Rapid detection method for zeolite loading capacity and uniformity in zeolite composite material

Abstract

The invention discloses a rapid detection method for zeolite loading capacity and uniformity in a zeolite composite material, and particularly relates to the technical field of material performance detection. Performing fluorescence labeling treatment on the to-be-detected composite material sample; acquiring a fluorescence image under a specific excitation wavelength; constructing a three-dimensional light intensity distribution map through an image processing algorithm; establishing a nonlinear fitting relationship between the fluorescence intensity and the standard zeolite loading capacity by using a local weighted regression model; further estimating the overall zeolite loading capacity of the sample; meanwhile, based on fluorescence integration, distribution skewness and anisotropy analysis, calculating a zeolite distribution variation coefficient, and judging the distribution uniformity of the zeolite according to the zeolite distribution variation coefficient; the detection method has the advantages of rapidness, high sensitivity, non-destructiveness and the like, can realize automatic image recognition and quantitative evaluation, is suitable for zeolite distribution detection of various types of composite materials, and has a wide application prospect in preparation and quality control of materials such as catalysts, adsorbents, zeolite membranes and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of material performance detection, and in particular to a method for quickly detecting zeolite loading and uniformity in a zeolite composite material. Background Art

[0002] Zeolites are a class of crystalline aluminosilicate materials with a regular pore structure, widely used in catalysts, adsorbents, ion exchangers, and other fields. To meet the performance requirements of specific industrial applications, zeolites are often loaded onto various composite matrices to form zeolite composites. The zeolite loading and distribution uniformity within the composite material have a significant impact on the performance of the final material.

[0003] Currently, methods for testing the zeolite loading and distribution uniformity in zeolite composites primarily include thermogravimetric analysis (TGA), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). However, these methods generally suffer from complex operations, long testing cycles, high sample pretreatment requirements, and high costs, making them difficult to meet the needs of rapid, large-scale testing.

[0004] Therefore, there is an urgent need for a simple, rapid, and accurate method to achieve quantitative or semi-quantitative detection of zeolite loading and its distribution uniformity in zeolite composites, so as to improve the quality control efficiency in the production process and provide a reliable basis for the evaluation of material properties. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for quickly detecting the zeolite loading and uniformity in a zeolite composite material, so as to overcome the shortcomings in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for rapidly detecting the zeolite loading and uniformity in a zeolite composite material, comprising: contacting a zeolite composite material sample to be tested with a solution containing a fluorescent ligand to form a fluorescent label; Under the excitation of the selected wavelength, the sample is subjected to fluorescence imaging to obtain an optical image of the marked area; Using image processing algorithms, the acquired optical images are rasterized and a three-dimensional light intensity distribution map is constructed; Based on the three-dimensional light intensity distribution map, the coverage of zeolite and the light intensity mean deviation in each region are quantified, and the zeolite distribution variation coefficient is output to evaluate the distribution uniformity of zeolite in the composite material; A fitting relationship curve between the fluorescence signal intensity and the standard zeolite loading was established. Based on the fitting relationship curve, the image grayscale value was correlated with the actual zeolite mass content, and the zeolite loading was estimated.

[0007] Preferably, the image processing algorithm includes: The optical fluorescence image obtained by excitation is processed into a two-dimensional pixel matrix, and the image area is grid-divided according to the preset spatial resolution to construct the light intensity data grid unit; Perform local variance normalization on the pixel light intensity value in each grid cell; The convolution nested spline interpolation model is used to perform longitudinal interpolation fitting on the light intensity data of each grid unit to generate a continuous three-dimensional light intensity distribution map.

[0008] Preferably, the calculation of the zeolite distribution coefficient of variation includes: Based on the three-dimensional light intensity distribution map, the map is divided into multiple sub-volume regions according to the spatial level, and the fluorescence intensity integral value of the zeolite in each sub-region is extracted; Construct a probability density distribution model of zeolite light intensity in the overall space and calculate its distribution skewness and kurtosis; A spherical scanning window structure is introduced, and the window is moved in each direction to calculate the local mean change rate and form an anisotropic distribution index. The integrated fluorescence intensity value, distribution skewness and kurtosis, and anisotropy distribution index are combined to output the zeolite distribution variation coefficient through a multi-factor weighted model as the zeolite uniformity test result.

[0009] Preferably, the obtained zeolite distribution variation coefficient is compared with a preset threshold. If the zeolite distribution variation coefficient is greater than the preset threshold, it indicates that the zeolite is unevenly distributed in the composite material; if the zeolite distribution variation coefficient is less than or equal to the preset threshold, it indicates that the zeolite is evenly distributed in the composite material.

[0010] Preferably, the establishment of the fitting relationship curve between the fluorescence signal intensity and the standard zeolite loading comprises: Multiple groups of composite material standard samples with known zeolite mass fractions were selected, and their corresponding fluorescence intensity averages were obtained through the same fluorescence labeling and image acquisition process; The collected data are processed to remove outliers and normalize the light intensity signal; Based on the local weighted regression model, the nonlinear mapping relationship between fluorescence intensity and actual zeolite loading was fitted; The model parameters were optimized by cross-validation and the minimum mean square error criterion.

[0011] Preferably, the establishment of the nonlinear mapping relationship is based on a local weighted regression model, specifically including: For each standard sample point, a local neighborhood sample subset is dynamically selected based on the position of its fluorescence intensity value in the input space; A first-order or second-order polynomial regression model is independently fitted in each local area to obtain the estimated value of the fluorescence intensity and zeolite loading in the area; Multiple local regression results are spliced ​​together to form a global fitting curve.

[0012] Preferably, the estimating the zeolite loading comprises: The three-dimensional light intensity distribution map obtained by image processing is normalized, and the average grayscale value of each grid voxel area is extracted; Substituting the average gray value as an input variable into the fitting relationship curve constructed based on the local weighted regression model to obtain the corresponding zeolite mass fraction prediction value; The total zeolite loading of the entire sample was calculated by taking the volume-weighted average of the predicted values ​​for all voxel regions.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. This method utilizes specific fluorescent labeling and high-resolution image processing techniques, combined with nonlinear modeling and spatial distribution analysis, to visualize, quantify, and identify the loading state of zeolite within composite structures. Compared to existing thermogravimetric analysis, electron microscopy, or X-ray analysis methods, this method offers a simpler procedure, shorter detection cycles, and richer data structures, making it suitable for rapid online screening and industrial process quality control.

[0014] 2. This method quantifies the spatial distribution characteristics of zeolites from multiple dimensions (integrated light intensity, distribution skewness, and anisotropy) by constructing a three-dimensional fluorescence spectrum and a coefficient of variation model. A locally weighted regression method is then used to establish a nonlinear mapping relationship between fluorescence signal and loading, significantly improving prediction accuracy and model generalization. This method, which does not rely on expensive instrumentation, possesses broad adaptability and strong engineering practicality, and can be widely applied to structural analysis and uniformity assessment in a variety of fields, including catalytic materials, adsorbents, and composite membranes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 This is a mind map of the method of the present invention. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, the method for quickly detecting the zeolite loading and uniformity in a zeolite composite material described in this embodiment includes: contacting a zeolite composite material sample to be tested with a solution containing a fluorescent ligand to form a fluorescent label; Under the excitation of the selected wavelength, the sample is subjected to fluorescence imaging to obtain an optical image of the marked area; Using image processing algorithms, the acquired optical images are rasterized and a three-dimensional light intensity distribution map is constructed; Based on the three-dimensional light intensity distribution map, the coverage of zeolite and the light intensity mean deviation in each region are quantified, and the zeolite distribution variation coefficient is output to evaluate the distribution uniformity of zeolite in the composite material; A fitting relationship curve between the fluorescence signal intensity and the standard zeolite loading was established. Based on the fitting relationship curve, the image grayscale value was correlated with the actual zeolite mass content, and the zeolite loading was estimated.

[0019] This embodiment provides a method for fluorescently labeling a zeolite composite material sample, which is used for subsequently detecting the loading amount and distribution of zeolite in the composite material by optical means.

[0020] Zeolite composite material sample to be tested (e.g., alumina-based particles loaded with ZSM-5); Fluorescent ligand solution: 0.1 mM 4-carboxyfluorescein dissolved in a mixed solvent of ethanol and water (volume ratio 7:3); Buffer: 50 mM phosphate buffer, pH = 6.8; Conventional experimental equipment such as glassware, magnetic stirrer, constant temperature water bath, centrifuge, drying oven, etc.

[0021] Sample pretreatment: 5 g of zeolite composite sample was dried in an oven at 60 °C for 4 h to remove physically adsorbed moisture; Fluorescent ligand reaction: The dried sample was added to 20 mL of fluorescent ligand solution and magnetically stirred at room temperature for 2 hours to allow the carboxylic acid groups in the fluorescent ligand to react with the acidic hydroxyl groups (Si–OH / Al–OH) on the zeolite surface through esterification or hydrogen bonding, forming a stable surface label. Elution and drying: The reacted sample was collected by centrifugation and washed three times with ethanol to remove unbound fluorescent molecules. It was then vacuum dried at 40 °C for 6 hours to obtain the fluorescently labeled zeolite composite material.

[0022] Under weakly acidic conditions, the carboxyl groups in the fluorescent ligand can form hydrogen bonds or ester bonds with the hydroxyl groups on the zeolite surface through condensation. This reaction can be expressed as: zeolite-OH + R-COOH → zeolite-OOC-R + HO; where R represents the fluorescein group. The labeled zeolite emits green light (approximately 520 nm) when excited at 490 nm, facilitating subsequent image analysis.

[0023] The treated samples exhibited uniform fluorescence under UV excitation. The fluorescence signal exhibited a spatial correspondence with the zeolite distribution, enabling visual and quantitative analysis of the zeolite distribution characteristics through image processing. Experiments demonstrated a good correlation between the fluorescence intensity and the zeolite loading of the standard sample (R² > 0.98).

[0024] This example illustrates how to perform fluorescence imaging at a specific excitation wavelength after completing the fluorescence labeling of a zeolite composite material sample to obtain an original optical image for loading and distribution analysis.

[0025] Excitation light source: LED ultraviolet exciter, main peak wavelength is 490 nm, bandpass filter width is ±10 nm; Sample fixture: standard glass stage, sample thickness controlled at 2–3 mm; Imaging equipment: CMOS fluorescence microscopy system equipped with a bandpass fluorescence emission filter with a central wavelength of 520 nm; Environmental conditions: Perform the experiment in a dark room without interference from natural light and maintain the temperature at 20–25 °C.

[0026] Sample preparation: Take the composite material sample that has been fluorescently labeled and dried, and spread it evenly on a black reflection-suppressing substrate; Excitation settings: Place the sample under the path of the excitation light source, turn on the excitation light source and adjust the angle and intensity so that the light spot evenly covers the entire sample surface; Image acquisition: Turn on the fluorescence microscope imaging system, adjust the focus and set the exposure time (usually 200–500 ms) to capture the sample fluorescence image; Image output: The acquired images were saved in 16-bit grayscale TIFF format with a resolution of no less than 2048 × 2048 for subsequent image analysis and light intensity quantitative processing.

[0027] When excited at 490 nm, the covalently bound fluorescent ligands of fluorescently labeled zeolites emit a characteristic green light with a central emission wavelength of approximately 520 nm. Because zeolite-enriched regions contain numerous ligand-binding sites, their localized fluorescence brightness is enhanced, resulting in a correlation between the image grayscale and the spatial distribution of the zeolite.

[0028] This imaging method eliminates the need for an electron microscope and relies solely on a standard fluorescence illumination system to acquire large-field, high-contrast optical images, significantly simplifying equipment requirements. Experiments have demonstrated a nonlinear positive correlation between the mean grayscale value of the imaged area and the zeolite mass fraction in that area, providing a foundation for developing a quantitative model.

[0029] This embodiment provides a method for processing a fluorescence image acquired by excitation and generating a continuous three-dimensional light intensity spectrum, which is suitable for spatial analysis of zeolite distribution in a zeolite composite material.

[0030] The TIFF format fluorescence image obtained in the embodiment has an image size of 2048×2048 pixels; Use the OpenCV library in Python to read the image grayscale value into a two-dimensional array. The pixel grayscale range is 0–65535 (16 bits). Grid division is performed according to the set spatial resolution (such as each 100×100 pixels is a unit) to generate two-dimensional grid units of equal size.

[0031] The light intensity information within each grid cell is stored as a collection of pixel grayscale values ​​in preparation for subsequent processing.

[0032] Calculate the local variance and mean of the grayscale values ​​of all pixels within each grid cell.

[0033] The 2D grid is used as X–Y plane data and the local normalized light intensity mean is used as the initial Z-axis value to build a sparse 3D point cloud. A convolution nested spline interpolation model is introduced, which first performs one-dimensional spline interpolation in each direction and then performs continuous smoothing through a three-dimensional convolution kernel. The model comprehensively utilizes the local gradient of the image and the global fitting curve to improve the reconstruction accuracy while suppressing abnormal mutations; Finally, a set of three-dimensional grid structures is output, in which each voxel unit contains a normalized light intensity value, forming a continuous and visual three-dimensional light intensity distribution map.

[0034] Compared with traditional two-dimensional projection or simple interpolation algorithms, this method is more continuous in spatial structure recovery and is suitable for describing the non-uniform distribution characteristics of zeolite in the thickness direction of the composite material, providing high-resolution structural input data for subsequent loading estimation and uniformity calculation.

[0035] This example provides a method combining image processing and spatial statistical analysis to assess the uniformity of zeolite distribution in composite materials. This method uses a three-dimensional fluorescence intensity map as data, integrates multiple spatial feature dimensions to establish a coefficient of variation for the zeolite distribution, and compares this coefficient with a preset threshold to determine whether the zeolite distribution in the material is uniform.

[0036] First, the three-dimensional light intensity map obtained through excitation light imaging and image processing algorithms is used as input data. This map has been processed through normalization, noise removal, and continuity reconstruction to provide complete spatial distribution information.

[0037] For spatial analysis, the entire 3D map is divided into several equal-volume subregions. Each subregion corresponds to a local spatial unit, representing the zeolite distribution at a specific location. This partitioning approach allows for more refined attribution of local light intensity within the composite material, facilitating subsequent statistical processing.

[0038] Within each subvolume, the light intensity signals of all voxels within it are extracted and integrated. The integrated result can be interpreted as an estimate of the total amount of zeolite loading in the region, reflecting the local cumulative distribution of zeolite.

[0039] By collecting the integrated light intensity values ​​across all regions, a spatial load distribution map of the entire composite sample is obtained, which will be used for subsequent distribution feature modeling and analysis.

[0040] After obtaining the light intensity integrals of all sub-areas, a spatial statistical model is constructed based on these integral values. This model focuses on two aspects of distribution characteristics: Skewness: It is used to reflect whether the zeolite has spatial enrichment or biased distribution, such as concentrated loading in some areas and sparse loading in other areas; Kurtosis: A measure of whether the zeolite distribution is highly concentrated or extreme, indicating that the zeolite accumulation in some areas is much higher than the average level.

[0041] Both parameters are derived from the statistical characteristics of spatial distribution, which help to quantitatively capture the "invisible" distribution laws and are particularly suitable for the evaluation of zeolite distribution status in complex material systems.

[0042] Considering that zeolite may exhibit asymmetric distribution in a specific direction (such as forming accumulation along the flow direction), this embodiment further introduces an anisotropic analysis mechanism.

[0043] The method uses a spherical scanning window with a fixed radius in the 3D atlas. This window is gradually slid along the x-, y-, and z-axes, and the local mean fluorescence intensity at each position is recorded. The variation in the mean intensity during the sliding window is then calculated to capture the distribution fluctuations in different directions.

[0044] This method can reveal the directional dependence of zeolite distribution in composite materials, that is, whether zeolite is more likely to form aggregation or sparseness in a certain direction, thereby judging the structural consistency and stability within the material.

[0045] This embodiment combines the above three independent parameters: the skewness and kurtosis of the light intensity integral distribution and the anisotropy distribution index in the three-dimensional direction, to construct a composite index reflecting the overall distribution characteristics of zeolite, namely the zeolite distribution variation coefficient.

[0046] The design philosophy of this coefficient is to characterize the spatial aggregation trend, abnormal concentration, and directional heterogeneity of zeolites from a multi-dimensional perspective. By assigning appropriate weights to each parameter, they are linearly combined into a standardized numerical indicator for easy use in subsequent discriminant logic.

[0047] The higher the ZDVC, the more uneven the spatial distribution of the zeolite, and there may be problems such as over-enrichment, regional vacancies or striped arrangement; the lower the ZDVC, the more balanced and stable the distribution is, and the quality control status is good.

[0048] To achieve engineering applications and automatic identification, this embodiment further sets an empirically determined preset threshold value, which is derived from comparative experiments with multiple standard samples and expert evaluation results, and is adjusted based on the quality requirements of the actual preparation process.

[0049] The judgment logic is as follows: If the calculated coefficient of variation of zeolite distribution is higher than the preset threshold, it indicates that the zeolite distribution in the sample is significantly uneven, which may affect its adsorption or catalytic performance and is judged as "uneven distribution"; If the coefficient of variation is less than or equal to the threshold, the zeolite in the sample is considered to be evenly distributed and can meet the quality or process requirements, and is judged to be "evenly distributed".

[0050] This logic can be automatically completed through a judgment module embedded in the image processing software or detection system, improving judgment efficiency and objectivity.

[0051] This embodiment provides an implementation method for constructing a zeolite loading prediction model based on a statistical learning method. This method converts the image information of an unknown sample into quantifiable zeolite loading data by establishing a nonlinear fitting relationship between the fluorescence intensity signal and the zeolite mass content.

[0052] First, several sets of composite materials with known zeolite mass fractions were selected as standard samples for training the model. The zeolite loading of these standard samples was accurately measured using traditional methods such as thermogravimetric analysis or chemical titration, representing a typical distribution of different loading levels.

[0053] Subsequently, these standard samples were fluorescently labeled one by one, and the type of fluorescent ligand, reaction conditions and image acquisition process used were completely consistent with the actual test samples, ensuring that the input data for model training was consistent and comparable.

[0054] For each sample, fluorescence images were acquired under the same imaging conditions, and the average grayscale value in the image was extracted as its corresponding "mean fluorescence intensity." This grayscale value was normalized and used for subsequent modeling.

[0055] To improve the model's stability and generalization capabilities, the collected standard sample data was cleaned. First, outlier samples that may have been affected by abnormal excitation, background interference, or localization offset during the experiment were removed. Then, a unified channel normalization operation was performed on the retained data to ensure that the fluorescence intensity values ​​of different batches and samples were comparable within the same numerical range.

[0056] Normalization processing helps reduce systematic errors, enhance the model's tolerance to different sample inputs, and ensure that subsequent fitting models more accurately reflect real physical relationships.

[0057] In order to accurately describe the nonlinear relationship between fluorescence intensity and zeolite loading, this embodiment uses a local weighted regression method for modeling.

[0058] The core idea of ​​this method is to select a group of samples with similar fluorescence intensity space near each input value to form a local data subset. A simple regression model, such as a first- or second-order polynomial, is then fitted to this subset. In this way, the entire input range is covered by multiple local regression results, which are then weighted together to form a continuous global fitting curve.

[0059] Compared with traditional global linear or high-order polynomial fitting methods, local weighted regression has higher flexibility and accuracy. It is especially suitable for situations where data distribution is uneven or the response relationship varies greatly, and can effectively avoid underfitting or overfitting.

[0060] In practice, for each standard sample point, a set of neighboring points in the fluorescence intensity space is dynamically selected, and a curve is fitted to that local region. The model assigns a weight to each sample point, which decreases with the distance between the sample point and the target point in the input space, thereby enhancing local correlation and weakening long-range interference.

[0061] During the modeling process, to ensure optimal predictive performance, the parameters of the local fitting model are optimized, such as neighborhood size, regression order, and weighting function form. This optimization is achieved through a cross-validation strategy, which randomly divides all standard samples into multiple subsets, which are used alternately as training and validation groups to evaluate the model's predictive ability on unseen data.

[0062] Finally, the minimum average error was used as the objective function, and the parameter combination with the best fitting accuracy was selected as the final model structure to complete the construction of the nonlinear prediction model of zeolite loading.

[0063] For the composite material sample under test, a three-dimensional light intensity distribution map is obtained through fluorescence imaging and image processing. This map is then channel-normalized to ensure that the grayscale range is consistent with the training model.

[0064] The atlas was segmented into voxel regions (i.e., three-dimensional pixel units), and the average grayscale value of each voxel was extracted as its representative fluorescence intensity input. For each voxel, its grayscale value was substituted into the previously constructed fitting relationship curve to obtain the corresponding zeolite mass fraction prediction value.

[0065] After the prediction is complete, the zeolite mass fractions for all voxels are volume-weighted and integrated to output the average loading for the entire sample. This weighting not only considers the predicted value of each voxel, but also reflects its spatial contribution within the overall sample structure, thereby improving the accuracy of the overall estimate.

[0066] Example 2: To verify the feasibility and superiority of the detection method provided by the present invention in practical applications, five groups of composite material standard samples with different zeolite loadings (5%, 10%, 15%, 20%, and 25%) were tested. Zeolite mass fraction was determined using both the fluorescence image analysis method of the present invention and traditional thermogravimetric analysis (TGA). The distribution was then evaluated and compared using scanning electron microscopy images combined with energy spectrum line scanning.

[0067] Sample preparation: A fixed mass of ZSM-5 zeolite was mixed with γ-alumina powder in a certain proportion, pressed and sintered to produce composite material sheets with a known loading amount.

[0068] Testing process of the method of the present invention: Fluorescent labeling: 4-carboxyfluorescein was used as the fluorescent ligand, and the solution treatment time was 2 hours; Fluorescence imaging: 490 nm excitation, acquisition of fluorescence images and image normalization; 3D atlas construction: based on image rasterization and spline interpolation; Load estimation: prediction based on local weighted regression model; Uniformity assessment: The zeolite distribution variation coefficient (ZDVC) was calculated based on the integrated light intensity distribution and the anisotropy index.

[0069] Traditional testing process: The TGA method measures the mass loss of the sample at 600 °C and infers the zeolite content; The element distribution map of the zeolite region was obtained by SEM-EDS, and the distribution uniformity was subjectively evaluated.

[0070]

[0071] As shown in Table 1, the product composition is the mass percentage of zeolite and alumina in each sample; the predicted value of the method of the present invention is obtained by image grayscale and fitting model estimation; the TGA measured value is obtained by thermogravimetric analysis; ZDVC is a quantitative indicator used by the present invention to characterize the uniformity of the spatial distribution of zeolite, with a larger value indicating a more uneven distribution; the SEM observation conclusion is derived from the subjective distribution judgment of scanning electron microscopy-energy spectrum imaging and is used to assist in comparison.

[0072] Effect analysis: In the five groups of samples, the maximum deviation between the method of the present invention and the actual TGA measurement results does not exceed ±0.4%, indicating that the established nonlinear mapping model between fluorescence intensity and zeolite loading has a good fitting effect; the method of the present invention takes about 15 minutes from image acquisition to result output, which is significantly better than the 2-3 hours required by TGA; the coefficient of variation (ZDVC) indicator is used to achieve quantifiable evaluation of distribution uniformity, which can clearly identify phenomena such as boundary accumulation and center aggregation. The traditional SEM method can only be observed qualitatively and is difficult to quantify; in addition, the entire processing flow supports software execution, with less manual intervention, avoiding subjective errors, and is suitable for batch detection scenarios.

[0073] The experimental results of this example show that the fluorescence image analysis and regression modeling detection method proposed in the present invention has high precision, rapid response and automation capabilities in the determination of zeolite loading. At the same time, by constructing a spatial variation coefficient index, it realizes the quantitative judgment of distribution uniformity, which is significantly better than existing traditional methods such as thermogravimetric analysis and microscopic observation.

[0074] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A rapid detection method for zeolite loading and uniformity in a zeolite composite material, characterized by: include: contacting a zeolite composite material sample to be tested with a solution containing a fluorescent ligand to form a fluorescent label; Under the excitation of the selected wavelength, the sample is subjected to fluorescence imaging to obtain an optical image of the marked area; Using image processing algorithms, the acquired optical images are rasterized and a three-dimensional light intensity distribution map is constructed; Based on the three-dimensional light intensity distribution map, the coverage of zeolite and the light intensity mean deviation in each region are quantified, and the zeolite distribution variation coefficient is output to evaluate the distribution uniformity of zeolite in the composite material; A fitting relationship curve between the fluorescence signal intensity and the standard zeolite loading was established. Based on the fitting relationship curve, the image grayscale value was correlated with the actual zeolite mass content, and the zeolite loading was estimated.

2. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 1, characterized in that: The image processing algorithm includes: The optical fluorescence image obtained by excitation is processed into a two-dimensional pixel matrix, and the image area is grid-divided according to the preset spatial resolution to construct the light intensity data grid unit; Perform local variance normalization on the pixel light intensity value in each grid cell; The convolution nested spline interpolation model is used to perform longitudinal interpolation fitting on the light intensity data of each grid unit to generate a continuous three-dimensional light intensity distribution map.

3. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 1, characterized in that: The calculation of the zeolite distribution coefficient of variation includes: Based on the three-dimensional light intensity distribution map, the map is divided into multiple sub-volume regions according to the spatial level, and the fluorescence intensity integral value of the zeolite in each sub-region is extracted; Construct a probability density distribution model of zeolite light intensity in the overall space and calculate its distribution skewness and kurtosis; A spherical scanning window structure is introduced, and the window is moved in each direction to calculate the local mean change rate and form an anisotropic distribution index. The integrated fluorescence intensity value, distribution skewness and kurtosis, and anisotropy distribution index are combined to output the zeolite distribution variation coefficient through a multi-factor weighted model as the zeolite uniformity test result.

4. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 3, characterized in that: The obtained zeolite distribution variation coefficient is compared with the preset threshold. If the zeolite distribution variation coefficient is greater than the preset threshold, it means that the zeolite is unevenly distributed in the composite material; if the zeolite distribution variation coefficient is less than or equal to the preset threshold, it means that the zeolite is evenly distributed in the composite material.

5. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 1, characterized in that: The establishment of the fitting relationship curve between the fluorescence signal intensity and the standard zeolite loading includes: Multiple groups of composite material standard samples with known zeolite mass fractions were selected, and their corresponding fluorescence intensity averages were obtained through the same fluorescence labeling and image acquisition process; The collected data are processed to remove outliers and normalize the light intensity signal; Based on the local weighted regression model, the nonlinear mapping relationship between fluorescence intensity and actual zeolite loading was fitted; The model parameters were optimized by cross-validation and the minimum mean square error criterion.

6. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 5, characterized in that: The establishment of the nonlinear mapping relationship is based on a local weighted regression model, specifically including: For each standard sample point, a local neighborhood sample subset is dynamically selected based on the position of its fluorescence intensity value in the input space; A first-order or second-order polynomial regression model is independently fitted in each local area to obtain the estimated value of the fluorescence intensity and zeolite loading in the area; Multiple local regression results are spliced ​​together to form a global fitting curve.

7. The method for rapid detection of zeolite loading and uniformity in a zeolite composite material according to claim 6, characterized in that: The estimated zeolite loading comprises: The three-dimensional light intensity distribution map obtained by image processing is normalized, and the average grayscale value of each grid voxel area is extracted; Substituting the average gray value as an input variable into the fitting relationship curve constructed based on the local weighted regression model to obtain the corresponding zeolite mass fraction prediction value; The total zeolite loading of the entire sample was calculated by taking the volume-weighted average of the predicted values ​​for all voxel regions.

Images