Graphitization degree detection method and device based on Raman spectrum, computer equipment and medium

Abstract

The invention provides a Raman spectrum-based graphitization degree detection method and device, computer equipment and a medium. The method comprises the following steps: acquiring Raman data and X-ray diffraction data of multiple groups of test samples; obtaining the graphitization degree R-1 of the test sample based on the Raman data, and obtaining the graphitization degree g of the test sample based on the X-ray diffraction data; taking the graphitization degree R-1 and the graphitization degree g of the same test sample as a group of benchmarking data, performing numerical fitting based on multiple groups of benchmarking data, and constructing a function relationship between the graphitization degree R-1 and the graphitization degree g; raman data of a to-be-detected sample are obtained, the graphitization degree R-1 of the to-be-detected sample is calculated, and the graphitization degree g of the to-be-detected sample is determined according to the function relation. According to the method, the graphitization degree R-1 and the graphitization degree g of the material are associated, the graphitization degree g of the product can be directly obtained through Raman analysis, online detection of the carbon powder material is achieved, and the sorting precision of the carbon powder is improved.

Description

Technical Field

[0001] The present application relates to the field of new energy battery technology, and in particular to a method, device, computer equipment and medium for detecting graphitization degree based on Raman spectroscopy. Background Art

[0002] In recent years, with increasing awareness of environmental and energy issues, lithium-ion batteries have gradually gained widespread application as an environmentally friendly, efficient, economical, and reliable new energy source. The energy density and safety performance of lithium-ion batteries rely on the rapid development of positive and negative electrode materials. Among them, artificial graphite is currently the mainstream commercial application of negative electrode materials. Highly graphitized carbon powder can improve the capacity of lithium-ion batteries. Therefore, the effective measurement of the graphitization degree of carbon powder on the production line has important application prospects.

[0003] The degree of graphitization (g) is a parameter used to characterize the degree of similarity between the arrangement of carbon atoms and the graphite crystal structure and is often used to measure the structural perfection of carbon materials. A higher g value indicates a more graphitized material and fewer lattice defects. As a battery anode material, graphite's ordered structure facilitates the insertion of lithium ions, forming a lithium-intercalated compound, LixC6 (0≤x≤1), with a theoretical capacity of up to 372mAh / g (x=1).

[0004] The current methods for detecting the degree of graphitization mainly include X-ray diffraction (XRD) or Raman spectroscopy. The XRD method calculates the degree of graphitization by analyzing the crystal structure characteristics of graphite. It is necessary to first determine the C(002) peak position and calculate the interlayer spacing d using the Bragg formula. 002 , and finally substituted into the Franklin formula to calculate the graphitization degree; however, the XRD method scans too large an area and cannot select micro-areas; and in the process of testing the graphitization degree using this method, due to the systematic error caused by inaccurate goniometer calibration and the random error caused by the sample surface deviating from the goniometer axis, it is usually necessary to add a small amount of high-purity silicon powder into the sample as an internal standard to calibrate the graphite C(002) peak position; because the data analysis process requires the precise diffraction peak positions of the graphite material and the silicon internal standard, there are usually peak vertex method, center of gravity method, peak fitting method, etc. Different analysis processes will result in relatively large errors in the final data. The above factors make the XRD method difficult to use as an automated detection method for the graphitization degree of carbon powder on the production line.

[0005] The existing laser Raman spectroscopy is to analyze the sp 2 The bond vibration information is used to characterize the degree of graphitization. The frequency range of the first-order region of the Raman spectrum of carbon materials is 1100-1800 cm -1 , 1580cm -1 is the G line, 1350cm -1 is the D line, the degree of graphitization R -1 =IG / I D It can be used to characterize the orderliness of the carbon material structure. -1 When the value is higher, the material is closer to graphite and the degree of graphitization is higher. -1 It can only judge the degree of graphitization of the material, and cannot be directly linked to the numerical value of the graphitization degree g; in addition, Raman data processing mainly relies on manual operation and experience judgment, including baseline removal, peak identification, peak fitting and other steps. In order to obtain more accurate results, data processing software (such as Origin, wire, etc.) is required to manually fit a large number of Raman test points. On the one hand, it places high requirements on the inspection personnel. On the other hand, operations such as baseline removal and peak fitting in this process are subjective, and it is impossible to guarantee the consistency of operations in each processing process. It cannot be directly applied to the automatic detection of the graphitization degree of carbon powder on the production line.

[0006] It should be noted that the above introduction to the technical background is merely intended to provide a clear and complete description of the technical solutions of this application and facilitate understanding by those skilled in the art. Simply because these solutions are described in the background technology section of this application, it should not be assumed that the above technical solutions are well known to those skilled in the art. Summary of the Invention

[0007] The object of the present invention is to provide a method and device for detecting the degree of graphitization based on Raman spectroscopy, so as to realize online detection of the degree of graphitization of carbon powder, thereby improving the sorting accuracy of carbon powder.

[0008] In order to solve the above problems, in a first aspect, a method for detecting the degree of graphitization based on Raman spectroscopy is provided below, comprising the following steps:

[0009] Obtain Raman data and X-ray diffraction data for multiple groups of test samples;

[0010] The graphitization degree R of the test sample is obtained based on the Raman data. -1 , obtaining the graphitization degree g of the test sample based on the X-ray diffraction data;

[0011] The graphitization degree R of the same test sample -1 and graphitization degree g are a set of benchmark data, and numerical fitting is performed based on multiple sets of the benchmark data to construct the graphitization degree R -1 and the functional relationship of the graphitization degree g;

[0012] Obtain Raman data of the sample to be tested and calculate the graphitization degree R of the sample to be tested -1 , determine the graphitization degree g of the sample to be tested according to the functional relationship.

[0013] This application is carried out by graphitizing the material to a certain degree R -1 By correlating it with the graphitization degree g, the graphitization degree g of the product can be directly obtained by Raman analysis, which realizes the online detection of carbon powder materials and improves the sorting accuracy of carbon powder.

[0014] The graphitization degree R of the test sample is obtained based on the Raman data. -1 The process includes: performing baseline removal processing on the Raman data; performing peak fitting processing on the baseline-removed data to obtain the peak intensity I of the G peak G and the peak intensity of peak D I D ; Calculate the degree of graphitization R -1 , graphitization degree R -1 =I G / I D .

[0015] The process of obtaining the graphitization degree g of the test sample based on the X-ray diffraction data includes: calculating the C(002) crystal plane spacing d based on the X-ray diffraction data; calculating the graphitization degree g, graphitization degree g=((0.3440-d / 2) / 0.0086)*100%.

[0016] The baseline removal process involves sequentially reading the frequency data and corresponding intensity data from the beginning and end of the Raman data, constructing a linear function, and performing baseline removal using this linear function as the baseline function. Using a linear function as the baseline function effectively removes background noise, is easy to construct, and processes quickly, making it particularly suitable for rapid, real-time testing on production lines.

[0017] The peak fitting process includes: using the Lorentz function as the fitting curve function, performing curve fitting by a regression algorithm, wherein the initial fitting parameters of the Lorentz function are: G peak peak intensity of 6000, G peak peak position of 1580, G peak half-peak width of 100, D peak peak intensity of 5000, D peak peak position of 1350, D peak half-peak width of 150; based on the curve fitting results, obtaining the G peak peak intensity I G and the peak intensity of peak D I D The Lorentz function is easy to call and, when combined with appropriate initial fitting parameters, can further shorten the fitting time and improve accuracy, making it more suitable for rapid real-time detection on the production line.

[0018] The process of acquiring Raman data for a sample includes: placing the sample in a sample container and smoothing the surface to obtain the sample; performing laser Raman analysis on the sample to obtain corresponding Raman data. The laser Raman analysis parameters are: laser wavelength of 532nm, integration time of 20s, and integration times of 3. Appropriate sample preparation methods can effectively improve Raman signal acquisition and avoid the influence of the substrate on the spectrum. Appropriate test parameters also ensure the accuracy of automatic data processing.

[0019] The exponential curve regression model was used to numerically fit multiple groups of the benchmark data to construct the graphitization degree R -1 and the functional relationship of the graphitization degree g, wherein the functional relationship is: g = 1-1.1825e -1.1833R-1 .

[0020] In a second aspect, the present invention further provides a graphitization degree detection device based on Raman spectroscopy, which is used to implement the graphitization degree detection method as described in any one of the first aspects, and the device includes: a Raman analysis module, which is used to perform laser Raman analysis on the sample to be tested and obtain Raman data of the sample to be tested; a data processing module, including a baseline removal module, a peak fitting module and a graphitization degree calculation module; the baseline removal module is used to perform baseline removal processing on the Raman data; the peak fitting module is used to perform curve fitting on the data after baseline removal processing to calculate the graphitization degree R of the sample to be tested. -1 The graphitization degree calculation module is used to calculate the graphitization degree R according to the preset functional relationship -1 Calculate the graphitization degree g of the sample to be tested.

[0021] In a third aspect, the present invention further provides a computer device comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, a computer program is stored in the memory, and the processor executes the graphitization degree detection method described in any one of the first aspects by executing the computer program.

[0022] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is used to enable a computer to execute the graphitization degree detection method described in any one of the first aspects.

[0023] Compared with the prior art, the beneficial effects of the present invention mainly include the following: 1) Compared with the XRD method, the microscopic distribution state of the graphitization degree of samples containing components with multiple graphitization degrees can be easily analyzed, thereby realizing the adjustment and control of the microscopic distribution of the graphitization degree and realizing more precise imaging test analysis in space; 2) No internal standard needs to be added in the Raman detection process, and only the standard silicon wafer needs to be used to adjust the peak position of the instrument, which saves process costs and reduces errors; 3) In terms of data processing, the graphitization degree of the product is automatically calculated by writing a program, and the dual functions of data acquisition and processing can be realized by scanning, making data processing more objective, avoiding the errors in the results caused by multiple processing methods in the XRD method, and simplifying the process flow and improving production efficiency; 4) Compared with the laser Raman spectroscopy method, the present device can obtain real-time spectral data and graphitization degree of different areas of the product by moving, which is equivalent to performing a surface scan on the product, and can understand the distribution of graphitization degree of different micro-areas in the detection area; 5) The graphitization degree g and graphitization degree R are given through research. -1 The relationship formula of the two parameters is linked together, and the difference in the degree of graphitization of different materials can be more intuitively seen from the comparison of the percentage values of the graphitization degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 This is a flow chart of a method for detecting degree of graphitization based on Raman spectroscopy provided by the present invention.

[0026] Figure 2 This is a schematic diagram of the sample preparation provided by the present invention.

[0027] Figure 3 This is a flow chart for obtaining and judging the optimal fitting parameters provided by the present invention.

[0028] Figure 4 Provides a comparison chart of manual fitting results and automated processing program results for the present invention.

[0029] Figure 5 The graphitization degree R provided by the present invention is -1 and the fitting results of graphitization degree g. DETAILED DESCRIPTION

[0030] The foregoing and other technical aspects, features, and functions of the present invention are clearly presented in the following detailed description of a preferred embodiment with reference to the accompanying drawings. Directional terms such as up, down, left, right, front, and back, used in the following embodiments, are intended solely to refer to the directions in the accompanying drawings. Therefore, the directional terms used are for illustrative purposes only and are not intended to limit the present invention.

[0031] The following detailed description of the various embodiments of the present application is provided in conjunction with the accompanying drawings. However, those skilled in the art will appreciate that many technical details are provided in the various embodiments of the present application to facilitate a better understanding of the present application. However, even without these technical details and the various variations and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0032] The steps in the following embodiments do not correspond one-to-one to the steps in the summary of the invention.

[0033] Example 1

[0034] like Figure 1 Shown is a flow chart of a method for detecting degree of graphitization based on Raman spectroscopy provided in an embodiment of the present invention.

[0035] refer to Figure 1 The present invention provides a method for detecting the degree of graphitization based on Raman spectroscopy, the method comprising the following steps:

[0036] Step 1: Acquire Raman data and X-ray diffraction data of multiple sets of test samples.

[0037] Specifically, multiple groups of graphite powder are obtained from the production line. Laser Raman and X-ray diffraction tests are performed on each group of graphite powder to obtain corresponding Raman and X-ray diffraction data. This testing process can refer to the process of existing technology. The following is the specific process of this embodiment.

[0038] S1. Obtain graphite powder and prepare the sample to obtain the test sample. The sample preparation method is to place the graphite powder material on the production line directly into the sample container, and then scrape its surface until it is flat. The sample needs to have a certain thickness. The results are referenced Figure 2 shown.

[0039] In this step, the sample preparation method is obtained after careful consideration and practical exploration. If the material is pressed during the sample preparation process, the surface flatness of the prepared test sample will be too high. The high flatness of the surface will cause a large amount of light to be specularly reflected, which will not be able to interact with the sample and produce an effective Raman scattering signal, which will have a great impact on the scattered light collection of the instrument. At the same time, the proportion of Raman signal in the scattered light is very low, and the strong reflected light signal may overwhelm the scattered light signal. It is preferred to prepare and detect samples at room temperature to avoid the influence of high temperature on scattered light collection. Finally, it is necessary to avoid the influence of the substrate on the spectral line. Therefore, during the sample preparation process, the sample needs to have a certain thickness (for example, greater than or equal to 1mm).

[0040] S2. Use Raman testing device to test. Parameter settings: The device uses a 532nm laser, selects a 10x objective lens, and adjusts the device parameters to 6.9mW laser power, 20s integration time, 3 integration times, and a detection band of 900-1850cm -1 .

[0041] The test parameters set here will also affect the test accuracy and results to a certain extent. -1 The size of the D peak and the G peak is determined by the intensity of the D peak and the G peak. Using different laser wavelengths for experiments will affect the intensity ratio of the two peaks, which may affect the graphitization degree g and the graphitization degree R. -1 If the laser power, integration time or number of integration times are changed, it may cause damage to the sample or cause greater noise in the Raman spectrum, thus affecting the accuracy of automatic data processing.

[0042] It is understood that before the test, in order to ensure the reliability of the test data, the Raman test device can be calibrated first. Specifically, the peak position of the device is calibrated using a silicon wafer: First, the peak position of the device is calibrated using 520.7 cm -1 The silicon wafer is used for wave number calibration of the Raman test device. When calibrating the standard sample, a 532nm laser is selected to perform real-time spectrum acquisition on the silicon wafer. The peak position of the spectrum is obtained by removing the background and marking the peak position. Then, the peak position of the silicon wafer is adjusted to the standard 520.7cm through "Instrument Calibration" in the "Settings" menu. -1 .

[0043] S3. Data acquisition: placing the test sample prepared in step S1 in a corresponding detection area of the Raman test device, collecting Raman spectrum data of the sample in the area according to the set parameters, and obtaining corresponding Raman spectrum data.

[0044] Similarly, the above process can be referred to, and X-ray diffractometer can be used to obtain X-ray diffraction data corresponding to each group of test samples. The specific process will not be described in detail here.

[0045] Step 2: Obtain the graphitization degree R of the test sample based on Raman data -1 , the graphitization degree g of the test sample was obtained based on the X-ray diffraction data.

[0046] In step 1 above, the Raman data and X-ray diffraction data of each test sample have been obtained. Then, the graphitization degree R of each test sample can be obtained according to the Raman data of each test sample. -1 , the graphitization degree g of each test sample is obtained according to the X-ray diffraction data of the test sample.

[0047] Obtain the corresponding graphitization degree R according to Raman data -1 The specific method can refer to the existing technology. In this embodiment, the process includes: performing baseline removal processing on the Raman data; performing peak fitting processing on the baseline-removed data to obtain the peak intensity I of the G peak G and the peak intensity of peak D I D ; Calculate the degree of graphitization R -1 , graphitization degree R -1 =I G / I D .

[0048] In order to reduce the influence of subjective factors of the test personnel, this embodiment is provided with a program for automatically processing Raman data (programmed in Python) to improve the processing efficiency and the objectivity of the analysis results. However, this application is not limited to the use of this automated analysis program. It is understood that the degree of graphitization R can be calculated by manually fitting and processing using data processing software (such as Origin, Wire, etc.) -1 It is also acceptable.

[0049] The process of automating the Raman data processing includes:

[0050] 1) Load the Raman spectrum data file. The data file to be loaded should be a ".txt" file saved after the Raman spectrum scan, and should have two columns of data. The first column is the Raman frequency data, and the second column is the Raman intensity data, which respectively form the horizontal and vertical coordinates of the Raman spectrum. The two horizontal and vertical coordinates in each row correspond to each other.

[0051] 2) Perform baseline removal. The purpose of baseline removal is to eliminate the background noise generated by residual Rayleigh scattering and fluorescence. In this embodiment, the baseline function form is set to a linear function, and the baseline removal parameters are set according to the called Raman spectrum data. Specifically, the starting and ending values of the horizontal coordinate data are selected: 900.118 and 1801.22, the corresponding vertical coordinate data are read, and the definite form of the baseline function is calculated by two sets of data; the baseline removal step can be completed by subtracting the value of the baseline function corresponding to each vertical coordinate data and its horizontal coordinate data, and the vertical coordinate data after subtraction is used as the final data for drawing the Raman spectrum (i.e., the data after baseline removal).

[0052] 3) Perform peak fitting. The purpose of this step is to find the D and G lines in the Raman spectrum and make smooth fitting, which is the basis for the subsequent graphitization degree R -1 In this embodiment, the Lorentz function is selected as the fitting curve function of the double peak because of its high fitting degree with the carbon powder Raman spectrum.

[0053] refer to Figure 3 , each Lorentz function corresponds to a set of parameters, which includes three parameter values: peak intensity, peak position, and half-peak width. Setting appropriate initial fitting parameters can effectively shorten the fitting time and improve accuracy. In this embodiment, the initial fitting parameters of the D spectrum line are set as follows: peak intensity 5000, peak position 1350, and half-peak width 150; the initial fitting parameters of the G spectrum line are set as follows: peak intensity 6000, peak position 1580, and half-peak width 100. The 6 initial parameters are sorted in the above order and saved in the form of a 1*6 array. After substituting into the Lorentz function, the curve is fitted by the regression algorithm. When the sum of the squares of the residuals is minimized, two sets of optimal fitting parameters are returned. The 6 optimal fitting parameter values are also arranged in the form of a 1*6 array. However, at this time, it is not possible to directly determine which peak the two sets of data correspond to. It is also necessary to compare the size of the peak position to make a judgment. The set of parameters with a smaller peak position corresponds to the D spectrum line, and the set of parameters with a larger peak position corresponds to the G spectrum line. They are used as the final parameters for the overall curve and the bimodal curve fitting. According to the obtained bimodal fitting curve, the peak intensities of the D and G lines are obtained by searching for the maximum ordinate data. The peak intensity of the G line I G and D line peak intensity I D The ratio is the degree of graphitization R -1 value.

[0054] In order to verify the rationality of the above-mentioned automated Raman data processing program, in this example, Raman data of 12 different test samples were selected, and the fitting results processed by the automated analysis program (Python) were compared with the manual fitting results of the Origin software. Figure 4 As shown, R -1The errors are concentrated below 1%, so it can be considered that the above program fitting can replace the manual fitting results of Origin.

[0055] Similarly, the X-ray diffraction data of each group of test samples are analyzed and processed to obtain the corresponding graphitization degree g of the test sample. The specific method can be carried out according to the existing technology. Usually, the C(002) peak position is first determined and the C(002) crystal plane interlayer spacing d is calculated by the Bragg formula. Then, the graphitization degree g is calculated according to the Franklin formula, where the graphitization degree g = ((0.3440-d / 2) / 0.0086)*100%.

[0056] Step 3: Construct the graphitization degree R -1 and the functional relationship of graphitization degree g.

[0057] After step 2 is completed, the graphitization degree R corresponding to each group of test samples has been obtained. -1 and the degree of graphitization g, which can be further analyzed by the degree of graphitization R -1 The relationship between the graphitization degree g and the graphitization degree R -1 Convert to the desired degree of graphitization g.

[0058] Specifically, the graphitization degree R of the same test sample -1 and graphitization degree g are a set of benchmark data. Multiple sets of benchmark data are used for numerical fitting to construct the graphitization degree R -1 and the functional relationship of the graphitization degree g. In this embodiment, four sets of benchmark data are taken as an example, and the four sets of benchmark data are shown in the following table. In other embodiments, more sets of benchmark data can also be used for numerical fitting.

[0059] XRD method g 0.529 0.710 0.840 0.949 Raman spectroscopy <![CDATA[R -1 ]]> 0.851 1.035 1.789 3.040

[0060] Programming in R via Python -1 A scatter plot was made with g as the horizontal axis and g as the vertical axis, and the exponential curve regression model was used in the Python program to perform fitting analysis and goodness of fit calculation (see the results in Figure 5 ), the following formula is obtained:

[0061] g=1-1.1825e -1.1833R-1

[0062] It can be seen that with the R -1 As the value of R increases, the value of g also increases, but the increasing trend gradually slows down, and the slope of each point on the curve gradually tends from a positive value to 0. In this embodiment, the variable g is related to R -1 The goodness of fit is 0.97, which has a high correlation, indicating that the above formula can better explain the relationship between the two variables.

[0063] According to the above steps, the graphitization degree R -1 The corresponding relationship between the graphitization degree g and the graphitization degree g can be obtained by using Raman spectroscopy, which can conveniently analyze the microscopic distribution state of the graphitization degree of samples containing components with various graphitization degrees, such as the measurement and characterization of the graphitization degree of different components or different parts of the same component, thereby realizing the adjustment and control of the microscopic distribution of the graphitization degree. It has great application prospects in industrial production line sorting to meet the production line's demand for controlling the graphitization degree of products.

[0064] Step 4: Obtain Raman data of the sample to be tested and calculate the graphitization degree R of the sample to be tested -1 , determine the graphitization degree g of the sample to be tested according to the functional relationship.

[0065] In this application, by pre-graphitization degree R -1 The relationship between the graphitization degree g and the graphitization degree R was obtained. -1 And the functional relationship of graphitization degree g. When the graphitization degree g of the material needs to be tested later, the Raman method can be used to test the sample to obtain the Raman data of the sample to be tested and calculate the corresponding graphitization degree R -1 Finally, the graphitization degree g of the sample to be tested is determined based on the obtained functional relationship.

[0066] It can be understood that in this step, the Raman data of the sample to be tested is obtained and the graphitization degree R of the sample to be tested is calculated. -1 The specific method can refer to the method in step 1 and step 2, which will not be repeated here.

[0067] Example 2

[0068] In the second embodiment, a graphitization degree detection device based on Raman spectroscopy is provided, which is used to implement the graphitization degree detection method in the above-mentioned embodiment 1. The graphitization degree detection device includes: a Raman analysis module, which is used to perform laser Raman analysis on the sample to be tested and obtain Raman data of the sample to be tested; a data processing module, which includes a baseline removal module, a peak fitting module and a graphitization degree calculation module; the baseline removal module is used to perform baseline removal processing on the Raman data; the peak fitting module is used to perform curve fitting on the data after baseline removal processing and calculate the graphitization degree R of the sample to be tested. -1 ; Graphitization degree calculation module, used to calculate the graphitization degree R according to the preset function relationship -1 Calculate the graphitization degree g of the sample to be tested. In this device, by selecting different analysis positions of the sample to be tested, the graphitization degree of products in different micro-areas within the detection area can be viewed in real time.

[0069] The present invention provides a graphitization detection method and device based on Raman spectroscopy. Compared with the existing XRD method, this method can conveniently analyze the microscopic distribution state of the graphitization degree of a sample containing components with multiple graphitization degrees, such as measuring and characterizing the graphitization degrees of different components or different parts of the same component, thereby achieving adjustment and control of the microscopic distribution of the graphitization degree and realizing more precise spatial imaging test analysis. At the same time, no internal standard needs to be added in the Raman detection process, and only a standard silicon wafer needs to be used to adjust the instrument peak position, which saves process costs and reduces errors. In terms of data processing, this process automatically calculates the graphitization degree of the product through programming, and only scanning is required to achieve the dual functions of data acquisition and processing, making data processing more objective and avoiding errors in the results caused by multiple processing methods in the XRD method. At the same time, the process flow is simplified and production efficiency is improved.

[0070] Compared with traditional laser Raman spectroscopy, this device can obtain real-time spectral data and graphitization degree of different areas of the product by moving, which is equivalent to scanning the surface of the product. It is more reasonable in data acquisition and data processing. It can not only understand the distribution of graphitization degree of different micro-areas in the detection area, but also avoid the subjective interference of manual fitting data processing. Furthermore, this process provides the graphitization degree g and graphitization degree R through research. -1 The relationship formula links the two parameters together, so the difference in graphitization degree of different materials can be more intuitively seen from the comparison of the percentage values of graphitization degree.

[0071] Based on the research and development of this method and device, toner manufacturers can conduct online detection of toner graphitization degree according to their own needs, thereby improving the sorting accuracy of toner. It has good application value in industrial production line sorting and meets the requirements of future industry development.

[0072] Example 3

[0073] Embodiment 3 of the present application provides a computer device for implementing the steps of the aforementioned Raman spectroscopy-based graphitization detection method, or the steps of the aforementioned Raman spectroscopy-based graphitization detection method. The computer device may include: a processor, a communications interface, a memory, and a communication bus. The processor, the communications interface, and the memory communicate with each other via the communication bus; the processor may call logic instructions in the memory, and the logic instructions in the memory may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as an independent product.

[0074] Example 4:

[0075] Embodiment 4 of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the steps of the above-mentioned Raman spectroscopy-based graphitization detection method.

[0076] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Based on this understanding, the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments or certain parts of the embodiments.

[0077] In order to facilitate the description of the present invention, some common English nouns or letters are used for illustrative reference only and are not intended to be restrictive or specific. The scope of protection of the present invention should not be limited by their possible Chinese translations or specific letters.

[0078] It should also be noted that, in this article, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

Claims

1. A method for detecting the degree of graphitization based on Raman spectroscopy, characterized in that: The steps include: Obtain Raman data and X-ray diffraction data for multiple groups of test samples; The graphitization degree R of the test sample is obtained based on the Raman data. -1 , obtaining the graphitization degree g of the test sample based on the X-ray diffraction data; The graphitization degree R of the same test sample -1 and graphitization degree g are a set of benchmark data, and numerical fitting is performed based on multiple sets of the benchmark data to construct the graphitization degree R -1 and the functional relationship of the graphitization degree g; Obtain Raman data of the sample to be tested and calculate the graphitization degree R of the sample to be tested -1 , determine the graphitization degree g of the sample to be tested according to the functional relationship.

2. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 1, wherein: The graphitization degree R of the test sample is obtained based on the Raman data. -1 The process includes: performing baseline removal processing on the Raman data; Perform peak fitting on the baseline-removed data to obtain the peak intensity I of the G peak. G and the peak intensity of peak D I D ; Calculation of graphitization degree R -1 , graphitization degree R -1 =I G / I D .

3. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 1, wherein: The process of obtaining the graphitization degree g of the test sample based on the X-ray diffraction data includes: Calculate the C(002) interplanar spacing d based on the X-ray diffraction data; Calculate the graphitization degree g, graphitization degree g=((0.3440-d / 2) / 0.0086)*100%.

4. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 2, wherein: The baseline removal process includes: The Raman data includes one-to-one corresponding frequency data and intensity data. The frequency data at the beginning and end and the corresponding intensity data are read in sequence to construct a linear function, which is used as a baseline function for baseline removal.

5. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 2, wherein: The peak fitting process includes: The Lorentz function was used as the fitting curve function, and the curve fitting was performed by regression algorithm. The initial fitting parameters of the Lorentz function were: G peak peak intensity of 6000, G peak peak position of 1580, G peak half-peak width of 100, D peak peak intensity of 5000, D peak peak position of 1350, and D peak half-peak width of 150; Based on the curve fitting results, the peak intensity of G peak I is obtained. G and the peak intensity of peak D I D .

6. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 2, wherein: The process of acquiring Raman data for a sample to be tested includes: Obtaining a material to be tested, placing it in a sample container and scraping the surface thereof to obtain the sample to be tested; Laser Raman analysis is performed on the sample to be tested to obtain corresponding Raman data. The laser Raman analysis parameters are: laser wavelength 532 nm, integration time 20 s, and integration times 3.

7. The method for detecting the degree of graphitization based on Raman spectroscopy according to claim 2, wherein: The exponential curve regression model was used to numerically fit multiple groups of the benchmark data to construct the graphitization degree R -1 and the functional relationship of the graphitization degree g, the functional relationship is:

8. A graphitization degree detection device based on Raman spectroscopy, characterized in that: For implementing the method for detecting the degree of graphitization according to any one of claims 1 to 7, the device comprises: A Raman analysis module is used to perform laser Raman analysis on the sample to be tested and obtain Raman data of the sample to be tested; Data processing module, including baseline removal module, peak fitting module and graphitization degree calculation module; The baseline removal module is used to perform baseline removal processing on the Raman data; The peak fitting module is used to perform curve fitting on the data after baseline removal and calculate the graphitization degree R of the sample to be tested. -1 ; The graphitization degree calculation module is used to calculate the graphitization degree R according to the preset functional relationship -1 Calculate the graphitization degree g of the sample to be tested.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, a computer program is stored in the memory, and the processor executes the graphitization degree detection method according to any one of claims 1 to 7 by executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute the method for detecting the degree of graphitization according to any one of claims 1 to 7.

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