A quantitative method for characterizing the dispersion of cnts in resin-based composite systems

Abstract

The application provides a quantitative method for characterizing dispersion of CNTs in a resin-based composite material system, relates to the field of image processing, and comprises the following steps: obtaining a two-dimensional distribution image of CNTs in a resin-based composite material, applying a gamma grayscale transformation to a grayscale image and generating a gradient amplitude image; realizing binarization by using a function im2bw; identifying a target region formed by a pixel set with the same pixel grayscale value and being connected with each other; dividing the target region into rectangular image units according to the common multiple of the horizontal and vertical resolutions; calculating the area proportion of CNTs in each image unit, flattening all the area proportion matrices of the units into a vector Q, and calculating the variance and standard deviation. The application solves the problem in the prior art that due to the lack of unified, objective and quantitative evaluation means, the dispersion of CNTs in a resin-based composite material is often analyzed by relying on artificial experience or subjective judgment, so that the characterization result is highly subjective and it is difficult to accurately reflect the real distribution of CNTs in the matrix.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and particularly relates to a quantitative method for representing dispersion of CNTs in a resin-based composite material system. BACKGROUND

[0002] At present, the evaluation method for dispersion of CNTs in a resin-based composite material system usually relies on electronic or optical imaging technology (such as ultraviolet-visible spectroscopy, optical microscopy, scanning electron microscopy SEM and transmission electron microscopy TEM), first acquires the distribution characteristics of CNTs in a resin matrix, and then performs qualitative analysis in combination with indexes such as aggregation degree and distribution uniformity.

[0003] For example, Yanhui Wang and other scholars qualitatively evaluated the dispersion effect of MWCNTs in an epoxy resin when evaluating the dispersion of MWCNTs functionalized by o-benzene triol in the epoxy resin by using ultraviolet-visible spectroscopy and optical microscopic observation method; Xiaohui Bao and other scholars qualitatively observed the dispersion state of functionalized CNTs in an epoxy resin when exploring the thermal degradation behavior of a modified carbon nanotube reinforced epoxy resin composite by using scanning electron microscopy (SEM); Amit K. Chakraborty and other scholars systematically represented the dispersion state of CNTs in a bisphenol F-based epoxy resin from mixing, curing to the formation of a final composite material by using an optical microscope.

[0004] In summary, the technical problem in the prior art is that the representation method based on imaging technology combined with artificial observation relies on subjective experience of researchers, it is difficult to objectively and uniformly define the uniformity of the distribution of CNTs in a resin matrix, and it is difficult to accurately reflect the improvement effect of different modification or dispersion processes on the dispersion state of CNTs by using a quantitative index. SUMMARY

[0005] The purpose of the present application is to provide a quantitative method for representing dispersion of CNTs in a resin-based composite material system, so as to solve the technical problem in the prior art that the representation method based on imaging technology combined with artificial observation relies on subjective experience of researchers, it is difficult to objectively and uniformly define the uniformity of the distribution of CNTs in a resin matrix, and it is difficult to accurately reflect the improvement effect of different modification or dispersion processes on the dispersion state of CNTs by using a quantitative index.

[0006] In view of the above problems, the application provides a quantitative method for characterizing the dispersion of CNTs in a resin-based composite system, wherein the image from a high-resolution imaging device is collected to obtain a two-dimensional distribution image of CNTs in the resin-based composite, the two-dimensional distribution image is a TIF image containing an Alpha channel, and the Alpha channel is deleted to convert the image into a grayscale image; a gamma grayscale transformation is applied to the grayscale image to achieve nonlinear enhancement of the image contrast; the enhanced image is input into an image derivation method based on a Sobel operator to calculate a convolution factor Gx along the horizontal direction and a convolution factor Gy along the vertical direction, and generate a gradient amplitude image to obtain a CNTs edge feature image; the edge feature image is converted into a binary image using a function im2bw, and an area threshold constraint condition is applied to the binary image to screen out small area noise regions; the binary image satisfying the area threshold condition is subjected to connected domain recognition and segmentation to identify a target region formed by a pixel set with the same pixel grayscale value and mutual connectivity; resolution analysis is performed on the target region, the target region is set to have a horizontal resolution W and a vertical resolution H, and the target region is divided into m x n rectangular image units according to the common divisor of the horizontal and vertical resolutions; the area proportion of CNTs in each image unit is calculated, and all the unit area proportion matrices are flattened into a vector Q, and the variance and standard deviation are calculated to quantify the dispersion uniformity of CNTs in the two-dimensional resin plane.

[0007] The one or more technical solutions provided in the application have at least the following beneficial effects:

[0008] By automatically extracting the features of the two-dimensional microscopic image of CNTs and combining standard deviation analysis, the problem of relying on subjective judgment of researchers in the traditional method is avoided, and the uniformity of the distribution of CNTs in the resin-based composite system is uniformly and objectively defined. A quantitative index based on standard deviation is established, which can directly reflect the improvement effect of different modification methods or dispersion processes on the dispersion state of CNTs, and overcomes the deficiency of lack of quantitative index in the prior art. By combining image processing and data analysis, the repeatability of the characterization process and the stability of the analysis results are ensured, thereby improving the scientificity and reliability of the evaluation of CNTs dispersion. The method of the application is not only suitable for the dispersion evaluation of CNTs in an epoxy resin matrix, but also can be extended to other types of resin-based composite systems, and has strong universality and application value. By providing accurate and intuitive quantitative results, the method of the application can provide a scientific basis for the optimization of CNTs dispersion process, and is helpful to promote the development and application of CNTs modification, dispersion and composite material preparation and other related technologies.

[0009] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0011] Figure 1 This is an image (original image) showing the distribution characteristics of CNTs inside the CNTs / PPESK composite film obtained in Embodiment 1 of the present invention.

[0012] Figure 2 for Figure 1 Image after gamma transformation;

[0013] Figure 3 for Figure 2 The image calculated using the Sobel operator;

[0014] Figure 4 for Figure 3 Image after thresholding and binarization;

[0015] Figure 5 for Figure 4 Image after noise reduction;

[0016] Figure 6 for Figure 5 The image after image region segmentation and standard deviation calculation;

[0017] Figure 7 This is the image before processing using the wet ball milling method in Embodiment 2 of the present invention; Figure 7 Image (a) shows the distribution of CNTs in the thin film without ball milling treatment; Figure 7 Image (b) shows the distribution of CNTs in the film after ball milling for 2 hours; Figure 7 (c) in the image represents the distribution of CNTs in the film after ball milling for 4 hours; Figure 7 (d) in the image represents the distribution of CNTs in the film after ball milling for 6 hours;Figure 7 (e) in the image represents the distribution of CNTs in the film after ball milling for 8 hours; Figure 7 (f) in the image represents the distribution of CNTs in the film after ball milling for 10 hours; Figure 7 In the image, (g) represents the distribution of CNTs in the film after ball milling for 12 hours; Figure 7 (h) in the image represents the distribution of CNTs in the film after 14 hours of ball milling. Figure 7 In the image, (i) represents the distribution of CNTs in the film after ball milling for 16 hours; Figure 7 In the image, (j) represents the distribution of CNTs in the film after ball milling for 18 hours; Figure 7 In the image, (k) represents the distribution of CNTs in the film after ball milling for 20 hours;

[0018] Figure 7 This is an image processed by wet ball milling in Embodiment 2 of the present invention; Figure 8 Image (a) in the image represents the dispersion characterization results of CNTs in the film without ball milling treatment; Figure 8 Image (b) in the image represents the dispersion characterization results of CNTs in the thin film after ball milling for 2 hours; Figure 8 (c) in the image represents the dispersion characterization result of CNTs in the film after ball milling for 4 hours; Figure 8 (d) in the image represents the dispersion characterization result of CNTs in the film after ball milling for 6 hours; Figure 8 (e) in the image represents the dispersion characterization result of CNTs in the film after ball milling for 8 hours; Figure 8 In the image, (f) represents the dispersion characterization result of CNTs in the thin film after ball milling for 10 hours; Figure 8 In the image, (g) represents the dispersion characterization result of CNTs in the thin film after ball milling for 12 hours; Figure 8 (h) in the image represents the dispersion characterization result of CNTs in the film after ball milling for 14 hours; Figure 8 In the image (i), the dispersion characterization result of CNTs in the film after ball milling for 16 hours is shown. Figure 8 In the image, (j) represents the dispersion characterization result of CNTs in the thin film after ball milling for 18 hours; Figure 8 In the image, (k) represents the dispersion characterization result of CNTs in the film after ball milling for 20 hours;

[0019] Figure 9 This is a flowchart of Embodiment 1 of the present invention. Detailed Implementation

[0020] The application solves the technical problems in the prior art that it is difficult to objectively determine the uniformity of CNTs distribution due to the dependence on manual observation and the lack of quantitative indicators, resulting in the lack of scientificity and universality of dispersion evaluation by providing a quantitative method for characterizing the dispersion of CNTs in a resin-based composite system. The area distribution characteristics of CNTs in each local region are determined by image microelement division and black pixel ratio calculation of the two-dimensional microscopic image of CNTs, and a standard deviation quantitative characterization model is constructed, thereby improving the accuracy, objectivity and repeatability of dispersion evaluation.

[0021] Below, the technical solutions in the application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the example embodiments described here. Based on the embodiments of the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application. In addition, it should be noted that only parts related to the application are shown in the drawings for convenience of description.

[0022] Embodiment one

[0023] The application provides a quantitative method for characterizing the dispersion of CNTs in a resin-based composite system, as shown in the accompanying Figure 9 The quantitative method for characterizing the dispersion of CNTs in a resin-based composite system specifically includes the following steps:

[0024] An image from a high-resolution imaging device is collected to obtain a two-dimensional distribution image of CNTs in a resin-based composite material, and the two-dimensional distribution image is a TIF image containing an Alpha channel. The Alpha channel is deleted, and the image is converted to a gray-scale image.

[0025] Specifically, a high-resolution imaging device (such as a scanning electron microscope or a high-resolution optical microscope) is used to image a resin-based composite sample to obtain a two-dimensional distribution image containing CNTs distribution information. The two-dimensional distribution image is saved in TIF format and contains an Alpha channel. First, the fourth channel (Alpha channel) value is deleted, and the four-channel image (RGB+Alpha) is converted to a gray-scale image. For example, a 3D digital optical microscope with a model RH-2000 produced by Haoshi Corporation is used to obtain a distribution characteristic image of CNTs inside a CNTs / PPESK composite film with a magnification of 1000 times, a mass fraction of 5wt%, and a thickness of 1mm, as shown in the accompanying Figure 1The color image is converted into a gray image by the method of histogram equalization. The processing step can effectively reduce the influence of color dimension on image analysis and enhance the performance of CNTs dispersion characteristics in the image.

[0026] A gamma gray scale transformation is applied to the gray image to achieve nonlinear enhancement of image contrast.

[0027] According to the plurality of heterogeneous storage units, data synchronization conflict identification is performed on the multi-source heterogeneous data set to determine the data unit to be synchronized.

[0028] Further, the application further comprises the following steps: obtaining a gray image processed by an Alpha channel; setting a gamma coefficient γ and a compensation coefficient eps according to the contrast requirement of the target image; applying a gamma transformation formula to each pixel gray value g as: f1(g)=f1(g)= ; wherein g is the gray value of the initial image pixel, f1(g) is the gray value of the image after gray transformation; and the gamma enhanced gray image is output for subsequent edge extraction or dispersion analysis.

[0029] Specifically, in order to enhance the contrast between CNTs and the resin background, a gamma gray scale transformation method is adopted; the transformation formula is to perform multiplication operation on each pixel value of the original image, and in the calculation process, the influence of the offset is usually considered, so the expression is usually written as formula (1). Wherein g is the gray value of the initial image pixel, f1(g) is the gray value of the image after gray transformation; eps represents the compensation coefficient, which is automatically set according to the expected gray range; γ represents the Gamma coefficient, which is used to adjust the gray mapping relationship;

[0030] f1(g)= .

[0031] When γ<1, the dark details can be enhanced, and when γ>1, the bright region can be suppressed. Through the gamma enhancement processing, the identifiable of CNTs distribution characteristics in the image can be effectively improved, and the precision and robustness of dispersion analysis can be improved. For example, the gray image obtained in step 100 is input into an image processing software (such as MATLAB or Python OpenCV library), and a gamma transformation formula f1(g)= is applied to each pixel gray value g, and γ is set to 5 to achieve nonlinear enhancement of image contrast. The enhanced gray image can obviously highlight the boundary between CNTs and background resin.

[0032] The enhanced image is input into an image derivation method based on a Sobel operator, and the convolution factor along the horizontal direction and the convolution factor along the vertical direction are calculated, and a gradient amplitude image is generated to obtain a CNTs edge feature image, as shown in FIG. 2B.Figure 2 as shown.

[0033] Further, the application further comprises the following steps: setting the position pointer of the upper left corner pixel of the gray image as (0, 0), and setting (i, j) as the pixel position of the i-th row and the j-th column in the image;

[0034] For the pixel point (i, j), a 3x3 neighborhood matrix A(i, j) is selected with (i, j) as the center, and the differential gray value along the horizontal direction at (i, j) is calculated (i, j) and the differential gray value along the vertical direction at (i, j) is calculated (i, j), and the formula is:

[0035] = ×A(i, j);

[0036] Wherein, A(i, j) is a 3x3 gray matrix with (i, j) as the center;

[0037] = ×A(i, j);

[0038] Based on and , the gradient amplitude is calculated to form a gradient amplitude image, and the formula is:

[0039] ;

[0040] Wherein, f2(i, j) is the gray value of the pixel at (i, j) in the derived image, and the gray values of all pixel points constitute the gradient amplitude image;

[0041] The gradient amplitude image is output as the CNT edge feature image, which is used for subsequent binarization and dispersity analysis.

[0042] Specifically, after the image contrast is nonlinearly enhanced by the gamma coefficient, the image gray range is adjusted; then the image derivation method based on Sobel operator is applied to the image after gray transformation, as shown in the accompanying drawings. Figure 3 The method can be represented as formula:

[0043] ;

[0044] Let (i, j) be the pixel position of the i-th row and j-th column in the image, the position pointer of the pixel in the upper left corner of the gray image is set as (0, 0), f2(i, j) is the gray value of the pixel at (i, j) in the derived image, for the pixel (i, j), a 3*3 neighborhood matrix A(i, j) is selected with (i, j) as the center, and the difference gray value of (i, j) along the horizontal direction is calculated (i,j) and the difference gray value along the vertical direction (i, j),

[0045] = ×A(i, j);

[0046] Wherein, A(i, j) is a 3*3 gray matrix with (i, j) as the center;

[0047] = ×A(i, j);

[0048] Wherein, And are the convolution factors of Sobel operator along the horizontal direction and the vertical direction, and the mathematical expression is:

[0049] ;

[0050] The mathematical expression of A(i, j) is:

[0051] ;

[0052] After pixel-by-pixel calculation, a complete gradient amplitude image is obtained. For example, the obtained gamma-enhanced gray image is input into the edge detection algorithm based on Sobel operator, the convolution factor along the horizontal direction And the convolution factor along the vertical direction , to generate a gradient amplitude image. The output result is a CNTs edge feature image, in which the CNTs contour is clear and distinguishable, which can be used for subsequent binarization processing. Compared with the original gray image, the CNTs edge contour of the gradient amplitude image is clearer, which can accurately reflect the distribution boundary of CNTs in the resin matrix composite material, and provides a reliable data basis for subsequent quantitative evaluation of dispersion based on edge distribution density, edge connectivity and other indicators.

[0053] The edge feature image is converted into a binary image by using the function im2bw, and an area threshold constraint condition is applied to the binary image to filter out small area noise regions.

[0054] Furthermore, this application also includes the following steps: normalizing the gradient magnitude image to a preset pixel value range to generate a normalized image. The binarization threshold T is determined based on the normalized image, where T is a fixed pixel value. The function im2bw is called to convert the normalized gradient image into a binary image f3(i,j) based on the binarization threshold T, using the following formula:

[0055] ;

[0056] Connected component labeling is performed on the binarized image f3(i,j) to obtain a set of connected components. Each connected component is a set of pixels that are connected to each other in the image. A pixel area threshold is set for each connected component. Each set of connected components is filtered. If the pixel area of ​​the current connected component is less than the threshold, it is removed from the binarized image. If the pixel area of ​​the current connected component is greater than or equal to the threshold, it is retained as a candidate target region.

[0057] Specifically, the obtained binarized image is further subjected to connected component analysis and area thresholding to remove noise regions and extract effective edge regions related to the CNT distribution. First, the image calculated by Sobel is transformed to grayscale to facilitate the extraction of CNT pixels. Then, the grayscale image is converted into a binarized image using the formula for binarization, as shown in the appendix. Figure 4 , Figure 5 As shown, the function "im2bw" is used to convert the grayscale image to a binary image and fill the holes in the binary image.

[0058] The gradient magnitude image is normalized to a preset pixel value range to generate a normalized image. ;

[0059] A binarization threshold T is determined based on the normalized image, wherein the binarization threshold T is a fixed pixel value;

[0060] The function `im2bw` is called to convert the normalized gradient image into a binary image `f3(i, j)` based on the binarization threshold `T`, using the following formula:

[0061] ;

[0062] Connected component labeling is performed on the binarized image f3(i,j) to obtain a set of connected components, where each connected component is a set of pixels in the image that are connected to each other.

[0063] Set the pixel area threshold t for connected components grayFor each set of connected components, a filter is applied. If the pixel area of ​​the current connected component is less than a threshold, it is removed from the binarized image; if the pixel area of ​​the current connected component is greater than or equal to the threshold, it is retained as a candidate target region. A connected component is a set of pixels that are spatially adjacent to each other in the image and have the same pixel value. By labeling connected components, the binarized image can be divided into several independent connected regions, each representing a candidate structure in the image. For example, the edge image is input into the function im2bw, with a threshold... =0.2, converting the image into a binary image. An area threshold constraint is applied to the binary image, setting the minimum area to 50 pixels, to remove isolated small noise regions, resulting in a denoised binary image. The denoised binary image is then subjected to connected component identification and segmentation, marking interconnected regions with the same pixel value to form independent target regions. Each connected component represents a cluster or distribution region of CNTs, providing an accurate regional basis for micro-element analysis. Significant impurity noise signals are observed in the image, exhibiting high density and small-scale (sub-millimeter) spatial distribution. To suppress this type of noise interference, this study applies an area threshold constraint to the binary image based on connected component analysis theory. Connected component identification and extraction is a crucial step in this image processing, used to identify and segment connected regions in the image.

[0064] The target region is analyzed for resolution. Let the horizontal resolution of the target region be L and the vertical resolution be W. The target region is divided into m×n rectangular image elements according to the common divisor of the horizontal and vertical resolutions.

[0065] Furthermore, this application also includes the following steps: performing resolution analysis on the target region, assuming the horizontal pixel resolution of the image is L and the vertical pixel resolution is W; dividing the image into several rectangular micro-regions according to the resolution, wherein the number of horizontal pixels in each micro-region is denoted as . The vertical pixel count is denoted as h. h is preferably chosen as the common divisor of L and W; the image is divided into m×n infinitesimal regions, where m= n= For each rectangular micro-region, set the coordinates of its top-left vertex in the image as follows: Set the column index of the micro-element region to , For column index, The row index is used; the x and y coordinates of the top left vertex of the image element are... ,as follows: Based on the length and width of the set micro-element Cropping is performed using the horizontal and vertical coordinates, resulting in a number of micro-elements per image. The total number of columns and rows of the segmented micro-elements in the total field of view of the image are respectively set as follows: and The formula for calculating is: = × Each micro-element region is trimmed according to the set length and width, resulting in a total number of micro-elements of m×n.

[0066] Specifically, this invention proposes a second part of quantitative characterization—standard deviation analysis. This analysis still includes two parts—"region segmentation" and "numerical calculation". The first step, region segmentation, involves segmenting the image obtained from the "CNTs feature extraction" part into regions. The number of pixels in the horizontal and vertical directions of a unit region, i.e., the length and width of the image micro-element, is usually taken as the common divisor of the number of pixels in the horizontal and vertical directions of the total resolution.

[0067] First, set the length and width of the micro-regions according to the image resolution (let the image length be L and the width be W). Then, set the coordinates of the top-left vertex of each rectangular image micro-region in the image to , and set the column index and row index of the image micro-region to and , respectively. Perform resolution analysis on the target region, assuming the horizontal pixel resolution of the image is L and the vertical pixel resolution is W. Based on the resolution, divide the image into several rectangular micro-regions, and denot the horizontal pixel count of each micro-region as . The vertical pixel count is denoted as h. h is preferably chosen as the common divisor of L and W; the image is divided into m×n infinitesimal regions, where m= n= For each rectangular micro-region, set the coordinates of its top-left vertex in the image as follows: Set the column index of the micro-element region to , For column index, The row index is used; the x and y coordinates of the top left vertex of the image element are... ,as follows: Based on the length and width of the set micro-element Cropping is performed using the horizontal and vertical coordinates, resulting in a number of micro-elements per image. The total number of columns and the total number of rows of the segmented micro-elements in the total field of view of the image are set respectively. and The formula for calculating is: = × Each micro-element region is cropped according to the set length and width, resulting in a total of m×n micro-elements. Each rectangular image micro-element consists of a group of adjacent pixel units, which maintain a consistent resolution division accuracy in both the horizontal and vertical directions, as shown in the attached figure. Figure 6As shown. For example, resolution analysis is performed on each target region. Assuming a horizontal resolution of 1000 pixels and a vertical resolution of 1000 pixels, the target region is divided into 10 × 10 = 100 rectangular image elements according to the common divisor of the horizontal and vertical resolutions. Each element is 100 × 100 pixels in size. This division method ensures that the elements have regular shapes and uniform sizes, thus facilitating subsequent region feature extraction and image analysis processing.

[0068] The area ratio of CNTs within each image micro-element is calculated, and the area ratio matrix of all micro-elements is flattened into a vector Q. The variance and standard deviation are calculated to quantify the uniformity of CNT dispersion in the two-dimensional resin plane.

[0069] Furthermore, this application also includes the following steps: for each image micro-element (p, q), calculate the area ratio of the black pixel block; ×100%; of which, The percentage of the area of ​​black pixels, i.e., the first... Liede The proportion of black pixels in a row image micro-element to the total number of pixels in the micro-element; For the first Liede The total number of black pixel blocks in a row image micro-element h is the width of the image micro-element, i.e., the number of horizontal pixels in each micro-element region, and h is the height of the image micro-element, i.e., the number of vertical pixels in each micro-element region. Liede The proportion of black pixels in a row image micro-element to the total number of pixels in the micro-element; For the first Liede The total number of black pixel blocks in a row image micro-element is the width of the image micro-element, i.e., the number of pixels horizontally in each micro-element region; h is the height of the image micro-element, i.e., the number of pixels vertically in each micro-element region; the area proportions of all micro-elements are flattened into a vector Q in row or column order for subsequent statistical analysis; the average area proportion is calculated. : Where μ is the average percentage of black pixels in the image micro-element. The total number of columns of the image elements. The total number of rows of image elements; calculate the dispersibility index of CNTs in the resin-based composite system. : ;in, The dispersion index of CNTs in resin-based composite systems; based on The size of CNTs is used to assess their dispersibility in the matrix. The closer the value is to 0, the more uniform the distribution of CNTs in the matrix.

[0070] Specifically, numerical calculation: quantifying the dispersion of CNTs in the resin matrix is ​​an innovative aspect of this study and a new research method in the process of dispersion analysis and research of composites of reinforcing phase / matrix type; using equation (9) to calculate the area ratio of CNTs in each divided micro-element region, and calculating the variance of the obtained data. In the process of standard deviation calculation, since the quantification target is the uniformity of CNTs dispersion in the "two-dimensional resin plane", it is necessary to first flatten the obtained CNTs pixel block ratio matrix into a vector and then calculate the variance and standard deviation. For each image micro-element (p, q), calculate the area ratio of black pixel blocks. ×100%; of which, The percentage of the area of ​​black pixels, i.e., the first... Liede The proportion of black pixels in a row image micro-element to the total number of pixels in the micro-element; For the first Liede The total number of black pixel blocks in a row image micro-element h is the width of the image micro-element, that is, the number of horizontal pixels in each micro-element region, and h is the height of the image micro-element, that is, the number of vertical pixels in each micro-element region.

[0071] The dispersibility (standard deviation) of CNTs in the resin matrix is ​​characterized by the following formula: flatten the area percentage of all infinitesimal elements into a vector Q in row or column order for subsequent statistical analysis; calculate the average area percentage. : ;

[0072] Where μ is the average percentage of black pixel blocks in the image micro-element. The total number of columns of the image elements. The total number of rows of image elements; calculate the dispersibility index of CNTs in the resin-based composite system. :

[0073] ;

[0074] in, The dispersion index of CNTs in resin-based composite systems;

[0075] according to The size of CNTs is used to assess their dispersibility in the matrix. The closer to 0, the more uniform the distribution of CNTs in the matrix. For example, calculate the area ratio of CNTs in each microelement to the total pixel of the microelement, for example, the CNTs pixel of a certain microelement accounts for 1100 / 10000, and the total ratio is 0.11. Flatten the area ratio of 100 microelements into a vector, and calculate the variance and standard deviation of the vector. For example, the variance is 0.0004, and the standard deviation is 0.02, which is used to quantify the uniformity of the dispersion of CNTs in the two-dimensional plane of the composite film. The smaller the variance / standard deviation, the more uniform the distribution of CNTs.

[0076] In summary, the quantitative method for characterizing the dispersion of CNTs in the resin-based composite material system provided by the present application has the following beneficial effects:

[0077] By collecting TIF images containing Alpha channels and removing Alpha channels, the integrity and processability of the original image information are ensured, and high-quality data basis is provided for subsequent processing. Nonlinear enhancement of the gray-scale image is performed using gamma gray-scale transformation, which effectively improves the contrast between CNTs and the background, making the edge and small distribution features more clear. The edge features of CNTs are extracted using gradient amplitude calculation based on the Sobel operator, which can accurately identify the outline of CNTs in the two-dimensional plane and improve the accuracy of edge detection. The edge image is binarized and subjected to area threshold constraint, effectively eliminating small-area noise regions and improving the signal-to-noise ratio to ensure that subsequent analysis is only for effective CNTs regions. The regions in the binarized image that are connected to each other and have the same pixel value are clearly segmented by the connected region identification and segmentation method to form independent target regions, providing accurate regional information for quantitative analysis. The microelements are divided based on the resolution of the target regions, and the rectangular image microelements are generated according to the greatest common divisor of the horizontal and vertical resolutions, making the statistical CNT area ratio more uniform and repeatable. The CNT area ratio of each microelement is calculated and flattened into a vector, and then the variance and standard deviation are calculated to quantitatively evaluate the uniformity of CNT dispersion, overcoming the subjectivity and low accuracy of traditional manual observation methods. The method of the present application can objectively and quantitatively evaluate the dispersion of CNTs in the resin-based composite material system while ensuring the accuracy of image processing, improving the accuracy and repeatability of the analysis, and providing reliable data support for composite material performance evaluation and process optimization.

[0078] Example 2

[0079] The present application provides a quantitative method for characterizing the dispersion of CNTs in a resin-based composite material system, which uses the mechanical grinding action of a planetary ball mill to reduce the length of carboxylated CNTs, thereby improving the dispersion of CNTs in PPESK. The specific steps include:

[0080] The planetary ball mill of the experiment is an oil-sealed mute vertical planetary ball mill with a model of YXQM-2L and a maximum revolution speed of 800 rpm / min produced by Guangzhou Guirui Company; the grinding tank is a corundum grinding tank with a volume of 250 ml produced by the same manufacturer as the ball mill; the grinding balls are zirconia balls with sizes of 3 mm, 5 mm and 8 mm, and the weight ratio of the grinding balls is 5:3:2. The carbon nanotubes of the experiment are carboxylated CNTs with a model of TNMC3 and a length of 10-30 microns produced by Zhongke Nano. The NMP dispersant is an NMP dispersant with a model of TNNDIS produced by Zhongke Times Nano, which is a white powder dispersant and is more suitable for dispersing carbon nanotubes in an NMP (N-methyl pyrrolidone) solvent.

[0081] Process flow:

[0082] 1. The ball milling method of wet ball milling is adopted; first, a CNTs / NMP dispersant / NMP dispersion liquid with a ratio of 2 g:4 g:80 ml is configured: first, 2 g of CNTs, 4 g of NMP dispersant and 80 ml of NMP solvent are weighed and added into a beaker in sequence, and then the dispersion system is subjected to 5 min of ultrasonic pre-dispersion treatment to uniformly disperse the CNTs in the NMP. The dispersion system is added into a 250 ml ball mill tank to serve as a ball milling medium (the dispersion system after filling the CNTs / NMP solution accounts for about one-third of the total volume of a single mill). The ball milling process is set as a ball milling cycle sequence of forward rotation 1 h+intermittent 30 min+reverse rotation 1 h+intermittent 30 min, a revolution speed of 500 rpm / min, and 11 groups of ball milling experiments with ball milling times of 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, 18 h and 20 h respectively; and the ball milling parameters (such as ball milling speed, ball size and proportion) of each group of parallel experiments are consistent except for the ball milling time. Finally, the CNTs with a typical length are obtained through the above ball milling process. The CNTs (5 wt%) with different lengths and distributions obtained through the above horizontal experiment are laid with PPESK to form a composite film with a thickness of 1 mm, and a high-magnification 3D optical microscope is used to observe the distribution state of the CNTs in the film.

[0083] 2. The quantitative characterization of the dispersion of the CNTs is performed by using the characterization means of the application, and the effect is shown in the accompanying Figure 7 Figure 7 ​The skilled in the art can see that the distribution characteristics of CNTs obtained by optical imaging technology can only be roughly judged by qualitative analysis method to improve the dispersion of CNTs (in PPESK resin) in Example Two and the influence of ball milling time on the dispersion of CNTs. Therefore, it is lack of scientificity and objectivity to analyze the dispersion of CNTs in resin-based composite material system or guide the update iteration of "dispersion improvement process" only by simple electron or optical imaging. Figure 7 (a)-(k) are distribution images of CNTs in the film after ball milling (the numerical value in the lower left corner of each figure is the ball milling time), Figure 7 (a) represents the distribution image of CNTs in the film without ball milling treatment, Figure 7 (b) represents the distribution image of CNTs in the film after ball milling treatment for 2 hours, Figure 7 (c) represents the distribution image of CNTs in the film after ball milling treatment for 4 hours, Figure 7 (d) represents the distribution image of CNTs in the film after ball milling treatment for 6 hours, Figure 7 (e) represents the distribution image of CNTs in the film after ball milling treatment for 8 hours, Figure 7 (f) represents the distribution image of CNTs in the film after ball milling treatment for 10 hours, Figure 7 (g) represents the distribution image of CNTs in the film after ball milling treatment for 12 hours, Figure 7 (h) represents the distribution image of CNTs in the film after ball milling treatment for 14 hours, Figure 7 (i) represents the distribution image of CNTs in the film after ball milling treatment for 16 hours, Figure 7 (j) represents the distribution image of CNTs in the film after ball milling treatment for 18 hours, Figure 7 (k) represents the distribution image of CNTs in the film after ball milling treatment for 20 hours. The above images are obtained by RH-2000 3D digital optical microscope produced by Haoshi Corporation: initial resolution is 1920x1200, magnification is 1000.

[0084] 3、to the attached Figure 8 After the obtained images are subjected to CNTs two-dimensional feature extraction and standard deviation analysis, the CNTs in the field of view are more obvious than the resin background, and the effect is shown in the attached Figure 8 figure, and the non-CNTs "impurities" interfering with "feature extraction and standard deviation analysis" are removed, and the uniformity of CNTs distribution in each field of view and the span difference of the uniformity of CNTs distribution in the same ball milling time span can be accurately quantitatively analyzed according to the standard deviation data results given in the lower left corner of the image, which is beneficial to promote the condition optimization and parameter optimization of ball milling process parameters and other dispersion improvement methods similar to "ball milling". Figure 8(a)-(k) are the dispersion characterization results of CNTs in the thin film (the values in the lower left corner of each figure are the standard deviation obtained by image processing program), Figure 7 (a) is Figure 8 the dispersion characterization results of the distribution images shown in (a), Figure 7 (b) is Figure 8 the dispersion characterization results of the distribution images shown in (b), Figure 7 (c) is Figure 8 the dispersion characterization results of the distribution images shown in (c), Figure 7 (d) is Figure 8 the dispersion characterization results of the distribution images shown in (d), Figure 7 (e) is Figure 8 the dispersion characterization results of the distribution images shown in (e), Figure 7 (f) is Figure 8 the dispersion characterization results of the distribution images shown in (f), Figure 7 (g) is Figure 8 the dispersion characterization results of the distribution images shown in (g), Figure 7 (h) is Figure 8 the dispersion characterization results of the distribution images shown in (h), Figure 7 (i) is Figure 8 the dispersion characterization results of the distribution images shown in (i), Figure 7 (j) is Figure 8 the dispersion characterization results of the distribution images shown in (j), Figure 7 (k) is Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure 8 Figure 7 Figure the dispersion characterization results of the distribution images shown in (k).

[0085] 4、In summary, the application can realize the accurate quantitative characterization of the dispersion of different contents and different types of CNTs in resin-based composite material system through "feature extraction of CNTs in two-dimensional plane" + "standard deviation analysis".

[0086] Obviously, for those skilled in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A quantitative method for characterizing the dispersibility of CNTs in a resin-based composite material system, characterized in that, include: Images from a high-resolution imaging device are acquired to obtain a two-dimensional distribution image of CNTs in a resin-based composite material. The two-dimensional distribution image is a TIF image containing an alpha channel. The alpha channel is then deleted, and the image is converted to a grayscale image. Gamma grayscale transformation is applied to the grayscale image to achieve nonlinear enhancement of image contrast; The enhanced image is input into an image derivation method based on the Sobel operator to calculate the convolution factor along the horizontal direction. and convolution factor along the vertical direction And generate gradient magnitude images to obtain CNT edge feature images; The edge feature image is converted into a binary image using the function im2bw, and an area threshold constraint is applied to the binary image to filter out small noise areas. Connected component identification and segmentation are performed on binarized images that meet the area threshold condition, and the target region is identified by a set of pixels with the same gray value and connected to each other. The target region is analyzed for resolution. Let the horizontal resolution of the target region be L and the vertical resolution be W. The target region is divided into m×n rectangular image elements according to the common divisor of the horizontal and vertical resolutions. The area ratio of CNTs within each image micro-element is calculated, and the area ratio matrix of all micro-elements is flattened into a vector Q. The variance and standard deviation are calculated to quantify the uniformity of CNT dispersion in the two-dimensional resin plane.

2. The quantitative method for characterizing the dispersibility of CNTs in a resin-based composite material system as described in claim 1, characterized in that, Applying gamma grayscale transformation to the grayscale image to achieve nonlinear enhancement of image contrast includes: Obtain the grayscale image after processing with the alpha channel; Set the gamma coefficient according to the contrast requirements of the target image. and compensation coefficient eps; The gamma transform formula applied to the grayscale value g of each pixel is: f1(g)= ; Where g is the gray value of the initial image pixel, and f1(g) is the gray value of the image after gray-level transformation; Output the gamma-enhanced grayscale image for subsequent edge extraction or dispersion analysis.

3. The quantitative method for characterizing the dispersibility of CNTs in a resin-based composite material system as described in claim 1, characterized in that, The method of inputting the enhanced image into the Sobel operator-based image derivation method, calculating the horizontal convolution factor Gx and the vertical convolution factor Gy, and generating a gradient magnitude image to obtain the CNT edge feature image includes: Set the position pointer of the top-left pixel of the grayscale image to (0, 0), and let (i, j) be the pixel position of the i-th row and j-th column in the image; For pixel (i, j), select a 3×3 neighborhood matrix A(i, j) centered on it, and calculate the difference gray value along the horizontal direction at (i, j). (i, j) and the difference in gray values ​​along the vertical direction (i, j), the formula is: = ×A(i, j); Where A(i, j) is a 3×3 grayscale matrix centered at pixel (i, j); = ×A(i, j); based on and The gradient magnitude is calculated and a gradient magnitude image is generated using the following formula: ； Where f2(i,j) is the gray value of the pixel at (i,j) in the derived image, and the gray values ​​of all pixels constitute the gradient magnitude image; The gradient magnitude image is output as the edge feature image of CNTs for subsequent binarization and dispersion analysis.

4. The quantitative method for characterizing the dispersibility of CNTs in a resin-based composite material system as described in claim 1, characterized in that, The edge feature image is converted into a binary image using the function im2bw, and an area threshold constraint is applied to the binary image to filter out small noise regions, including: The gradient magnitude image is normalized to a preset pixel value range to generate a normalized image. ; A binarization threshold T is determined based on the normalized image, wherein the binarization threshold T is a fixed pixel value; The function `im2bw` is called to convert the normalized gradient image into a binary image `f3(i, j)` based on the binarization threshold `T`, using the following formula: ； Connected component labeling is performed on the binarized image f3(i,j) to obtain a set of connected components, where each connected component is a set of pixels in the image that are connected to each other. Set a pixel area threshold for connected components, and filter each set of connected components. If the pixel area of ​​the current connected component is less than the threshold, it is removed from the binarized image. If the pixel area of ​​the current connected component is greater than or equal to the threshold, it is retained as a candidate target region.

5. The quantitative method for characterizing the dispersibility of CNTs in a resin-based composite material system as described in claim 1, characterized in that, A resolution analysis is performed on the target region. Let the horizontal resolution of the target region be L and the vertical resolution be W. The target region is divided into m×n rectangular image elements according to the common divisor of the horizontal and vertical resolutions, including: Perform resolution analysis on the target region, assuming the horizontal pixel resolution of the image is L and the vertical pixel resolution is W; Based on the resolution, the image is divided into several rectangular micro-regions, and the number of horizontal pixels in each micro-region is denoted as . The vertical pixel count is denoted as h. The optimal choice for h is the common divisor of L and W; The image is divided into m×n infinitesimal regions, where m= n= ; For each rectangular micro-element region, set the coordinates of its top-left vertex in the image as follows: Set the column index of the micro-element region to , For column index, Indexed by row number; The x and y coordinates of the top left vertex of the image element are ,as follows: ； Based on the length and width of the set micro-element Cropping is performed using the horizontal and vertical coordinates, resulting in a number of micro-elements per image. The total number of columns and rows of the segmented micro-elements in the total field of view of the image are respectively set as follows: and The formula for calculating is: = × ； Each micro-element region is trimmed according to the set length and width, resulting in a total number of micro-elements of m×n.

6. The quantitative method for characterizing the dispersibility of CNTs in a resin-based composite system as described in claim 1, characterized in that, Calculate the area proportion of CNTs within each image micro-element, and flatten all micro-element area proportion matrices into a vector Q. Calculate the variance and standard deviation to quantify the uniformity of CNT dispersion within the two-dimensional resin plane, including: For each image element (p, q), calculate the area ratio of the black pixel block; ×100％； in, The percentage of the area of ​​black pixels, i.e., the first... Liede The proportion of black pixels in a row image micro-element to the total number of pixels in the micro-element; For the first Liede The total number of black pixel blocks in a row image micro-element h is the width of the image micro-element, that is, the number of horizontal pixels in each micro-element region; h is the height of the image micro-element, that is, the number of vertical pixels in each micro-element region. Flatten all the area proportions of the infinitesimal elements into a vector Q in row or column order for subsequent statistical analysis; Calculate the average area percentage : ； Where μ is the average percentage of black pixel blocks in the image micro-element. The total number of columns of the image elements. This represents the total number of rows of image elements; Calculate the dispersibility index of CNTs in resin-based composite systems. : ； in, The dispersion index of CNTs in resin-based composite systems; according to The size of CNTs is used to assess their dispersibility in the matrix. The closer the value is to 0, the more uniform the distribution of CNTs in the matrix.

Images