A Quantitative Evaluation Method for the Dispersion Effect of Carbon Nanomaterials in a Transparent Solution

Abstract

The present invention discloses a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution. S1, Select a dispersion of CNM and obtain a photograph through microphotography; S2, Perform binarization processing on the photograph to obtain connected regions; S3, Fill the connected regions of the current photograph and obtain the area value, the number value, and the ratio of the area of the connected regions to the area of the photograph image. Compared with the existing evaluation methods for the dispersion effect of carbon nanomaterials, the present invention combines image recognition with microscopic observation of the CNM dispersion liquid, and can obtain a more clear and intuitive CNM dispersion effect diagram through image processing, while obtaining a data report on the agglomeration situation of CNM, including parameters such as the number of agglomerates, the size of each agglomerate, and the ratio of the area of the agglomerates to the area of the picture, which helps to quickly compare and judge the dispersion quality. Experimental personnel can directly give analysis data, and there is no need to dilute the solution, and the dispersion situation can be directly and truly reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of nanomaterials, and in particular, to a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution. Background Art

[0002] Inorganic cementitious materials have the disadvantages of low tensile strength and poor strain capacity, and are considered quasi-brittle materials. Adding fibers of different scales can inhibit the initiation and propagation of cracks and achieve the enhancement and toughening of cementitious materials. Carbon nanomaterials (CNM), including graphene oxide (GO), carbon nanotubes (CNT), and carbon nanofibers (CNF), are nanoscale carbon fibers with characteristics such as small size, high strength and hardness, and large specific surface area, and can improve the mechanical properties, fracture properties, etc. of materials from the nanoscale.

[0003] The uniform dispersion of CNM is the key and prerequisite for it to become a material reinforcement and interact with the matrix. CNM without any treatment often exhibits hydrophobicity and cannot be directly dissolved in water. Therefore, researchers have used physical methods, chemical methods, etc. to help its dispersion, including mechanical stirring, ball milling dispersion, ultrasonic treatment, acid treatment, adding surfactants, etc. The most commonly used dispersion method at present is to add surfactants and supplement with ultrasonic dispersion. Among them, the surfactant chemically modifies CNM through non-covalent bonds, uses the hydrophobic groups in the surfactant molecular structure to coat CNM, and forms electrostatic repulsion or steric hindrance between the hydrophilic groups to help CNF disperse; ultrasonic dispersion belongs to a physical dispersion method, which uses ultrasonic energy to excite the cavitation effect, causes the bubbles in the dispersion liquid to burst, and breaks up the aggregates.

[0004] Although these methods help the dispersion of CNM in water and overcome the aggregation formed under the action of van der Waals forces, existing studies have found that the dispersion becomes difficult in alkaline solutions with a complex electrolytic environment. For example, in the pore solution simulating cement hydration, carbon nanomaterials will form obvious aggregates again.

[0005] For the evaluation of the aggregation state of CNM in the above-mentioned solutions, the methods currently used include visual inspection, microscopic observation, calculating the solution concentration after ultraviolet absorbance measurement, zeta potential measurement after solution dilution, etc. There are few quantitative characterization methods that can intuitively understand the number and size of aggregates.

[0006] The existing evaluation methods for the dispersion effect of CNM have the following problems: The evaluation of the dispersion effect of CNM mainly adopts qualitative judgment methods, such as visual inspection method, microscope observation method, etc., to judge the dispersion effect by the naked eye and compare the sizes of the formed aggregates, which is not accurate enough; Ultraviolet absorbance test and zeta potential belong to quantitative methods, but they cannot directly give the results. The ultraviolet absorbance method needs to dilute the solution by about 50 times, measure the absorbance value and then calculate to obtain the solubility of CNM, and then compare the dispersion effect based on this, which is not intuitive enough; The zeta potential test also needs dilution because it cannot directly measure the potential value under strong alkaline solutions. However, the pH value has a very significant impact on the zeta potential itself, and the detection after dilution cannot reflect the true potential situation of the dispersion liquid. Summary of the Invention

[0007] The present invention aims to provide a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution to solve or improve at least one of the above technical problems.

[0008] In view of this, the first aspect of the present invention provides a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution.

[0009] The first aspect of the present invention provides a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution, including: S1, selecting a dispersion liquid of carbon nanomaterials (CNM) and obtaining a photo through microphotography; S2, performing binarization processing on the photo to obtain connected domains; S3, filling the connected domains of the photo and obtaining the area value, the number value, and the ratio of the area of the connected domains to the area of the photo image; wherein, the area of the connected domain is equal to the area of the aggregation region of CNM.

[0010] The quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution provided by the present invention, compared with the existing evaluation methods for the dispersion effect of carbon nanomaterials, combines image recognition with microscopic observation of the CNM dispersion liquid. While being able to obtain a clearer and more intuitive CNM dispersion effect diagram through image processing, it can also obtain a data report on the CNM aggregation situation, including parameters such as the number of aggregates, the size of each aggregate, and the ratio of the aggregate area to the picture area, which helps to quickly compare and judge the dispersion quality;

[0011] Directly output various required data, enabling experimenters to directly give analysis data without the need for operations such as recalculation;

[0012] The method of the present invention can directly operate on the solution without diluting the solution, and can directly and truly reflect the dispersion situation, facilitating the subsequent obtaining and use of results.

[0013] In addition, the technical solution provided by the embodiment of the present invention may also have the following additional technical features:

[0014] In any of the above technical solutions, the steps of S1 specifically include: S11, dropping a preset range of CNM dispersion droplets on a glass slide and covering them with a coverslip; S12, after standing for 3 min - 5 min until the particles are stationary, taking photos using an optical microscope at a magnification of 150 - 400; wherein, the preset range is 10 droplets of 0.04 ml - 0.05 ml, and 8 to 12 photos are taken for each droplet.

[0015] In this technical solution, by dropping multiple droplets of CNM dispersion on the glass slide, then covering them with a coverslip, and taking 8 to 12 photos for each droplet respectively, it helps to obtain photos with image quality meeting the requirements in subsequent screening, avoiding the interference of uncertain factors during shooting, which may cause the photos to be unusable and affect the progress of the experiment.

[0016] In any of the above technical solutions, before the step of S2, the steps of S1 further include: S13, automatically identifying the scale and pixel information of the image in the photo and calibrating the actual size of the image area; S14, judging the severity of the noise in the photo, adjusting the window size of the filter according to the severity, and optimizing the photo.

[0017] In this technical solution, in order to avoid the image quality of the photo affecting the subsequent processing and information extraction of the image content, pre - judgment and processing of the photo image noise are carried out. By adjusting the window size of the filter, photos with different noise levels can be optimized to the quality required for subsequent information extraction.

[0018] In any of the above technical solutions, the steps of S2 specifically include: S21, determining the threshold for globally segmenting the photo image using a preset method; S22, globally segmenting the photo image into a background and a foreground according to the threshold and obtaining a binary image; wherein, the foreground is a set of multiple connected domains.

[0019] In this technical solution, by pre - selecting a set threshold to segment the photo image, the image becomes a binary image with a foreground and a background, so as to distinguish the parts where CNM agglomerates from other places in the image, facilitating the extraction of information such as the area and quantity of this part in the subsequent process.

[0020] In any of the above technical solutions, S21 specifically includes: S211, when the image brightness distribution of the picture is uniform, the threshold is the segmentation threshold, the preset method is the maximum inter-class variance method, and the segmentation threshold is determined by it; S212, when the image brightness distribution of the picture is non-uniform, the threshold is the local threshold, the preset method is the local adaptive method, and the local threshold is determined by it.

[0021] In this technical solution, due to the different shapes and distributions of CNM agglomerates, they may be concentrated or distributed in small areas everywhere. Different methods can be used to determine the threshold according to different photo image content forms for the binarization of the photo, which can enhance the success of content differentiation. When the brightness distribution is uniform, the maximum inter-class variance method is used to determine the segmentation threshold, and when the image brightness distribution is non-uniform, the local adaptive method is used to determine the local threshold.

[0022] In any of the above technical solutions, the steps of the maximum inter-class variance method specifically include: dividing the pixels of the photo into two categories C0 and C1 with the segmentation threshold T, where T takes values in the range [0, L - 1], and using the following formula to calculate the optimal segmentation threshold of the image:

[0023] σ B 2 = w0(u0 - u T ) 2 + w1(u1 - u T ) 2 = w0w1(u0 - u1) 2

[0024] where, σ B is the inter-class variance between the foreground and the background, w0 is the proportion of the number of foreground points in the image, u0 is the average gray value of the number of foreground points in the image, w1 is the proportion of the number of background points in the image, u1 is the average gray value of the number of background points in the image, u T is the average gray value of the entire image, and L is the maximum gray value of the image.

[0025] In this technical solution, when the background brightness distribution of the picture is uniform, assuming the number of pixels of the image is N, the gray range is [0, L - 1], and the number of pixels corresponding to the gray level i is n i , the probability is:

[0026] p i = n i / N, i = 1, 2, 3,..., L - 1

[0027]

[0028] The pixels are divided into two classes, C0 and C1, using a segmentation threshold T. C0 consists of pixels with gray values in the range [0, T], and C1 consists of pixels with gray values in the range [T + 1, L - 1]. The mean of the image is calculated as:

[0029]

[0030] The means of C0 and C1 are:

[0031]

[0032]

[0033] where w0 and u0 are the proportion of foreground points in the image and the average gray value respectively; w1 and u1 are the proportion of background points in the image and the average gray value respectively. The calculation formulas for w0 and w1 are:

[0034]

[0035]

[0036] From equations (2)-(5), we can get:

[0037] u T = w0u0 + w1u1 (7)

[0038] The between-class variance is defined as:

[0039] σ B 2 = w0(u0 - u T ) 2 + w1(u1 - u T ) 2 = w0w1(u0 - u1) 2 (8)

[0040] T takes values in the range [0, L - 1]. When the value obtained from equation (8) is the maximum between-class variance, T is the best.

[0041] In any of the above technical solutions, the steps of the local adaptive method specifically include: assuming that the gray value at the pixel point (i, j) is f(i, j), and T(i, j) is the local threshold, taking the surrounding (2k + 1)×(2k + 1) region centered on the pixel point (i, j) and calculating using the following formula:

[0042]

[0043] Among them, (2k + 1) represents the side length of the region, k is a natural number, x is the offset of the pixel point on the abscissa, y is the offset of the pixel point on the ordinate, T(i, j) is the local threshold of the pixel point (i, j), Z is the gray value offset adjustment amount, and it is a constant.

[0044] In this technical solution, when the brightness distribution of the picture background is uneven, a binary image can be obtained by combining the local adaptive method. Among them, the core theory of calculating its local threshold T(i, j) by the local adaptive method is: Suppose the gray value at the pixel point (i, j) is f(i, j). Take the weighted average value of the pixels in the surrounding (2w + 1)×(2w + 1) region centered on (i, j) and subtract an offset adjustment amount C (constant) to obtain T(i, j).

[0045] In any of the above technical solutions, the steps of S3 specifically include: S31, prepare a two-dimensional integer array equal to the number of pixels of the picture to record the distribution of connected regions in the picture, and select a pixel point in any area of the photo as the seed pixel point; S32, search for adjacent pixels along the circumferential direction of the seed pixel point, and determine whether the adjacent pixels are the boundary pixel points of the connected region through a preset judgment criterion; S33, when the pixel points in the photo are determined, the corresponding element storage values in the two-dimensional array are determined. After obtaining the above two-dimensional array, the area and number of the connected regions surrounded by the boundary pixel points can be obtained by traversing the two-dimensional array, and further obtain the proportion of the connected region in the image area of the photo and the aggregate area distribution histogram.

[0046] In this technical solution, by selecting any point in the photo as the seed pixel point and searching for adjacent pixels in the eight directions of up, down, left, right, upper left, lower left, upper right, and lower right until all pixel points in the photo are determined, the filling inside the photo image can be completed.

[0047] Specifically, on the basis of the seed filling method, the distribution of connected regions is marked with a two-dimensional array, and relevant values such as quantity and area are determined through the characteristics in the obtained two-dimensional array;

[0048] Moreover, the area, number, etc. are directly and automatically output by the algorithm without manual calculation. The area ratio, histogram, etc. can be obtained through simple quantity statistics and basic operations, and can be directly implemented by the program. For the calculation of other methods, such as the absorbance method, the measured value is the absorbance, and the solubility needs to be calculated manually through the absorbance to characterize the dispersion effect. Therefore, this method is more convenient and fast in terms of result output.

[0049] Specifically, the two-dimensional integer array has the following characteristics:

[0050] ① The integer numbers stored in the corresponding elements of the pixels not belonging to the connected domain are negative, and the integer numbers stored in the corresponding elements of the pixels within the connected domain are positive;

[0051] ② The integer numbers stored in the corresponding elements of the pixels within the same connected domain have the same value;

[0052] ③ The integer numbers stored in the corresponding elements of the pixels in different connected domains have different values.

[0053] In any of the above technical solutions, the preset judgment criterion is: obtaining the gray values of its three components according to the reference color C set by the seed pixel point, C r 、C g 、C b , setting the maximum color error between two pixel points with similar colors as d, then r, g, b should satisfy:

[0054] max(abs(r - C r ), abs(g - C g ), abs(b - C b )) = d

[0055] Among them, if d does not satisfy the above formula, this pixel point is the boundary pixel point, otherwise it is a non - boundary pixel point.

[0056] In this technical solution, the pixel points are judged through the above formula. When the maximum color error d is greater than the values of r, g, b in the above formula, then this pixel point is the boundary pixel point, and the judgment can be carried out through the above formula.

[0057] The additional aspects and advantages of the embodiments according to the present invention will become apparent in the following description part, or be learned through the practice of the embodiments according to the present invention.

[0058] The beneficial effects of the present invention are as follows:

[0059] 1. It can quantitatively and accurately analyze the dispersion effect of the dispersion liquid, and realize quantitative comparison by identifying the number, size, and area ratio of the aggregates in the microscopic picture of the dispersion liquid.

[0060] 2. It can directly give the analysis data without the need for operations such as recalculation.

[0061] 3. It does not dilute the solution and truly reflects the dispersion situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention.

[0063] Figure 1 It is the technical route diagram of the photo - analysis of the present invention;

[0064] Figure 2 Original images of PC, PVP, and MC of the present invention without treatment;

[0065] Figure 3 Images of PC, PVP, and MC of the present invention after binarization processing;

[0066] Figure 4 Histogram of the aggregate area distribution of MC, PC, and PVP of the present invention. Detailed implementation manners

[0067] To more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below in conjunction with the drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0068] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0069] As Figures 1-3 shown, an embodiment of the first aspect of the present invention provides a quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution, including:

[0070] S1, Select a dispersion of carbon nanomaterials (CNM) and obtain a photo through microphotography, including:

[0071] S11, Drop a preset amount of the CNM dispersion on a glass slide and cover it with a coverslip;

[0072] S12, After standing for 3 min - 5 min until the particles are stationary, use an optical microscope to take a photo at a magnification of 150 - 400;

[0073] S13, Automatically identify the scale and pixel information of the image in the photo and calibrate the actual size of the image area;

[0074] S14, Judge the severity of the noise in the photo, adjust the window size of the filter according to the severity, and optimize the photo.

[0075] In this embodiment, as Figure 2 shown, in order to avoid the influence of the image quality of the photo on the subsequent processing of the image content and information extraction, a preliminary judgment and processing of the photo image noise are performed. By adjusting the window size of the filter, photos with different noise levels can be optimized to the quality required for subsequent information extraction.

[0076] S2. Perform binaryzation on the photo to obtain connected components, including:

[0077] S21. Determine the threshold for globally segmenting the photo image using a preset method;

[0078] S22. Globally segment the photo image into background and foreground according to the threshold, and obtain a binary image; where the foreground is a set of multiple connected components.

[0079] In this embodiment, as Figure 3 shown, segment the photo image by pre-selecting a set threshold, so that the image becomes a binary image with foreground and background, in order to distinguish the parts where CNM agglomerates from other places in the image, facilitating the extraction of information such as the area and quantity of this part in the subsequent process.

[0080] S211. When the image brightness distribution of the picture is uniform, the threshold is the segmentation threshold, the preset method is the maximum inter-class variance method, and the segmentation threshold is determined by it;

[0081] The steps of the maximum inter-class variance method specifically include: divide the pixels of the photo into two categories C0 and C1 with the segmentation threshold T, T takes values in the range of [0, L - 1], and use the following formula to calculate the optimal segmentation threshold of the image:

[0082] σ B 2 = w0(u0 - u T ) 2 + w1(u1 - u T ) 2 = w0w1(u0 - u1) 2

[0083] where, σ B is the inter-class variance between foreground and background, w0 is the proportion of foreground points in the image, u0 is the average gray value of foreground points in the image, w1 is the proportion of background points in the image, u1 is the average gray value of background points in the image, u T is the average gray value of the entire image, and L is the maximum gray value of the image.

[0084] When the background brightness distribution of the picture is uniform, assume the number of pixels of the image is N, the gray range is [0, L - 1], and the number of pixels corresponding to the gray level i is n i , and the probability is:

[0085] p i = n i / N, i = 1, 2, 3,..., L - 1

[0086]

[0087] The pixels are divided into two classes, C0 and C1, using a segmentation threshold T. C0 consists of pixels with gray values between [0, T], and C1 consists of pixels between [T + 1, L - 1]. The mean of the image is calculated as:

[0088]

[0089] The means of C0 and C1 are:

[0090]

[0091]

[0092] where w0 and u0 are the proportion of foreground points in the image and the average gray value respectively; w1 and u1 are the proportion of background points in the image and the average gray value respectively. The calculation formulas for w0 and w1 are:

[0093]

[0094]

[0095] From equations (2)-(5), we can get:

[0096] u T = w0u0 + w1u1 (15)

[0097] The between-class variance is defined as:

[0098] σ B 2 = w0(u0 - u T ) 2 + w1(u1 - u T ) 2 = w0w1(u0 - u1) 2 (16)

[0099] T takes values in the range of [0, L - 1]. When the value obtained from equation (8) is the maximum between-class variance, T is the best.

[0100] S3. Fill the connected regions of the photo and obtain the area value, the number value, and the ratio of the area of the connected regions to the area of the photo image, including:

[0101] S31. Prepare a two-dimensional integer array equal to the number of pixels in the picture to record the distribution of connected regions in the picture, and select a pixel point in any area of the photo as the seed pixel point;

[0102] S32. Search for adjacent pixels along the circumference of the seed pixel point and determine whether the adjacent pixels are boundary pixel points of the connected region through a preset judgment criterion;

[0103] S33. After the pixel points in the photo are determined, the stored values of the corresponding elements in the two-dimensional array are determined. After obtaining the above two-dimensional array, the area and number of connected regions surrounded by the boundary pixel points can be obtained by traversing the two-dimensional array, and further the proportion of the connected region in the image area of the photo and the aggregate area distribution histogram can be obtained.

[0104] In this embodiment, an arbitrary point in the photo is selected as the seed pixel point, and adjacent pixels are searched in eight directions: up, down, left, right, upper left, lower left, upper right, and lower right, until all pixel points in the photo are determined. Thus, the filling inside the photo image can be completed.

[0105] The preset judgment criterion is: obtaining the gray values of its three components according to the reference color C set by the seed pixel, C r 、C g 、C b . Setting the maximum color error between two pixel points with similar colors as d, then r, g, and b should satisfy:

[0106] max(abs(r - C r ), abs(g - C g ), abs(b - C b )) = d

[0107] Among them, if d does not satisfy the above formula, this pixel point is the boundary pixel point, otherwise it is a non-boundary pixel point.

[0108] In this embodiment, when the image brightness distribution of the picture is uniform, the maximum inter-class variance method is used to determine the segmentation threshold, and the binary segmentation of the photo image is performed according to the segmentation threshold.

[0109] Embodiment 1

[0110] Another embodiment of the first aspect of the present invention provides a method for quantitatively evaluating the dispersion effect of carbon nanomaterials in a transparent solution, including:

[0111] The selected CNM is carbon nanofiber CNF, but not limited to CNF, and all GO and CNT are applicable; regarding the solution, the present invention mainly elaborates on the evaluation of the dispersion effect in NaOH solution, and it is applicable in other transparent solution media such as alkaline solutions and acid solutions.

[0112] The CNF used in the present invention is commercially available XFM60, with a diameter of 50 - 200 nm and a length of 1 - 15 μm, produced by chemical vapor deposition. Three commonly used nanofiber dispersants are selected, namely methyl cellulose (MC), Sika 540P high-performance polycarboxylate water reducer (PC), and polyvinylpyrrolidone (PVP).

[0113] First, calculate the materials required to prepare the dispersion according to a water-cement ratio of 0.30, a CNF mass fraction of 0.05%, and a NaOH concentration of 8 mol / L. Among them, distilled water is equally divided into two parts, one for preparing the CNF dispersion and the other for preparing the NaOH solution. The specific operation process is as follows:

[0114] (1) Weigh 0.17 g of ground CNF with an electronic balance, and weigh 3 different surfactants at a fixed ratio of 6:1, that is, 1.02 g of each;

[0115] (2) Dissolve the surfactant in 50 g of distilled water and stir manually for 3 min;

[0116] (3) Add CNF to the surfactant solution and stir for 2 min. Finally, put the prepared dispersion into an ultrasonic crusher (the specific equipment model is the commercially available VCX800), disperse for 30 min, set the amplitude to 80%, run for 15 s, pause for 15 s, and set the upper temperature limit to 60 °C to prevent agglomeration or flocculation of the surfactant caused by excessive temperature.

[0117] After ultrasonic treatment, add the NaOH solution to each dispersion to prepare a CNF alkaline solution. They are named MC-Na, PC-Na, and PVP-Na respectively, representing the NaOH solutions with MC, PC, and PVP as surfactants.

[0118] Then, according to the above steps, take micrographs at a magnification of 160×.

[0119] Automatically identify the scale and pixel information of the image in the photo, and calibrate the actual size of the image area;

[0120] Judge the severity of the noise in the photo, adjust the window size of the filter according to the severity, and optimize the photo.

[0121] Perform binarization processing on the photo to obtain connected regions, including:

[0122] Use a preset method to determine the threshold for globally segmenting the photo image;

[0123] Globally segment the photo image into background and foreground according to the threshold, and obtain a binarized image; among them, the foreground is a set of multiple connected regions.

[0124] Fill the connected regions of the current photo and obtain the area value, the number value, and the ratio of the area of the connected regions to the area of the photo image, including:

[0125] Select a pixel point in any area of the photo as the seed pixel point;

[0126] Search for adjacent pixels along the circumferential direction of the seed pixel, and determine whether the adjacent pixel is a boundary pixel of the connected domain through a preset judgment criterion;

[0127] After all the pixels in the photo have been determined, calculate the area and number of the connected domains surrounded by the boundary pixels, and further obtain the proportion of the connected domain in the image area of the photo and the histogram of the aggregate area distribution.

[0128] Embodiment 2

[0129] As Figures 1-3 shown, another embodiment of the first aspect of the present invention provides a method for quantitatively evaluating the dispersion effect of carbon nanomaterials in a transparent solution, which is only different from Embodiment 2 in that step s211 is replaced by s212, and includes:

[0130] When the image brightness distribution of the picture is uneven, the threshold is a local threshold, the preset method is a local adaptive method, and the local threshold is determined through it.

[0131] The steps of the local adaptive method specifically include: assuming that the gray value at the pixel point (i, j) is f(i, j), and T(i, j) is the local threshold, taking the surrounding (2k + 1)×(2k + 1) area centered on the pixel point (i, j) and calculating using the following formula:

[0132]

[0133] where, (2k + 1) represents the side length of the area, k is a natural number, x is the offset of the pixel point on the abscissa, y is the offset of the pixel point on the ordinate, T(i, j) is the local threshold of the pixel point (i, j), and Z is the gray value offset adjustment amount and is a constant.

[0134] In this embodiment, when the image brightness distribution of the picture is uneven, the local adaptive method is used to determine the local threshold, and the binary segmentation of the photo image is performed according to the local threshold.

[0135] As Figure 4 shown, the data in Table 1 can be obtained, which can quantitatively and accurately analyze the dispersion effect of the dispersion liquid, and realize quantitative comparison by identifying the number, size, proportion of the picture area, etc. of the aggregates in the microscopic picture of the dispersion liquid.

[0136] Table 1 Aggregate-related data (maximum inter-class variance method)

[0137] MC-Na PC-Na PVP-Na <![CDATA[Total number of aggregates / mm 2 > 1819 1710 1818 <![CDATA[Area > 1000μm 2 Number of aggregates]]> 113 14 11 Aggregate area proportion (%) 18.84 4.83 6.00 <![CDATA[Average area of aggregates (μm 2 )]]> 325 87 105 <![CDATA[Maximum area of the aggregate (μm 2 )]]> 30598 7345 52013

[0138] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention.

[0139] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A quantitative evaluation method for the dispersion effect of carbon nanomaterials in a transparent solution, characterized in that, Including: S1, select the dispersion of carbon nanomaterials CNM and obtain a photo through microphotography; S2, perform binarization processing on the photo to obtain connected regions; S3, fill the connected regions of the photo and obtain the area value, the number value, and the ratio of the area of the connected regions to the area of the photo image; Wherein, the area of the connected region is equal to the area of the agglomeration region of CNM; The steps of S2 specifically include: S21, use a preset method to determine the threshold for globally segmenting the photo image; S22, segment the photo image globally into background and foreground according to the threshold and obtain a binarized image; Wherein, the foreground is a set of multiple connected regions; S21 specifically includes: S211, when the image brightness distribution of the picture is uniform, the threshold is the segmentation threshold, the preset method is the maximum between-class variance method, and the segmentation threshold is determined through it; S212, when the image brightness distribution of the picture is non-uniform, the threshold is the local threshold, the preset method is the local adaptive method, and the local threshold is determined through it; The steps of S3 specifically include: S31, prepare a two-dimensional integer array equal to the number of pixels of the picture to record the distribution of connected regions in the picture, and select a pixel point in any area of the photo as the seed pixel point; S32, search for adjacent pixels along the circumference of the seed pixel point and determine whether the adjacent pixels are boundary pixel points of the connected region through a preset judgment criterion; S33, when the pixel points in the photo are determined, the corresponding element storage values of the two-dimensional array are determined. After obtaining the above two-dimensional array, the area and the number of the connected regions surrounded by the boundary pixel points can be obtained by traversing the two-dimensional array, and further the ratio of the area of the connected region to the area of the photo image and the agglomerate area distribution histogram can be obtained; The steps of the maximum between-class variance method specifically include: Divide the pixels of the photo into two categories C0 and C1 with the segmentation threshold T, T takes values in the range of [0, L - 1], and use the following formula to calculate the optimal segmentation threshold of the image: Among them, is the between-class variance of the foreground and background, w0 is the proportion of foreground points in the image, u0 is the average gray value of foreground points in the image, w1 is the proportion of background points in the image, u1 is the average gray value of background points in the image, and u T is the average gray value of the entire image, and L is the maximum gray value of the image; ； The steps of the local adaptive method specifically include: Suppose the gray value at the pixel point (i, j) is f(i, j), T(i, j) is the local threshold, and take the surrounding (2k + 1)×(2k + 1) area centered on the pixel point (i, j) and calculate using the following formula: Wherein, (2k + 1) represents the side length of the area, k is a natural number, x is the offset of the pixel point on the abscissa, y is the offset of the pixel point on the ordinate, T(i, j) is the local threshold of the pixel point (i, j), Z is the gray value offset adjustment amount, and is a constant.

2. The quantitative evaluation method for the dispersion effect of a carbon nanomaterial in a transparent solution according to claim 1, wherein The steps of S1 specifically include: S11, drop a preset amount of CNM dispersion on a glass slide and cover it with a coverslip; S12, after standing for 3 min - 5 min until the particles are stationary, use an optical microscope to take photos at a magnification of 150 - 400; Wherein, the preset amount is 10 droplets of 0.04 ml - 0.05 ml, and 8 to 12 photos are taken for each droplet.

3. The quantitative evaluation method for the dispersion effect of a carbon nanomaterial in a transparent solution according to claim 2, characterized in that, Before S2, the steps of S1 further include: S13, automatically identify the scale and pixel information of the image in the photo and calibrate the actual size of the image area; S14. Determine the severity of the noise in the photo, adjust the window size of the filter according to the severity, and perform optimization processing on the photo.

4. A quantitative evaluation method for the dispersion effect of a carbon nanomaterial in a transparent solution according to claim 1, characterized in that, The preset judgment criterion is as follows: Obtain the grayscale values of the three components based on the reference color C set by the seed pixel, C r , C g , C b . Set the maximum color error between two pixel points with similar colors as d, then r, g, and b should satisfy: Among them, if d does not satisfy the above formula, this pixel point is a boundary pixel point; otherwise, it is a non-boundary pixel point.

Images