Characterization method and system for dispersion uniformity of carbon nanotubes in composite materials

Abstract

The present invention discloses a method and system for characterizing the uniformity of carbon nanotube dispersion in a composite material. The method comprises the following steps: preparing a sample; analyzing the prepared sample under a scanning electron microscope, adjusting the charging effect voltage, and obtaining an electron microscope image showing the conductivity differences between the carbon nanotubes and the resin material; and obtaining a carbon nanotube dispersion characterization index based on the obtained electron microscope image showing the conductivity differences between the carbon nanotubes and the resin material. The method for characterizing the uniformity of carbon nanotube dispersion in a composite material provided herein utilizes the charging effect in a scanning electron microscope to accurately and quantitatively evaluate the dispersion state of the carbon nanotubes at a relatively low magnification, thereby enabling quality evaluation of finished carbon nanotube composite material samples and components.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile material analysis and testing method research, and in particular to a method and system for characterizing the dispersion uniformity of carbon nanotubes in a composite material. Background Art

[0002] Since their discovery, carbon nanotubes (CNTs) have attracted widespread attention due to their excellent mechanical, electrical, and thermal properties. Currently, polymer-based composites prepared using CNTs have been applied in fields such as automotive and aviation. During the preparation of composite materials, the dispersion state of CNTs is a key factor that directly affects the ultimate performance of the material. Currently, there are two main characterization methods: the first is microscopic characterization—morphology—using optical microscopy, scanning electron microscopy, and transmission electron microscopy to directly characterize the structural morphology of CNTs in a dispersed system; the second is spectroscopy, which uses the absorbance curve of a UV-visible spectrophotometer to determine the sample's absorbance value (often the peak value around 260°, the characteristic peak of CNTs). This value is related to the number and volume of nanoparticles per unit volume, and therefore can characterize the degree of dispersion of CNTs.

[0003] In recent years, researchers have paid more and more attention to the characterization method of using morphology to characterize the dispersion state of carbon nanotubes and then numerically processing the obtained images. Numerical processing of images can express the rich information contained in the dispersion state of carbon nanotubes (such as concentration, carbon nanotube diameter, carbon nanotube length, and dispersion conditions) with a single value, thereby achieving a quantitative evaluation of the dispersion state of carbon nanotubes. The literature Carbon, 2011, 49(4): 1473-1478 published a method of using the position randomness index to characterize the dispersion state of carbon nanotubes in scanning electron microscope images. The smaller the position randomness index, the smaller the probability of adjacent carbon nanotubes agglomerating together, and the better the dispersion state of the carbon nanotubes. A fractal dimension-based numerical characterization method for the dispersion state of carbon nanotubes obtains a scanning electron microscope image of the dispersion state of carbon nanotubes in a dispersed system, then uses image processing software to binarize the image, extracts the boundary contours of single carbon nanotubes or carbon nanotube aggregates in the image, and finally uses a box algorithm to calculate the fractal dimension of the processed image. The obtained fractal dimension value is a quantitative description of the rich information in the dispersion state of carbon nanotubes, thereby realizing the numerical characterization of the dispersion state of carbon nanotubes in the dispersed system.

[0004] The existing technologies mainly realize the numerical characterization of the dispersion state of carbon nanotubes in the dispersion system through direct topography, spectroscopy, topography and image processing methods, topography + image processing + random index method and topography + image processing + fractal dimension method. The direct topography method can reflect the disentanglement and size change of carbon nanotube agglomeration in the dispersion system, so it can comprehensively characterize and evaluate the dispersion state of carbon nanotubes. However, the limitation of this method is that it can only qualitatively describe the dispersion state of carbon nanotubes, which makes the topography also have certain deficiencies in evaluating the dispersion state of carbon nanotubes; the spectroscopy method can only evaluate the number of carbon nanotubes in the dispersion system suspension, but cannot explain the size and morphology information of carbon nanotube agglomeration. Therefore, this method has great deficiencies in evaluating the dispersion state of carbon nanotubes; the topography + image processing method can only evaluate the number of carbon nanotubes in the dispersion system suspension, but cannot explain the size and morphology information of carbon nanotube agglomeration. Image processing method Due to the small diameter of carbon nanotubes, within the range of 2-30nm, the direct morphology method needs to identify carbon nanotubes, and its magnification is large and the area taken is small. Therefore, there is a disadvantage that a large sample amount (a large number of reference points) is required, and the calculation error strongly depends on the number of particles, the number of reference points and the grid size; Morphology method + image processing + random index Due to the small diameter of carbon nanotubes, the direct morphology method needs to identify carbon nanotubes, and its scanning electron microscope magnification is required to be large, which makes the image area taken small and the image processing program complex. In order to obtain a more accurate dispersion uniformity, a complex calculation formula needs to be introduced; Morphology method + image processing + fractal dimension is only applicable to the preparation process of carbon nanotube composite materials. The direct morphology method needs to identify carbon nanotubes under the premise of large magnification and small imaging range, and the fractal dimension calculation method is relatively complex.

[0005] To address these technical challenges, direct identification of carbon nanotubes using topography, in particular, requires high magnification, resulting in a small selected area and limiting analysis of the carbon nanotube dispersion state to a microscopic region. To address these technical difficulties, a characterization method is needed that can accurately evaluate the uniformity of carbon nanotube dispersion in composite materials at lower magnifications. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the above-mentioned background technology and to provide a method and system for characterizing the dispersion uniformity of carbon nanotubes in a composite material.

[0007] In a first aspect, the present application provides a method for characterizing the dispersion uniformity of carbon nanotubes in a composite material, comprising the following steps:

[0008] Prepare samples;

[0009] The prepared samples were analyzed under a scanning electron microscope, and the charging effect voltage was adjusted to obtain electron microscope images showing the conductivity differences between the carbon nanotubes and the resin material.

[0010] Based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material, the dispersion characterization index of the carbon nanotubes is obtained.

[0011] According to the first aspect, in a first possible implementation manner of the first aspect, the sample preparation step specifically includes the following steps:

[0012] Prepare standard test specimens;

[0013] Perform impact tests on standard test specimens to obtain samples whose cross sections meet the requirements after the impact test.

[0014] According to the first aspect, in a second possible implementation of the first aspect, the step of analyzing the prepared sample under a scanning electron microscope, adjusting the charging effect voltage, and obtaining a conductivity-differentiated electron microscope image of the carbon nanotubes and the resin material specifically includes the following steps:

[0015] The prepared samples were placed under a scanning electron microscope for analysis, the charging effect voltage was adjusted, and adverse factors affecting image observation were reduced or eliminated to obtain differential conductivity electron microscope images of carbon nanotubes and resin materials.

[0016] According to the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the step of placing the prepared sample under a scanning electron microscope for analysis, adjusting the charging effect voltage and reducing or eliminating adverse factors affecting image observation, and obtaining conductivity-differentiated electron microscope images of the carbon nanotubes and the resin material specifically includes the following steps:

[0017] The prepared samples are analyzed under a scanning electron microscope at an arithmetic progression of accelerating voltages. When the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages is less than a difference threshold, the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression is obtained.

[0018] Comparing the area difference of the non-charge accumulation region corresponding to two adjacent acceleration voltages, and obtaining the adjacent voltage with the minimum area difference of the non-charge accumulation region;

[0019] The larger value of the adjacent voltages with the smallest difference in the area of ​​the non-charge accumulation region is taken as the optimal voltage for the charge effect.

[0020] According to the first aspect, in a fourth possible implementation of the first aspect, the dispersion characterization indicators of the carbon nanotubes in the composite material include dispersion grade grading information, the average carbon nanotube percentage content and the standard deviation of the carbon nanotube distribution, and a normal distribution diagram of the carbon nanotube percentage content in the composite material.

[0021] According to the fourth possible implementation manner of the first aspect, in the fifth possible implementation manner of the first aspect, the step of obtaining a dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material specifically includes the following steps:

[0022] Based on the obtained conductivity difference electron microscope images of carbon nanotubes and resin materials, the dispersion level classification information of nanotubes in the composite material is obtained;

[0023] The obtained conductivity difference electron microscope images of carbon nanotubes and resin materials are input into Photoshop software, and binarization and graphic grid division are performed to obtain the divided graphics. Based on the divided graphics, the average carbon nanotube percentage content and the standard deviation of the carbon nanotube distribution and the normal distribution diagram of the carbon nanotube percentage content in the composite material are obtained.

[0024] According to a fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, the step of obtaining information on the dispersion level of the carbon nanotubes in the composite material based on the obtained electron microscope images of the conductivity differences of the carbon nanotubes and the resin material specifically includes the following steps:

[0025] Acquiring a conductive differential electron microscope image of the carbon nanotube and the resin material at a first magnification;

[0026] Acquire a conductive differential electron microscope image of the carbon nanotube and the resin material at a second magnification;

[0027] Based on the light and dark contrast, distribution and charging phenomenon of the conductive differential electron microscope images of the carbon nanotubes and the resin material at a first magnification and the morphology and distribution of the carbon nanotubes in the conductive differential electron microscope images at a second magnification, the dispersion level grading information of the nanotubes in the composite material is obtained, and the second magnification is greater than the first magnification.

[0028] In a second aspect, the present application provides a system for characterizing the dispersion uniformity of carbon nanotubes in a composite material, comprising:

[0029] A sample preparation module, used for preparing samples;

[0030] An electron microscope analysis module, which is in communication with the sample preparation module and is used to analyze the prepared sample under a scanning electron microscope, adjust the charging effect voltage, and obtain a conductivity-differentiated electron microscope image of the carbon nanotubes and the resin material;

[0031] The dispersion characterization module is in communication with the electron microscope analysis module and is used to obtain the dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material.

[0032] According to the second aspect, in a first possible implementation manner of the second aspect, the electron microscope analysis module includes:

[0033] The differential electron microscope image acquisition unit is communicatively connected to the sample preparation module and is used to place the prepared sample under a scanning electron microscope for analysis, adjust the charging effect voltage and reduce or eliminate adverse factors affecting image observation, and obtain conductive differential electron microscope images of carbon nanotubes and resin materials.

[0034] According to a first possible implementation manner of the second aspect, in a second possible implementation manner of the second aspect, the differential electron microscope image acquisition unit includes:

[0035] a voltage difference acquisition subunit, communicatively connected to the sample preparation module, for analyzing the prepared sample under a scanning electron microscope at an arithmetic progression of accelerating voltages, and acquiring the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression when the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression is less than a difference threshold;

[0036] The optimal voltage acquisition subunit is in communication with the voltage difference acquisition subunit and is used to take the larger value of the adjacent voltages with the minimum difference in the non-charge accumulation area as the charge effect optimal voltage.

[0037] Compared with the prior art, the advantages of the present invention are as follows:

[0038] The method for characterizing the dispersion uniformity of carbon nanotubes in composite materials provided in this application utilizes the charging effect in a scanning electron microscope to accurately and quantitatively evaluate the dispersion state of carbon nanotubes at a relatively low magnification, thereby achieving quality evaluation of finished carbon nanotube composite material samples and components. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a method for characterizing the dispersion uniformity of carbon nanotubes in a composite material according to an embodiment of the present invention;

[0040] Figure 2 is the distribution diagram of carbon nanotubes in the composite material;

[0041] Figure 3 Gridding map for the image;

[0042] Figure 4(a) shows the charging effect of the image at 20KV voltage;

[0043] Figure 4(b) shows the charging effect of the image under 10KV voltage;

[0044] Figure 4(c) shows the charging effect of the image under 5KV voltage;

[0045] Figure 4(d) shows the charging effect of the image under 3KV voltage;

[0046] Figure 4(e) shows the image binarization result at 20KV voltage;

[0047] Figure 4(f) shows the image binarization result at 10KV voltage;

[0048] Figure 4(g) shows the image binarization result at 5KV voltage;

[0049] Figure 4(h) shows the image binarization result at 3KV voltage;

[0050] Figure 5 This is an electron microscope image of a composite material of PE resin and carbon nanotubes observed at 1500X;

[0051] Figure 6 This is an electron microscope image of a composite material of PE resin and carbon nanotubes observed at 20000X;

[0052] Figure 7 Schematic diagram of the binarization processing of the composite image of PE resin and carbon nanotubes;

[0053] Figure 8 Schematic diagram of the total pixels in the segmented image;

[0054] Figure 9 Schematic diagram of the segmentation of carbon tube pixels in the image;

[0055] Figure 10 This is the normal distribution diagram of the dispersion uniformity of the composite material of PE resin and carbon nanotubes;

[0056] Figure 11 This is an electron microscope image of a composite material of PP resin and carbon nanotubes observed at 1500X;

[0057] Figure 12 This is an electron microscope image of the composite material of PP resin and carbon nanotubes observed at 20000X;

[0058] Figure 13 This is the normal distribution diagram of the dispersion uniformity of the composite material of PP resin and carbon nanotubes. DETAILED DESCRIPTION

[0059] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that the present invention is not intended to be limited to those embodiments. On the contrary, it is intended to cover variations, modifications, and equivalents within the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of the two.

[0060] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Note: The following example is only a specific example and is not intended to limit the embodiments of the present invention to the following specific steps, values, conditions, data, sequence, etc. Those skilled in the art can apply the concepts of the present invention to construct more embodiments not described in this specification by reading this specification.

[0062] Existing methods for characterizing the dispersion uniformity of carbon nanotubes in composite materials have the following technical problems: a large magnification factor must be selected, resulting in a small selected area, so that the analysis of the dispersion state of carbon nanotubes is limited to a microscopic area.

[0063] When the high-energy electron beam of a scanning electron microscope hits the surface of a sample, the incident electrons interact with the electrons outside the nuclei of the sample atoms, ionizing the electrons outside the nuclei to produce secondary electrons. If the sample is a conductor, the excess charge will be conducted away through the sample grounding. If the sample is non-conductive, the excess charge cannot be conducted away, resulting in a local charging phenomenon, namely the charging effect. The electrons absorbed by the non-conductive sample will generate an electrostatic field (charging effect), which interferes with the emission of the incident electrons and secondary electrons, thereby producing a series of effects on the scanning electron image, as follows: (1) Abnormal contrast: Due to the charging effect, the emission of secondary electrons is irregularly affected, causing one part of the image to be abnormally bright and the other part to be dark. (2) Image distortion: Due to the electrostatic field, the electron beam is irregularly deflected, resulting in image distortion or phase difference. (3) Image drift: The electron beam produces irregular offset under the action of the electrostatic field, causing image drift. (4) Bright spots and bright lines: Irregular discharge often occurs in charged samples, resulting in irregular bright spots and bright lines in the image. Since the charging effect will affect the imaging quality of the scanning electron microscope, especially the observation of carbon nanotube materials, it is generally necessary to reduce or eliminate the charging phenomenon in the scanning electron microscope (methods to reduce the charging effect: coating the sample, reducing the voltage, low vacuum).

[0064] The present application utilizes the abnormal contrast among the factors affecting the charging effect of a scanning electron microscope, while also eliminating other adverse factors of the charging effect, such as image deformity, image drift, bright spots and bright lines. The method for achieving the above purpose is to utilize the difference in conductivity between carbon nanotubes and resin materials. When observing with a scanning electron microscope: charge accumulation will occur on the surface of the resin material matrix, appearing as a discharge-like white area that cannot be imaged, while the carbon nanotubes have better conductivity, and the image is dark gray and clear. Therefore, the carbon nanotubes can be well distinguished from the resin matrix material at a lower magnification, and the carbon nanotube morphology can be directly identified without a large magnification, such as Figure 1 .

[0065] In view of this, see Figure 2 As shown, an embodiment of the present invention provides a method for characterizing the dispersion uniformity of carbon nanotubes in a composite material, comprising the following steps:

[0066] Step S1, preparing a sample;

[0067] Step S2: analyzing the prepared sample under a scanning electron microscope, adjusting the charging effect voltage, and obtaining a differential electron microscope image of the conductivity of the carbon nanotubes and the resin material;

[0068] Step S3: obtaining a dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material.

[0069] The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material provided in this application has the following technical effects:

[0070] This application utilizes the charging effect of a scanning electron microscope to accurately and conveniently distinguish carbon nanotubes from resin materials at low magnifications. The difference in conductivity between carbon nanotubes and resin materials is that during scanning electron microscope observation, charge accumulates on the surface of the resin matrix, appearing as a discharge-like white area that cannot be imaged. However, carbon nanotubes have better conductivity and appear as a dark gray image with clear images. Therefore, carbon nanotubes can be well distinguished from resin matrix materials at low magnifications, and low-magnification images of the distribution of carbon nanotubes in composite materials can be provided.

[0071] The present application provides a method for quantitatively characterizing the dispersion uniformity of carbon nanotubes in a composite material based on the charging effect of a scanning electron microscope. The quantitative evaluation method includes two steps: first, a direct qualitative evaluation of the carbon nanotube distribution state using a morphology method; second, a low-magnification image of the dispersion of the carbon nanotubes in the composite material is obtained using the charging effect, and the image is processed and calculated to obtain a quantitative value of the dispersion uniformity of the carbon nanotubes in the composite material, thereby achieving a simple and accurate quantitative evaluation of the dispersion state of the carbon nanotubes in the composite material;

[0072] The present application provides a method for quantitatively characterizing the dispersion uniformity of carbon nanotubes in composite materials based on the charging effect of a scanning electron microscope, which can be applied to the quality evaluation of finished carbon nanotube composite material samples and components.

[0073] In one embodiment, the sample preparation step specifically includes the following steps:

[0074] Prepare standard test specimens according to GB / T 11997-2008 "Plastic Multi-purpose Test Specimens";

[0075] Impact tests are performed on standard test specimens according to GB / T 1843-2008 "Determination of Izod Impact Strength of Plastics" to obtain samples whose cross-sections meet the requirements after the impact test. The requirements are that the end faces are flat and there is no obvious plastic deformation. If the cross-section of the specimen does not meet the requirements, such as the cross-section is uneven or the plastic deformation is large, the specimen needs to be immersed in liquid nitrogen at low temperature for several minutes before the impact test is performed again until the cross-section of the test specimen meets the requirements after the impact.

[0076] In one embodiment, the step of analyzing the prepared sample under a scanning electron microscope, adjusting the charging effect voltage, and obtaining a conductivity-differential electron microscope image of the carbon nanotubes and the resin material specifically includes the following steps:

[0077] The prepared samples were placed under a scanning electron microscope for analysis, the charging effect voltage was adjusted, and adverse factors affecting image observation were reduced or eliminated to obtain differential conductivity electron microscope images of carbon nanotubes and resin materials.

[0078] In one embodiment, the step of placing the prepared sample under a scanning electron microscope for analysis, adjusting the charging effect voltage, and reducing or eliminating adverse factors affecting image observation to obtain differential conductivity electron microscope images of the carbon nanotubes and the resin material specifically includes the following steps:

[0079] The prepared sample is analyzed under a scanning electron microscope, and the charging effect is adjusted to a level suitable for observing the image. When the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages is less than a difference threshold, the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in an arithmetic progression is obtained;

[0080] Comparing the area difference of the non-charge accumulation region corresponding to two adjacent acceleration voltages, and obtaining the adjacent voltage with the minimum area difference of the non-charge accumulation region;

[0081] The larger value of the adjacent voltages with the smallest difference in the area of ​​the non-charge accumulation region is taken as the optimal voltage for the charge effect.

[0082] In one embodiment, the step of adjusting the charging effect to be suitable for observing the image is specifically implemented as follows:

[0083] If there is a large charging effect reaction in the microscopic morphology of the sample cross section, such as adverse factors such as image distortion, image drift, bright spots and bright lines, which affect the observation of the entire image, the voltage needs to be further reduced;

[0084] If the charging effect is not obvious in the microscopic morphology of the sample cross section, for example, the abnormal contrast is not obvious: it is impossible to clearly distinguish the white area of ​​charge accumulation on the surface of the resin material matrix and the dark gray color of the carbon nanotubes with good conductivity, it is necessary to increase the voltage until it is adjusted to the charging effect suitable for observing the image, so that the white area of ​​charge accumulation on the surface of the resin material matrix and the dark gray color of the carbon nanotubes with good conductivity can be better distinguished.

[0085] Taking FIG. 4 as an example, the selection criteria for the degree of charge effect in an image are explained.

[0086] (1) Excessive charging effect (large charging effect reaction affects the entire image observation): Figure 4a The voltage shown is 20KV, and the image shows image distortion, image drift, abnormal bright spots and bright lines. After the voltage is reduced to 10KV, the ratio of bright and dark areas in the image changes significantly. Figure 4b ), so it is believed that the charge effect phenomenon in the image is excessive at a voltage of 20KV, and the voltage should be reduced;

[0087] (2) Insufficient charge effect phenomenon: Figure 4d For the image at 3KV, the abnormal contrast is not obvious and there is no obvious white bright area. Therefore, it is necessary to increase the voltage to enhance the charging effect of the image.

[0088] (3) Best choice for charging effect: Figure 4b This is the image under voltage 10KV; Figure 4c For the image under voltage 5KV, the ratio of bright area to dark area does not change much. Therefore, the image with large charge effect is selected without affecting the image analysis (select Figure 4b (Image at voltage 10KV).

[0089] In a more specific embodiment, the larger value of the adjacent voltages with the smallest difference in the non-charge accumulation area is used as the optimal voltage for the charge effect, which is specifically implemented as follows:

[0090] The acceleration voltage of the scanning electron microscope is generally 0.5 to 30 kV (usually around 5 to 20 kV). Therefore, the images taken at the scanning electron microscope voltage of 5 kV, 10 kV, 15 kV and 20 kV (Note: 5 kV, 8 kV, 11 kV, 14 kV, 17 kV, 20 kV can also be selected, but the sequence of voltage values ​​needs to be an arithmetic progression) are binarized using software (such as Figure 4e 、 Figure 4f 、 Figure 4gand Figure 4h ), and the specific results are shown in the table below.

[0091] When performing scanning electron microscope image analysis at a higher acceleration voltage of the scanning electron microscope, there is a large charging effect reaction in the microscopic morphology on the cross section of the sample (adverse factors appear: image deformity, image drift, bright spots and bright lines), which leads to an abnormal increase in the charge accumulation area. After lowering the voltage, this abnormal charging effect is correspondingly reduced. Therefore, when the difference in the area of ​​the non-charge accumulation area corresponding to two adjacent acceleration voltages is less than the difference threshold, the difference threshold is used to limit the difference between the two to be small. When the difference in the area of ​​the non-charge accumulation area corresponding to two adjacent acceleration voltages is small, it means that the influence of the abnormal charging effect is low. Therefore, the difference between the two adjacent values ​​is calculated (ax1-ax2=△x1), and the size of the difference in the area of ​​the non-charge accumulation area corresponding to each adjacent acceleration voltage △x1 is compared, and the minimum value of the difference in the area of ​​the non-charge accumulation area corresponding to each adjacent acceleration voltage is taken. And for the two adjacent voltages in the arithmetic progression with the smallest difference (both voltages are suitable, but the higher the voltage, the better the charging effect, so the larger voltage is taken), the larger voltage is taken as the optimal voltage for the charging effect (for example, the difference in the area of ​​the non-charge accumulation region between 5KV and 10KV is the smallest, so 10KV is taken as the optimal voltage for the charging effect).

[0092] △1=a1-a2=0.676-0.615=0.061;

[0093] △2=a2-a3=0.615-0.488=0.127;

[0094] △3=a3-a4=0.488-0.328=0.16;

[0095] △1<△2<△3;

[0096] Table 2. Arithmetic progression of non-charge accumulation area corresponding to different acceleration voltages

[0097]

[0098] In one embodiment, the dispersion characterization index of the carbon nanotubes in the composite material includes dispersion grade classification information, average carbon nanotube percentage content and standard deviation of carbon nanotube distribution, and a normal distribution diagram of carbon nanotube percentage content in the composite material.

[0099] In one embodiment, the step of obtaining a dispersion index characterizing the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material specifically includes the following steps:

[0100] Based on the obtained conductivity difference electron microscope images of carbon nanotubes and resin materials, the dispersion level classification information of nanotubes in the composite material is obtained;

[0101] The obtained conductivity difference electron microscope images of carbon nanotubes and resin materials are input into Photoshop software, and binarization and graphic grid division are performed to obtain the divided graphics. Based on the divided graphics, the average carbon nanotube percentage content and the standard deviation of the carbon nanotube distribution and the normal distribution diagram of the carbon nanotube percentage content in the composite material are obtained.

[0102] In one embodiment, the step of obtaining the dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material is specifically implemented as follows:

[0103] Input the electron microscope photos that need to be quantitatively analyzed for dispersion into Photoshop software and perform binarization processing. At the same time, use grids to divide the electron microscope images. According to actual needs, the graphic grid division can be 3X3, 4X4 or 5X5, such as Figure 3 As shown;

[0104] The percentage of the dark part (containing carbon nanotubes) in the gridded image was calculated using image processing software. The average percentage and standard deviation of carbon nanotubes were calculated according to formula (1) and formula (2), respectively. The normal distribution diagram of the percentage of carbon nanotubes in the composite material was drawn using SPSS software, and the dispersion uniformity of carbon nanotubes was expressed using the normal distribution diagram:

[0105] The image after a single grid division is processed into black and white binary, where the dark part is the area containing carbon nanotubes. The percentage P of the area occupied by the dark area is calculated using image processing tools. According to this method, the percentage content P of carbon nanotubes in different grid division images is calculated. i , and then calculate the average carbon nanotube percentage according to formula (1).

[0106]

[0107] Where: P average - average value of carbon nanotube content, %;

[0108] N——Number of image division grids (N=m×m);

[0109] P i ——Percentage of carbon nanotubes in a single partition diagram, %;

[0110] The standard deviation of the carbon nanotube distribution is calculated according to formula (2).

[0111]

[0112] Where: σ——standard deviation of carbon nanotube distribution;

[0113] In one embodiment, the step of obtaining information on the dispersion level of the carbon nanotubes in the composite material based on the obtained electron microscope images of the conductivity differences of the carbon nanotubes and the resin material specifically includes the following steps:

[0114] Acquiring a conductive differential electron microscope image of the carbon nanotube and the resin material at a first magnification;

[0115] Acquire a conductive differential electron microscope image of the carbon nanotube and the resin material at a second magnification;

[0116] Based on the light and dark contrast, distribution and charging phenomenon of the conductive differential electron microscope images of the carbon nanotubes and the resin material at a first magnification and the morphology and distribution of the carbon nanotubes in the conductive differential electron microscope images at a second magnification, the dispersion level grading information of the nanotubes in the composite material is obtained, and the second magnification is greater than the first magnification.

[0117] In one embodiment, the step of obtaining information on the dispersion level of the carbon nanotubes in the composite material based on the obtained electron microscope images of the conductivity differences of the carbon nanotubes and the resin material is specifically implemented as follows:

[0118] The sample was observed under a scanning electron microscope at 1500X magnification. The area with the worst carbon nanotube dispersion in the composite material was selected for evaluation and photographed. A representative area within this area was then magnified at 20,000X for further observation and evaluation. The results of the 1500X and 20,000X analyses were combined to determine the dispersion of the carbon nanotubes in the composite material.

[0119] The grading of the dispersion of carbon nanotubes in composite materials is mainly based on the degree of light and dark contrast, distribution and charging phenomenon of the image under an electron microscope at 1500X. At the same time, combined with the morphology and distribution of carbon nanotubes under an electron microscope at 20000X, the dispersion level is divided into four levels. The description of each level is shown in Table 1.

[0120] Table 1 Qualitative evaluation of the dispersion of carbon nanotubes in composite materials

[0121]

[0122]

[0123] In Example 1, the method for characterizing the dispersion uniformity of carbon nanotubes in a composite material provided by the present application was applied to the evaluation of the dispersion of carbon nanotubes in a composite material (3%) of PE resin + carbon nanotubes, and was implemented by the following steps:

[0124] Step 1: Qualitative evaluation

[0125] Figure 5Observed under an electron microscope at 1500X: the black area (carbon nanotube distribution area) and the bright white area with charge accumulation (resin matrix material) have a large difference in brightness and darkness in the image, and the bright white area has a more serious charging phenomenon.

[0126] Figure 6 Observed under an electron microscope at 20000X: The black dot-like clusters are carbon nanotubes. It can be seen that the carbon nanotubes have a certain degree of agglomeration, but no strip-like distribution is seen, indicating that their orientation is good.

[0127] Judging from the qualitative evaluation table, the dispersion of the PE resin + carbon nanotube composite material (3%) is level D, that is, the carbon nanotubes are agglomerated to a certain extent in the PE resin.

[0128] Step 2: Quantitative evaluation

[0129] The PE+CNTs photos obtained by qualitative evaluation ( Figure 5 , 1500X) into photoshop and perform grid division (all images are in 4X4 mode). Figure 7 As shown in FIG, the segmented image is binarized to distinguish the dark area with carbon nanotubes and the non-conductive white area without carbon nanotubes. The percentage of the dark carbon nanotube area P1 = 25% is calculated using graphics processing software, as shown in FIG. Figure 8 and Figure 9 According to the above method, the percentage of carbon nanotubes in the remaining 15 segmented images was calculated, and the average percentage of carbon nanotubes and the standard deviation of carbon nanotube distribution were calculated using statistical software (SPSS software). The results are shown in Table 2. Figure 10 As shown, the dispersion of carbon nanotubes in the composite material is shown using a normal distribution diagram.

[0130] Table 2 Percentage and standard deviation of PE resin + carbon nanotubes

[0131]

[0132] Example 2: The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material provided by this application is applied to the evaluation of the dispersion of carbon nanotubes in a composite material (3%) of PP resin + carbon nanotubes, and is implemented by the following steps:

[0133] Step 1: Qualitative evaluation

[0134] Figure 11 Observed under an electron microscope at 1500X: the image has little difference in brightness and darkness, the charged areas are point-shaped, and are evenly distributed.

[0135] Figure 12 Observed under an electron microscope at 20,000X: The carbon nanotubes appear as black dots and stripes, and are evenly distributed. A small amount of charged white areas can be seen.

[0136] Based on the above analysis and judgment, the dispersion degree of this PP resin + carbon nanotube composite material (3%) is Class B, that is, the carbon nanotubes are uniformly distributed in the PP resin.

[0137] Step 2: Quantitative evaluation

[0138] Will Figure 10 The photos were input into Photoshop, meshed (all images were converted to 4x4 format) and binarized. The percentage of carbon nanotubes in each segmented image was calculated according to formula (1). The average carbon nanotube percentage and the standard deviation of the carbon nanotube distribution were calculated using SPSS software. The results are shown in Table 3. Figure 13 As shown, the dispersion of carbon nanotubes in the composite material can be shown using a normal distribution diagram.

[0139] Table 3 Percentage and standard deviation of PP resin + carbon nanotubes

[0140]

[0141] In a second aspect, the present application provides a system for characterizing the dispersion uniformity of carbon nanotubes in composite materials, comprising a sample preparation module, an electron microscope analysis module and a dispersion characterization module, wherein the sample preparation module is used to prepare samples; the electron microscope analysis module is communicatively connected to the sample preparation module, and is used to analyze the prepared samples under a scanning electron microscope, adjust the charging effect voltage, and obtain conductivity-differentiated electron microscope images of the carbon nanotubes and the resin material; the dispersion characterization module is communicatively connected to the electron microscope analysis module, and is used to obtain dispersion characterization indicators of the carbon nanotubes based on the obtained conductivity-differentiated electron microscope images of the carbon nanotubes and the resin material.

[0142] In one embodiment, the electron microscopy analysis module includes:

[0143] The differential electron microscope image acquisition unit is communicatively connected to the sample preparation module and is used to place the prepared sample under a scanning electron microscope for analysis, adjust the charging effect voltage and reduce or eliminate adverse factors affecting image observation, and obtain conductive differential electron microscope images of carbon nanotubes and resin materials.

[0144] In one embodiment, the differential electron microscope image acquisition unit includes:

[0145] a voltage difference acquisition subunit, communicatively connected to the sample preparation module, for analyzing the prepared sample under a scanning electron microscope at an arithmetic progression of accelerating voltages, and acquiring the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression when the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression is less than a difference threshold;

[0146] The optimal voltage acquisition subunit is in communication with the voltage difference acquisition subunit and is used to take the larger value of the adjacent voltages with the minimum difference in the non-charge accumulation area as the charge effect optimal voltage.

[0147] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.

[0148] The present application implements all or part of the processes in the above-mentioned method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0149] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, all or part of the method steps in the above method are implemented.

[0150] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of a computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0151] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, servers, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0153] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), servers and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0156] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for characterizing the uniformity of dispersion of carbon nanotubes in a composite material, characterized in that: The following steps are involved: Prepare samples; The prepared samples were placed under a scanning electron microscope for analysis, and the charging effect voltage was adjusted to reduce or eliminate adverse factors affecting image observation, thereby obtaining differential conductivity electron microscope images of carbon nanotubes and resin materials. Obtaining a dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material; Placing the prepared sample under a scanning electron microscope for analysis, adjusting the charging effect voltage to reduce or eliminate adverse factors affecting image observation, and obtaining differential conductivity electron microscope images of the carbon nanotubes and the resin material, specifically includes the following steps: The prepared samples are analyzed under a scanning electron microscope at an arithmetic progression of accelerating voltages. When the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages is less than a difference threshold, the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression is obtained. Comparing the area difference of the non-charge accumulation region corresponding to two adjacent acceleration voltages, and obtaining the adjacent voltage with the minimum area difference of the non-charge accumulation region; The larger value of the adjacent voltages with the smallest difference in the area of ​​the non-charge accumulation region is taken as the optimal voltage for the charge effect.

2. The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material according to claim 1, wherein: The sample preparation step specifically includes the following steps: Prepare standard test specimens; Perform impact tests on standard test specimens to obtain samples whose cross sections meet the requirements after the impact test.

3. The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material according to claim 1, wherein: The dispersion characterization indexes of the carbon nanotubes in the composite material include dispersion grade classification information, average carbon nanotube percentage content and standard deviation of carbon nanotube distribution, and a normal distribution diagram of carbon nanotube percentage content in the composite material.

4. The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material according to claim 3, wherein: The step of obtaining the dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material specifically includes the following steps: Based on the obtained conductivity difference electron microscope images of carbon nanotubes and resin materials, the dispersion level classification information of nanotubes in the composite material is obtained; The obtained conductivity difference electron microscope images of carbon nanotubes and resin materials are input into Photoshop software, and binarization and graphic grid division are performed to obtain the divided graphics. Based on the divided graphics, the average carbon nanotube percentage content and the standard deviation of the carbon nanotube distribution and the normal distribution diagram of the carbon nanotube percentage content in the composite material are obtained.

5. The method for characterizing the dispersion uniformity of carbon nanotubes in a composite material according to claim 4, wherein: The step of obtaining information on the dispersion level of the carbon nanotubes in the composite material based on the obtained electron microscope images of the conductivity differences of the carbon nanotubes and the resin material specifically includes the following steps: Acquiring a conductive differential electron microscope image of the carbon nanotube and the resin material at a first magnification; Acquire a conductive differential electron microscope image of the carbon nanotube and the resin material at a second magnification; Based on the light and dark contrast, distribution and charging phenomenon of the conductive differential electron microscope images of the carbon nanotubes and the resin material at a first magnification and the morphology and distribution of the carbon nanotubes in the conductive differential electron microscope images at a second magnification, the dispersion level grading information of the nanotubes in the composite material is obtained, and the second magnification is greater than the first magnification.

6. A system for characterizing the uniformity of dispersion of carbon nanotubes in a composite material, used for executing the method for characterizing the uniformity of dispersion of carbon nanotubes in a composite material according to any one of claims 1 to 5, characterized in that: include: A sample preparation module, used for preparing samples; An electron microscope analysis module, which is in communication with the sample preparation module and is used to place the prepared sample under a scanning electron microscope for analysis, adjust the charging effect voltage, and obtain a conductivity-differentiated electron microscope image of the carbon nanotubes and the resin material; a dispersion characterization module, communicating with the electron microscope analysis module, for obtaining a dispersion characterization index of the carbon nanotubes based on the obtained conductivity difference electron microscope images of the carbon nanotubes and the resin material; The electron microscope analysis module includes: A differential electron microscope image acquisition unit, in communication with the sample preparation module, is used to place the prepared sample under a scanning electron microscope for analysis, adjust the charging effect voltage to reduce or eliminate adverse factors affecting image observation, and obtain a differential electron microscope image of the conductivity of the carbon nanotubes and the resin material; The differential electron microscope image acquisition unit includes: a voltage difference acquisition subunit, communicatively connected to the sample preparation module, for analyzing the prepared sample under a scanning electron microscope at an arithmetic progression of accelerating voltages, and acquiring the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression when the difference in the area of ​​the non-charge accumulation region corresponding to two adjacent accelerating voltages in the arithmetic progression is less than a difference threshold; The optimal voltage acquisition subunit is in communication with the voltage difference acquisition subunit and is used to take the larger value of the adjacent voltages with the minimum difference in the non-charge accumulation area as the charge effect optimal voltage.

Images