Quantum dot labeling-based method for detecting interface diffusion behavior of polyimide coating layer

Abstract

The present application belongs to the technical field of material detection and characterization, and discloses a polyimide coating layer interface diffusion behavior detection method based on quantum dot markers; by doping surface modified quantum dot markers in the polyimide coating layer, the interface diffusion dynamic process is monitored in real time by using fluorescence microscopy technology. In view of the fluorescence signal distortion problem caused by local aggregation of quantum dots, an aggregation area recognition algorithm and an adaptive aggregation correction model based on a multi-dimensional feature vector are constructed, and accurate correction of the fluorescence intensity is realized. Based on the corrected fluorescence microscopic image, the interface diffusion distance and diffusion uniformity and other characteristic parameters are extracted through fluorescence intensity gradient analysis, and a quantitative evaluation standard for the abnormal degree of interface diffusion behavior is established. The present application realizes high sensitivity and high resolution detection of the interface diffusion behavior of the polyimide coating layer.

Description

Technical Field

[0001] This invention relates to the field of materials testing and characterization technology, and more specifically, to a method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labeling. Background Technology

[0002] Polyimide materials, with their excellent thermal stability, superior mechanical strength, and electrical insulation properties, have become indispensable key functional materials in high-tech fields such as aerospace, microelectronic packaging, flexible displays, and 5G communications. In practical applications, the interfacial diffusion behavior between the polyimide coating and the substrate material directly affects the adhesion performance, thermal stability, and long-term reliability of the material system, and has a decisive impact on the overall performance and lifespan of the device. Traditional methods for detecting interfacial diffusion behavior mainly rely on techniques such as Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and electron microscopy. In recent years, fluorescent labeling technology has gradually become an important tool for studying material interfacial behavior due to its high sensitivity and real-time monitoring capabilities.

[0003] However, traditional fluorescent markers exhibit poor photostability and short fluorescence lifetimes under high temperature and humidity conditions, leading to rapid signal decay or even complete disappearance during long-term service condition simulations, making it impossible to monitor interface behavior throughout the entire lifecycle. More challenging is the uneven dispersion of conventional markers within the polyimide matrix, particularly in areas of interfacial stress concentration where local aggregation easily occurs. The abnormally strong fluorescence signals generated by these aggregation areas mask the actual interfacial diffusion process, resulting in severely distorted data obtained by researchers, leading to biased material performance assessments and incorrect predictions of device failure. Furthermore, existing detection methods have limited resolution, making it difficult to capture nanoscale interfacial micro-changes, especially in the context of miniaturization in electronic devices, where the ability to characterize interface structures of hundreds of nanometers or even finer details is particularly insufficient. In practical applications, the interfacial behavior of polyimide coatings under complex thermo-mechanical cyclic loading in aero-engines cannot be accurately quantified. Design engineers can only rely on empirical estimations and over-design to ensure safety margins, increasing material development costs and extending the verification cycle of new materials. The lack of standardized quantitative parameter systems and evaluation standards in existing technologies makes it difficult to compare and verify test results between different research institutions.

[0004] In view of this, the present invention proposes a method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labeling to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labeling, comprising:

[0006] Multiple fluorescence microscopic images of a polyimide-coated sample were acquired within a preset detection period. The polyimide-coated sample was doped with surface-modified quantum dot markers.

[0007] Based on the change trend of fluorescence intensity of each pixel in the multi-frame fluorescence microscopy images over time, the local aggregation region of quantum dot markers in the polyimide coated sample is obtained.

[0008] Based on the fluorescence intensity distribution and spatial relationship of the local aggregation region, an aggregation correction model for the local aggregation region is constructed. Based on the aggregation correction model, the fluorescence intensity of the multi-frame fluorescence micrographs is corrected to obtain a corrected fluorescence micrograph.

[0009] Based on the fluorescence intensity gradient and diffusion direction of the quantum dot markers in the corrected fluorescence microscopy image, the interfacial diffusion characteristic parameters of the polyimide coated sample are obtained; the interfacial diffusion behavior of the polyimide coated sample is detected based on the interfacial diffusion characteristic parameters.

[0010] Further, obtaining the localized aggregation region of quantum dot markers in the polyimide-coated sample includes:

[0011] A time-series analysis was performed on the fluorescence intensity of each pixel in the multi-frame fluorescence microscopy images to obtain the fluorescence intensity change rate of each pixel.

[0012] Based on the comparison between the fluorescence intensity change rate and the preset change rate threshold, the pixels in the multi-frame fluorescence microscopy images with a fluorescence intensity change rate greater than the preset change rate threshold are marked as candidate clustered pixels.

[0013] Spatial clustering analysis is performed on the candidate clustered pixels to obtain the connected regions formed by the candidate clustered pixels, which are denoted as the local clustered regions.

[0014] Furthermore, constructing the clustering correction model for the local clustering region includes:

[0015] The fluorescence intensity of each pixel within the local clustering region is statistically analyzed and compared with the fluorescence intensity of pixels in the neighboring region outside the local clustering region. The ratio of the two fluorescence intensities is calculated and denoted as the clustering intensity factor of the local clustering region.

[0016] Based on the spatial relationship of the local clustered regions, the geometric center and boundary contour of the local clustered regions are obtained, and the area and shape complexity of the local clustered regions are calculated. The shape complexity is the ratio of the square of the perimeter of the boundary contour to the area.

[0017] Based on the aggregation intensity factor, area, and shape complexity, an aggregation correction model for the local aggregation region is constructed.

[0018] Further, the step of correcting the fluorescence intensity of the multi-frame fluorescence micrographs based on the aggregation correction model to obtain corrected fluorescence micrographs includes:

[0019] Based on the aggregation correction model, the fluorescence intensity correction coefficient of each pixel in the local aggregation region is obtained, and the fluorescence intensity correction coefficient is negatively correlated with the aggregation intensity factor.

[0020] The original fluorescence intensity of each pixel in the local aggregation region of the multi-frame fluorescence micrograph is multiplied by its corresponding fluorescence intensity correction coefficient to obtain the corrected fluorescence intensity;

[0021] The corrected fluorescence microscopy image is generated based on the corrected fluorescence intensity.

[0022] Further, obtaining the interfacial diffusion characteristic parameters of the polyimide coating sample includes:

[0023] Gradient analysis of the fluorescence intensity of each pixel in the corrected fluorescence microscopy image is performed to obtain the fluorescence intensity gradient vector of the quantum dot marker.

[0024] The diffusion direction and diffusion rate of the quantum dot marker are determined based on the direction and magnitude of the fluorescence intensity gradient vector.

[0025] Based on the diffusion direction and diffusion rate, the interfacial diffusion distance and diffusion uniformity of the polyimide coated sample within a preset detection period are calculated and denoted as the interfacial diffusion characteristic parameters.

[0026] Further, the step of performing spatial clustering analysis on the candidate clustered pixels to obtain the connected regions formed by the candidate clustered pixels, denoted as the local clustered region, includes:

[0027] The spatial coordinates, fluorescence intensity change rate, and temporal correlation of each candidate clustered pixel in the multi-frame fluorescence microscopy image are obtained. A multi-dimensional feature vector of the candidate clustered pixel is constructed. Based on the multi-dimensional feature vector, the dynamic feature distance between any two candidate clustered pixels is calculated.

[0028] Adaptive spatial clustering analysis based on the dynamic feature distance specifically includes: classifying candidate clustered pixels in the multi-frame fluorescence microscopy images whose dynamic feature distance is less than a preset initial distance threshold into the same initial clustering region; for each initial clustering region, calculating the variance of the fluorescence intensity change rate of all candidate clustered pixels within the initial clustering region; adaptively adjusting the preset initial distance threshold based on the variance to obtain an adaptive distance threshold for the initial clustering region; and re-clustering based on the adaptive distance threshold to obtain the connected regions formed by the candidate clustered pixels.

[0029] Temporal consistency verification is performed on the connected regions, and regions in the connected regions whose mean temporal correlation is less than a preset correlation threshold are removed to obtain preliminary local clustering regions;

[0030] Morphological optimization is performed on the initial local aggregation region to obtain the local aggregation region.

[0031] Furthermore, the surface modification of the quantum dot marker includes:

[0032] A hydrophobic organic ligand is coated on the surface of the quantum dot marker to enhance the dispersion uniformity of the quantum dot marker in the polyimide coated sample.

[0033] The molecular chain length of the hydrophobic organic ligand matches the molecular chain length of the polyimide coating sample.

[0034] Further, the step of detecting the interfacial diffusion behavior of the polyimide coating sample based on the interfacial diffusion characteristic parameters includes:

[0035] Obtain standard interfacial diffusion characteristic parameters of standard polyimide coated samples;

[0036] The Euclidean distance between the interfacial diffusion characteristic parameters of the polyimide coating sample and the standard interfacial diffusion characteristic parameters is calculated respectively, and the degree of abnormality of the interfacial diffusion behavior of the polyimide coating sample is judged based on the magnitude of the Euclidean distance.

[0037] Further, the calculation of the interfacial diffusion distance and diffusion uniformity of the polyimide coated sample within a preset detection period based on the diffusion direction and diffusion rate includes:

[0038] Based on the diffusion direction, the interfacial diffusion path of the polyimide coating sample is determined, and the cumulative length of the interfacial diffusion path is calculated and denoted as the interfacial diffusion distance.

[0039] The variance of the fluorescence intensity distribution of the quantum dot marker in the corrected fluorescence microscopy image is statistically analyzed, and the diffusion uniformity is obtained based on the ratio of the variance to the mean fluorescence intensity.

[0040] Furthermore, the method for morphological optimization of the initial local aggregation region includes:

[0041] Based on the boundary contour of the preliminary local clustering region, the curvature distribution of the boundary contour is calculated; according to the curvature distribution, high curvature points in the boundary contour are identified, and the high curvature points are used as segmentation points to segment the preliminary local clustering region to obtain segmented sub-regions; for each sub-region, the ratio of the area of ​​the sub-region to the total area of ​​the candidate clustered pixels in the multi-frame fluorescence microscopy image is calculated; if the ratio is less than a preset ratio threshold, the sub-region is removed, and the local clustering region is finally obtained.

[0042] The technical effects and advantages of the present invention regarding the detection method for interfacial diffusion behavior of polyimide coatings based on quantum dot labeling are as follows:

[0043] This invention effectively eliminates the masking effect of abnormally enhanced fluorescence intensity on real interfacial diffusion behavior by identifying and precisely correcting local aggregation regions of quantum dots, thus solving the problem of fluorescence signal distortion. This allows for the accurate reconstruction of the microscopic dynamic processes of interfacial diffusion, improving the accuracy and reliability of evaluating the interfacial properties of polyimide materials. By eliminating local aggregation interference, this invention achieves high-fidelity extraction of interfacial diffusion characteristic parameters, providing a clear perspective for understanding the molecular chain motion mechanism and the evolution of interfacial structures. This precise characterization of real interfacial diffusion behavior not only deepens the understanding of the structure-property relationship of polyimide interfaces but also provides crucial decision-making basis for material optimization design. Furthermore, this invention significantly shortens the development cycle of high-performance polyimide materials, reduces development costs, and improves the predictability and stability of material properties. Attached Figure Description

[0044] Figure 1 This is a flowchart of the steps of the method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labels according to the present invention;

[0045] Figure 2 This is a flowchart illustrating the steps involved in obtaining a localized clustering region according to the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labeling provided by this invention.

[0049] This invention proposes a method for detecting the interfacial diffusion behavior of polyimide coatings based on quantum dot labeling. Please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a flowchart of a method for detecting the interfacial diffusion behavior of a polyimide coating based on quantum dot labeling, according to an embodiment of the present invention. The method includes:

[0050] Step S1: Obtain multiple frames of fluorescence microscopic images of the polyimide coated sample within a preset detection period, wherein the polyimide coated sample is doped with surface-modified quantum dot markers.

[0051] Polyimide materials are widely used in aerospace, electronic packaging, and other fields due to their excellent thermal stability, mechanical properties, and electrical insulation characteristics. Their interfacial diffusion behavior has a significant impact on material properties. By doping polyimide coated samples with surface-modified quantum dot markers, fluorescence microscopy can be used to achieve highly sensitive and spatially resolved detection of the interfacial diffusion behavior of the coating. Because quantum dots possess high quantum yield, good photostability, and long fluorescence lifetime, they can maintain stable signal output during long-term tracking and detection.

[0052] The surface-modified quantum dot markers are uniformly dispersed in the polyimide-coated sample through covalent bonds or physical adsorption. As the coating material diffuses at the interface, the quantum dot markers also move. Multiple fluorescence microscopic images of this process are captured by fluorescence microscopy, providing basic data for subsequent analysis.

[0053] In one implementation of this invention, the preset detection period is set to 30 minutes, and one frame of fluorescence microscopy image is acquired every 5 seconds, for a total of 360 frames.

[0054] In one implementation of this invention, a confocal fluorescence microscope is used to acquire fluorescence microscopic images. The excitation wavelength is 405 nm, the fluorescence collection wavelength range is 500-600 nm, the scanning rate is 1 frame / second, and the image resolution is 1024×1024 pixels, ensuring that the acquired fluorescence microscopic images have sufficient signal intensity and spatial resolution.

[0055] Step S2: Based on the trend of fluorescence intensity change of each pixel in the multi-frame fluorescence microscopy image over time, obtain the local aggregation region of quantum dot markers in the polyimide coated sample.

[0056] The distribution of quantum dot markers in polyimide-coated samples may be uneven, with localized aggregation of quantum dots in some areas. The abnormally enhanced fluorescence intensity in these localized aggregation regions can mask the true interfacial diffusion behavior, leading to distorted detection results. Therefore, it is necessary to identify and correct these localized aggregation regions to improve the accuracy of interfacial diffusion behavior detection. The fluorescence intensity variation over time in aggregation regions differs from that in uniformly dispersed regions. By analyzing the temporal variation characteristics of fluorescence intensity for each pixel in multiple frames of fluorescence microscopy images, localized aggregation regions can be effectively identified.

[0057] Preferably, in some possible implementations of the embodiments of the present invention, obtaining the local aggregation region of quantum dot markers in the polyimide coated sample includes: performing time-series analysis on the fluorescence intensity change trend of each pixel in multiple frames of fluorescence microscopy images to obtain the fluorescence intensity change rate of each pixel; based on the comparison result of the fluorescence intensity change rate and a preset change rate threshold, marking the pixels in the multiple frames of fluorescence microscopy images with fluorescence intensity change rates greater than the preset change rate threshold as candidate aggregation pixels; performing spatial clustering analysis on the candidate aggregation pixels to obtain the connected regions formed by the candidate aggregation pixels, which are denoted as local aggregation regions.

[0058] The fluorescence intensity change rate characterizes the degree of drastic change in the fluorescence intensity of a pixel over time. Due to the fluorescence resonance energy transfer effect and self-quenching effect, the fluorescence intensity of quantum dot aggregate regions typically exhibits a different change pattern than that of non-aggregated regions. The fluorescence intensity change rate is obtained by calculating the first derivative of the fluorescence intensity of each pixel and normalizing it. The fluorescence intensity change rate of quantum dot aggregate regions is usually higher than that of non-aggregated regions; therefore, a preset change rate threshold is set to screen out candidate aggregated pixels.

[0059] In one implementation of this invention, the formula for calculating the fluorescence intensity change rate is:

[0060] Where R(i,j) is the fluorescence intensity change rate of pixel (i,j), I(i,j,t) is the fluorescence intensity of pixel (i,j) at time t, and I(i,j,t+1) is the fluorescence intensity of pixel (i,j) at time t+1.

[0061] In one implementation of this invention, a preset change rate threshold is set to 0.15. That is, when the fluorescence intensity change rate of a pixel is greater than 0.15, the pixel is marked as a candidate cluster pixel.

[0062] Since the aggregation of quantum dots has spatial continuity, it is difficult for a single pixel to form an effective aggregation region. Therefore, spatial clustering analysis is required for candidate aggregation pixels to group candidate aggregation pixels that are spatially close into the same connected region, i.e., local aggregation region.

[0063] In this embodiment of the invention, spatial clustering analysis is performed on candidate clustered pixels to obtain connected regions formed by the candidate clustered pixels. This includes: selecting one candidate clustered pixel from multiple frames of fluorescence microscopy images as the initial cluster center; classifying other candidate clustered pixels whose distance from the initial cluster center is less than a preset distance threshold into the same cluster region; using the pixels among the candidate clustered pixels that are not cluster centers as new cluster centers, and repeating the clustering process until all candidate clustered pixels are classified or cannot be classified into any cluster region; and recording each cluster region as a connected region.

[0064] This spatial clustering analysis method is based on a region growing algorithm, which iteratively groups candidate pixels with similar spatial locations into the same cluster region. A preset distance threshold is used to determine whether candidate pixels should be included in the current cluster region, ensuring the spatial continuity of the cluster regions.

[0065] In one implementation of this invention, a preset distance threshold is set to 5 pixels. That is, when the Euclidean distance between a candidate cluster pixel and the cluster center is less than 5 pixels, the candidate cluster pixel is assigned to the current cluster region.

[0066] Please see Figure 2The following is a flowchart of the steps for obtaining local clustering regions. In another embodiment of the present invention, a more complex multidimensional feature vector method is used for spatial clustering analysis of candidate clustered pixels, including: obtaining the spatial coordinates, fluorescence intensity change rate, and temporal correlation of each candidate clustered pixel in multiple frames of fluorescence microscopy images; constructing a multidimensional feature vector of the candidate clustered pixels, wherein the multidimensional feature vector includes two-dimensional components of spatial coordinates, fluorescence intensity change rate, and temporal correlation, wherein the temporal correlation is obtained by calculating the Pearson correlation coefficient between the fluorescence intensity sequence of the candidate clustered pixels in a preset detection period and the global fluorescence intensity mean sequence in multiple frames of fluorescence microscopy images; calculating the dynamic feature distance between any two candidate clustered pixels based on the multidimensional feature vector; and based on the dynamic feature vector... The adaptive spatial clustering analysis based on dynamic feature distance includes: classifying candidate clustered pixels in multiple frames of fluorescence microscopy images whose dynamic feature distance is less than a preset initial distance threshold into the same initial clustering region; for each initial clustering region, calculating the variance of the fluorescence intensity change rate of all candidate clustered pixels within the initial clustering region, and adaptively adjusting the preset initial distance threshold based on the variance to obtain an adaptive distance threshold for the initial clustering region, wherein the adaptive distance threshold is positively correlated with the variance; re-clustering based on the adaptive distance threshold to obtain connected regions composed of candidate clustered pixels; performing temporal consistency verification on the connected regions, and removing regions where the mean of the temporal correlation is less than a preset correlation threshold to obtain preliminary local clustering regions; and performing morphological optimization on the preliminary local clustering regions to obtain local clustering regions.

[0067] This method constructs a multidimensional feature vector that incorporates spatial information, fluorescence intensity change rate, and temporal correlation to comprehensively describe the characteristics of candidate clustered pixels, making clustering analysis more accurate. Temporal correlation is used to evaluate the consistency between the fluorescence intensity change pattern of candidate clustered pixels and the global fluorescence intensity change pattern, helping to distinguish between true clustering regions and noise interference. The adaptive distance threshold dynamically adjusts the clustering criteria based on the variance of the fluorescence intensity change rate within the initial clustering region, improving clustering accuracy.

[0068] In one implementation of this invention, the dynamic feature distance calculation formula is as follows:

[0069] Where Dij is the dynamic feature distance between the i-th and j-th candidate clustered pixels, (xi, yi) and (xj, yj) are the spatial coordinates of the i-th and j-th candidate clustered pixels, ri and rj are the fluorescence intensity change rates of the i-th and j-th candidate clustered pixels, ci and cj are the temporal correlations of the i-th and j-th candidate clustered pixels, ti and tj are the timestamps of the frames where the i-th and j-th candidate clustered pixels are located, T is the total duration of the preset detection period, w1, w2, and w3 are preset weight coefficients that satisfy w1+w2+w3=1, and are set to 0.4, 0.3, and 0.3 respectively; used to balance the contributions of spatial distance, fluorescence intensity change rate, and temporal correlation, and λ is a preset time decay factor used to adjust the influence of time difference on the dynamic feature distance.

[0070] In one implementation of this invention, the preset initial distance threshold is set to 0.5, and the preset correlation threshold is set to 0.6.

[0071] The method for morphological optimization of the preliminary local clustering region includes: calculating the curvature distribution of the boundary contour based on the boundary contour of the preliminary local clustering region; identifying high curvature points in the boundary contour according to the curvature distribution, and using the high curvature points as segmentation points to segment the preliminary local clustering region to obtain segmented sub-regions; for each sub-region, calculating the ratio of the area of ​​the sub-region to the total area of ​​candidate clustered pixels in multiple frames of fluorescence microscopy images; if the ratio is less than a preset ratio threshold, the sub-region is removed, and the final local clustering region is obtained.

[0072] Morphological optimization identifies and addresses potential misclustered regions by analyzing the boundary contour features of initial local clustering regions. High curvature points on the boundary contours typically correspond to abrupt changes in region shape, potentially representing junctions between multiple clustered regions or abnormal protrusions caused by noise. By segmenting the initial local clustering regions at high curvature points and filtering sub-regions based on area ratio thresholds, the effects of noise and edge effects can be removed, resulting in more accurate local clustering regions.

[0073] In one implementation of this invention, a high curvature point is defined as a point whose curvature value is greater than the mean curvature plus twice the standard deviation, and the preset proportion threshold is set to 0.05.

[0074] Step S3: Based on the fluorescence intensity distribution and spatial relationship of the local aggregation region, construct an aggregation correction model for the local aggregation region. Based on the aggregation correction model, perform fluorescence intensity correction on multiple frames of fluorescence micrographs to obtain corrected fluorescence micrographs.

[0075] After identifying local clustering regions, a clustering correction model needs to be constructed to eliminate the interference of local clustering on the detection of interface diffusion behavior. The clustering correction model, based on the fluorescence intensity distribution and spatial relationship of local clustering regions, quantitatively characterizes the degree to which local clustering masks the actual diffusion behavior, providing a basis for subsequent fluorescence intensity correction.

[0076] Preferably, in some possible implementations of the embodiments of the present invention, constructing a clustering correction model for a local clustering region includes: statistically analyzing the average fluorescence intensity of each pixel within the local clustering region and the average fluorescence intensity of pixels in neighboring regions outside the local clustering region, calculating the ratio of their fluorescence intensities, and denoting it as the clustering intensity factor of the local clustering region; obtaining the geometric center and boundary contour of the local clustering region based on its spatial positional relationship, calculating the area and shape complexity of the local clustering region, where the shape complexity is the ratio of the square of the perimeter of the boundary contour to the area; and constructing a clustering correction model for the local clustering region based on the clustering intensity factor, area, and shape complexity. The clustering correction model is used to characterize the degree to which the fluorescence intensity distribution of the local clustering region masks the actual diffusion behavior.

[0077] The aggregation intensity factor reflects the degree of fluorescence intensity enhancement in local aggregated regions relative to neighboring non-aggregated regions. A larger aggregation intensity factor indicates a higher degree of quantum dot aggregation and a more severe interference with actual diffusion behavior. Area characterizes the size of the local aggregated region, while shape complexity reflects the degree of irregularity in its shape. Larger areas and higher shape complexity of local aggregated regions interfere with interfacial diffusion behavior over a wider range, requiring more stringent corrections.

[0078] In one implementation of this invention, the neighboring region is defined as the region within 10 pixels surrounding the local clustering region.

[0079] In one implementation of this invention, the mathematical expression for the aggregation correction model is:

[0080] Where CM(i,j) is the aggregation correction factor for pixel (i,j) in the local aggregation region, IF is the aggregation strength factor, A is the normalized area, SC is the normalized shape complexity, and α, β and γ are weight coefficients, set to 0.6, 0.2 and 0.2 respectively.

[0081] Preferably, in some possible implementations of the embodiments of the present invention, fluorescence intensity correction of multiple frames of fluorescence microscopy images based on the aggregation correction model is performed to obtain a corrected fluorescence microscopy image, including: obtaining the fluorescence intensity correction coefficient of each pixel in the local aggregation region according to the aggregation correction model, wherein the fluorescence intensity correction coefficient is negatively correlated with the aggregation intensity factor; multiplying the original fluorescence intensity of each pixel in the local aggregation region of the multiple frames of fluorescence microscopy images with its corresponding fluorescence intensity correction coefficient to obtain the corrected fluorescence intensity; and generating a corrected fluorescence microscopy image based on the corrected fluorescence intensity.

[0082] The fluorescence intensity correction factor is used to adjust the fluorescence intensity of pixels within local aggregation regions. The fluorescence intensity correction factor is negatively correlated with the aggregation intensity factor; that is, the larger the aggregation intensity factor, the smaller the fluorescence intensity correction factor and the greater the correction magnitude, in order to eliminate the masking effect of excessive aggregation on the true diffusion behavior.

[0083] In one implementation of this invention, the formula for calculating the fluorescence intensity correction coefficient is as follows:

[0084] Where C(i,j) is the fluorescence intensity correction coefficient of pixel (i,j) in the local aggregation region, and CM(i,j) is the aggregation correction factor of the pixel.

[0085] The corrected fluorescence intensity is obtained by multiplying the original fluorescence intensity by a fluorescence intensity correction factor, as expressed by:

[0086] Where I'(i,j,t) is the corrected fluorescence intensity of pixel (i,j) at time t, I(i,j,t) is the original fluorescence intensity, and C(i,j) is the fluorescence intensity correction coefficient.

[0087] In one implementation of this invention, for pixels in non-locally clustered regions, the fluorescence intensity correction coefficient is set to 1 to keep the original fluorescence intensity unchanged.

[0088] Step S4: Obtain the interfacial diffusion characteristic parameters of the polyimide coated sample based on the fluorescence intensity gradient and diffusion direction of the quantum dot markers in the corrected fluorescence microscopy image.

[0089] Corrected fluorescence microscopy eliminates the interference of local aggregation on the detection of interfacial diffusion behavior, and can truly reflect the diffusion distribution of quantum dot markers in polyimide-coated samples. By analyzing the fluorescence intensity gradient and diffusion direction of quantum dot markers in corrected fluorescence microscopy images, interfacial diffusion characteristic parameters of polyimide-coated samples can be quantitatively obtained, providing an objective basis for the detection of interfacial diffusion behavior.

[0090] Preferably, in some possible implementations of the embodiments of the present invention, obtaining the interface diffusion characteristic parameters of the polyimide coated sample includes: performing gradient analysis on the fluorescence intensity of each pixel in the calibrated fluorescence microscopy image to obtain the fluorescence intensity gradient vector of the quantum dot marker; determining the diffusion direction and diffusion rate of the quantum dot marker based on the direction and magnitude of the fluorescence intensity gradient vector; and calculating the interface diffusion distance and diffusion uniformity of the polyimide coated sample within a preset detection period based on the diffusion direction and diffusion rate, which are recorded as interface diffusion characteristic parameters.

[0091] The fluorescence intensity gradient vector characterizes the spatial trend of fluorescence intensity change, pointing in the direction of the fastest increase in fluorescence intensity, and its modulus represents the magnitude of the rate of change. During interfacial diffusion, the quantum dot marker moves along the diffusion direction, forming a fluorescence intensity gradient. The gradient vector is in the opposite direction to the diffusion direction, and its modulus is positively correlated with the diffusion rate.

[0092] In one implementation of this invention, the fluorescence intensity gradient vector is calculated using the Sobel operator, expressed as:

[0093] ;

[0094] ;

[0095] ;

[0096] Where Gx(i,j) and Gy(i,j) are the fluorescence intensity gradient components of pixel (i,j) in the x and y directions, respectively, G(i,j) is the fluorescence intensity gradient vector, and I'(i,j) is the corrected fluorescence intensity of pixel (i,j).

[0097] The diffusion direction is opposite to the direction of the fluorescence intensity gradient vector, and the diffusion rate is proportional to the magnitude of the fluorescence intensity gradient vector, as expressed by:

[0098] ;

[0099] ;

[0100] Where D_dir(i,j) is the unit vector of diffusion direction at pixel (i,j), D_rate(i,j) is the diffusion rate, k is the scaling factor, and ||G(i,j)|| is the magnitude of the fluorescence intensity gradient vector.

[0101] Preferably, in some possible implementations of the present invention, calculating the interfacial diffusion distance and diffusion uniformity of the polyimide-coated sample within a preset detection period based on the diffusion direction and diffusion rate includes: determining the interfacial diffusion path of the polyimide-coated sample according to the diffusion direction, calculating the cumulative length of the interfacial diffusion path and recording it as the interfacial diffusion distance; statistically correcting the variance of the fluorescence intensity distribution of quantum dot markers in the fluorescence microscopy image, and obtaining the diffusion uniformity based on the ratio of the variance to the mean fluorescence intensity.

[0102] Interfacial diffusion path is the trajectory of quantum dot markers during interfacial diffusion, and its cumulative length reflects the degree of interfacial diffusion. Diffusion uniformity characterizes the uniformity of quantum dot marker distribution in polyimide-coated samples and is an important indicator of interfacial diffusion quality.

[0103] In one implementation of this invention, the interface diffusion distance calculation method is as follows: in the calibrated fluorescence micrographs at the start and end times of the preset detection period, multiple feature points are selected, the diffusion path of each feature point is determined according to the diffusion direction, and the average value of the diffusion path length of all feature points is calculated as the interface diffusion distance.

[0104] In one implementation of this invention, the formula for calculating diffusion uniformity is:

[0105] Where U is the diffusion uniformity, σ² is the variance of the fluorescence intensity distribution, and μ is the mean fluorescence intensity. The diffusion uniformity range is [0, 1], and a larger value indicates more uniform diffusion.

[0106] Step S5: Detect the interfacial diffusion behavior of the polyimide coating sample based on interfacial diffusion characteristic parameters.

[0107] Interfacial diffusion characteristic parameters objectively reflect the interfacial diffusion behavior of polyimide coated samples. By comparing with standard parameters, the normality and abnormality of interfacial diffusion behavior can be evaluated, providing a basis for material performance optimization and quality control.

[0108] Preferably, in some possible implementations of the embodiments of the present invention, detecting the interfacial diffusion behavior of a polyimide coating sample based on interfacial diffusion characteristic parameters includes: obtaining standard interfacial diffusion characteristic parameters of a standard polyimide coating sample; calculating the Euclidean distance between the interfacial diffusion characteristic parameters of the polyimide coating sample and the standard interfacial diffusion characteristic parameters, and determining the degree of abnormality of the interfacial diffusion behavior of the polyimide coating sample based on the magnitude of the Euclidean distance.

[0109] The standard polyimide coating sample serves as a reference sample with ideal interfacial diffusion behavior, and its standard interfacial diffusion characteristic parameters are used as the evaluation benchmark. The Euclidean distance is a quantitative indicator of the difference between the interfacial diffusion characteristic parameters and the standard interfacial diffusion characteristic parameters. The smaller the Euclidean distance, the closer the interfacial diffusion behavior is to the standard, and the more normal it is; the larger the Euclidean distance, the higher the degree of abnormality in the interfacial diffusion behavior.

[0110] In one implementation of this invention, the Euclidean distance calculation formula is as follows:

[0111] Where D is the Euclidean distance, D_test and U_test are the interfacial diffusion distance and diffusion uniformity of the sample to be tested, respectively, and D_std and U_std are the interfacial diffusion distance and diffusion uniformity of the standard sample, respectively.

[0112] In one implementation of this invention, the Euclidean distance threshold is set to 0.2. When the Euclidean distance is less than 0.2, the interface diffusion behavior is determined to be normal; when the Euclidean distance is greater than or equal to 0.2 and less than 0.5, the interface diffusion behavior is determined to be slightly abnormal; when the Euclidean distance is greater than or equal to 0.5, the interface diffusion behavior is determined to be severely abnormal.

[0113] Preferably, in some possible implementations of the embodiments of the present invention, the surface modification of the quantum dot marker includes: coating the surface of the quantum dot marker with a layer of hydrophobic organic ligand, the hydrophobic organic ligand being used to enhance the dispersion uniformity of the quantum dot marker in the polyimide coated sample; the molecular chain length of the hydrophobic organic ligand matching the molecular chain length of the polyimide coated sample to reduce the aggregation tendency of the quantum dot marker.

[0114] Surface modification of quantum dot markers significantly impacts their dispersibility in polyimide-coated samples. Hydrophobic organic ligands can enhance the compatibility between quantum dots and polyimide materials, improving dispersion uniformity. Matching the ligand chain length with the polyimide chain length helps quantum dots form a stable interface with polyimide materials at the molecular scale, reducing aggregation tendency and improving the accuracy of interfacial diffusion behavior detection.

[0115] In one implementation of this invention, the hydrophobic organic ligand is selected from oleylamine or oleic acid with an alkyl chain length of 12-18, and the polyimide molecular chain length is about 20-30 repeating units.

[0116] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0119] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0120] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0121] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0123] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for detecting the interfacial diffusion behavior of a quantum dot marker-based polyimide coating layer, characterized in that, The method comprises the following steps: acquire a plurality of frames of fluorescence microscopic images of a polyimide coating layer sample within a preset detection period, wherein the polyimide coating layer sample is doped with surface-modified quantum dot markers; acquire a local aggregation area of the quantum dot markers in the polyimide coating layer sample according to the change trend of the fluorescence intensity of each pixel point in the plurality of frames of fluorescence microscopic images over time, comprising: perform time series analysis on the change trend of the fluorescence intensity of each pixel point in the plurality of frames of fluorescence microscopic images to obtain the fluorescence intensity change rate of each pixel point; according to the comparison result of the fluorescence intensity change rate and a preset change rate threshold, mark the pixel points in the plurality of frames of fluorescence microscopic images whose fluorescence intensity change rate is greater than the preset change rate threshold as candidate aggregation pixel points; perform spatial clustering analysis on the candidate aggregation pixel points to obtain a connected region formed by the candidate aggregation pixel points, denoted as the local aggregation area; construct an aggregation correction model of the local aggregation area according to the fluorescence intensity distribution and spatial position relationship of the local aggregation area, and correct the fluorescence intensity of the plurality of frames of fluorescence microscopic images based on the aggregation correction model to obtain a corrected fluorescence microscopic image; acquire interface diffusion characteristic parameters of the polyimide coating layer sample according to the fluorescence intensity gradient and diffusion direction of the quantum dot markers in the corrected fluorescence microscopic image; and detect the interface diffusion behavior of the polyimide coating layer sample based on the interface diffusion characteristic parameters.

2. The method for detecting the interfacial diffusion behavior of a quantum dot marker based polyimide coating layer according to claim 1, characterized in that, The construction of the aggregation correction model of the local aggregation area comprises: statistically acquire the mean fluorescence intensity of each pixel point in the local aggregation area and the mean fluorescence intensity of pixel points in a neighboring area outside the local aggregation area, calculate the fluorescence intensity ratio of the two, and denote it as the aggregation intensity factor of the local aggregation area; according to the spatial position relationship of the local aggregation area, acquire the geometric center and boundary contour of the local aggregation area, calculate the area and shape complexity of the local aggregation area, and the shape complexity is the ratio of the square of the perimeter to the area of the boundary contour; based on the aggregation intensity factor, area and shape complexity, construct the aggregation correction model of the local aggregation area.

3. The method of claim 2, wherein the quantum dot-labeled polyimide coating layer interface diffusion behavior detection method is characterized by, The fluorescence intensity correction of the plurality of frames of fluorescence microscopic images based on the aggregation correction model to obtain a corrected fluorescence microscopic image comprises: according to the aggregation correction model, acquire the fluorescence intensity correction coefficient of each pixel point in the local aggregation area, wherein the fluorescence intensity correction coefficient is negatively correlated with the aggregation intensity factor; multiply the original fluorescence intensity of each pixel point in the local aggregation area in the plurality of frames of fluorescence microscopic images by the corresponding fluorescence intensity correction coefficient to obtain the corrected fluorescence intensity; based on the corrected fluorescence intensity, generate the corrected fluorescence microscopic image.

4. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 1, characterized in that, The acquisition of the interface diffusion characteristic parameters of the polyimide coating layer sample comprises: perform gradient analysis on the fluorescence intensity of each pixel point in the corrected fluorescence microscopic image to obtain the fluorescence intensity gradient vector of the quantum dot markers; According to the direction and length of the fluorescence intensity gradient vector, a diffusion direction and a diffusion rate of the quantum dot marker are determined; Based on the diffusion direction and the diffusion rate, an interface diffusion distance and a diffusion uniformity of the polyimide coating sample within a preset detection period are calculated, which are denoted as the interface diffusion characteristic parameters.

5. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 1, characterized in that, The spatial clustering analysis on the candidate aggregated pixel points is performed to obtain a connected region formed by the candidate aggregated pixel points, which is denoted as the local aggregated region, including: The spatial coordinates, the fluorescence intensity change rate, and the time sequence correlation of each candidate aggregated pixel point in the multiple frames of fluorescence microscopic images are obtained, a multi-dimensional feature vector of the candidate aggregated pixel point is constructed, and a dynamic feature distance between any two candidate aggregated pixel points is calculated according to the multi-dimensional feature vector; Adaptive spatial clustering analysis is performed based on the dynamic feature distance, specifically including: the candidate aggregated pixel points in the multiple frames of fluorescence microscopic images with a dynamic feature distance less than a preset initial distance threshold are classified into the same initial clustering region; for each initial clustering region, the variance of the fluorescence intensity change rate of all the candidate aggregated pixel points in the initial clustering region is calculated, the preset initial distance threshold is adaptively adjusted based on the variance to obtain an adaptive distance threshold of the initial clustering region, and clustering is performed again based on the adaptive distance threshold to obtain a connected region formed by the candidate aggregated pixel points; The connected region is subjected to time sequence consistency verification, and a region with a mean value of the time sequence correlation less than a preset correlation threshold is removed to obtain a preliminary local aggregated region; The preliminary local aggregated region is subjected to morphological optimization to obtain a local aggregated region.

6. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 1, characterized in that, The surface modification of the quantum dot marker includes: A layer of hydrophobic organic ligand is coated on the surface of the quantum dot marker, and the hydrophobic organic ligand is used to enhance the dispersion uniformity of the quantum dot marker in the polyimide coating sample; The molecular chain length of the hydrophobic organic ligand matches the molecular chain length of the polyimide coating sample.

7. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 1, characterized in that, The interface diffusion behavior of the polyimide coating sample is detected based on the interface diffusion characteristic parameters, including: Standard interface diffusion characteristic parameters of a standard polyimide coating sample are obtained; The Euclidean distances of the interface diffusion characteristic parameters of the polyimide coating sample and the standard interface diffusion characteristic parameters are calculated respectively, and the abnormal degree of the interface diffusion behavior of the polyimide coating sample is determined based on the size of the Euclidean distances.

8. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 4, characterized in that, The interface diffusion distance and the diffusion uniformity of the polyimide coating sample within a preset detection period are calculated based on the diffusion direction and the diffusion rate, including: According to the diffusion direction, an interface diffusion path of the polyimide coating sample is determined, and a cumulative length of the interface diffusion path is calculated, which is denoted as the interface diffusion distance; The variance of the fluorescence intensity distribution of the quantum dot marker in the corrected fluorescence microscopic image is counted, and the diffusion uniformity is obtained based on the ratio of the variance to the mean value of the fluorescence intensity.

9. The quantum dot label-based polyimide coating layer interfacial diffusion behavior detection method according to claim 5, characterized in that, The morphological optimization of the preliminary local aggregation region comprises: Based on the boundary contour of the preliminary local aggregation region, a curvature distribution of the boundary contour is calculated, high curvature points in the boundary contour are identified according to the curvature distribution, the preliminary local aggregation region is segmented by taking the high curvature points as segmentation points to obtain segmented sub-regions, and for each sub-region, a ratio of the area of the sub-region to the total area of the candidate aggregation pixel points in the multiple frames of fluorescence microscopic images is calculated, and if the ratio is less than a preset proportion threshold, the sub-region is removed, and finally the local aggregation region is obtained.

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