Graphene defect detection method, device and equipment and storage medium

Abstract

The invention relates to the field of graphene defect detection, and discloses a graphene defect detection method, device and equipment and a storage medium, and the method comprises the following steps: carrying out surface image acquisition on a graphene detection sample through a scanning electron microscope, and carrying out surface defect analysis and positioning on the surface image of the graphene detection sample; evaluating the surface defect state of the graphene detection sample, and finally performing X-ray diffraction test on the graphene detection sample to determine the lattice state of the graphene detection sample. According to the method, the purpose of defect detection is achieved by performing surface image acquisition and analysis and X-ray diffraction on the graphene detection sample, and the method has important significance in the aspects of evaluating the quality of a graphene material, optimizing a graphene preparation process and the like.

Description

Technical Field

[0001] The present invention relates to the field of graphene defect detection, and in particular to a graphene defect detection method, device, equipment and storage medium. Background Art

[0002] Graphene, also known as single-layer graphite or carbon monolayer, is a two-dimensional material composed of a single atomic layer of carbon atoms. The thickness of graphene is only equivalent to the diameter of a carbon atom, about 0.34 nanometers, making it the thinnest material known. There are intrinsic defects and externally introduced defects in graphene. Since graphene is not a product that exists under natural conditions, but a product that is prepared, various defects are easily introduced during the preparation process, such as mechanical exfoliation and chemical vapor deposition. At the same time, temperature, pressure, reactant concentration, etc. may affect the structure and properties of graphene, thereby causing defects. Graphene defect detection is imperative because when graphene is used in electronic devices, sensors, biomedicine and other fields, defects may affect its stability and reliability. Therefore, defect detection is an important means to ensure application safety. Understanding the causes and distribution of defects will help optimize the preparation process of graphene, reduce the generation of defects, and improve the performance of materials. It is of great significance for evaluating material quality, optimizing preparation processes, exploring new functions, and ensuring application safety. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a graphene defect detection method, device, equipment and storage medium.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides a graphene defect detection method, comprising the following steps: A graphene detection sample is prepared, and a scanning electron microscope is debugged at the same time, and a surface image of the graphene detection sample is acquired through the electron microscope after the device debugging, so as to obtain a surface image of the graphene detection sample; Perform image surface defect analysis and positioning on the surface image of the graphene test sample, determine the location and defect size information of the graphene surface defects, and evaluate the surface defect status of the graphene test sample; An X-ray diffraction test is performed on the graphene test sample, and a lattice state of the graphene test sample is determined after the X-ray diffraction test.

[0005] Furthermore, in a preferred embodiment of the present invention, the graphene detection sample is prepared, and the scanning electron microscope is debugged at the same time, and the surface image of the graphene detection sample is acquired by the electron microscope after the equipment debugging, so as to obtain the surface image of the graphene detection sample, specifically: Obtaining a batch of graphene that needs to be tested, and selecting sample graphene from the batch of graphene that needs to be tested, and calibrating the sample graphene as a graphene sample; Obtain a carbon layer spray gun and graphene slurry, add the graphene slurry into the carbon layer spray gun, and perform equipment debugging on the carbon layer spray gun, wherein the equipment debugging of the carbon layer spray gun is to debug the spraying pressure and spraying speed of the carbon layer spray gun so that the spraying pressure and spraying speed of the carbon layer spray gun meet a preset threshold; Using a carbon layer spray gun, the graphene sample surface is evenly sprayed with graphene slurry. When the graphene sample surface is evenly covered with graphene slurry, a preliminary graphene detection sample is obtained. Drying the preliminary graphene detection sample and obtaining a big data network, retrieving a standard drying time after the graphene slurry is sprayed based on the big data network, stopping the drying of the preliminary graphene detection sample after the standard drying time after the graphene slurry is sprayed, and detecting the conductivity of the preliminary graphene detection sample after drying by a plane resistance meter; If the conductivity of the preliminary graphene detection sample after drying meets the preset threshold, a graphene detection sample is obtained; if the conductivity of the preliminary graphene detection sample after drying does not meet the preset threshold, the preliminary graphene detection sample after drying is repeatedly sprayed; Introducing a scanning electron microscope, preventing graphene detection samples on the scanning electron microscope, and performing equipment debugging on the scanning electron microscope, wherein the equipment debugging on the scanning electron microscope includes adjusting the electron beam, focusing, and magnification; The surface image of the graphene detection sample is obtained through the scanning electron microscope after equipment debugging, and is calibrated as the surface image of the graphene detection sample.

[0006] Furthermore, in a preferred embodiment of the present invention, the surface defect analysis and positioning of the graphene detection sample surface image is performed to determine the location and defect size information of the graphene surface defects, and the surface defect state of the graphene detection sample is evaluated, specifically: The grayscale values ​​of different pixels on the surface image of the graphene detection sample are obtained, and a weighted average method is introduced to perform weighted average calculation on the grayscale values ​​of different pixels on the surface image of the graphene detection sample, so as to obtain the grayscale processed surface image of the graphene detection sample, which is calibrated as the grayscale image of the surface of the graphene detection sample; Wherein, the grayscale image of the surface of the graphene detection sample is mapped in the coordinate space of the imaging plate of the scanning electron microscope; Performing spatial transformation on the grayscale image of the surface of the graphene detection sample, and performing grayscale interpolation transformation on the grayscale image of the surface of the graphene detection sample after spatial transformation based on the nearest neighbor interpolation method, so that the pixels of the grayscale image of the surface of the graphene detection sample are mapped on the integer coordinates of the coordinate space of the imaging plate of the scanning electron microscope; Performing a two-dimensional Fourier transform on the grayscale image of the graphene detection sample surface, and combining the high-pass filtering method to enhance the characteristic edge high-frequency signal of the grayscale image of the graphene detection sample surface, extracting the edge features of the grayscale image of the graphene detection sample surface, and calibrating it as the edge features of the graphene detection sample surface; In the coordinate space of the imaging plate of the scanning electron microscope, the size of the edge features of the graphene detection sample surface is calculated to obtain the size information of the defects on the graphene detection sample surface, and the coordinate position of the defects on the graphene detection sample surface is located; In the big data network, the surface defect state comparison map of graphene is retrieved, and the surface defect size information of the graphene detection sample and the coordinate position of the surface defect of the graphene detection sample are imported into the surface defect state comparison map of the graphene detection sample to obtain the surface defect state of the graphene detection sample.

[0007] Furthermore, in a preferred embodiment of the present invention, the graphene test sample is subjected to an X-ray diffraction test, and the lattice state of the graphene test sample is determined after the X-ray diffraction test, specifically: Introducing an XRD instrument, wherein the XRD instrument is an instrument that can perform X-ray diffraction on a graphene test sample; Determine the maximum particle size of the graphene detection sample supported by the XRD instrument when the graphene detection sample is subjected to X-ray diffraction in the XRD instrument, calibrate it as the target particle size, and place the graphene detection sample in a grinding device for grinding treatment so that the particle size of the graphene detection sample is equal to the target particle size, thereby obtaining graphene detection sample powder; Obtaining a sample holder groove and a pressing glass slide in the XRD instrument, placing graphene detection sample powder in the sample holder groove, and pressing the graphene detection sample powder by pressing the glass slide so that the graphene detection sample powder is evenly distributed in the sample holder groove; In the XRD instrument, an X-ray source diffraction device is aligned with a groove of a sample holder, a cooling water circulation device is started in the XRD instrument, and all X-ray sources that can diffract are determined in the XRD instrument, and the X-ray source with the highest utilization rate when performing X-ray diffraction on graphene is retrieved from a big data network, and calibrated as a target X-ray source, and the area of ​​the groove of the sample holder is determined at the same time, and the diffraction angle range of the X-ray source diffraction device is calculated based on the area of ​​the groove of the sample holder; Based on the diffraction angle range of the X-ray source diffraction device, the target X-ray source is diffracted in the groove of the sample holder by the X-ray source diffraction device, and diffraction test data is collected; Introducing drawing software, importing the diffraction test data into the drawing software, performing data conversion and data difference correction on the diffraction test data through the drawing software, constructing an XRD spectrum of the graphene test sample, and calibrating it as a target XRD spectrum; The target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis results.

[0008] Furthermore, in a preferred embodiment of the present invention, the target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis result, specifically: In the target XRD spectrum, the peak area and peak height of the diffraction peak of the graphene detection sample are calculated, the intensity of the diffraction peak is calculated based on the peak area and peak height of the diffraction peak, and the full width half height of the diffraction peak of the graphene detection sample is calculated at the same time; Determine the wavelength of the target X-ray source, and based on the wavelength of the target X-ray source, the full width at half maximum of the diffraction peak, the diffraction angle range of the X-ray source diffraction device, and the target particle size, introduce the Scherrer formula to calculate the lattice size of the graphene test sample; In the target XRD spectrum, the total number of atomic occupancy and the energy required to form atomic vacancies in the graphene test sample are determined based on the intensity of the diffraction peak, and the corresponding Boltzmann constant and the absolute diffraction temperature of the X-ray source diffraction device in the graphene test sample are retrieved based on the big data network; A mathematical model formula of the lattice defect index of the graphene detection sample is constructed, wherein the mathematical model formula of the lattice defect index of the graphene detection sample is: ; in, is the lattice defect index of the graphene test sample, is the total number of atomic occupancy in the graphene test sample, The energy required to form an atomic vacancy is is the corresponding Boltzmann constant in the graphene test sample, is the absolute diffraction temperature of the X-ray source diffraction device; The lattice state of the graphene test sample is determined in combination with the lattice size of the graphene test sample and the lattice defect index of the graphene test sample.

[0009] Furthermore, in a preferred embodiment of the present invention, the lattice state of the graphene test sample is determined by combining the lattice size of the graphene test sample and the lattice defect index of the graphene test sample, specifically: Retrieving the controllable range of the defect index of the lattice of the graphene test sample at the current lattice size in the big data network, and determining whether the lattice defect index of the graphene test sample is maintained within the controllable range of the defect index of the lattice at the current lattice size; If yes, the lattice state of the graphene test sample is calibrated as qualified; If not, the lattice state of the graphene test sample is marked as unqualified, wherein the lattice state of the graphene test sample is unqualified because the diffraction peak of the graphene test sample is shifted, resulting in lattice distortion; Based on the lattice defect index of the graphene test sample, the lattice distortion position is marked in the target XRD spectrum, and the defect position is located in the graphene test sample based on the lattice distortion position.

[0010] The second aspect of the present invention further provides a graphene defect detection device, characterized in that the defect detection device comprises: A data retrieval module, wherein the data retrieval module is used to retrieve all schemes for defect detection of graphene; A graphene sample drying processing module, wherein the graphene sample drying processing module is used to dry the graphene detection sample sprayed with graphene slurry; A scanning electron microscope module, wherein the scanning electron microscope module is used to obtain a surface defect image of a graphene test sample, and to perform defect location and feature extraction on the surface defect image of the graphene test sample; An X-ray diffraction module is used to perform X-ray diffraction on the graphene test sample and analyze the obtained diffraction test data to determine the lattice state of the graphene test sample; The data processing module includes a memory and a processor. The processor analyzes and processes the data obtained by each module, and the memory can store the data of each module.

[0011] S102: preparing a graphene detection sample, debugging a scanning electron microscope, and acquiring a surface image of the graphene detection sample through the electron microscope after debugging, to obtain a surface image of the graphene detection sample; S104: performing image surface defect analysis and positioning on the surface image of the graphene test sample, determining the location and defect size information of the graphene surface defects, and evaluating the surface defect state of the graphene test sample; S106: Perform an X-ray diffraction test on the graphene test sample, and determine the lattice state of the graphene test sample after the X-ray diffraction test.

[0012] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the surface image of the graphene test sample is acquired by scanning electron microscope, and the surface defect analysis and positioning of the surface image of the graphene test sample is performed, the surface defect state of the graphene test sample is evaluated, and finally the graphene test sample is subjected to X-ray diffraction test to determine the lattice state of the graphene test sample. The present invention achieves the purpose of defect detection by acquiring and analyzing the surface image of the graphene test sample and performing X-ray diffraction, which is of great significance for evaluating the quality of graphene materials and optimizing the graphene preparation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A flow chart of a graphene defect detection method is shown; Figure 2 A flow chart of a method for determining the lattice state of a graphene test sample is shown; Figure 3 A process view of a graphene defect detection device is shown; Figure 4 A process diagram of a graphene defect detection device is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] Figure 1 A flow chart of a graphene defect detection method is shown, comprising the following steps: S102: preparing a graphene detection sample, debugging a scanning electron microscope, and acquiring a surface image of the graphene detection sample through the electron microscope after debugging, to obtain a surface image of the graphene detection sample; S104: performing image surface defect analysis and positioning on the surface image of the graphene test sample, determining the location and defect size information of the graphene surface defects, and evaluating the surface defect state of the graphene test sample; S106: Perform an X-ray diffraction test on the graphene test sample, and determine the lattice state of the graphene test sample after the X-ray diffraction test.

[0018] Furthermore, in a preferred embodiment of the present invention, the graphene detection sample is prepared, and the scanning electron microscope is debugged at the same time, and the surface image of the graphene detection sample is acquired by the electron microscope after the equipment debugging, so as to obtain the surface image of the graphene detection sample, specifically: Obtaining a batch of graphene that needs to be tested, and selecting sample graphene from the batch of graphene that needs to be tested, and calibrating the sample graphene as a graphene sample; Obtain a carbon layer spray gun and graphene slurry, add the graphene slurry into the carbon layer spray gun, and perform equipment debugging on the carbon layer spray gun, wherein the equipment debugging of the carbon layer spray gun is to debug the spraying pressure and spraying speed of the carbon layer spray gun so that the spraying pressure and spraying speed of the carbon layer spray gun meet a preset threshold; Using a carbon layer spray gun, the graphene sample surface is evenly sprayed with graphene slurry. When the graphene sample surface is evenly covered with graphene slurry, a preliminary graphene detection sample is obtained. Drying the preliminary graphene detection sample and obtaining a big data network, retrieving a standard drying time after the graphene slurry is sprayed based on the big data network, stopping the drying of the preliminary graphene detection sample after the standard drying time after the graphene slurry is sprayed, and detecting the conductivity of the preliminary graphene detection sample after drying by a plane resistance meter; If the conductivity of the preliminary graphene detection sample after drying meets the preset threshold, a graphene detection sample is obtained; if the conductivity of the preliminary graphene detection sample after drying does not meet the preset threshold, the preliminary graphene detection sample after drying is repeatedly sprayed; Introducing a scanning electron microscope, preventing graphene detection samples on the scanning electron microscope, and performing equipment debugging on the scanning electron microscope, wherein the equipment debugging on the scanning electron microscope includes adjusting the electron beam, focusing, and magnification; The surface image of the graphene detection sample is obtained through the scanning electron microscope after equipment debugging, and is calibrated as the surface image of the graphene detection sample.

[0019] It should be noted that graphene needs to be defect-tested to ensure that it does not pose risks when used. Graphene defects include defects on the outer surface and defects in the internal lattice. First, the surface defects of graphene are analyzed, and image analysis can be performed by acquiring images. Graphene samples are extracted and images are acquired through a scanning electron microscope. The use of a scanning electron microscope requires that the graphene sample has conductivity, so graphene slurry needs to be sprayed to increase its conductivity, and the spraying needs to be uniform during the spraying period. If the conductivity does not meet the preset value, it needs to be re-sprayed. The drying treatment after spraying can remove solvents and volatiles and improve conductivity. After the scanning electron microscope equipment is debugged, high-definition images of graphene test samples can be acquired.

[0020] Furthermore, in a preferred embodiment of the present invention, the surface defect analysis and positioning of the graphene detection sample surface image is performed to determine the location and defect size information of the graphene surface defects, and the surface defect state of the graphene detection sample is evaluated, specifically: The grayscale values ​​of different pixels on the surface image of the graphene detection sample are obtained, and a weighted average method is introduced to perform weighted average calculation on the grayscale values ​​of different pixels on the surface image of the graphene detection sample, so as to obtain the grayscale processed surface image of the graphene detection sample, which is calibrated as the grayscale image of the surface of the graphene detection sample; Wherein, the grayscale image of the surface of the graphene detection sample is mapped in the coordinate space of the imaging plate of the scanning electron microscope; Performing spatial transformation on the grayscale image of the surface of the graphene detection sample, and performing grayscale interpolation transformation on the grayscale image of the surface of the graphene detection sample after spatial transformation based on the nearest neighbor interpolation method, so that the pixels of the grayscale image of the surface of the graphene detection sample are mapped on the integer coordinates of the coordinate space of the imaging plate of the scanning electron microscope; Performing a two-dimensional Fourier transform on the grayscale image of the graphene detection sample surface, and combining the high-pass filtering method to enhance the characteristic edge high-frequency signal of the grayscale image of the graphene detection sample surface, extracting the edge features of the grayscale image of the graphene detection sample surface, and calibrating it as the edge features of the graphene detection sample surface; In the coordinate space of the imaging plate of the scanning electron microscope, the size of the edge features of the graphene detection sample surface is calculated to obtain the size information of the defects on the graphene detection sample surface, and the coordinate position of the defects on the graphene detection sample surface is located; In the big data network, the surface defect state comparison map of graphene is retrieved, and the surface defect size information of the graphene detection sample and the coordinate position of the surface defect of the graphene detection sample are imported into the surface defect state comparison map of the graphene detection sample to obtain the surface defect state of the graphene detection sample.

[0021] It should be noted that the purpose of graying the surface image of the graphene detection sample is to make the image clearer after graying, eliminate irrelevant information in the image, and restore useful real information. There is a coordinate system in the imaging plate of the electron microscope, and the image needs to be mapped on the integer coordinates of the coordinate system to facilitate the positioning of the defect position in the image and the calculation of the area of ​​the defect. The frequency domain method is used to indirectly enhance the image. The image is regarded as a two-dimensional signal. The two-dimensional Fourier transform can realize signal enhancement processing. Combined with the high-pass filtering method, high-frequency signals such as edges can be enhanced to make the blurred picture clear. Since there is a coordinate system in the imaging plate of the electron microscope, the pre-processed image is analyzed in the coordinate system, and the surface defect size information of the graphene detection sample and the coordinate position of the surface defect of the graphene detection sample are obtained, and the surface defect state of the graphene detection sample is obtained by combining the surface defect state comparison map of graphene. Among them, different surface defect size information and coordinate positions correspond to different surface defect states.

[0022] Figure 2 A flow chart of a method for determining the lattice state of a graphene detection sample is shown, comprising the following steps: S202: performing an X-ray diffraction test on the graphene test sample, and determining a lattice state of the graphene test sample after the X-ray diffraction test; S204: performing a spectrum analysis on the target XRD spectrum, and determining the lattice state of the graphene test sample based on the spectrum analysis result; S206: Determine the lattice state of the graphene test sample based on the lattice size of the graphene test sample and the lattice defect index of the graphene test sample.

[0023] Furthermore, in a preferred embodiment of the present invention, the graphene test sample is subjected to an X-ray diffraction test, and the lattice state of the graphene test sample is determined after the X-ray diffraction test, specifically: Introducing an XRD instrument, wherein the XRD instrument is an instrument that can perform X-ray diffraction on a graphene test sample; Determine the maximum particle size of the graphene detection sample supported by the XRD instrument when the graphene detection sample is subjected to X-ray diffraction in the XRD instrument, calibrate it as the target particle size, and place the graphene detection sample in a grinding device for grinding treatment so that the particle size of the graphene detection sample is equal to the target particle size, thereby obtaining graphene detection sample powder; Obtaining a sample holder groove and a pressing glass slide in the XRD instrument, placing graphene detection sample powder in the sample holder groove, and pressing the graphene detection sample powder by pressing the glass slide so that the graphene detection sample powder is evenly distributed in the sample holder groove; In the XRD instrument, an X-ray source diffraction device is aligned with a groove of a sample holder, a cooling water circulation device is started in the XRD instrument, and all X-ray sources that can diffract are determined in the XRD instrument, and the X-ray source with the highest utilization rate when performing X-ray diffraction on graphene is retrieved from a big data network, and calibrated as a target X-ray source, and the area of ​​the groove of the sample holder is determined at the same time, and the diffraction angle range of the X-ray source diffraction device is calculated based on the area of ​​the groove of the sample holder; Based on the diffraction angle range of the X-ray source diffraction device, the target X-ray source is diffracted in the groove of the sample holder by the X-ray source diffraction device, and diffraction test data is collected; Introducing drawing software, importing the diffraction test data into the drawing software, performing data conversion and data difference correction on the diffraction test data through the drawing software, constructing an XRD spectrum of the graphene test sample, and calibrating it as a target XRD spectrum; The target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis results.

[0024] It should be noted that the XRD instrument is an instrument that can perform X-ray diffraction. The graphene sample to be tested needs to be dry and pollution-free, meet the requirements of X-ray diffraction, and suitable samples, such as graphene powder, film, etc., need to be selected according to the purpose of the test. After the graphene test sample is ground into powder, its maximum particle size needs to be determined. If the particle size is larger or smaller, it will affect the accuracy of the diffraction data. The function of the sample rack groove is to store the graphene test sample powder, and the function of pressing the glass slide is to make the powder flat and easier to diffract. The purpose of ensuring the normal operation of the cooling water circulation device is to provide a stable cooling environment for the instrument, and based on the diffraction angle range of the X-ray source diffraction device, the target X-ray source is diffracted in the sample rack groove through the X-ray source diffraction device, and the diffraction test data is collected. Among them, the diffraction angle range is determined according to the placement area of ​​the powder. Different diffraction angles will result in different data such as the strength of the lattice after obtaining the XRD spectrum. The target XRD spectrum intuitively describes the lattice state of the graphene test sample. The drawing software performs data conversion and data difference correction on the diffraction test data to construct the target XRD spectrum. The lattice state of the graphene test sample is the state between the atoms in the graphene test sample, and it indicates whether there is ectopicity or misalignment between the atoms.

[0025] Furthermore, in a preferred embodiment of the present invention, the target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis result, specifically: In the target XRD spectrum, the peak area and peak height of the diffraction peak of the graphene detection sample are calculated, the intensity of the diffraction peak is calculated based on the peak area and peak height of the diffraction peak, and the full width half height of the diffraction peak of the graphene detection sample is calculated at the same time; Determine the wavelength of the target X-ray source, and based on the wavelength of the target X-ray source, the full width at half maximum of the diffraction peak, the diffraction angle range of the X-ray source diffraction device, and the target particle size, introduce the Scherrer formula to calculate the lattice size of the graphene test sample; In the target XRD spectrum, the total number of atomic occupancy and the energy required to form atomic vacancies in the graphene test sample are determined based on the intensity of the diffraction peak, and the corresponding Boltzmann constant and the absolute diffraction temperature of the X-ray source diffraction device in the graphene test sample are retrieved based on the big data network; A mathematical model formula of the lattice defect index of the graphene detection sample is constructed, wherein the mathematical model formula of the lattice defect index of the graphene detection sample is: ; in, is the lattice defect index of the graphene test sample, is the total number of atomic occupancy in the graphene test sample, is the energy required to form an atomic vacancy, k is the corresponding Boltzmann constant in the graphene test sample, and T is the absolute diffraction temperature of the X-ray source diffraction device; Determine the lattice state of the graphene test sample by combining the lattice size of the graphene test sample and the lattice defect index of the graphene test sample; It should be noted that in the target XRD spectrum, the intensity of the diffraction peak is measured, which is related to the peak height and peak area, and the full width at half maximum FWHM is measured. The intensity of the diffraction peak is usually related to the volume of the lattice in the sample, while the peak width is related to the lattice size and lattice defects. First, the Scherrer formula is introduced to calculate the lattice size of the graphene test sample, and the Scherrer formula is: ; Where D is the lattice size of the graphene test sample, k is the Scherrer constant, which is usually taken as 0.89, λ is the X-ray wavelength, β is the full width half height of the diffraction peak, and θ is the diffraction angle range of the X-ray source diffraction device. After the lattice size is obtained, it is necessary to simultaneously obtain the lattice defect index of the graphene test sample, and analyze it together with the lattice size and lattice defect index to determine the real-time state of the lattice, that is, to determine whether it has defects. Lattice defects are areas where the microscopic atomic arrangement of a substance is affected by crystal formation conditions, thermal motion of atoms, impurity filling and other conditions, resulting in the structure deviating from the ideal crystal structure. Therefore, based on the intensity of the diffraction peak, the total number of atomic occupancy in the graphene test sample and the energy required to form atomic vacancies are determined, and the corresponding Boltzmann constant and the absolute temperature of the diffraction of the X-ray source diffraction device in the graphene test sample are retrieved based on the big data network to realize the calculation of the defect index of the lattice and the lattice appearance judgment.

[0026] Furthermore, in a preferred embodiment of the present invention, the lattice state of the graphene test sample is determined by combining the lattice size of the graphene test sample and the lattice defect index of the graphene test sample, specifically: Retrieving the controllable range of the defect index of the lattice of the graphene test sample at the current lattice size in the big data network, and determining whether the lattice defect index of the graphene test sample is maintained within the controllable range of the defect index of the lattice at the current lattice size; If yes, the lattice state of the graphene test sample is calibrated as qualified; If not, the lattice state of the graphene test sample is marked as unqualified, wherein the lattice state of the graphene test sample is unqualified because the diffraction peak of the graphene test sample is shifted, resulting in lattice distortion; Based on the lattice defect index of the graphene test sample, the lattice distortion position is marked in the target XRD spectrum, and the defect position is located in the graphene test sample based on the lattice distortion position.

[0027] It should be noted that the defect index of graphene test samples is different under different lattice sizes. Under the current lattice size, it is determined whether the lattice defect index of the graphene test sample is maintained within the standard range. If so, the lattice state of the graphene test sample is determined to be qualified. Otherwise, it is unqualified. The reason for the unqualified graphene test sample is that the diffraction peak shift causes lattice distortion. The lattice distortion position needs to be marked in the spectrum. The purpose is to find the true lattice defect position in the graphene test sample and provide conditions for subsequent graphene repair treatment.

[0028] In addition, the graphene defect detection method further includes the following steps: introducing a Raman spectrometer, storing a graphene detection sample on the Raman spectrometer, emitting a laser to the graphene detection sample through the Raman spectrometer, and adjusting the laser wavelength of the Raman spectrometer until the laser wavelength completely covers the graphene detection sample, generating Raman spectrum data of the graphene detection sample, and calibrating it as target Raman spectrum data; Analyze the target Raman spectrum data, perform peak data detection on the G peak, D peak and D1 peak of the graphene detection sample, and determine the G peak, D peak and D1 peak; Based on the G peak, the D peak and the D1 peak, the intensity ratio of the D peak to the G peak is determined and calibrated as a type I intensity ratio, and the intensity ratio of the D peak to the D1 peak is determined and calibrated as a type II intensity ratio; Wherein, the first type of intensity ratio is used to determine the defect density of the lattice in the graphene test sample, and the second type of intensity ratio is used to determine the defect type of the lattice in the graphene test sample; The comparison portrait of Raman spectrum peak data ratio and defect category is retrieved in the big data network and calibrated as peak defect comparison portrait. The first type intensity ratio and the second type intensity ratio are imported into the peak defect comparison portrait to determine the defect purpose and defect type of the graphene detection sample.

[0029] It should be noted that the Raman spectrometer performs laser scanning on the graphene test sample and analyzes the Raman spectrum data to achieve the purpose of determining the defect type and density of the graphene test sample. Among them, the G peak is one of the main characteristic peaks of graphene, which is closely related to the energy band structure of graphene, and the D peak corresponds to the defects, edges and amorphous parts in graphene. By detecting the intensity of the D peak, the defect density can be quantitatively analyzed. The D1 peak usually appears in the multilayer structure of graphene, corresponding to the vibration mode perpendicular to the graphene layer. The intensity ratio of the D peak and the D1 peak is closely related to the type of defects on the graphene surface. By comparing the intensity of different peaks, the purpose of the defects and the purpose of the defect type of the graphene test sample can be determined in advance.

[0030] like Figure 3 As shown, the second aspect of the present invention further provides a graphene defect detection device, characterized in that the defect detection device comprises: A data retrieval module, wherein the data retrieval module is used to retrieve all schemes for defect detection of graphene; A graphene sample drying processing module, wherein the graphene sample drying processing module is used to dry the graphene detection sample sprayed with graphene slurry; A scanning electron microscope module, wherein the scanning electron microscope module is used to obtain a surface defect image of a graphene test sample, and to perform defect location and feature extraction on the surface defect image of the graphene test sample; An X-ray diffraction module is used to perform X-ray diffraction on the graphene test sample and analyze the obtained diffraction test data to determine the lattice state of the graphene test sample; The data processing module includes a memory and a processor. The processor analyzes and processes the data obtained by each module, and the memory can store the data of each module.

[0031] like Figure 3 As shown, the present invention also provides a graphene defect detection device, characterized in that the defect detection device comprises: A scanning electron microscope device, wherein the scanning electron microscope device is used to obtain a surface defect image of a graphene test sample, and to perform defect location and feature extraction on the surface defect image of the graphene test sample; X-ray diffraction equipment, the function of the X-ray diffraction equipment is to perform X-ray diffraction on the graphene detection sample, and analyze the obtained diffraction test data to determine the lattice state of the graphene detection sample.

[0032] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0033] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0034] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0035] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0036] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0037] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A graphene defect detection method, characterized in that: The following steps are involved: A graphene detection sample is prepared, and a scanning electron microscope is debugged at the same time, and a surface image of the graphene detection sample is acquired through the electron microscope after the device debugging, so as to obtain a surface image of the graphene detection sample; Perform image surface defect analysis and positioning on the surface image of the graphene test sample, determine the location and defect size information of the graphene surface defects, and evaluate the surface defect status of the graphene test sample; An X-ray diffraction test is performed on the graphene test sample, and a lattice state of the graphene test sample is determined after the X-ray diffraction test.

2. A graphene defect detection method according to claim 1, characterized in that: The graphene detection sample is prepared, and the scanning electron microscope is debugged at the same time, and the surface image of the graphene detection sample is acquired by the electron microscope after the equipment debugging, so as to obtain the surface image of the graphene detection sample, specifically: Obtaining a batch of graphene that needs to be tested, and selecting sample graphene from the batch of graphene that needs to be tested, and calibrating the sample graphene as a graphene sample; Obtain a carbon layer spray gun and graphene slurry, add the graphene slurry into the carbon layer spray gun, and perform equipment debugging on the carbon layer spray gun, wherein the equipment debugging of the carbon layer spray gun is to debug the spraying pressure and spraying speed of the carbon layer spray gun so that the spraying pressure and spraying speed of the carbon layer spray gun meet a preset threshold; Using a carbon layer spray gun, the graphene sample surface is evenly sprayed with graphene slurry. When the graphene sample surface is evenly covered with graphene slurry, a preliminary graphene detection sample is obtained. Drying the preliminary graphene detection sample and obtaining a big data network, retrieving a standard drying time after the graphene slurry is sprayed based on the big data network, stopping the drying of the preliminary graphene detection sample after the standard drying time after the graphene slurry is sprayed, and detecting the conductivity of the preliminary graphene detection sample after drying by a plane resistance meter; If the conductivity of the preliminary graphene detection sample after drying meets the preset threshold, a graphene detection sample is obtained; if the conductivity of the preliminary graphene detection sample after drying does not meet the preset threshold, the preliminary graphene detection sample after drying is repeatedly sprayed; Introducing a scanning electron microscope, preventing graphene detection samples on the scanning electron microscope, and performing equipment debugging on the scanning electron microscope, wherein the equipment debugging on the scanning electron microscope includes adjusting the electron beam, focusing, and magnification; The surface image of the graphene detection sample is obtained through the scanning electron microscope after equipment debugging, and is calibrated as the surface image of the graphene detection sample.

3. A graphene defect detection method according to claim 1, characterized in that: The image surface defect analysis and positioning of the graphene test sample surface image is performed to determine the location and defect size information of the graphene surface defect, and the surface defect state of the graphene test sample is evaluated, specifically: The grayscale values ​​of different pixels on the surface image of the graphene detection sample are obtained, and a weighted average method is introduced to perform weighted average calculation on the grayscale values ​​of different pixels on the surface image of the graphene detection sample, so as to obtain the grayscale processed surface image of the graphene detection sample, which is calibrated as the grayscale image of the surface of the graphene detection sample; Wherein, the grayscale image of the surface of the graphene detection sample is mapped in the coordinate space of the imaging plate of the scanning electron microscope; Performing spatial transformation on the grayscale image of the surface of the graphene detection sample, and performing grayscale interpolation transformation on the grayscale image of the surface of the graphene detection sample after spatial transformation based on the nearest neighbor interpolation method, so that the pixels of the grayscale image of the surface of the graphene detection sample are mapped on the integer coordinates of the coordinate space of the imaging plate of the scanning electron microscope; Performing a two-dimensional Fourier transform on the grayscale image of the graphene detection sample surface, and combining the high-pass filtering method to enhance the characteristic edge high-frequency signal of the grayscale image of the graphene detection sample surface, extracting the edge features of the grayscale image of the graphene detection sample surface, and calibrating it as the edge features of the graphene detection sample surface; In the coordinate space of the imaging plate of the scanning electron microscope, the size of the edge features of the graphene detection sample surface is calculated to obtain the size information of the defects on the graphene detection sample surface, and the coordinate position of the defects on the graphene detection sample surface is located; In the big data network, the surface defect state comparison map of graphene is retrieved, and the surface defect size information of the graphene detection sample and the coordinate position of the surface defect of the graphene detection sample are imported into the surface defect state comparison map of the graphene detection sample to obtain the surface defect state of the graphene detection sample.

4. A graphene defect detection method according to claim 1, characterized in that: The method of performing an X-ray diffraction test on the graphene test sample and determining the lattice state of the graphene test sample after the X-ray diffraction test is specifically as follows: Introducing an XRD instrument, wherein the XRD instrument is an instrument that can perform X-ray diffraction on a graphene test sample; Determine the maximum particle size of the graphene detection sample supported by the XRD instrument when the graphene detection sample is subjected to X-ray diffraction in the XRD instrument, calibrate it as the target particle size, and place the graphene detection sample in a grinding device for grinding treatment so that the particle size of the graphene detection sample is equal to the target particle size, thereby obtaining graphene detection sample powder; Obtaining a sample holder groove and a pressing glass slide in the XRD instrument, placing graphene detection sample powder in the sample holder groove, and pressing the graphene detection sample powder by pressing the glass slide so that the graphene detection sample powder is evenly distributed in the sample holder groove; In the XRD instrument, an X-ray source diffraction device is aligned with a groove of a sample holder, a cooling water circulation device is started in the XRD instrument, and all X-ray sources that can diffract are determined in the XRD instrument, and the X-ray source with the highest utilization rate when performing X-ray diffraction on graphene is retrieved from a big data network, and calibrated as a target X-ray source, and the area of ​​the groove of the sample holder is determined at the same time, and the diffraction angle range of the X-ray source diffraction device is calculated based on the area of ​​the groove of the sample holder; Based on the diffraction angle range of the X-ray source diffraction device, the target X-ray source is diffracted in the groove of the sample holder by the X-ray source diffraction device, and diffraction test data is collected; Introducing drawing software, importing the diffraction test data into the drawing software, performing data conversion and data difference correction on the diffraction test data through the drawing software, constructing an XRD spectrum of the graphene test sample, and calibrating it as a target XRD spectrum; The target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis results.

5. A graphene defect detection method according to claim 4, characterized in that: The target XRD spectrum is analyzed, and the lattice state of the graphene test sample is determined based on the spectrum analysis result, specifically: In the target XRD spectrum, the peak area and peak height of the diffraction peak of the graphene detection sample are calculated, the intensity of the diffraction peak is calculated based on the peak area and peak height of the diffraction peak, and the full width half height of the diffraction peak of the graphene detection sample is calculated at the same time; Determine the wavelength of the target X-ray source, and based on the wavelength of the target X-ray source, the full width at half maximum of the diffraction peak, the diffraction angle range of the X-ray source diffraction device, and the target particle size, introduce the Scherrer formula to calculate the lattice size of the graphene test sample; In the target XRD spectrum, the total number of atomic occupancy and the energy required to form atomic vacancies in the graphene test sample are determined based on the intensity of the diffraction peak, and the corresponding Boltzmann constant and the absolute diffraction temperature of the X-ray source diffraction device in the graphene test sample are retrieved based on the big data network; A mathematical model formula of the lattice defect index of the graphene detection sample is constructed, wherein the mathematical model formula of the lattice defect index of the graphene detection sample is: ； in, is the lattice defect index of the graphene test sample, is the total number of atomic occupancy in the graphene test sample, The energy required to form an atomic vacancy, is the corresponding Boltzmann constant in the graphene test sample, for The absolute diffraction temperature of the ray source diffraction device; The lattice state of the graphene test sample is determined in combination with the lattice size of the graphene test sample and the lattice defect index of the graphene test sample.

6. A graphene defect detection method according to claim 5, characterized in that: The lattice state of the graphene test sample is determined by combining the lattice size of the graphene test sample and the lattice defect index of the graphene test sample, specifically: Retrieving the controllable range of the defect index of the lattice of the graphene test sample at the current lattice size in the big data network, and determining whether the lattice defect index of the graphene test sample is maintained within the controllable range of the defect index of the lattice at the current lattice size; If yes, the lattice state of the graphene test sample is calibrated as qualified; If not, the lattice state of the graphene test sample is marked as unqualified, wherein the lattice state of the graphene test sample is unqualified because the diffraction peak of the graphene test sample is shifted, resulting in lattice distortion; Based on the lattice defect index of the graphene test sample, the lattice distortion position is marked in the target XRD spectrum, and the defect position is located in the graphene test sample based on the lattice distortion position.

7. A graphene defect detection device, characterized in that: The defect detection device comprises: A data retrieval module, wherein the data retrieval module is used to retrieve all schemes for defect detection of graphene; A graphene sample drying processing module, wherein the graphene sample drying processing module is used to dry the graphene detection sample sprayed with graphene slurry; A scanning electron microscope module, wherein the scanning electron microscope module is used to obtain a surface defect image of a graphene test sample, and to perform defect location and feature extraction on the surface defect image of the graphene test sample; An X-ray diffraction module is used to perform X-ray diffraction on the graphene test sample and analyze the obtained diffraction test data to determine the lattice state of the graphene test sample; The data processing module includes a memory and a processor. The processor analyzes and processes the data obtained by each module, and the memory can store the data of each module.

8. A graphene defect detection device, characterized in that: The defect detection equipment comprises: A scanning electron microscope device, wherein the scanning electron microscope device is used to obtain a surface defect image of a graphene test sample, and to perform defect location and feature extraction on the surface defect image of the graphene test sample; X-ray diffraction equipment, the function of the X-ray diffraction equipment is to perform X-ray diffraction on the graphene detection sample, and analyze the obtained diffraction test data to determine the lattice state of the graphene detection sample.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium contains a graphene defect detection method program, and when the graphene defect detection method program is executed by a processor, the steps of the graphene defect detection method described in any one of claims 1 to 6 are implemented.

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