Method for detecting graphitization degree of carbon material and method for detecting gravimetric capacity of carbon material
By constructing a multiple linear regression model combined with multiple Raman characteristic parameters, the complexity and accuracy problems of graphitization degree determination in traditional methods are solved, and rapid and accurate detection of graphitization degree and specific capacity is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG KAIJIN NEW ENERGY TECH CORP LTD
- Filing Date
- 2026-03-13
- Publication Date
- 2026-05-29
Smart Images
Figure CN122109047A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of carbon material testing technology, and in particular to a method for detecting the graphitization degree of carbon materials and a method for detecting the specific capacity of carbon materials. Background Technology
[0002] Lithium-ion batteries are widely used due to their advantages such as high voltage, high capacity, long cycle life, and no memory effect. Graphite-based carbon materials are the preferred anode material for lithium-ion batteries, and the degree of graphitization is a core parameter for measuring the orderliness of the microstructure of carbon materials. It is a key indicator of the perfection of the crystal structure of carbon materials and directly determines key properties such as electrical conductivity, thermal conductivity, mechanical strength, and chemical stability. By accurately measuring the degree of graphitization of carbon materials, we can gain a deeper understanding of the intrinsic relationship between heat treatment processes and the evolution of the material's microstructure, thereby optimizing the preparation process and achieving targeted design and control of material properties.
[0003] Traditional methods for determining graphitization primarily rely on X-ray diffraction, which measures the (002) interplanar spacing d002 and calculates it using the Franklin equation. However, X-ray diffraction involves complex sample preparation, requiring uniform mixing with internal standard silicon powder before grinding and testing. Furthermore, it is time-consuming, hindering rapid screening, has low spatial resolution, cannot perform micro-area analysis, and is inadequate for characterizing the non-uniformity of graphitization in materials.
[0004] Raman spectroscopy is a rapid, non-destructive, and high spatial resolution analytical technique that is highly sensitive to the crystal structure of carbon materials. Current techniques typically utilize the D peak (1350 cm⁻¹) of disordered carbon. - ¹) and the G peak of graphite crystal (1580 cm⁻¹) - The intensity ratio (ID / IG) is used to qualitatively or semi-quantitatively assess the degree of disorder in carbon materials. However, the relationship between (ID / IG) and graphitization degree is not a simple linear or monotonic one, especially in the high graphitization region (graphitization degree > 0.5), where ID / IG is insensitive to structural changes, making it impossible for traditional Raman spectroscopy to perform accurate quantitative determination. Summary of the Invention
[0005] This application provides a method for detecting the graphitization degree of carbon materials, a method for detecting the specific capacity of carbon materials, an apparatus, a computer device, a computer-readable storage medium, and a computer program product, which can solve at least one of the technical problems mentioned in the background art.
[0006] In view of this, firstly, embodiments of this application provide a method for detecting the graphitization degree of carbon materials, comprising: A multiple linear regression model is constructed, which is the correlation between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient. The degree of graphitization of several selected samples was determined, and Raman spectral data of several of the samples were collected and the Raman characteristic parameters were extracted. The graphitization degree of each sample and the extracted Raman characteristic parameters are substituted into the multiple linear regression model and fitted to obtain the values of each weight coefficient. Raman spectral data of the sample to be tested are collected and the Raman characteristic parameters of the sample to be tested are extracted and then input into the multiple linear regression model to calculate the degree of graphitization of the sample to be tested.
[0007] Optionally, the multiple linear regression model is: g =k0+ k1*(PosG-1580)+k2*(ID / IG) +k3*FWHMG+k4(I2D / IG) Where g is the degree of graphitization, PosG-1580, ID / IG, FWHMG, and I2D / IG are Raman characteristic parameters, representing the offset of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak, respectively. k1, k2, k3, and k4 are weighting coefficients, and k0 is a constant term.
[0008] Secondly, embodiments of this application also provide a method for detecting the specific capacity of carbon materials, including the method for detecting the graphitization degree of carbon materials as described above, and further including: Obtain the test gram capacity of several of the samples; Substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b between specific capacity and degree of graphitization, where Y is the specific capacity, g is the degree of graphitization, and a and b are coefficients. The values of a and b are obtained by fitting the data. After calculating the degree of graphitization of the sample to be tested, the degree of graphitization of the sample to be tested is substituted into the correlation function Y=ag-b to obtain the specific capacity of the sample to be tested.
[0009] Thirdly, embodiments of this application also provide a device for detecting the graphitization degree of carbon materials, comprising: A construction module is used to construct a multiple linear regression model, which is the correlation between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient. The measurement and acquisition module is used to measure the degree of graphitization of several selected samples and to acquire Raman spectral data of several samples and extract the Raman characteristic parameters. The substitution and fitting module is used to substitute the graphitization degree measured for each sample and the extracted Raman feature parameters into the multiple linear regression model and then fit the model to obtain the values of each weight coefficient. The calculation module is used to acquire Raman spectral data of the sample to be tested and extract the Raman characteristic parameters of the sample to be tested, and then input them into the multiple linear regression model to calculate the degree of graphitization of the sample to be tested.
[0010] Optionally, the multiple linear regression model is: g =k0+ k1*(PosG-1580)+k2*(ID / IG) +k3*FWHMG+k4(I2D / IG) Where g is the degree of graphitization, PosG-1580, ID / IG, FWHMG, and I2D / IG are Raman characteristic parameters, representing the offset of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak, respectively. k1, k2, k3, and k4 are weighting coefficients, and k0 is a constant term.
[0011] Fourthly, embodiments of this application also provide a carbon material specific capacity detection device, including the carbon material graphitization degree detection device as described above, and further including: An acquisition module is used to acquire the test gram capacity of several of the samples; The substitution module is used to substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b between specific capacity and degree of graphitization, where Y is the specific capacity, g is the degree of graphitization, and a and b are coefficients. The fitting module is used to obtain the values of a and b through fitting. The specific capacity calculation module is used to calculate the specific capacity of the sample by substituting the degree of graphitization of the sample into the correlation function Y=ag-b after calculating the degree of graphitization of the sample.
[0012] Fifthly, embodiments of this application also provide an apparatus, including a memory and a processor; The memory is connected to the processor. The memory is used to store computer programs. The processor is used to call the computer programs so that the computer device can execute the carbon material graphitization degree detection method or the carbon material specific capacity detection method as described above.
[0013] Optionally, the device further includes: a multi-sample carrier plate, a three-dimensional displacement device, and a Raman spectrometer. The processor is equipped with a motion control module, a focus control module, and a spectral acquisition trigger module. The multi-sample carrier plate is used to carry multiple samples and is disposed on the three-dimensional displacement device. The three-dimensional displacement device and the Raman spectrometer are communicatively connected to the processor. The motion control module is used to control the movement of the three-dimensional displacement device so that the samples on the multi-sample carrier plate are moved in a preset order to the area below the laser path of the Raman spectrometer. The focus control module is used to control the Raman spectrometer to automatically focus. The spectral acquisition trigger module is used to trigger the Raman spectrometer to acquire data after focusing is completed.
[0014] Sixthly, embodiments of this application also provide a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed as described above for the carbon material graphitization degree detection method or the carbon material specific capacity detection method as described above.
[0015] In this application, the graphitization degree of the sample to be tested does not require the complex sample preparation required by X-ray diffraction, which helps to solve the technical problems associated with X-ray diffraction (e.g., single-point measurement can be completed in seconds, and combined with rapid scanning, statistical evaluation of macroscopic samples can be achieved, meeting the needs of industrial batch testing). Furthermore, this application constructs a multiple linear regression model using multiple Raman characteristic parameters, which effectively overcomes the problem of single Raman characteristic parameter (ID / IG) failing in the high graphitization degree region, facilitating accurate determination of graphitization degree from low to high. In addition, after calculating the graphitization degree of the sample to be tested, this application can immediately substitute it into the correlation function to quickly predict the specific capacity of the sample. Attached Figure Description
[0016] Figure 1 This is a schematic flowchart of the method for detecting the graphitization degree of carbon materials according to an embodiment of this application.
[0017] Figure 2 This is a schematic flowchart of the carbon material specific capacity detection method according to an embodiment of this application.
[0018] Figure 3 This is a schematic diagram of a carbon material graphitization degree detection device according to an embodiment of this application.
[0019] Figure 4 This is a schematic diagram of a carbon material specific capacity detection device according to an embodiment of this application.
[0020] Figure 5 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation
[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0022] Please see Figure 1 This application discloses a method for detecting the graphitization degree of carbon materials. It should be noted that the carbon materials in this application refer to graphitized carbon materials, that is, the graphitized products of carbon materials such as petroleum coke and pitch coke.
[0023] The method for detecting the graphitization degree of carbon materials includes: S1. Construct a multiple linear regression model. The multiple linear regression model is the correlation between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient.
[0024] In some embodiments, the multiple linear regression model is: g =k0+ k1*(PosG-1580)+k2*(ID / IG) +k3*FWHMG+k4(I2D / IG) Where g represents the degree of graphitization, PosG-1580, ID / IG, FWHMG, and I2D / IG are Raman characteristic parameters, representing the shift of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak, respectively. k1, k2, k3, and k4 are weighting coefficients, and k0 is a constant term. k0 can be set based on the type of carbon material and empirical data.
[0025] The shift of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak are all Raman characteristic parameters that are sensitive to changes in graphitization. By integrating these parameters to construct a multiple linear regression model, it is beneficial to achieve accurate measurement of graphitization from low to high.
[0026] The Raman characteristic parameters mentioned above are explained in detail below: The offset of the G peak position (PosG-1580): The G peak position relative to 1580 (cm) - ¹) offset. PosG is typically at 1580cm. - The ¹ represents a regular graphite structure, specifically a layer of sp² hybridized carbon atoms. As the degree of graphitization increases, the G peak shifts to lower wavenumbers because the vibrational modes of carbon atoms change in the highly ordered graphite structure.
[0027] The ratio of the peak intensity of D peak to G peak (ID / IG): The larger the ID / IG ratio, the more defects there are in the material and the lower the degree of graphitization; conversely, the smaller the ratio, the closer the material is to ideal graphite and the higher the degree of graphitization.
[0028] Full width at half maximum (FWHMG) of G peak: The smaller the FWHMG, the more concentrated the frequency distribution of the G peak, the more uniform the vibrational environment of carbon atoms at different positions, indicating fewer defects in the material and a higher degree of graphitization.
[0029] The peak intensity ratio of 2D peak to G peak (I²D / IG): The peak shape, position, and intensity of the 2D peak are sensitive to the packing order of the graphite layers. Although the carbon layers with low graphitization are locally ordered, the interlayer arrangement is disordered. In this case, the 2D peak is a very broad, weak, and asymmetrical peak. The 2D peak with high graphitization exhibits a typical, high-intensity, and fixed-shape asymmetrical peak.
[0030] S2, determine the degree of graphitization of several selected samples and collect Raman spectral data of several samples and extract Raman characteristic parameters.
[0031] Specifically, several samples underwent graphitization treatment at different temperatures.
[0032] In some embodiments, the degree of graphitization of selected samples is determined by X-ray diffraction.
[0033] In some embodiments, the Raman testing device needs to be calibrated before acquiring Raman spectral data of several samples.
[0034] Specifically, the peak position of the device is calibrated using a silicon wafer: first, a 520.7 cm⁻¹ silicon wafer is used for wavenumber calibration of the Raman test device, and a 532 nm laser is selected for standard sample calibration.
[0035] Specifically, when testing each sample, Raman spectra of multiple effective regions are collected under the same experimental conditions (such as laser wavelength, integration time, number of integrations, detection band, etc.). For example, the integration time is 20 s, the number of integrations is 1, and the detection band is 800 cm⁻¹. -1 -3000cm -1 .
[0036] In some embodiments, the device of this application may include a Raman spectrometer, a multi-sample carrier plate, a three-dimensional displacement device, a motion control module, a focusing control module, and a spectral acquisition triggering module. The multi-sample carrier plate is used to carry multiple samples and is set on the three-dimensional displacement device. The motion control module is used to control the movement of the three-dimensional displacement device so that the samples on the multi-sample carrier plate are moved in a preset order to the area below the laser beam path of the Raman spectrometer. The focusing control module is used to control the Raman spectrometer to automatically focus to ensure that the laser focus falls accurately on the sample surface during each measurement. The spectral acquisition triggering module is used to trigger the Raman spectrometer to acquire data after focusing is completed.
[0037] Specifically, the multi-sample carrier can have multiple regularly arranged sample positions, each of which is used to hold one sample. The three-dimensional displacement device can be precisely positioned in the XYZ plane and, under the control of the motion control module, can quickly move any sample position to below the laser path of the Raman spectrometer.
[0038] Specifically, when using a Raman spectrometer for scanning, a large amount of spectral data can be collected through area scanning or line scanning, and the final result can be obtained by statistical averaging, thereby improving the representativeness and accuracy of the measurement.
[0039] After the Raman spectral data is acquired, the Raman characteristic parameters can be extracted.
[0040] Before extracting Raman characteristic parameters, the sample spectrum can be preprocessed using the data processing module, such as noise filtering, baseline correction, peak position detection, and peak fitting.
[0041] By seamlessly integrating functional modules such as motion control, focus control, spectral acquisition triggering, and data processing into the software, the system lowers the technical threshold by requiring only simple sample placement and software clicks. Even non-experts can obtain professional and reliable analytical results with the help of this system, reducing test differences caused by human factors and making test results more comparable and reproducible.
[0042] S3. The graphitization degree of each sample and the extracted Raman characteristic parameters are substituted into the multiple linear regression model and fitted to obtain the values of each weight coefficient.
[0043] S4. Collect the Raman spectral data of the sample to be tested and extract the Raman characteristic parameters of the sample to be tested. Then input them into the multiple linear regression model to calculate the graphitization degree (predicted value) of the sample to be tested.
[0044] It should be noted that when collecting Raman spectral data of the sample to be tested, the same experimental conditions as in step S1 are usually used.
[0045] In this embodiment, a multiple linear regression model is constructed to establish the correlation between graphitization degree and multiple Raman characteristic parameters. The graphitization degree of several selected samples is measured, and Raman spectral data of several samples are collected and Raman characteristic parameters are extracted. Then, the measured graphitization degree of each sample and the extracted Raman characteristic parameters are substituted into the multiple linear regression model for fitting to obtain the values of each weight coefficient. Subsequently, when it is necessary to detect the graphitization degree of a sample, it is only necessary to collect the Raman spectral data of the sample to be tested and extract the Raman characteristic parameters of the sample to be tested, and then substitute them into the multiple linear regression model to calculate the graphitization degree of the sample to be tested. Therefore, the carbon material graphitization degree detection method of this application embodiment does not require complex sample preparation like X-ray diffraction when detecting the graphitization degree of the sample to be tested. This helps to solve the technical problems brought about by X-ray diffraction (for example, single-point measurement can be completed in seconds, and combined with rapid scanning, it can realize the statistical evaluation of macroscopic samples, and the process meets the needs of industrial batch testing). In addition, this application constructs a multiple linear regression model by using multiple Raman characteristic parameters, which can effectively overcome the problem of the failure of a single Raman characteristic parameter (ID / IG) in the high graphitization degree region, and is conducive to achieving accurate determination of graphitization degree from low to high.
[0046] Please see Figure 2 This application also discloses a method for detecting the specific capacity of carbon materials, including the method for detecting the graphitization degree of carbon materials as described above, and further including: S5, obtain the test gram capacity of several samples.
[0047] S6. Substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b and obtain the values of a and b by fitting, where Y is the specific capacity, g is the degree of graphitization, and a and b are coefficients.
[0048] Specifically, several samples that have undergone graphitization treatment at different temperatures are assembled into half-cells (with lithium sheets as the counter electrode), and the half-cells are tested to obtain the test capacity for the first charge, which is the aforementioned test capacity.
[0049] S7. After calculating the degree of graphitization of the sample to be tested, substitute the degree of graphitization of the sample to be tested into the correlation function Y=ag-b to obtain the specific capacity (prediction) of the sample to be tested.
[0050] In this application, the graphitization degree of the sample to be tested does not require the complex sample preparation required by X-ray diffraction, which helps to solve the technical problems associated with X-ray diffraction (e.g., single-point measurement can be completed in seconds, and combined with rapid scanning, statistical evaluation of macroscopic samples can be achieved, meeting the needs of industrial batch testing). Furthermore, this application constructs a multiple linear regression model using multiple Raman characteristic parameters, which effectively overcomes the problem of single Raman characteristic parameter (ID / IG) failing in the high graphitization degree region, facilitating accurate determination of graphitization degree from low to high. In addition, after calculating the graphitization degree of the sample to be tested, the specific capacity of the sample can be quickly predicted by substituting it into the correlation function.
[0051] To better understand this application, the following examples are provided: First, select 50-100 graphite samples (9 of which are listed below), and each sample is graphitized at different temperatures.
[0052] Then, the degree of graphitization g of each sample was accurately determined by X-ray diffraction, and was 0.481, 0.482, 0.558, 0.611, 0.645, 0.761, 0.829, 0.907, and 0.942, respectively.
[0053] Next, the peak positions of the device were calibrated using a silicon wafer: First, a 520.7 cm⁻¹ silicon wafer was used for wavenumber calibration of the Raman testing device. A 532 nm laser was selected for standard sample calibration, with an integration time of 20 s and one integration cycle. The detection wavelength range was 800 cm⁻¹ to 3000 cm⁻¹. All samples were placed sequentially into a multi-sample carrier tray, and the test sequence was entered into the motion control module. After automatic focusing calibration of each sample, 10 different points were selected, and the test began. After the test, the average spectrum was taken.
[0054] Next, the data processing module was started to perform baseline correction on the spectrum and then fit the spectrum to extract the PosG, ID / IG, FWHMG and I2D / IG for each sample (as shown in Table 1).
[0055] Next, the PosG, ID / IG, FWHMG, I2D / IG values and their corresponding graphitization degrees were substituted into the multiple linear regression model, and then the weight coefficients were obtained through fitting. The resulting multiple linear regression model is as follows: g=0.860-0.022(PosG-1580)-0.165(ID / IG)-0.0080(FWHM G )+0.250(I2D / IG) (R²>95%).
[0056] Next, the test specific capacity (as shown in Table 1) and graphitization degree of several samples were substituted into the correlation function Y=ag-b, and the values of a and b were obtained by fitting, thus obtaining the correlation function Y=373.41G-1.5965 (R²=0.9987).
[0057]
[0058] Table 1 The following five verification examples are provided for validation: Verification Example 1 When the same type of graphite was tested under the same conditions, its PosG = 1580.7 cm⁻¹ was obtained. - ¹, ID / IG=0.09, FWHMG=19.3cm - ¹, I2D / IG=0.823. Substituting into the model, we get g=0.881. Substituting into the correlation function Y=373.41g-1.5965, we get the predicted value Y=327.42. We then performed a discharge-charge test on the coin cell to obtain the specific capacity (mAh / g) test value Y, as shown in Table 2.
[0059] Verification Example 2 When the same type of graphite was tested under the same conditions, its PosG = 1581.2 cm⁻¹ was obtained. - ¹, ID / IG=0.254, FWHMG=24.9 cm - ¹, I2D / IG=0.722. Substituting into the model, we get g=0.751. Substituting into the correlation function Y=307.7g+57.8, we get the predicted value Y=278.83. We then performed a discharge-charge test on the coin cell to obtain the specific capacity (mAh / g) test value Y, as shown in Table 2.
[0060] Verification Example 3 When the same type of graphite was tested under the same conditions, its PosG = 1582.8 cm⁻¹ was obtained. - ¹, ID / IG=0.382, FWHMG=26.5 cm - ¹, I2D / IG=0.577. Substituting into the model, we get g=0.668. Substituting into the correlation function Y=307.7g+57.8, we get the predicted value Y=247.70. We then performed a discharge-charge test on the coin cell to obtain the specific capacity (mAh / g) test value Y, as shown in Table 2.
[0061] Verification Example 4 When the same type of graphite was tested under the same conditions, its PosG = 1580.3 cm⁻¹ was obtained. - ¹, ID / IG=0.052, FWHMG=19.2 cm -¹, I2D / IG=0.921. Substituting into the model, we get g=0.921. Substituting into the correlation function Y=307.7g+57.8, we get the predicted value Y=342.49. We then performed a discharge-charge test on the coin cell to obtain the specific capacity (mAh / g) test value Y, as shown in Table 2.
[0062] Verification Example 5 When the same type of graphite was tested under the same conditions, its PosG = 1581.7 cm⁻¹ was obtained. - ¹, ID / IG=0.194, FWHMG=23.3 cm - ¹, I2D / IG=0.812, substituting into the model, we get g=0.807. Substituting into the correlation function Y=307.7g+57.8, we get the predicted value Y=299.82. We conduct discharge and charge tests on the button cell and obtain the specific capacity (mAh / g) test value Y, as shown in Table 2.
[0063]
[0064] Table 2 As shown in Table 2, the error of the predicted specific capacity Y relative to the measured specific capacity Y was calculated for the above 5 sets of verification examples, and the relative error was found to be <3mAh / g.
[0065] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0066] Based on the same inventive concept, this application also provides an apparatus for implementing the method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations / descriptions in the verification apparatus embodiments provided below can be found in the limitations / descriptions of the method above, and will not be repeated here.
[0067] Please see Figure 3 This application discloses a device for detecting the graphitization degree of carbon materials, comprising: Module 201 is used to construct a multiple linear regression model. The multiple linear regression model is the relationship between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient. The measurement and acquisition module 202 is used to measure the graphitization degree of several selected samples and to acquire Raman spectral data of several samples and extract Raman characteristic parameters. The substitution and fitting module 203 is used to substitute the graphitization degree measured for each sample and the extracted Raman characteristic parameters into the multiple linear regression model and then fit the model to obtain the values of each weight coefficient. The calculation module 204 is used to acquire the Raman spectral data of the sample to be tested and extract the Raman characteristic parameters of the sample to be tested, and then input them into the multiple linear regression model to calculate the degree of graphitization of the sample to be tested.
[0068] The carbon material graphitization degree detection device of this application eliminates the need for complex sample preparation as required by X-ray diffraction when detecting the graphitization degree of the sample. This helps to solve the technical problems caused by X-ray diffraction (for example, single-point measurement can be completed in seconds, and combined with rapid scanning, it can achieve statistical evaluation of macroscopic samples, and the process meets the needs of industrial batch testing). In addition, this application constructs a multiple linear regression model through multiple Raman characteristic parameters, which can effectively overcome the problem of failure of a single Raman characteristic parameter (ID / IG) in the high graphitization degree region, and facilitates the accurate determination of graphitization degree from low to high.
[0069] Please see Figure 4 This application discloses a carbon material specific capacity detection device, including: the carbon material graphitization degree detection device as described above, and further including: The acquisition module 205 is used to acquire the test gram capacity of several samples; Substitute into module 206, which is used to substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b between specific capacity and degree of graphitization, where Y is specific capacity, g is degree of graphitization, and a and b are coefficients. Fitting module 207 is used to obtain the values of a and b through fitting. The specific capacity calculation module 208 is used to calculate the specific capacity of the sample by substituting the degree of graphitization of the sample into the correlation function Y=ag-b.
[0070] In this application, the graphitization degree of the sample to be tested does not require the complex sample preparation required by X-ray diffraction, which helps to solve the technical problems associated with X-ray diffraction (e.g., single-point measurement can be completed in seconds, and combined with rapid scanning, statistical evaluation of macroscopic samples can be achieved, meeting the needs of industrial batch testing). Furthermore, this application constructs a multiple linear regression model using multiple Raman characteristic parameters, which effectively overcomes the problem of single Raman characteristic parameter (ID / IG) failing in the high graphitization degree region, facilitating accurate determination of graphitization degree from low to high. In addition, after calculating the graphitization degree of the sample to be tested, the specific capacity of the sample can be quickly predicted by substituting it into the correlation function.
[0071] In this application embodiment, the term "module" refers to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.
[0072] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 4 As shown, the computer device may include a processor 601 and a memory 602. The memory 602 is connected to the processor 601 and is used to store computer programs. The processor 601 calls the computer programs to cause the computer device to execute the methods described in the above embodiments. Furthermore, the computer device may also include at least one communication bus 603. The communication bus 603 is used to implement communication between components. The memory 602 may be a high-speed RAM or a non-volatile memory, such as at least one disk storage device.
[0073] This application also provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute the methods described in the above embodiments.
[0074] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods described in the above embodiments.
[0075] It should be understood that, in the embodiments of this application, the processor may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0076] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by hardware related to computer program instructions. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0077] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0078] The above-disclosed examples are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall fall within the scope of this application.
Claims
1. A method for detecting the graphitization degree of carbon materials, characterized in that, include: A multiple linear regression model is constructed, which is the correlation between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient. The degree of graphitization of several selected samples was determined, and Raman spectral data of several of the samples were collected and the Raman characteristic parameters were extracted. The graphitization degree of each sample and the extracted Raman characteristic parameters are substituted into the multiple linear regression model and fitted to obtain the values of each weight coefficient. Raman spectral data of the sample to be tested are collected and the Raman characteristic parameters of the sample to be tested are extracted and then input into the multiple linear regression model to calculate the degree of graphitization of the sample to be tested.
2. The method for detecting the graphitization degree of carbon materials according to claim 1, characterized in that, The multiple linear regression model is as follows: g =k0+ k1*(PosG-1580)+k2*(ID / IG) +k3*FWHMG+k4(I2D / IG) Where g is the degree of graphitization, PosG-1580, ID / IG, FWHMG, and I2D / IG are Raman characteristic parameters, representing the offset of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak, respectively. k1, k2, k3, and k4 are weighting coefficients, and k0 is a constant term.
3. The method for detecting the graphitization degree of carbon materials according to claim 1, characterized in that, The degree of graphitization of the selected samples was determined by X-ray diffraction.
4. A method for detecting the specific capacity of carbon materials, characterized in that, The method includes the graphitization degree detection method for carbon materials according to any one of claims 1 to 3, and further includes: Obtain the test gram capacity of several of the samples; Substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b between specific capacity and degree of graphitization, where Y is the specific capacity, g is the degree of graphitization, and a and b are coefficients. The values of a and b are obtained by fitting the data. After calculating the degree of graphitization of the sample to be tested, the degree of graphitization of the sample to be tested is substituted into the correlation function Y=ag-b to obtain the specific capacity of the sample to be tested.
5. A device for detecting the graphitization degree of carbon materials, characterized in that, include: A construction module is used to construct a multiple linear regression model, which is the correlation between the graphitization degree of carbon materials and multiple Raman characteristic parameters. The graphitization degree is equal to the sum of the constant term and the product of each Raman characteristic parameter and its respective weight coefficient. The measurement and acquisition module is used to measure the degree of graphitization of several selected samples and to acquire Raman spectral data of several samples and extract the Raman characteristic parameters. The substitution and fitting module is used to substitute the graphitization degree measured for each sample and the extracted Raman feature parameters into the multiple linear regression model and then fit the model to obtain the values of each weight coefficient. The calculation module is used to acquire Raman spectral data of the sample to be tested and extract the Raman characteristic parameters of the sample to be tested, and then input them into the multiple linear regression model to calculate the degree of graphitization of the sample to be tested.
6. The carbon material graphitization degree detection device according to claim 1, characterized in that, The multiple linear regression model is as follows: g =k0+ k1*(PosG-1580)+k2*(ID / IG) +k3*FWHMG+k4(I2D / IG) Where g is the degree of graphitization, PosG-1580, ID / IG, FWHMG, and I2D / IG are Raman characteristic parameters, representing the offset of the G peak position, the peak intensity ratio of the D peak to the G peak, the full width at half maximum (FWHM) of the G peak, and the peak intensity ratio of the 2D peak to the G peak, respectively. k1, k2, k3, and k4 are weighting coefficients, and k0 is a constant term.
7. A carbon material specific capacity detection device, characterized in that, The device for detecting the graphitization degree of carbon materials as described in claim 5 or 6, and further includes: An acquisition module is used to acquire the test gram capacity of several of the samples; The substitution module is used to substitute the measured specific capacity and the measured degree of graphitization of several samples into the correlation function Y=ag-b between specific capacity and degree of graphitization, where Y is the specific capacity, g is the degree of graphitization, and a and b are coefficients. The fitting module is used to obtain the values of a and b through fitting. The specific capacity calculation module is used to calculate the specific capacity of the sample by substituting the degree of graphitization of the sample into the correlation function Y=ag-b after calculating the degree of graphitization of the sample.
8. A device, characterized in that, Including memory and processor; The memory is connected to the processor, the memory is used to store computer programs, and the processor is used to call the computer programs so that the computer device executes the carbon material graphitization degree detection method according to any one of claims 1 to 3 or the carbon material specific capacity detection method according to claim 4.
9. The device according to claim 8, characterized in that, The device further includes: a multi-sample carrier plate, a three-dimensional displacement device, and a Raman spectrometer. The processor is equipped with a motion control module, a focusing control module, and a spectral acquisition triggering module. The multi-sample carrier plate is used to carry multiple samples and is positioned on the three-dimensional displacement device. The three-dimensional displacement device and the Raman spectrometer are communicatively connected to the processor. The motion control module is used to control the movement of the three-dimensional displacement device so that the samples on the multi-sample carrier plate move in a preset order to the area below the laser path of the Raman spectrometer. The focusing control module is used to control the Raman spectrometer to automatically focus. The spectral acquisition triggering module is used to trigger the Raman spectrometer to acquire data after focusing is completed.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed by the carbon material graphitization degree detection method according to any one of claims 1 to 3 or the carbon material specific capacity detection method according to claim 4.