solve the problem of overlapping infrared spectra of multiple escaping gases, accurately decompose the intensity of each characteristic peak into the corresponding escaping gas component, and improve the accuracy of quantitative analysis. Quantitative correction of escaping gases can be performed based on the characteristic peak intensity separation data, and a quantitative relationship between the characteristic peak intensity and the escaping gas content can be established to obtain a quantitative calibration curve, which provides a basis for the subsequent accurate calculation of the content of each escaping gas. By using quantitative calibration curves and characteristic peak intensity data to analyze the composition of evolved gases, the qualitative analysis results of infrared spectroscopy can be converted into quantitative results, and the content of each evolved gas can be ultimately determined, providing accurate and reliable data support for subsequent analysis of the evolution of the evolved gas composition, its source, and its relationship with coating performance.

[0049] Preferably, step S55 includes the following steps:

[0050] Step S551: quantifying the components of the evolved gas using the quantitative calibration curve on the characteristic peak intensity data to obtain quantitative result data of the evolved gas components;

[0051] Step S552: Drawing a composition evolution map based on the quantitative result data of the evolved gas composition to obtain an evolved gas composition evolution map;

[0052] Step S553: ​​performing principal component analysis on the characteristic peak intensity data according to the evolution spectrum of the escaping gas composition to obtain principal component analysis result data;

[0053] Step S554: performing component cluster analysis based on the principal component analysis result data to obtain the escaping gas component analysis result data.

[0054] The present invention uses a quantitative calibration curve to quantify the escaping gas components based on the characteristic peak intensity data, which can convert abstract spectral peak intensity information into specific escaping gas content, such as ppm or vol%, making the analysis results more intuitive, easier to understand and apply. Drawing a component evolution map based on the quantitative result data of the escaping gas components can intuitively show the changing trend of the content of different escaping gas components with time or temperature, which helps to more clearly understand the gas release law of the coating during the thermal decomposition process and provide a basis for analyzing the source and interaction of different components. Performing principal component analysis on the characteristic peak intensity data can reduce the dimensionality of high-dimensional infrared spectral data, extract the most important component information, simplify data analysis, and reveal the potential correlation between different escaping gas components, providing a better data basis for subsequent cluster analysis. Performing component cluster analysis based on the results of principal component analysis can classify samples or time points with similar escaping gas component characteristics together, simplify data analysis, and help identify different types of thermal decomposition processes or products, providing a clearer direction for in-depth research on the thermal decomposition mechanism of coatings.

[0055] Preferably, step S6 includes the following steps:

[0056] Step S61: performing a correlation analysis between the escaped gas composition and the coating performance according to the weight loss interval division result data and the escaped gas composition analysis result data to obtain an escaped gas composition-coating performance correlation analysis matrix;

[0057] Step S62: identifying key escaping gas components according to the escaping gas component-coating performance correlation analysis matrix to obtain a list of key escaping gas components;

[0058] Step S63: Calculate thermogravimetric-infrared spectrum kinetic parameters using the infrared spectrum and the thermogravimetric curve to obtain a thermal decomposition kinetic parameter table;

[0059] Step S64: inferring the thermal decomposition mechanism based on the escaping gas source analysis report, the key escaping gas component list, and the thermal decomposition kinetic parameter table to obtain thermal decomposition mechanism description data;

[0060] Step S65: Evaluate the thermal stability of the coating according to the thermal decomposition mechanism description data and the list of key escaping gas components to obtain thermal stability data of the coating;

[0061] Step S66: Perform coating thermal stability and performance correlation analysis on the coating thermal stability data and the escaping gas composition-coating performance correlation analysis matrix to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum test operation.

[0062] The present invention can link the thermal decomposition behavior of the coating with its macroscopic properties, such as mechanical properties, heat resistance, weather resistance, etc., through the correlation analysis of the escaped gas components and the coating properties, thereby revealing the degree and direction of the influence of different escaped gas components on the coating properties, and providing a reference for the subsequent optimization of the coating formula and performance. Identifying the key escaped gas components can help researchers focus on those components that have the greatest impact on the coating properties, so as to carry out subsequent analysis more targeted, such as studying their sources, generation mechanisms, and the mechanisms of influence on performance. Calculating thermal decomposition kinetic parameters, such as activation energy, reaction order, and pre-exponential factor, can quantitatively describe the thermal decomposition process of the coating, providing important parameters for a deep understanding of the thermal decomposition mechanism and predicting the thermal stability of the coating. Combining the escaped gas sources, key escaped gas components, and thermal decomposition kinetic parameters, the thermal decomposition mechanism of the coating can be more comprehensively and accurately inferred, such as the main reaction paths, intermediate products, and final products, providing a theoretical basis for explaining the performance changes of the coating. According to the thermal decomposition mechanism and key escaped gas components, the thermal stability of the coating can be more accurately assessed, such as judging its thermal decomposition temperature range, thermal decomposition rate, and the hazards generated, providing a guarantee for the safe use of the coating. By analyzing the relationship between the thermal stability of coatings and their performance, we can gain a deeper understanding of the mechanism by which the thermal decomposition behavior of coatings affects their performance, such as explaining which factors lead to performance degradation and how to improve the thermal stability and overall performance of coatings by adjusting the formulation or process.

[0063] Preferably, step S61 includes the following steps:

[0064] Step S611: performing mapping of the escaping gas component analysis result data with the escaping gas component according to the weight loss interval division result data, and obtaining a weight loss interval-escaping gas component mapping table;

[0065] Step S612: Calculate the weight loss rate-escaped gas content using the weight loss interval-escaped gas composition correspondence table to obtain a weight loss rate-escaped gas content data table;

[0066] Step S613: Analyze the source of the escaped gas according to the weight loss rate-escaped gas content data table to obtain an escaped gas source analysis report;

[0067] Step S614: performing a coating performance index test on the pretreated coating sample to obtain a coating performance test data table;

[0068] Step S615: performing component-performance correlation analysis on the weight loss rate-escaped gas content data table and the coating performance test data table to obtain an escaping gas component-coating performance correlation analysis matrix.

[0069] The present invention can link the weight loss phenomenon in different temperature ranges with the specific escaped gas components by establishing a corresponding table of weight loss intervals and escaped gas components, providing basic data for subsequent analysis of the source and contribution of each escaped gas. By calculating the weight loss rate-escape gas content data table, the weight loss can be associated with the escaped gas content, more intuitively reflecting the contribution of different escaped gas components to the weight loss of the coating, and providing a quantitative basis for subsequent analysis of the impact of escaped gas on coating performance. By analyzing the source of escaped gas based on the weight loss rate-escape gas content data table, it can be determined which components in the coating each escaped gas component mainly comes from, such as resin, pigment, filler, etc., providing a reference for optimizing coating formulations and reducing harmful gas release. By testing the coating performance index of pretreated coating samples, various performance data of the coating, such as mechanical properties, heat resistance, weather resistance, etc., can be obtained, providing evaluation indicators for subsequent analysis of the impact of escaped gas components on coating performance. By performing component-performance correlation analysis on the weight loss rate-escaped gas content data table and the coating performance test data table, a correlation model between the escaped gas composition and coating performance can be established, such as a linear model, a nonlinear model, etc., thereby revealing the influence of different escaped gas components on coating performance and providing theoretical guidance for optimizing coating formulations and improving coating performance.

[0070] Preferably, the present invention also provides a water-based paint obtained by testing the water-based paint test method as described above. The present invention can remove impurity interference through sample pretreatment and material analysis, providing high-quality samples and basic data for subsequent analysis. Through thermogravimetric analysis, a thermal decomposition curve of the paint can be obtained, which intuitively reflects its weight loss process and provides raw data for subsequent analysis of weight loss intervals and thermal decomposition behavior. By dividing the thermogravimetric curve into weight loss intervals, the complex thermal decomposition process can be decomposed into multiple stages, and the temperature range and weightless materials of each stage can be preliminarily determined, providing a reference for subsequent analysis of the composition of the evolved gas and the thermal decomposition mechanism. Through thermogravimetric-infrared combined testing, the thermal decomposition information of the paint and the infrared spectrum information of the evolved gas can be obtained in real time and synchronously, and the two can be correlated to provide a direct basis for analyzing the composition and evolution of the evolved gas. By performing multi-component spectral analysis and quantitative analysis on the infrared spectrum, the various evolved gases generated during the thermal decomposition process of the paint can be accurately identified and quantified, providing data support for subsequent analysis of their source, contribution and relationship with the paint performance. By integrating thermogravimetric analysis, infrared spectroscopy and coating performance test data, the correlation between the evolved gas composition and the coating performance can be established, the coating thermal decomposition mechanism can be revealed, the coating thermal stability can be evaluated, and the relationship between it and its performance can be analyzed, which provides an important theoretical basis and technical support for optimizing coating formulations, improving coating performance, and broadening the scope of coating applications. Therefore, the present invention provides a testing method for water-based coatings, which effectively solves the defects of traditional coating infrared spectroscopy testing methods by introducing weight loss interval division, multi-component spectral analysis, evolved gas composition-coating performance correlation analysis and thermal decomposition kinetics analysis. It achieves a refined analysis of coating thermal stability, reveals the correlation between the coating thermal decomposition mechanism and performance, and provides important technical support for coating research and development, production and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The figure is a flow chart of the steps of a testing method for water-based coatings;

[0072] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0073] Figure 3 for Figure 1 Detailed implementation steps of step S5 are shown in the flowchart.

[0074] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0075] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0077] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve this, please refer to Figures 1 to 3 , a testing method for water-based coatings, comprising the following steps:

[0079] Step S1: pre-treating the coating sample to be tested to obtain a pre-treated coating sample;

[0080] Step S2: performing thermogravimetric analysis on the pretreated coating sample to obtain a thermogravimetric curve;

[0081] Step S3: performing an initial weight loss interval division according to the thermogravimetric curve to obtain an initial weight loss interval list; performing weight loss interval division correction on the initial weight loss interval list to obtain weight loss interval division correction data; visualizing the weight loss intervals on the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data;

[0082] Step S4: performing a thermogravimetric-infrared test on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence; performing a weight loss interval correspondence on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain an infrared spectrum corresponding to the weight loss interval; performing infrared spectrum extraction on the infrared spectrum corresponding to the weight loss interval to obtain an infrared spectrum;

[0083] Step S5: performing multi-component spectrum analysis on the infrared spectrum to obtain characteristic peak intensity separation data; performing escaping gas component analysis based on the characteristic peak intensity data to obtain escaping gas component analysis result data;

[0084] Step S6: Perform a correlation analysis between the escaping gas composition and the coating performance based on the weight loss interval division result data and the escaping gas composition analysis result data to obtain an escaping gas composition-coating performance correlation analysis matrix; perform a thermal decomposition kinetics analysis based on the escaping gas composition-coating performance correlation analysis matrix to obtain thermal decomposition mechanism description data; perform a coating thermal stability and performance correlation analysis based on the thermal decomposition mechanism description data to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum testing operation.

[0085] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of the testing method of the water-based coating of the present invention. In this example, the testing method of the water-based coating includes the following steps:

[0086] Step S1: pre-treating the coating sample to be tested to obtain a pre-treated coating sample;

[0087] In an embodiment of the present invention, the coating sample to be tested is ground into a fine powder, subjected to ultrasonic-assisted extraction and cyclic washing using a solvent such as ethyl acetate or acetone to remove impurities, and then the washed precipitate is vacuum-dried to obtain a pretreated coating sample.

[0088] Step S2: performing thermogravimetric analysis on the pretreated coating sample to obtain a thermogravimetric curve;

[0089] In an embodiment of the present invention, an analytical balance is used to weigh a pretreated coating sample and record the initial mass data of the sample. An empty crucible is placed in a thermogravimetric analyzer for thermogravimetric testing to obtain an empty crucible thermogravimetric curve. The pretreated coating sample is then placed in the crucible, the heating program parameters are set, and a thermogravimetric test is performed using the thermogravimetric analyzer to obtain a sample thermogravimetric curve. Finally, the sample thermogravimetric curve is calibrated with the empty crucible thermogravimetric curve using thermogravimetric analysis software to obtain the final thermogravimetric curve.

[0090] Step S3: performing an initial weight loss interval division according to the thermogravimetric curve to obtain an initial weight loss interval list; performing weight loss interval division correction on the initial weight loss interval list to obtain weight loss interval division correction data; visualizing the weight loss intervals on the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data;

[0091] In an embodiment of the present invention, the Savitzky-Golay smoothing algorithm is used to smooth the thermogravimetric curve and calculate the weight loss rate curve. A continuous wavelet transform is used to identify the peaks and shoulders on the weight loss rate curve and preliminarily divide the weight loss intervals. Peak shape fitting is performed on each initial weight loss interval, and the interval weight loss rate is calculated. This is then compared with a preset material thermogravimetric database to preliminarily infer the substance type. Finally, the weight loss interval division is corrected based on expert experience, and the weight loss interval of the thermogravimetric curve is visualized to obtain the weight loss interval division result data.

[0092] Step S4: performing a thermogravimetric-infrared test on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence; performing a weight loss interval correspondence on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain an infrared spectrum corresponding to the weight loss interval; performing infrared spectrum extraction on the infrared spectrum corresponding to the weight loss interval to obtain an infrared spectrum;

[0093] In an embodiment of the present invention, a thermogravimetric analyzer and a Fourier transform infrared spectrometer are connected, and the transmission line temperature is preheated and optimized. After collecting a background infrared spectrum, a sample thermogravimetric-infrared test is performed to obtain a time-resolved infrared spectral sequence. The resulting data is divided according to the weight loss interval, and the time-resolved infrared spectral sequence is aligned with the weight loss interval. The infrared spectra corresponding to the weight loss interval are extracted, and the spectral data is preprocessed, characteristic peaks are identified, and peak intensity normalization is performed. Finally, a three-dimensional infrared spectrum is constructed and a two-dimensional infrared spectrum is extracted.

[0094] Step S5: performing multi-component spectrum analysis on the infrared spectrum to obtain characteristic peak intensity separation data; performing escaping gas component analysis based on the characteristic peak intensity data to obtain escaping gas component analysis result data;

[0095] In the embodiments of the present invention, atmospheric correction is performed on the infrared spectrum and characteristic peaks are compared with a standard infrared spectrum database. Multi-component spectral analysis is performed on the characteristic peak intensity data to separate the characteristic peak intensity corresponding to each gas. A quantitative calibration curve is established using standard gas samples of known concentrations, and the characteristic peak intensity separation data is converted into the content of each escaping gas, ultimately obtaining the escaping gas composition analysis result data.

[0096] Step S6: performing a correlation analysis between the gas composition and the coating performance based on the weight loss interval division result data and the gas composition analysis result data to obtain a gas composition-coating performance correlation analysis matrix; performing a thermal decomposition kinetics analysis based on the gas composition-coating performance correlation analysis matrix to obtain thermal decomposition mechanism description data; performing a coating thermal stability and performance correlation analysis based on the thermal decomposition mechanism description data to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum testing operation;

[0097] In an embodiment of the present invention, a weight loss interval division result data and the escaping gas composition analysis result data are combined to establish a weight loss interval-escaping gas composition correspondence table, and a weight loss rate-escaping gas content data table is calculated. By performing component-performance correlation analysis on the weight loss rate-escaping gas content data table and the coating performance test data table, an escaping gas composition-coating performance correlation analysis matrix is ​​obtained. Based on the correlation analysis matrix, the key escaping gas components are identified, and the thermal decomposition mechanism of the coating is inferred by combining the thermal decomposition kinetic parameters and the escaping gas source analysis report. Finally, the thermal stability of the coating is evaluated, and its correlation with the performance is analyzed to generate a coating thermal stability and performance correlation report.

[0098] Preferably, step S1 includes the following steps:

[0099] Step S11: Grind the coating to be tested to obtain sample powder data;

[0100] Step S12: Selecting a solvent based on the sample powder data to obtain solvent data;

[0101] Step S13: performing ultrasonic-assisted extraction according to the sample powder data and the solvent data, and performing cyclic washing to obtain a washed precipitate;

[0102] Step S14: vacuum drying the washed precipitate to obtain a pretreated coating sample.

[0103] In this embodiment of the present invention, 10 grams of the paint sample to be tested was first ground into a fine powder using an agate mortar for 10 minutes until the particle size was less than 100 microns. This step increases the sample's specific surface area, facilitating subsequent solvent penetration and extraction. Based on the sample powder data, an appropriate solvent was selected for extraction. For example, if the paint sample is an alkyd resin, ethyl acetate could be used as the solvent; if the paint sample is an epoxy resin, acetone could be used as the solvent. The solvent selection should consider its solubility for the target component and its effectiveness in removing other components. The sample powder was placed in a 100-ml centrifuge tube, and 40 ml of the solvent selected in step S12 was added. Ultrasonic-assisted extraction was performed at room temperature for 30 minutes. The ultrasonic frequency was set to 40 kHz and the power to 200 W. After extraction, the centrifuge tube was centrifuged at 5000 rpm for 10 minutes, and the supernatant was collected. The extraction and centrifugation steps were repeated three times, and all supernatants were combined. Finally, the precipitate was washed with 30 ml of solvent, and the washings were discarded. This step utilizes the cavitation effect of ultrasound to promote the dissolution and extraction of the target component by the solvent. Centrifugation is then used to separate the solid and liquid phases and remove insoluble impurities. The washed precipitate obtained in step S13 is transferred to a clean Petri dish and placed in a vacuum drying oven at 60°C for 12 hours, until the sample weight no longer changes. The dried sample is the pretreated coating sample.

[0104] Preferably, step S2 includes the following steps:

[0105] Step S21: weighing the pretreated coating sample to obtain initial sample mass data;

[0106] Step S22: performing an empty crucible thermogravimetric test on the crucible to obtain an empty crucible thermogravimetric curve;

[0107] Step S23: transferring the pretreated coating sample to a crucible, setting the heating program parameters, and obtaining the heating program parameters;

[0108] Step S24: using a thermogravimetric analyzer to perform a thermogravimetric test on the pretreated coating sample in the crucible according to the temperature ramp parameters to obtain a thermogravimetric curve of the sample;

[0109] Step S25: performing thermogravimetric curve correction on the sample thermogravimetric curve and the empty crucible thermogravimetric curve to obtain a thermogravimetric curve.

[0110] In an embodiment of the present invention, 5 mg of pre-treated coating samples are weighed using an analytical balance, and the sample initial mass data is recorded, accurate to 0.01 mg. A clean alumina crucible is placed on the sample holder of a thermogravimetric analyzer, and a heating program is set to rise from room temperature to 800 degrees Celsius, a heating rate of 10 degrees Celsius per minute, a nitrogen atmosphere, and a flow rate of 50 milliliters per minute. The thermogravimetric analyzer is started to perform an empty crucible thermogravimetric test, and a temperature-mass change relationship curve is recorded, which is the empty crucible thermogravimetric curve. The pre-treated coating sample weighed in step S21 is carefully transferred to the alumina crucible used in step S22 to ensure that the sample is evenly spread on the bottom of the crucible. The crucible is placed on the sample holder of a thermogravimetric analyzer, and a heating program is set to rise from room temperature to 800 degrees Celsius, a heating rate of 10 degrees Celsius per minute, a nitrogen atmosphere, and a flow rate of 50 milliliters per minute, and the heating program parameters are saved. Start the thermogravimetric analyzer and perform a thermogravimetric test on the pretreated coating sample in the crucible according to the heating program parameters set in step S23. The thermogravimetric analyzer records the relationship data between temperature and sample mass change in real time, and automatically generates a sample thermogravimetric curve after the test. Use the thermogravimetric analysis software to open the empty crucible thermogravimetric curve obtained in step S22 and the sample thermogravimetric curve obtained in step S24. Using the "curve correction" function of the software, the mass data corresponding to each temperature point in the sample thermogravimetric curve is subtracted from the corresponding mass data in the empty crucible thermogravimetric curve at the same temperature to obtain a corrected thermogravimetric curve. This curve eliminates the influence of the mass change of the crucible itself on the sample thermogravimetric analysis results, and truly reflects the mass change of the coating sample during the heating process.

[0111] Preferably, step S3 includes the following steps:

[0112] Step S31: performing data smoothing processing on the thermogravimetric curve to obtain a thermogravimetric smooth curve; calculating the weight loss rate of the curve based on the thermogravimetric smooth curve to obtain a weight loss rate curve;

[0113] Step S32: performing peak identification on the weight loss rate curve to obtain a weight loss peak temperature list; performing peak shoulder identification on the weight loss rate curve to obtain a peak shoulder temperature range list;

[0114] Step S33: performing initial weight loss interval division on the thermogravimetric curve according to the weight loss peak temperature list and the peak shoulder temperature interval list to obtain an initial weight loss interval list;

[0115] Step S34: performing peak shape fitting on each interval in the initial weight loss interval list to obtain a weight loss interval fitting list; calculating the interval weight loss rate based on the weight loss interval fitting list to obtain a weight loss interval weight loss rate list;

[0116] Step S35: performing a database comparison on the weight loss rate list of the weight loss interval and the weight loss interval fitting list according to a preset material thermogravimetric database to obtain initial material type inference data;

[0117] Step S36: performing interval division based on expert experience correction based on the initial material type inference data to obtain weight loss interval division correction data;

[0118] Step S37: Visualizing the weight loss intervals of the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data.

[0119] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0120] Step S31: performing data smoothing processing on the thermogravimetric curve to obtain a thermogravimetric smooth curve; calculating the weight loss rate of the curve based on the thermogravimetric smooth curve to obtain a weight loss rate curve;

[0121] In the embodiment of the present invention, the Savitzky-Golay smoothing algorithm is used to smooth the thermogravimetric curve obtained in step S25, and the smoothing window size is set to 5 to obtain a thermogravimetric smoothed curve. Based on the thermogravimetric smoothed curve, the weight loss rate corresponding to each temperature point is calculated using the formula: weight loss rate = (initial mass - current mass) / initial mass * 100%. The weight loss rate corresponding to each temperature point is plotted into a curve, which is the weight loss rate curve.

[0122] Step S32: performing peak identification on the weight loss rate curve to obtain a weight loss peak temperature list; performing peak shoulder identification on the weight loss rate curve to obtain a peak shoulder temperature range list;

[0123] In this embodiment of the present invention, a continuous wavelet transform (CWT) was used to identify peaks in the weight loss rate curve. The Morlet wavelet was selected as the wavelet basis function, and the scale parameter was set to 2. All peak points were identified, and the temperature corresponding to each peak point was recorded to obtain a list of weight loss peak temperatures. Simultaneously, the left and right inflection points corresponding to each peak point were identified, and the temperature range between the two inflection points was used as the peak shoulder temperature range to obtain a list of peak shoulder temperature ranges.

[0124] Step S33: performing initial weight loss interval division on the thermogravimetric curve according to the weight loss peak temperature list and the peak shoulder temperature interval list to obtain an initial weight loss interval list;

[0125] In this embodiment of the present invention, the thermogravimetric curve is initially divided into weight loss intervals based on the weight loss peak temperature list and the shoulder temperature interval list obtained in step S32. The shoulder temperature interval corresponding to each weight loss peak temperature is defined as an initial weight loss interval. If two adjacent shoulder temperature intervals overlap, the overlapping portion is assigned to the shoulder temperature interval with the greater weight loss rate variation.

[0126] Step S34: performing peak shape fitting on each interval in the initial weight loss interval list to obtain a weight loss interval fitting list; calculating the interval weight loss rate based on the weight loss interval fitting list to obtain a weight loss interval weight loss rate list;

[0127] In an embodiment of the present invention, peak shape fitting is performed on each interval in the initial weight loss interval list obtained in step S33. For different peak shapes, a suitable function is selected for fitting, such as a Gaussian function, a Lorentz function, or a combination thereof. The peak shape curve of each weight loss interval is obtained by fitting, and a weight loss interval fitting list is formed. Based on the fitting curve of each weight loss interval, the weight loss rate of the interval is calculated, that is, the ratio of the mass difference corresponding to the starting temperature and the ending temperature of the interval to the initial mass, to form a weight loss rate list of the weight loss intervals.

[0128] Step S35: performing a database comparison on the weight loss rate list of the weight loss interval and the weight loss interval fitting list according to a preset material thermogravimetric database to obtain initial material type inference data;

[0129] In this embodiment of the present invention, a preset thermogravimetric material database is established, containing thermogravimetric data for common coating components, such as the weight loss temperature ranges and weight loss rate ranges for resins, pigments, and fillers. The weight loss interval weight loss rate list and weight loss interval fitting list obtained in step S34 are compared with the preset thermogravimetric material database. Based on the degree of match between the weight loss temperature range and the weight loss rate range, the substance type corresponding to each weight loss interval is preliminarily inferred, thereby obtaining initial substance type inference data.

[0130] Step S36: performing interval division based on expert experience correction based on the initial material type inference data to obtain weight loss interval division correction data;

[0131] In this embodiment of the present invention, based on the initial substance type inference data obtained in step S35, expert experience is used to correct the initial weight loss interval division. For example, if the initial substance type inference data indicates that a certain weight loss interval may correspond to two substances, and expert experience data indicates that the content of one of the substances is extremely low, the weight loss interval can be assigned to the substance with the higher content. Ultimately, corrected weight loss interval division data is obtained.

[0132] Step S37: Visualizing the weight loss intervals of the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data;

[0133] In this embodiment of the present invention, the weight loss intervals of the thermogravimetric curve are visualized based on the weight loss interval calibration data obtained in step S36. For example, different colors or shaded areas can be used to distinguish different weight loss intervals, and the starting and ending temperatures of each weight loss interval, as well as the corresponding substance type, are annotated on the graph. The resulting weight loss interval data provides a reference for subsequent infrared spectroscopy data analysis.

[0134] Preferably, step S4 includes the following steps:

[0135] Step S41: connecting a thermogravimetric analyzer and an infrared spectrometer, and preheating the coupled instrument to obtain a preheated coupled instrument; optimizing the transmission line temperature of the preheated coupled instrument according to the temperature range data of the weight loss interval division result data to obtain transmission line temperature data;

[0136] Step S42: collecting a background spectrum based on the preheated coupled instrument and the transmission line temperature data to obtain a background infrared spectrum;

[0137] Step S43: performing a thermogravimetric-infrared test on the pretreated coating sample using a preheated coupled instrument according to the background infrared spectrum to obtain a time-resolved infrared spectrum sequence;

[0138] Step S44: performing weight loss interval mapping on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain infrared spectra corresponding to the weight loss intervals;

[0139] Step S45: performing spectral data preprocessing on the infrared spectrum corresponding to the weightlessness interval to obtain a preprocessed infrared spectrum; performing characteristic peak identification on the preprocessed infrared spectrum to obtain a characteristic peak position list; performing peak intensity normalization on the characteristic peak position list to obtain a normalized characteristic peak intensity list;

[0140] Step S46: constructing a three-dimensional spectrum according to the normalized characteristic peak intensity list to obtain a three-dimensional infrared spectrum; and extracting a two-dimensional spectrum from the three-dimensional infrared spectrum to obtain an infrared spectrum.

[0141] In an embodiment of the present invention, a thermogravimetric analyzer (e.g., TA Instruments' Q500) and a Fourier transform infrared spectrometer (e.g., Thermo Fisher Scientific's Nicolet iS50) are connected using a dedicated interface to form a thermogravimetric-infrared coupled instrument. The coupled instrument is preheated to 800 degrees Celsius and stabilized for 30 minutes to reduce the impact of instrument drift on the test results. The temperature range of each weightlessness interval is determined based on the weightlessness interval division result data obtained in step S37, and the temperature range data is input into the coupled instrument control software. The software automatically optimizes the temperature of the gas transmission line connecting the thermogravimetric analyzer and the infrared spectrometer according to a preset algorithm to ensure that the escaping gas does not condense during the transmission process, and obtains the transmission line temperature data. The parameters of the infrared spectrometer are set, including the scanning range (4000-400cm -1 ), resolution (4cm -1 ) and the number of scans (32 times). At the transmission line temperature optimized in step S41, background spectrum collection is performed to obtain background infrared spectrum data. The remaining pretreated coating samples are placed in the sample crucible of the thermogravimetric analyzer, and the sample thermogravimetric-infrared test is performed according to the heating program parameters set in step S23. The thermogravimetric analyzer records the mass change of the sample in real time, and sends the escaping gas into the gas pool of the infrared spectrometer through the transmission line for detection. The infrared spectrometer collects infrared spectrum data once at each set time interval (for example, 1 second), and finally obtains a series of infrared spectrum data that changes with time, that is, a time-resolved infrared spectrum sequence. According to the temperature range of each weightlessness interval in the weightlessness interval division result data obtained in step S37, the time-resolved infrared spectrum sequence obtained in step S43 is matched with the weightlessness interval, and the infrared spectrum data corresponding to each weightlessness interval is extracted to obtain the infrared spectrum corresponding to the weightlessness interval. The infrared spectrum corresponding to the weightlessness interval obtained in step S44 is subjected to spectral data preprocessing, including atmospheric background subtraction, baseline correction and smoothing, to obtain a preprocessed infrared spectrum. Use peak finding algorithm to carry out characteristic peak identification to pre-treatment infrared spectrum, record the peak position of each characteristic peak, obtain characteristic peak position list.For the ease of the variation trend of characteristic peak under comparative different temperatures, peak intensity normalization is carried out to characteristic peak position list, be about to the intensity value of each characteristic peak divided by the maximum peak intensity value in this spectrum, obtain normalized characteristic peak intensity list.According to the normalized characteristic peak intensity list obtained in step S45, take time as X-axis, wave number as Y-axis, and normalized peak intensity as Z-axis, construct three-dimensional infrared spectrogram, intuitively show the variation trend of characteristic peak intensity under different temperatures.For the convenience of observing the variation of characteristic peak within the specific wave number range, two-dimensional spectrogram is carried out to three-dimensional infrared spectrogram and extracted, for example, extract the infrared spectrogram in specific time point or specific temperature range, obtain infrared spectrogram.

[0142] Preferably, step S5 includes the following steps:

[0143] Step S51: performing original spectrum correction on the infrared spectrum to obtain a corrected infrared spectrum;

[0144] Step S52: performing characteristic peak comparison on the corrected infrared spectrum using a preset standard infrared spectrum database to obtain characteristic peak comparison data;

[0145] Step S53: performing intensity analysis on the characteristic peak comparison data to obtain characteristic peak intensity data; performing multi-component spectrum analysis on the characteristic peak intensity data to obtain characteristic peak intensity separation data;

[0146] Step S54: performing quantitative correction of the escaped gas based on the characteristic peak intensity separation data to obtain a quantitative calibration curve;

[0147] Step S55: Analyze the components of the evolved gas using the quantitative calibration curve and the characteristic peak intensity data to obtain the evolved gas component analysis result data.

[0148] As an example of the present invention, refer to Figure 3 As shown, in this example, step S5 includes:

[0149] Step S51: performing original spectrum correction on the infrared spectrum to obtain a corrected infrared spectrum;

[0150] In this embodiment of the present invention, infrared spectrum analysis software (e.g., OMNIC) is used to open the infrared spectrum obtained in step S46. The software's built-in "atmospheric correction" function automatically identifies and subtracts the water vapor (H2O) and carbon dioxide (CO2) absorption peaks from the infrared spectrum to obtain a corrected infrared spectrum.

[0151] Step S52: performing characteristic peak comparison on the corrected infrared spectrum using a preset standard infrared spectrum database to obtain characteristic peak comparison data;

[0152] In the present embodiment, a standard infrared spectrum database is pre-established, containing infrared spectrum data for common escaping gases, such as water (H2O), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), benzene (C6H6), etc. Using a spectrum matching algorithm, the characteristic peaks of the corrected infrared spectrum are compared with those in the standard infrared spectrum database. The degree of match between each characteristic peak and the corresponding gas characteristic peak in the database is calculated to obtain characteristic peak comparison data.

[0153] Step S53: performing intensity analysis on the characteristic peak comparison data to obtain characteristic peak intensity data; performing multi-component spectrum analysis on the characteristic peak intensity data to obtain characteristic peak intensity separation data;

[0154] In an embodiment of the present invention, characteristic peaks with a matching degree higher than a preset threshold (for example, 80%) are screened out based on the characteristic peak comparison data, and the intensity information of these characteristic peaks is extracted to obtain characteristic peak intensity data. Since the infrared spectra of multiple escaping gases may overlap, it is necessary to perform multi-component spectral analysis on the characteristic peak intensity data. Commonly used multi-component spectral analysis methods include least squares method, principal component analysis method, independent component analysis method, etc. For example, using the least squares method, the corrected infrared spectrum is represented as a linear combination of the infrared spectra of multiple standard gases. Through iterative calculation, the contribution ratio of each gas is determined, thereby separating the characteristic peak intensity corresponding to each gas and obtaining characteristic peak intensity separation data.

[0155] Step S54: performing quantitative correction of the escaped gas based on the characteristic peak intensity separation data to obtain a quantitative calibration curve;

[0156] In the embodiments of the present invention, quantitative calibration is required to convert the characteristic peak intensity into the actual content of the escaping gas. A commonly used quantitative calibration method is to establish a calibration curve. For each gas to be measured, a series of standard gas samples of known concentrations are prepared, their infrared spectra are measured using a thermogravimetric-infrared instrument, and the characteristic peak intensity separation data is extracted. A calibration curve is drawn with the standard gas concentration as the horizontal axis and the corresponding characteristic peak intensity separation data as the vertical axis, and the quantitative calibration curve equation is obtained using linear regression or other fitting methods.

[0157] Step S55: Analyze the components of the evolved gas using the quantitative calibration curve and the characteristic peak intensity data to obtain the evolved gas component analysis result data;

[0158] In the embodiment of the present invention, the characteristic peak intensity separation data obtained in step S53 is converted into the content of each evolved gas using the quantitative calibration curve equation obtained in step S54 to obtain the quantitative results of the evolved gas composition. The quantitative results of the evolved gas composition are normalized to obtain the final evolved gas composition analysis results, for example, the percentage of each evolved gas in the total evolved gas.

[0159] Preferably, step S55 includes the following steps:

[0160] Step S551: quantifying the components of the evolved gas using the quantitative calibration curve on the characteristic peak intensity data to obtain quantitative result data of the evolved gas components;

[0161] Step S552: Drawing a composition evolution map based on the quantitative result data of the evolved gas composition to obtain an evolved gas composition evolution map;

[0162] Step S553: ​​performing principal component analysis on the characteristic peak intensity data according to the evolution spectrum of the escaping gas composition to obtain principal component analysis result data;

[0163] Step S554: performing component cluster analysis based on the principal component analysis result data to obtain the escaping gas component analysis result data.

[0164] In an embodiment of the present invention, the characteristic peak intensity separation data obtained in step S53 is input into the quantitative calibration curve equation established in step S54 to calculate the content of each escaping gas at each time point or each temperature point, for example, in units of ppm (parts per million) or vol% (volume percentage), to form the quantitative result data of the escaping gas composition. Using data processing software such as Origin or MatLab, with time or temperature as the horizontal coordinate and the content of each escaping gas as the vertical coordinate, a composition evolution spectrum is drawn. For example, the content change curves of all escaping gases can be drawn in one figure to intuitively compare the changing trends of different gases over time or temperature; or a composition evolution spectrum can be drawn separately for each escaping gas to more clearly observe its content change pattern. The characteristic peak intensity data obtained in step S53 are subjected to dimensionality reduction analysis using the principal component analysis (PCA) method. The characteristic peak intensity data at each time point or each temperature point are taken as a sample to construct a data matrix. After the data matrix is ​​standardized, the principal components are calculated using the PCA algorithm, and the first few principal components are selected based on the size of the eigenvalues, for example, the principal components that explain more than 80% of the total variance are selected. The original data is projected into the principal component space to obtain principal component analysis result data, for example, the coordinates of each sample in the principal component space. Based on the principal component analysis result data obtained in step S553, the samples are clustered using the K-means clustering algorithm or the hierarchical clustering algorithm. For example, using the K-means algorithm, the number of clusters K is pre-set, and the samples are divided into K categories based on the distance between the samples in the principal component space. Each category represents a group of samples with similar escaping gas composition characteristics. Based on the clustering results, the escaping gas components are classified, for example, escaping gas components belonging to the same category are marked as the same type, and escaping gas composition analysis result data are obtained, for example, the category to which each escaping gas belongs and the characteristic description of each category.

[0165] Preferably, step S6 includes the following steps:

[0166] Step S61: performing a correlation analysis between the escaped gas composition and the coating performance according to the weight loss interval division result data and the escaped gas composition analysis result data to obtain an escaped gas composition-coating performance correlation analysis matrix;

[0167] Step S62: identifying key escaping gas components according to the escaping gas component-coating performance correlation analysis matrix to obtain a list of key escaping gas components;

[0168] Step S63: Calculate thermogravimetric-infrared spectrum kinetic parameters using the infrared spectrum and the thermogravimetric curve to obtain a thermal decomposition kinetic parameter table;

[0169] Step S64: inferring the thermal decomposition mechanism based on the escaping gas source analysis report, the key escaping gas component list, and the thermal decomposition kinetic parameter table to obtain thermal decomposition mechanism description data;

[0170] Step S65: Evaluate the thermal stability of the coating according to the thermal decomposition mechanism description data and the list of key escaping gas components to obtain thermal stability data of the coating;

[0171] Step S66: Perform coating thermal stability and performance correlation analysis on the coating thermal stability data and the escaping gas composition-coating performance correlation analysis matrix to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum test operation.

[0172] In an embodiment of the present invention, in combination with the weightlessness interval division result data obtained in step S37 and the escaping gas component analysis result data obtained in step S55, the association between the escaping gas component and the coating performance is established. For example, the content of each escaping gas is subjected to correlation analysis with the mechanical properties (such as tensile strength, elongation at break), heat resistance (such as glass transition temperature, thermal decomposition temperature), weather resistance (such as resistance to ultraviolet aging, water resistance) of the coating, and the like. The degree of association between the two is measured using indicators such as the Pearson correlation coefficient or the Spearman correlation coefficient, and finally a matrix is ​​obtained, which contains the degree of association between each escaping gas component and each coating performance, i.e., the escaping gas component-coating performance correlation analysis matrix. According to the escaping gas component-coating performance correlation analysis matrix obtained in step S61, the escaping gas component with a higher correlation with the coating performance is identified, defined as a key escaping gas component, and recorded in a key escaping gas component list. For example, a threshold value can be set, and the escaping gas component with a correlation coefficient higher than the threshold value is regarded as a key escaping gas component. Utilize the infrared spectrogram obtained in step S46 and the thermogravimetric curve obtained in step S25, in conjunction with thermogravimetric-infrared spectroscopy kinetic analysis method (for example, Kissinger method, Ozawa method, Flynn-Wall-Ozawa method), calculate the thermal decomposition kinetic parameters of coating, such as activation energy (Ea), reaction order (n) and pre-exponential factor (A).The kinetic parameters under the different temperatures or conversion ratios calculated are recorded in the thermal decomposition kinetic parameter table.In conjunction with the thermal decomposition kinetic parameter table obtained in the escaping gas source analysis report obtained in step S61, the key escaping gas component list obtained in step S62 and the thermal decomposition kinetic parameter table obtained in step S63, infer the thermal decomposition mechanism of coating.For example, according to the kind and release order of key escaping gas components, in conjunction with the variation rule of thermal decomposition kinetic parameters, analyze the thermal decomposition reaction type, reaction path and main product of coating in different temperature stages, finally describe the thermal decomposition process of coating in detail in the mode of text description, form thermal decomposition mechanism description data.According to the thermal decomposition mechanism description data obtained in step S64 and the key escaping gas component list obtained in step S62, evaluate the thermal stability of coating. For example, the thermal stability level of the coating can be determined based on indicators such as the thermal decomposition starting temperature, the temperature of the maximum weight loss rate, and the activation energy. The effects of key escaping gas components on the thermal stability of the coating can be analyzed, ultimately yielding coating thermal stability data, such as the coating's thermal stability level, thermal decomposition temperature range, and the primary factors influencing thermal stability. Combining the coating thermal stability data obtained in step S65 with the escaping gas component-coating performance correlation analysis matrix obtained in step S61, the correlation between the coating's thermal stability and its performance can be analyzed, and a report on the correlation between the coating's thermal stability and performance can be generated.For example, the differences in mechanical properties, heat resistance, weather resistance, etc. of coatings with different thermal stability levels can be analyzed, as well as the impact mechanism of key escaping gas components on these properties. The analysis results can finally be presented in the form of a report to provide guidance for the research and development, production and application of coatings.

[0173] Preferably, step S61 includes the following steps:

[0174] Step S611: performing mapping of the escaping gas component analysis result data with the escaping gas component according to the weight loss interval division result data, and obtaining a weight loss interval-escaping gas component mapping table;

[0175] Step S612: Calculate the weight loss rate-escaped gas content using the weight loss interval-escaped gas composition correspondence table to obtain a weight loss rate-escaped gas content data table;

[0176] Step S613: Analyze the source of the escaped gas according to the weight loss rate-escaped gas content data table to obtain an escaped gas source analysis report;

[0177] Step S614: performing a coating performance index test on the pretreated coating sample to obtain a coating performance test data table;

[0178] Step S615: performing component-performance correlation analysis on the weight loss rate-escaped gas content data table and the coating performance test data table to obtain an escaping gas component-coating performance correlation analysis matrix.

[0179] In an embodiment of the present invention, based on the weight loss interval division result data obtained in step S37, each weight loss interval is matched with its corresponding temperature range. Then, the escaping gas component analysis result data obtained in step S55 is arranged in time or temperature order and compared with the temperature range of the weight loss interval. Each escaping gas component is classified into its corresponding weight loss interval, forming a weight loss interval-escaping gas component correspondence table. For example, if the release time of a certain escaping gas component is primarily concentrated within the temperature range of a certain weight loss interval, then the escaping gas component is considered to correspond to that weight loss interval. Based on the weight loss interval-escaping gas component correspondence table obtained in step S611, the weight loss rate data for each weight loss interval is associated with the corresponding escaping gas component content data to form a weight loss rate-escaping gas content data table. For example, the content of each escaping gas component within each weight loss interval can be accumulated to obtain the total content of the escaping gas component within that weight loss interval, and this total content can be associated with the weight loss rate of that weight loss interval. And the weight loss rate-escaped gas content data table obtained in step S612, analyze the source of each escaping gas component. For example, if a certain escaping gas component mainly appears in a certain weight loss interval, and the coating component corresponding to the weight loss interval is known, it can be inferred that the escaping gas component mainly comes from the coating component. The source analysis results of each escaping gas component are summarized to form an escaping gas source analysis report. The pretreated coating sample obtained in step S14 is subjected to a series of coating performance index tests, for example, using a universal material testing machine to test the tensile strength, elongation at break and other mechanical performance indicators of the coating; using a differential scanning calorimeter to test the glass transition temperature, thermal decomposition temperature and other heat resistance performance indicators of the coating; using ultraviolet aging test chamber, water immersion test and other methods to evaluate the weather resistance performance indicators of the coating. Record the coating performance index data obtained from all tests in the coating performance test data table. Perform component performance correlation analysis on the weight loss rate-escaped gas content data table obtained in step S612 and the coating performance test data table obtained in step S614. For example, multivariate statistical analysis methods, such as principal component analysis (PCA) or partial least squares regression (PLSR), can be used to analyze the relationship between the content of different escaping gas components and different coating performance indicators, and to build a model to predict coating performance. Ultimately, a matrix is ​​obtained that contains the degree of correlation between the content of each escaping gas component and each coating performance indicator, namely the escaping gas component-coating performance correlation analysis matrix.

[0180] Preferably, the present invention further provides a water-based paint testing system for performing the water-based paint testing method described above, the water-based paint testing system comprising:

[0181] A sample pretreatment module, which pretreats the paint sample to be tested to obtain a pretreated paint sample;

[0182] Thermogravimetric analysis module, used to perform thermogravimetric analysis on the pre-treated coating sample to obtain a thermogravimetric curve;

[0183] The weight loss interval division module is used to perform initial weight loss interval division according to the thermogravimetric curve to obtain an initial weight loss interval list; perform weight loss interval division correction on the initial weight loss interval list to obtain weight loss interval division correction data; perform weight loss interval visualization on the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data;

[0184] The infrared spectrum extraction module is used to perform thermogravimetric-infrared testing on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence; the time-resolved infrared spectrum sequence is matched to the weight loss interval according to the temperature range data of the weight loss interval division result data to obtain the infrared spectrum corresponding to the weight loss interval; the infrared spectrum corresponding to the weight loss interval is extracted to obtain the infrared spectrum;

[0185] The spectrum data analysis module is used to perform multi-component spectrum analysis on the infrared spectrum to obtain characteristic peak intensity separation data; analyze the escaped gas composition based on the characteristic peak intensity data to obtain the escaped gas composition analysis result data;

[0186] The result correlation analysis module is used to perform a correlation analysis between the gas composition and the coating performance based on the weight loss interval division result data and the gas composition analysis result data, thereby obtaining a gas composition-coating performance correlation analysis matrix; perform a thermal decomposition kinetic analysis based on the gas composition-coating performance correlation analysis matrix to obtain thermal decomposition mechanism description data; and perform a coating thermal stability and performance correlation analysis based on the thermal decomposition mechanism description data to obtain a coating thermal stability and performance correlation report, thereby realizing the coating infrared spectrum testing operation. For example, the water-based coating can be three types: water-soluble coating, water-dilutable coating, and water-dispersible coating (latex coating).

[0187] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0188] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for testing water-based coatings, characterized in that: The following steps are involved: Step S1: pre-treating the coating sample to be tested to obtain a pre-treated coating sample; Step S2: performing thermogravimetric analysis on the pretreated coating sample to obtain a thermogravimetric curve; Step S3: performing initial weight loss interval division according to the thermogravimetric curve to obtain an initial weight loss interval list; performing weight loss interval division correction on the initial weight loss interval list to obtain weight loss interval division correction data; performing weight loss interval visualization on the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data; Step S4: performing a thermogravimetric-infrared test on the pretreated coating sample to obtain a time-resolved infrared spectrum sequence; performing a weight loss interval correspondence on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data to obtain an infrared spectrum corresponding to the weight loss interval; performing an infrared spectrum extraction on the infrared spectrum corresponding to the weight loss interval to obtain an infrared spectrum; Step S5: performing multi-component spectrum analysis on the infrared spectrum to obtain characteristic peak intensity separation data; Performing component analysis of the evolved gas according to the characteristic peak intensity data to obtain the evolved gas component analysis result data; Step S6: performing an escaping gas composition-coating performance correlation analysis based on the weight loss interval division result data and the escaping gas composition analysis result data to obtain an escaping gas composition-coating performance correlation analysis matrix; performing a thermal decomposition kinetic analysis based on the escaping gas composition-coating performance correlation analysis matrix to obtain thermal decomposition mechanism description data; According to the thermal decomposition mechanism description data, the thermal stability and performance correlation analysis of the coating is carried out to obtain the thermal stability and performance correlation report of the coating, so as to realize the infrared spectrum testing operation of the coating.

2. The testing method for water-based coating according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Grind the sample of the coating to be tested to obtain sample powder data; Step S12: Selecting a solvent according to the sample powder data to obtain solvent data; Step S13: performing ultrasonic-assisted extraction according to the sample powder data and the solvent data, and performing cyclic washing to obtain a washed precipitate; Step S14: vacuum drying the washed precipitate to obtain a pretreated coating sample.

3. The testing method for water-based coating according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: weighing the pre-treated coating sample to obtain initial sample mass data; Step S22: performing an empty crucible thermogravimetric test on the crucible to obtain an empty crucible thermogravimetric curve; Step S23: transferring the pretreated coating sample to a crucible, setting the heating program parameters, and obtaining the heating program parameters; Step S24: using a thermogravimetric analyzer to perform a thermogravimetric test on the pretreated coating sample in the crucible according to the temperature rising program parameters to obtain a thermogravimetric curve of the sample; Step S25: performing thermogravimetric curve correction on the sample thermogravimetric curve and the empty crucible thermogravimetric curve to obtain a thermogravimetric curve.

4. The testing method for water-based coating according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing data smoothing processing on the thermogravimetric curve to obtain a thermogravimetric smooth curve; calculating the weight loss rate of the curve according to the thermogravimetric smooth curve to obtain a weight loss rate curve; Step S32: performing peak identification on the weight loss rate curve to obtain a weight loss peak temperature list; performing peak shoulder identification on the weight loss rate curve to obtain a peak shoulder temperature range list; Step S33: performing initial weight loss interval division on the thermogravimetric curve according to the weight loss peak temperature list and the peak shoulder temperature interval list to obtain an initial weight loss interval list; Step S34: performing peak shape fitting on each interval of the initial weight loss interval list to obtain a weight loss interval fitting list; performing interval weight loss rate calculation based on the weight loss interval fitting list to obtain a weight loss interval weight loss rate list; Step S35: performing database comparison on the weight loss interval weight loss rate list and the weight loss interval fitting list according to a preset material thermogravimetric database to obtain initial material type inference data; Step S36: performing interval division based on expert experience correction according to the initial material type inference data to obtain weight loss interval division correction data; Step S37: Visualize the weight loss interval of the thermogravimetric curve according to the weight loss interval division correction data to obtain weight loss interval division result data.

5. The testing method for water-based coating according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: connecting a thermogravimetric analyzer and an infrared spectrometer, and preheating the combined instrument to obtain a preheated combined instrument; optimizing the transmission line temperature of the preheated combined instrument according to the temperature range data of the weight loss interval division result data to obtain transmission line temperature data; Step S42: collecting background spectrum according to the preheated combined instrument and the transmission line temperature data to obtain a background infrared spectrum; Step S43: performing a sample thermogravimetric-infrared test on the pretreated coating sample using a preheated coupled instrument according to the background infrared spectrum to obtain a time-resolved infrared spectrum sequence; Step S44: performing weight loss interval correspondence on the time-resolved infrared spectrum sequence according to the temperature range data of the weight loss interval division result data, and obtaining infrared spectra corresponding to the weight loss intervals; Step S45: performing spectral data preprocessing on the infrared spectrum corresponding to the weightlessness interval to obtain a preprocessed infrared spectrum; performing characteristic peak identification on the preprocessed infrared spectrum to obtain a characteristic peak position list; performing peak intensity normalization on the characteristic peak position list to obtain a normalized characteristic peak intensity list; Step S46: constructing a three-dimensional spectrum according to the normalized characteristic peak intensity list to obtain a three-dimensional infrared spectrum; extracting a two-dimensional spectrum from the three-dimensional infrared spectrum to obtain an infrared spectrum.

6. The testing method for water-based coating according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing original spectrum correction on the infrared spectrum to obtain a corrected infrared spectrum; Step S52: using a preset standard infrared spectrum database to perform characteristic peak comparison on the corrected infrared spectrum to obtain characteristic peak comparison data; Step S53: performing intensity analysis on the characteristic peak comparison data to obtain characteristic peak intensity data; performing multi-component spectrum analysis on the characteristic peak intensity data to obtain characteristic peak intensity separation data; Step S54: performing quantitative correction of the evolved gas according to the characteristic peak intensity separation data to obtain a quantitative correction curve; Step S55: Analyze the components of the evolved gas using the quantitative calibration curve and the characteristic peak intensity data to obtain the evolved gas component analysis result data.

7. The testing method for water-based coating according to claim 6, characterized in that: Step S55 includes the following steps: Step S551: quantifying the components of the evolved gas using the quantitative calibration curve for the characteristic peak intensity data to obtain quantitative result data of the components of the evolved gas; Step S552: Drawing a component evolution spectrum according to the quantitative result data of the evolved gas components to obtain an evolved gas component evolution spectrum; Step S553: ​​performing principal component analysis on the characteristic peak intensity data according to the evolution spectrum of the escaping gas composition to obtain principal component analysis result data; Step S554: Perform component cluster analysis based on the principal component analysis result data to obtain the escaping gas component analysis result data.

8. The method for testing water-based coatings according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: performing an escaping gas component-coating performance correlation analysis based on the weight loss interval division result data and the escaping gas component analysis result data to obtain an escaping gas component-coating performance correlation analysis matrix; Step S62: identifying key escaping gas components according to the escaping gas component-coating performance correlation analysis matrix to obtain a list of key escaping gas components; Step S63: Calculate the thermogravimetric-infrared spectrum kinetic parameters using the infrared spectrum and the thermogravimetric curve to obtain a thermal decomposition kinetic parameter table; Step S64: inferring the thermal decomposition mechanism according to the escaping gas source analysis report, the key escaping gas component list and the thermal decomposition kinetic parameter table to obtain thermal decomposition mechanism description data; Step S65: evaluating the thermal stability of the coating according to the thermal decomposition mechanism description data and the list of key escaping gas components to obtain thermal stability data of the coating; Step S66: Perform coating thermal stability and performance correlation analysis on the coating thermal stability data and the escaping gas composition-coating performance correlation analysis matrix to obtain a coating thermal stability and performance correlation report to implement the coating infrared spectrum test operation.

9. The method for testing water-based coatings according to claim 8, characterized in that: Step S61 includes the following steps: Step S611: performing correspondence between the evolved gas component and the evolved gas component analysis result data according to the weight loss interval division result data, and obtaining a weight loss interval-escape gas component correspondence table; Step S612: Calculate the weight loss rate-escaped gas content using the weight loss interval-escaped gas composition correspondence table to obtain a weight loss rate-escaped gas content data table; Step S613: Analyze the source of the escaped gas according to the weight loss rate-escaped gas content data table to obtain an escaped gas source analysis report; Step S614: testing the coating performance index of the pre-treated coating sample to obtain a coating performance test data table; Step S615: Perform component-performance correlation analysis on the weight loss rate-escaped gas content data table and the coating performance test data table to obtain an escaped gas component-coating performance correlation analysis matrix.

10. A water-based paint, characterized in that: Obtained by testing according to the testing method for water-based coatings as claimed in claim 1.

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