Quality detection method for graphite composite structure

Abstract

Provided in the present application is a quality detection method for a graphite composite structure. The method comprises: selecting a graphite composite structure as a test sample, wherein the graphite composite structure comprises a graphite core and a coating layer coating the graphite core; selecting a test region of the test sample, and using a Raman spectrometer to sequentially perform Raman tests on different sites in the test region, so as to acquire Raman spectra of at least 100 sites; performing peak fitting on data of the Raman spectra of the test sample, so as to obtain a D peak and a G peak; on the basis of the intensities or areas of the D peak and the G peak, acquiring a Raman characteristic parameter A for the at least 100 sites; on the basis of the Raman characteristic parameter A, detecting the relative concentration of defects at different sites on the surface of the test sample; and / or on the basis of the Raman characteristic parameter A, detecting coating information of the coating layer on the surface of the test sample, wherein the coating information comprises whether a coating layer is present and whether the coating layer is excessively coated.

Description

Quality testing methods for graphite composite structures Technical Field

[0001] This application relates to the field of lithium-ion batteries, and in particular to a method for quality testing of graphite composite structures. Background Technology

[0002] With the continuous development of the energy storage and power battery manufacturing industry, people have placed higher demands on the fast charging and long cycle performance of lithium-ion batteries. Graphite, as a standard commercial lithium-ion battery anode material, has a capacity of 372 mAh / g. -1 While graphite anodes offer high reversible specific capacity, their high graphitization and strong orientation lead to slow lithium insertion / extraction kinetics, limiting their application in high-rate fast charging. Current research utilizes surface coating methods to load carbon precursors onto the surface of highly crystalline carbon materials like graphite, creating an amorphous carbon coating layer to form a graphite composite structure. This composite structure combines the high capacity of highly crystalline graphite with the good electrolyte compatibility of the amorphous carbon coating. For graphite with its large specific surface area, the amorphous carbon coating can fill the pores, increasing the tap density, reducing the specific surface area, suppressing interfacial side reactions, and improving the initial coulombic efficiency. Furthermore, the highly ionicly conductive carbon coating helps lithium ions reach the edge active sites of graphite, lowering the lithium ion desolvation barrier, increasing the lithium ion migration rate, and preventing graphite exfoliation and pulverization caused by solvent molecule co-intercalation, thus improving the material's cycle stability. Therefore, the coating effect of the carbon coating layer on the graphite surface, including the coating rate, coating thickness and uniformity, has a great influence on the material properties. Therefore, a stable and effective detection method is needed to test the surface carbon coating layer and its distribution. Summary of the Invention

[0003] This application provides a method for quality testing of graphite composite structures, comprising: selecting a graphite composite structure as a test sample, wherein the graphite composite structure includes a graphite core and a coating layer covering the graphite core;

[0004] The test area of ​​the test sample is selected, and Raman spectrometer is used to perform Raman tests on different sites in the test area in sequence to obtain Raman spectra of at least 100 sites;

[0005] Peak fitting was performed on the Raman spectrum data of the test sample to obtain the D peak and the G peak;

[0006] The Raman characteristic parameters A of the at least 100 sites are obtained based on the intensity or area of ​​the D and G peaks, where A = I D / I G I D I represents the intensity or area of ​​peak D.G This represents the intensity or area of ​​the G peak;

[0007] The relative concentration of defects at different sites on the surface of the test sample is detected based on the Raman characteristic parameter A; and / or the coating information of the coating layer on the surface of the test sample is detected based on the Raman characteristic parameter A, wherein the coating information includes whether a coating layer exists and whether the coating layer is over-coated.

[0008] In some embodiments of this application, the method further includes:

[0009] A frequency histogram or a cumulative frequency histogram is plotted based on the Raman feature parameter A. Based on the frequency histogram or the cumulative frequency histogram, the A value Ik corresponding to the distribution percentage of the Raman feature parameter A reaching k is obtained, where k is any one or more of 10, 20, 30, 40, 50, 60, 70, 80, and 90.

[0010] The quality of the graphite composite structure is determined based on the Ik value.

[0011] In some embodiments of this application, determining the quality of the graphite composite structure based on the Ik value includes:

[0012] According to the I 50 The value evaluates the overall surface disorder level of the graphite composite structure, where I 50 The value of A corresponding to k being 50; and / or

[0013] The distribution of surface defect concentration of the graphite composite structure is evaluated based on the Raman characteristic parameter A values ​​of the at least 100 sites; and / or

[0014] The coating uniformity is evaluated based on the distribution width of the frequency histogram or the cumulative frequency histogram, wherein the coating uniformity = (1-(I 80 -I 20 ) / 1)×100%, I 80 I is the value of A corresponding to k being 80. 20 This is the value of A when k is 20.

[0015] In some embodiments of this application, detecting the coating information of the coating layer on the surface of the test sample based on the Raman characteristic parameter A includes: when the Raman characteristic parameter A is greater than 0.05 and less than or equal to 0.2, the coating layer exists on the surface of the test sample, and the coating rate R1 of the coating layer is N. A1 / N 总 ×100%, N A1 N represents the number of locations where the coating layer exists. 总 This represents the total number of locations where Raman testing was performed.

[0016] When the Raman characteristic parameter A is greater than or equal to 0.5 and less than or equal to 0.9, the coating layer on the surface of the test sample is over-coated, and the over-coating rate R2 of the coating layer is N. A2 / N 总 ×100%, N A2 N represents the number of locations where the coating layer exists. 总 This indicates the total number of locations where Raman testing was performed.

[0017] In some embodiments of this application, the test area includes at least 20 graphite composite structure particles.

[0018] In some embodiments of this application, the method further includes: obtaining a defect concentration distribution heat map of the test sample surface based on the coordinate data of the at least 100 sites in the test area and the feature parameter A value, and detecting the coating information of the coating layer on the test sample surface based on the defect concentration distribution heat map.

[0019] In some embodiments of this application, the at least 100 sites are arranged in an array. Optionally, the at least 100 sites form a 10×10 array, located over a region of at least 10×10 μm on the surface of the test sample. Optionally, the at least 100 sites are 2500 sites, forming a 50×50 array, located over a region of at least 50×50 μm on the surface of the test sample.

[0020] In some embodiments of this application, the test samples have different particle sizes, and / or the test samples have coatings of different thicknesses, and / or the coatings have different residual carbon content.

[0021] In some embodiments of this application, the scanning wavenumber of the Raman spectrometer is 1050 cm⁻¹. -1 Centered on [a specific point], the wavenumber range is 98.58–1869.89 cm. -1 .

[0022] Compared with existing technologies, this application provides a method for quality testing of graphite composite structures.

[0023] The relative concentration of defects at different sites on the surface of the test sample is detected based on the Raman characteristic parameter A; and / or the coating information of the coating layer on the surface of the test sample is detected based on the Raman characteristic parameter A, the coating information including whether a coating layer exists and whether the coating layer is over-coated; further, a frequency histogram or a cumulative frequency histogram is plotted based on the Raman characteristic parameter A, and the A value Ik corresponding to the distribution percentage of the Raman characteristic parameter A reaching k is obtained according to the frequency histogram or the cumulative frequency histogram, where k is any one or more of 10, 20, 30, 40, 50, 60, 70, 80, 90; the quality of the graphite composite structure is judged based on the Ik value. For example, by I... 10 I 20 I 50 I 80 I 90 To describe the distribution of the coating layer at different sites on the surface of the graphite composite structure; through I 50 The overall surface disorder level of the graphite composite structure is evaluated; at the same time, based on the Raman distribution of the coating layer, parameters such as the coating uniformity and coating rate of the coating layer are determined, so as to realize the quantitative analysis of the overall coating level of the coating layer using Raman spectroscopy; hotspot distribution maps are drawn based on the Raman characteristic parameter A at different sites to intuitively understand the distribution and distribution pattern of the coating layer on the material surface. Attached Figure Description

[0024] Figure 1 shows the peak fitting result of a Raman spectrum;

[0025] Figures 2a and 2b show the frequency histogram and cumulative frequency histogram of feature parameter A obtained by Raman surface scanning in Example 1;

[0026] Figures 3a, 3b, 3c, and 3d are the Raman characteristic parameter A-frequency histograms of the test samples from Examples 1, 6, 7, and 8, respectively.

[0027] Figures 4a, 4b, 4c, and 4d are the Raman characteristic parameter A-frequency histograms of the test samples from Examples 9, 10, 11, and 12, respectively.

[0028] Figures 5a and 5b show the relationship between characteristic parameters and residual carbon, respectively.

[0029] Figure 6 shows the hotspot diagram of characteristic parameter A distribution of graphite with the same residual carbon at different particle sizes;

[0030] Figure 7 shows the heat map of characteristic parameter A distribution of graphite with different residual carbon particles of the same size. Detailed Implementation

[0031] The following description provides specific application scenarios and requirements for this application, intended to enable those skilled in the art to make and use the content of this application. Various partial modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of this application. Therefore, this application is not limited to the embodiments shown, but rather to the widest scope consistent with the claims.

[0032] Graphite raw materials undergo a graphitization process at temperatures above 2700°C to form graphene sheets with a stacked, ordered structure. Using pitch and resin as raw materials, carbonization at 1100°C forms a carbon coating layer on the surface of the graphene sheets. This carbon coating layer, having only undergone carbonization, has an amorphous carbon structure with small grains, low crystallinity, and many incompletely precipitated heteroatoms, containing a large number of dopants, vacancies, and edge structures. Therefore, the presence and distribution of the surface carbon layer can be detected by identifying structural differences in the material. Currently, the main methods for detecting carbon coatings include transmission electron microscopy (TEM), graphitization degree, and Raman spectroscopy. TEM is costly, its results are highly unpredictable and lack representativeness, making it unsuitable as a routine quality control method. Graphitization degree and Raman spectroscopy have limited detection capabilities, only confirming the presence of a carbon coating. For example, by obtaining the relationship curve between the graphitization degree and the Raman R value of the tested material, the deviation between the actual and theoretical values ​​is calculated to comprehensively determine the presence of a carbon coating on the graphite surface. However, this method has a lengthy process, and its accuracy is significantly affected by the relationship curve, failing to meet the need for accurate identification of the carbon coating effect on the graphite surface. In the industry, graphite composite structures are mostly measured by parameters such as particle size, BET, and tap rate to assess the carbon coating effect on the graphite surface. However, graphite production involves many steps and numerous factors influencing these parameters, making it impossible to accurately measure the coating effect from the graphite composite structure itself, thus hindering quality control during factory production.

[0033] Raman spectroscopy is the most commonly used method for testing differences in the surface structure of materials. For graphitic carbon materials, due to differences in atomic arrangement, carbon bonding, and corresponding vibrational modes, each structural type has a corresponding vibrational mode and characteristic Raman spectrum. For graphitized carbon structures, the G peak is the main characteristic peak of graphite, which is generated by SP. 2 Caused by in-plane vibrations of carbon atoms, appearing at 1580 cm⁻¹. -1 Nearby. However, for graphite composite structures with a carbon coating on the graphite surface, the edges of the carbon coating often have numerous defects, except at 1580 cm⁻¹. -1 A G-peak appeared nearby, and it will be at 1350cm.-1 D peaks appear on the left and right sides, and at 1620cm. -1 A D′ peak appears nearby.

[0034] Currently, Raman spectroscopy is rarely used in the production of graphite composite structures with carbon coatings on the graphite surface. This is because the distribution of carbon coatings on the graphite surface is complex. Depending on the preparation process, the carbon coating is usually distributed in a dotted or patchy pattern on the graphite particle surface. Although the presence of the carbon coating can significantly improve the fast-charging performance of the material, the inhomogeneity of the carbon coating can lead to graphite exposure and carbon accumulation on the graphite surface, resulting in reduced first-pass yield and rate capability. Conventional point scanning or area scanning has a small number of points and area, which can only reflect local information of the carbon coating, and the test fluctuations and stability are poor, making it difficult to evaluate the coating level of carbon, etc. Therefore, a more complete and accurate method is needed to quantitatively evaluate the coating effect of carbon coatings on graphite in graphite composite structures, and to further solve the problem of quality control in the production of graphite composite structures.

[0035] This application embodiment performs Raman tests on different sites within the test area sequentially. For example, it samples different sites on the surface of a 50μm×50μm graphite composite structure and performs Raman tests to obtain coating information covering more than 20 conventionally coated graphite particles. The overall coating level of the graphite composite structure is obtained through statistical analysis. On the other hand, the coating situation of the graphite composite structure is presented intuitively through heat maps, thereby providing effective support for the research and development, process improvement, and production quality control of the graphite composite structure.

[0036] One aspect of this application provides a method for quality testing of graphite composite structures, comprising:

[0037] Step S1: Select a graphite composite structure as a test sample. The graphite composite structure includes a graphite core and a coating layer covering the graphite core.

[0038] Step S2: Select the test area of ​​the test sample, and use a Raman spectrometer to perform Raman tests on different sites in the test area in sequence to obtain Raman spectra of at least 100 sites;

[0039] Step S3: Perform peak fitting on the Raman spectrum data of the test sample to obtain the D peak and G peak;

[0040] Step S4: Obtain the Raman characteristic parameters A of the at least 100 sites based on the intensity or area of ​​the D peak and G peak, where A = I D / I G I D I represents the intensity or area of ​​peak D. G This represents the intensity or area of ​​the G peak;

[0041] Step S5: Detect the relative concentration of defects at different sites on the surface of the test sample based on the Raman characteristic parameter A; and / or detect the coating information of the coating layer on the surface of the test sample based on the Raman characteristic parameter A, wherein the coating information includes whether a coating layer exists and whether the coating layer is over-coated.

[0042] In step S1, a graphite composite structure is selected as the test sample. The graphite composite structure includes a graphite core and a coating layer covering the graphite core, such as a carbon coating layer. In some embodiments of this application, the test samples from different batches may have different particle sizes, which may be average particle sizes, and / or the test samples may have coating layers of different thicknesses, and / or the coating layers may have different residual carbon contents. The different residual carbon contents refer to the proportion of residual carbon contained in the coating layer after carbonization.

[0043] For test samples from the same batch, the particle size of the test samples can be kept uniform. For example, a 300-mesh sieve can be used to sieve the test samples to remove large particles and obtain the test samples. This application embodiment requires that the particle size of the test samples entering the Raman spectrometer be uniform. In this application embodiment, the average particle size range of the test samples is 11 to 18 μm.

[0044] Step S2: Select the test area of ​​the test sample, and use a Raman spectrometer to perform Raman tests on different sites in the test area in sequence to obtain Raman spectra of at least 100 sites.

[0045] The selection principle for the test area is as follows: the test area contains multiple graphite composite structure particles with uniform particle size and similar average particle diameter, so that there is sufficient space within the test area to meet the requirements of test stability. In some embodiments of this application, the test area includes at least 20 of the graphite composite structure particles.

[0046] In this embodiment of the application, the test area includes at least 100 sites to be tested in order to form a suitable Raman spectrum.

[0047] In some embodiments of this application, the at least 100 sites are arranged in an array. Optionally, the at least 100 sites form at least a 10×10 array, located in an area of ​​at least 10×10 μm on the surface of the test sample. Optionally, the test area includes at least 2500 sites to be tested, for example, 2500 sites forming a 50×50 array, located in an area of ​​at least 50×50 μm on the surface of the test sample.

[0048] The Raman spectrometer in this application embodiment can be any commercially available Raman spectrometer, such as a Renishaw confocal micro Raman spectrometer. In some embodiments, a confocal Raman spectrometer with a 532 nm wavelength light source can be selected, configured with a slit width of 1800 l / mm (vis), optical path calibration using an internal silicon chip, and the Raman spectral scanning wavenumber range set to 1050 cm⁻¹. -1 Centered at 98.58–1869.89 cm -1 The laser intensity irradiating the surface of the test area of ​​the test sample can be 0.5% to 50% of the laser intensity emitted by the Raman spectrometer, for example, 10%, 20%, 30%, or 40%, to avoid excessive laser intensity causing carbonization of the coating layer on the surface of the test sample. The single-point scanning time is 0.1 s to 1 s. In some embodiments of this application, the laser intensity irradiating the surface of the test area of ​​the test sample can be 10% of the laser intensity emitted by the Raman spectrometer, and the single-point scanning time is 0.5 s.

[0049] In this embodiment, a test area can be selected for surface scanning based on the particle size of the test sample, for example, 50×50μm, with a step size of 0.5~5μm, for example 1μm, to form an array of 50×50 sites, for a total of 2500 sites. Raman testing is performed on each site sequentially to obtain the Raman spectra of different sites on the surface of the test sample within the test area.

[0050] Step S3: Perform peak fitting on the Raman spectrum data of the test sample to obtain the D peak and G peak; wherein, the Raman spectrum data consists of Raman spectral data from at least 100 sites. First, remove the background from the Raman spectrum, and then perform peak fitting on the Raman characteristic peaks of the graphite coating to obtain the D peak and G peak, which are located at 1350 cm⁻¹. -1 Nearby and 1580cm -1 Nearby, the peak fitting also yielded peak D′, located at 1620 cm⁻¹. -1 Nearby, as shown in Figure 1, is a peak fitting result of a Raman spectrum.

[0051] Step S4: Obtain the Raman characteristic parameters A of the at least 100 sites based on the intensity or area of ​​the D peak and G peak, where A = I D / I G I D I represents the intensity or area of ​​peak D. G This represents the intensity or area of ​​the G peak;

[0052] Step S5: Detect the relative concentration of defects at different sites on the surface of the test sample based on the Raman characteristic parameter A; and / or detect the coating information of the coating layer on the surface of the test sample based on the Raman characteristic parameter A, wherein the coating information includes whether a coating layer exists and whether the coating layer is over-coated.

[0053] Among them, I D For SP 3 Hybrid structures, which are disordered structures, represent defects in ordered structures. The more defects there are, the higher the quality. D The higher the value, the better for defect-free graphite materials I D =0; I G The value represents the ordered arrangement of SP in graphite. 2 With a hybrid structure, the Raman signal is constant; however, due to differences in laser intensity at different sites, the signal varies with I. G As the denominator for different sites I D Values ​​are all in I G The relative concentration is used as a reference, therefore I D / I G This represents the relative defect concentration relative to the ordered structure signal. In this embodiment, for the test sample, the relative defect concentration at different sites on the surface of the test sample can be detected based on the Raman characteristic parameter A.

[0054] In some embodiments of this application, detecting the coating information of the coating layer on the surface of the test sample based on the Raman feature parameter A includes: when the Raman feature parameter A is greater than 0.05 and less than or equal to 0.2, the coating layer exists on the surface of the test sample, and the coating rate R1 = N of the coating layer is obtained based on the Raman feature parameter A. A1 / N 总 ×100%, N A1 N represents the number of locations where the coating layer exists. 总 This represents the total number of locations where Raman testing is performed. In other embodiments of this application, the Raman characteristic parameter A is preferably greater than 0.15 and less than or equal to 0.2.

[0055] In some embodiments of this application, when the Raman characteristic parameter A is greater than or equal to 0.5 and less than or equal to 0.9, the coating layer on the surface of the test sample is over-coated, and the over-coating rate R2 of the coating layer is N. A2 / N 总 ×100%, N A2 N represents the number of locations where the coating layer exists. 总 This indicates the total number of locations where Raman testing is performed. In other embodiments of this application, when the coating layer on the surface of the test sample is overcoated, the Raman characteristic parameter A is greater than 0.7 and less than or equal to 0.0.

[0056] In some embodiments of this application, the method further includes step S6: drawing a frequency histogram or a cumulative frequency histogram based on the Raman feature parameter A; obtaining the A value Ik corresponding to the distribution percentage of the Raman feature parameter A reaching k based on the frequency histogram or the cumulative frequency histogram, where k is a positive integer, and further, k is taken from any one or more of 10, 20, 30, 40, 50, 60, 70, 80, and 90; and determining the quality of the graphite composite structure based on the Ik value.

[0057] In some embodiments of this application, determining the quality of the graphite composite structure based on the Ik value includes:

[0058] According to the I 50 The value evaluates the overall surface disorder level of the graphite composite structure, where I 50 The value of A corresponding to k being 50; and / or

[0059] The distribution of surface defect concentration of the graphite composite structure is evaluated based on the Raman characteristic parameter A values ​​of the at least 100 sites; and / or

[0060] The coating uniformity is evaluated based on the distribution width of the frequency histogram or the cumulative frequency histogram, wherein the coating uniformity = (1-(I 80 -I 20 ) / 1)×100%, I 80 I is the value of A corresponding to k being 80. 20 This is the value of A when k is 20.

[0061] In some embodiments of this application, the method further includes step S7: obtaining a defect concentration distribution heat map of the test sample surface based on the coordinate data of the at least 100 sites in the test area and the characteristic parameter A value of the at least 100 sites, and detecting the coating information of the coating layer on the test sample surface based on the defect concentration distribution heat map, wherein the coating information includes whether a coating layer exists and whether the coating layer is over-coated.

[0062] In the defect concentration distribution heat map, feature parameter A values ​​between 0 and 0.2 are displayed in green, indicating no coating layer; feature parameter A values ​​between 0.2 and 0.8 are displayed in blue, indicating the coating layer is graphite; and feature parameter A values ​​between 0.8 and 1 are displayed in red, indicating the coating layer is overcoated. The distribution of the surface coating layer can be visually understood from the defect concentration distribution heat map. Figures 6 and 7 show feature parameter A distribution heat maps of graphite coated with different particle sizes and the same residual carbon, and graphite coated with the same particle size but different residual carbon, respectively. The different residual carbon amounts refer to the proportion of residual carbon contained in the coating layer after carbonization.

[0063] The order of steps S5, S6, and S7 is not limited in this embodiment. Steps S5, S6, and S7 can be performed simultaneously or sequentially.

[0064] This application provides a method for evaluating the surface coating effect of coated graphite. Raman spectroscopy is used to scan the surface of the coated layer to test the structural differences at various sites in the test area. The Raman image is then fitted to obtain the D peak, G peak, and D′ peak. Characteristic parameter A is used to evaluate the relative concentration of defects at a certain point on the material surface. Statistical analysis is performed on characteristic parameter A at different sites, and frequency histograms and cumulative frequency histograms are plotted. 10 I 20 I 50 I 80 I 90 The distribution of defect concentration on the material surface can be described by the characteristic parameter B, i.e., I. 50 This study aims to determine the overall carbon coating level of coated graphite, and based on the Raman distribution of coated graphite, to determine parameters such as coating uniformity, coating rate, and over-coating rate, thereby achieving quantitative analysis of the overall coating level of coated graphite using Raman spectroscopy.

[0065] Simultaneously, by plotting hotspot distribution maps using the characteristic parameter A at different sites, the distribution of relative defect concentration and the pattern of defect distribution on the material surface can be intuitively understood. This provides effective support and guidance for the process development and parameter optimization of coated graphite for fast charging, and has a significant impact on improving the fast charging performance of power batteries and optimizing material interfaces.

[0066] The implementation methods of this application are further discussed below with reference to Examples 1 to 13.

[0067] Example 1:

[0068] The test sample was a graphite composite structure with a particle size of D50 = 11.56 μm, comprising a graphite core and a carbon coating layer covering the graphite core. The residual carbon content of the graphite composite structure was 2%.

[0069] (1) Turn on the confocal Raman spectrometer with a 532nm wavelength light source, configure the slit to 18001 / mm (vis), and use the built-in silicon chip for optical path calibration;

[0070] (2) Use a 300-mesh sieve to sieve the sample to be tested, remove a small amount of large particles, and make the sample particle size uniform.

[0071] (3) Prepare a test sample comprising a graphite core and a coating layer covering the graphite core using a glass slide;

[0072] (4) Using Raman testing software, set the Raman spectral range to 1050 cm⁻¹.-1 Centered at 98.58–1869.89 cm -1 The region was scanned with a step size of 1 μm, the laser intensity was attenuated by 10%, the single-point scanning time was 0.5 s, and a 50×50 μm region was selected for Raman surface scanning. A total of 2500 sites were scanned to obtain Raman surface scanning data of graphite-coated graphite.

[0073] (5) Using Raman data processing software, the Raman spectrum of each site of the Raman surface scan is fitted with peaks and the Raman characteristic parameters A, B, uniformity, coverage and overcoverage are calculated, as shown in Figure 1.

[0074] (6) Draw frequency histograms, cumulative frequency histograms and heatmaps based on the location coordinates of each site and the corresponding characteristic parameter A data, as shown in Figure 2a and Figure 2b;

[0075] (7) Evaluate the coating effect of the graphite surface layer based on the frequency histogram, and obtain I from the cumulative frequency histogram. 10 I 20 I 50 I 80 I 90 The data is shown in Figure 3.

[0076] (8) The distribution of the surface coating can be intuitively understood from the heat map, as shown in Figures 6 and 7.

[0077] Example 2:

[0078] The difference from Example 1 is: different laser intensity: 5%.

[0079] Example 3:

[0080] The difference from Example 1 is: different laser intensity: 50%.

[0081] Example 4:

[0082] The difference from Example 1 is: different scanning area: 100×100μm, step size: 2μm.

[0083] Example 5:

[0084] The difference from Example 1 is: different scanning area: 150×150μm, step size: 3μm.

[0085] Example 6:

[0086] The difference from Example 1 is that the test sample was uncoated graphite aggregate with a particle size of D50 = 11.56 μm.

[0087] Example 7:

[0088] The difference from Example 1 is that the test sample is a graphite aggregate with a particle size of D50 = 15.03 μm coated with a graphite coating layer with a residual carbon content of 2%.

[0089] Example 8:

[0090] The difference from Example 1 is that the test sample is a graphite aggregate with a particle size of D50 = 7.08 μm coated with a graphite coating layer with a residual carbon content of 2%.

[0091] Example 9:

[0092] The difference from Example 1 is that the test sample is graphite aggregate with a particle size of D50 = 13.8 μm.

[0093] Example 10:

[0094] The difference from Example 1 is that the test sample is graphite aggregate with a particle size of D50 = 13.8 μm coated with a coating agent with a residual carbon content of 1%.

[0095] Example 11:

[0096] The difference from Example 1 is that the test sample is graphite aggregate with a particle size of D50 = 13.8 μm coated with a coating agent with a residual carbon content of 2%.

[0097] Example 12:

[0098] The difference from Example 1 is that the test sample is graphite aggregate with a particle size of D50 = 13.8 μm coated with a coating agent with a residual carbon content of 3%.

[0099] Example 13:

[0100] The difference from Example 1 is that the same sample was tested 9 times.

[0101] The test samples from Examples 1, 6, and 7-12 were subjected to the following tests: the D50 of the test samples was measured using a laser particle size analyzer MS3000; the tapped density of the test samples was measured using a tap tester (BT-302); the specific surface area of ​​the test samples was measured using a specific surface area analyzer (NOVATouch2000); and the compacted density of the test samples was measured using an automatic powder compaction density meter (FT-100F). The test results are shown in Table 1.

[0102] Table 1

[0103] Confocal Raman spectroscopy (Renishaw) was performed on sites in the test areas of Examples 1 to 12, and the test results are shown in Table 2.

[0104] Table 2

[0105] As can be seen from Examples 1 to 3, different laser intensities affect the Raman spectroscopy test results. When the laser intensity is relatively high (10%), it is easy to cause the carbon material in the test sample to burn, which will further carbonize the material and cause its disorder to decrease. At a laser intensity of 5%, the Raman signal generated is weak and fluctuates strongly due to the relatively weak laser intensity, which will cause the test accuracy to decrease. Therefore, the laser intensity needs to be set within a reasonable range.

[0106] As can be seen from the comparison of Examples 1 and 4-5, the disorder degree changes little under different scanning area areas, indicating that increasing the step size and scanning area does not affect the test results to a certain extent.

[0107] As can be seen from the comparison of Examples 6 to 12, the disorder of the test sample surface after surface coating is significantly increased, as shown in Figures 3 and 4. As the aggregate particle size decreases, the disorder decreases, the coating uniformity increases, the coating rate gradually decreases, and the over-coating rate decreases. As the amount of coating agent added increases, the disorder continuously increases, the uniformity gradually decreases, the coating rate gradually increases, and the over-coating rate increases.

[0108] Electrical performance testing:

[0109] The carbon-coated graphite anode materials of Examples 1, 6-12 were used to prepare coin cells according to the following method, and the discharge capacity, first efficiency and rate performance were tested.

[0110] The preparation method of the coin cell is as follows:

[0111] (1) A negative electrode slurry was prepared by uniformly mixing 95.5% of the carbon-coated graphite anode material from Examples 1, 6-12, 1.5% carbon black, 1.5% styrene-butadiene rubber (SBR), and 1.5% sodium carboxymethyl cellulose (CMC) in water. The slurry was then coated onto battery-grade copper foil and dried in a vacuum oven at 110°C for 4 hours. The resulting carbon-coated graphite anode sheet was then cut by roll forming. The areal density of the carbon-coated graphite anode sheet was 10 mg / cm³. 2 The compacted density is 1.65 g / cm³. 3 ;

[0112] (2) The counter electrode is a lithium metal sheet, the separator is polyethylene, and the electrolyte includes 1M LiPF6 and solvent (the solvent includes ethylene carbonate (EC), ethyl methyl carbonate (EMC) and dimethyl carbonate (DMC) in a volume ratio of 1:1:1) to prepare a coin cell.

[0113] The above-mentioned button half-cells were tested for discharge capacity, first efficiency, rate performance and cycle performance using an Arbin BT2000 battery tester.

[0114] (1) Initial discharge capacity and initial coulombic efficiency test: Discharge: In the first week, discharge with a constant current of 0.6mA to a voltage of 5mV, and discharge with a constant voltage to a current of 0.06mA. Q1 is recorded as the discharge capacity. Charge: Charge with a constant current of 0.1C to 2V. Q2 is recorded as the charging capacity. The discharge voltage range is 0.005-2.0V, the charging rate is 0.1C, and Q2 / Q1 is recorded as the initial coulombic efficiency. The test results are shown in Table 1.

[0115] (2) Rate performance test: Discharge: Discharge with a constant current of 0.6mA to a voltage of 5mV, and the capacity is recorded as Q3; discharge with a constant voltage to a current of 0.06mA, and the capacity is recorded as Q4; Charge: Charge with a constant current of 0.1C to 2V, and the constant capacity ratio Q3 / Q4 is recorded as 0.1C / 0.1C. 0.5C / 0.1C, 1C / 0.1C, 2C / 0.1C, and 3C / 0.1C are tested in the same way. The rate performance test results are shown in Table 3.

[0116] Table 3

[0117] Figures 3a, 3b, 3c, and 3d are the Raman characteristic parameter A-frequency histograms of the test samples from Examples 1, 6, 7, and 8, respectively. The comparison of the test results of Examples 1, 6-8 shows that surface coating can improve the fast-charging performance of the test samples. As the particle size of the coated aggregate decreases, the coating uniformity of the test samples improves. Non-uniformity of coating will cause graphite exposure and surface carbon accumulation, resulting in a decrease in rate capability and a reduction in first-time efficiency.

[0118] Figures 4a, 4b, 4c, and 4d are the Raman characteristic parameter A frequency histograms of the test samples from Examples 9, 10, 11, and 12, respectively. The test results from Examples 9-12 show that with the increase of residual carbon, the characteristic parameter B of the coated graphite increases accordingly, and the rate performance of the coated graphite anode is significantly improved, as shown in Figures 5a and 5b. Figure 5a shows the characteristic parameter B corresponding to the test samples prepared when the residual carbon content increased from 0 to 3% and to 5% in Examples 9-12. Figure 5a shows a linear relationship between the residual carbon content and the characteristic parameter B of the coated graphite. Figure 5b shows the relationship between the rate performance of different materials under 2C and 3C conditions and the characteristic parameter B. It can be seen from the figure that the rate performance of the material is also correlated with the characteristic parameter B. Meanwhile, with the increase of the amount of coating agent added, the coating uniformity decreases, resulting in a large amount of over-coating, which gradually reduces the initial efficiency.

[0119] Method stability test: Take the sample from Example 1 and retest the sample 10 times according to the test method in Example 1 to verify the stability of the test method. The test results are shown in Table 4. The results in Table 4 show that the test method meets the stability requirements.

[0120] Table 4

[0121] As can be seen from Table 4, after 10 retests, the characteristic parameter B, i.e., I, of the sample in Example 1... 50 The fluctuations are small, with the data fluctuation range around ±0.01, indicating that under the test conditions, the evaluation parameters of the carbon layer on the graphite surface exhibit good test stability.

[0122] Finally, it should be understood that the embodiments disclosed herein are illustrative of the principles of the embodiments of this application. Other modified embodiments are also within the scope of this application. Therefore, the embodiments disclosed herein are merely examples and not limitations. Those skilled in the art can implement the applications in this application by adopting alternative configurations based on the embodiments in this application. Therefore, the embodiments of this application are not limited to those embodiments precisely described in the application.

Claims

1. A method for detecting the mass of a graphite composite structure, characterized by, include: A graphite composite structure was selected as the test sample. The graphite composite structure includes a graphite core and a coating layer covering the graphite core. The test area of ​​the test sample is selected, and Raman spectrometer is used to perform Raman tests on different sites in the test area in sequence to obtain Raman spectra of at least 100 sites. Peak fitting was performed on the Raman spectrum data of the test sample to obtain the D peak and the G peak; Raman characteristic parameter A of the at least 100 sites is obtained according to the intensity or area of the D peak and the G peak, wherein A = I D / I G , I D represents the intensity or area value of the D peak, I G represents the intensity or area value of the G peak; The relative concentration of defects at different sites on the surface of the test sample is detected based on the Raman characteristic parameter A; and / or the coating information of the coating layer on the surface of the test sample is detected based on the Raman characteristic parameter A, wherein the coating information includes whether a coating layer exists and whether the coating layer is over-coated.

2. The method of claim 1, wherein The method further includes: A frequency histogram or a cumulative frequency histogram is plotted based on the Raman feature parameter A. Based on the frequency histogram or the cumulative frequency histogram, the A value Ik corresponding to the distribution percentage of the Raman feature parameter A reaching k is obtained, where k is any one or more of 10, 20, 30, 40, 50, 60, 70, 80, and 90. The quality of the graphite composite structure is determined based on the Ik value.

3. The method of claim 2, wherein the graphite composite structure quality detection method is characterized by, Judging the quality of the graphite composite structure based on the Ik value includes: The value of A is evaluated for k = 50; and / or 50 The overall surface disorder level of the graphite composite structure is evaluated according to the value of I 50 The value of A is evaluated for k = 50; and / or The distribution of surface defect concentration of the graphite composite structure is evaluated based on the Raman characteristic parameter A values ​​of the at least 100 sites; and / or The coating uniformity is evaluated based on the distribution width of the frequency histogram or the cumulative frequency histogram, wherein the coating uniformity = (1-(I 80 -I 20 ) / 1)×100%, I 80 I is the value of A corresponding to k being 80. 20 This is the value of A when k is 20.

4. The method of claim 2, wherein the graphite composite structure is a graphite composite structure for a lithium ion battery. The step of detecting the coating information of the coating layer on the surface of the test sample based on the Raman characteristic parameter A includes: The Raman characteristic parameter A is greater than 0.05 and less than or equal to 0.2, and the test sample surface has the coating layer, and the coating rate R1=N A1 / N 总 x 100%, N A1 represents the number of positions where the coating layer exists, N 总 represents the total number of positions subjected to Raman testing; When the Raman characteristic parameter A is greater than or equal to 0.5 and less than or equal to 0.9, the coating layer on the surface of the test sample is over-coated, and the over-coating rate R2 of the coating layer is N. A2 / N 总 ×100%, N A2 N represents the number of locations where the coating layer exists. 总 This indicates the total number of locations where Raman testing was performed.

5. The method of claim 3, wherein the graphite composite structure quality detection method is characterized by, The test area includes at least 20 graphite composite structure particles.

6. The method of claim 1, wherein, The method further includes: obtaining a defect concentration distribution heat map of the test sample surface based on the coordinate data of at least 100 sites in the test area and the characteristic parameter A value, and detecting the coating information of the coating layer on the test sample surface based on the defect concentration distribution heat map.

7. The method of claim 6, wherein the graphite composite structure quality detection method is characterized by, The at least 100 sites are arranged in an array.

8. The method of claim 7, wherein the graphite composite structure quality detection method is characterized by, The at least 100 sites form an array of at least 10×10, which is located in a region of at least 10×10 μm on the surface of the test sample.

9. The method of claim 8, wherein the graphite composite structure quality detection method is characterized by, The at least 100 sites are 2500 sites, and the 2500 sites form a 50×50 array, which is located in a region of at least 50×50 μm on the surface of the test sample.

10. The method of claim 1, wherein, The test samples have different particle sizes, and / or the test samples have coatings of different thicknesses, and / or the coatings have different residual carbon content.

11. The method of claim 1, wherein, The scanning wave number of the Raman spectrometer is 1050 cm -1 with the center at 98.58 ~ 1869.89 cm -1 The scanning wave number of the Raman spectrometer is 1050 cm -1 with the center at 98.58 ~ 1869.89 cm -1 The scanning wave number of the Raman

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