A method for detecting the uniformity of silicon deposition in a sample to be detected

Abstract

The embodiment of the present application relates to a kind of detection methods of the uniformity of silicon deposition in the sample to be detected, comprising: the sample to be detected containing silicon-carbon material is placed on field emission scanning electron microscope sample stage;Field emission scanning electron microscope is adjusted to preset magnification, and first sample to be detected picture is collected;First sample to be detected picture is input in brightness adjustment, and second sample to be detected picture is obtained;Second sample to be detected picture is carried out binaryzation processing, and silicon-carbon particle picture and particle gap picture are obtained;The number of silicon-carbon particle in silicon-carbon particle picture is counted, and the area of each silicon-carbon particle is calculated, and silicon-carbon particle statistical picture is obtained;Each silicon-carbon particle statistical picture is mapped to each second sample to be detected picture, and the confusion degree in the particle of sample to be detected is calculated;Particle gap picture and second sample to be detected picture are processed, and third sample to be detected picture is obtained, and the deposition uniformity between particles in sample to be detected is calculated.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and in particular to a method for detecting the uniformity of silicon deposition in a sample to be tested. Background Technology

[0002] As the application of lithium-ion batteries expands in consumer, power, and energy storage products, the requirements for capacity and performance are also gradually increasing. The choice of anode material has a significant impact on the overall performance of lithium-ion batteries. In recent years, silicon-carbon anode materials have received widespread attention and research due to their high capacity characteristics.

[0003] Silicon-carbon anode materials can be prepared by various methods, among which chemical vapor deposition (CVD) is one of the commonly used methods. CVD technology can deposit silicon on a carbon matrix to form a composite material. However, at present, the process parameters for silicon deposition in CVD are not yet clear, especially the uniformity of silicon deposition or loading on the carbon matrix, which lacks a unified quantitative standard.

[0004] Currently, the uniformity of silicon deposition in silicon-carbon anode materials still relies on manual analysis of scanning electron microscopy (SEM) energy dispersive spectroscopy (EDS) images, visual observation of backscattered electron imaging (BSO), estimation of physicochemical properties, and empirical judgment. This method is time-consuming, yields inaccurate results, and fails to meet the requirements for objective evaluation of the materials. In short, this lack of quantitative indicators poses certain challenges to the optimization and mass production of silicon-carbon anode materials. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for detecting the uniformity of silicon deposition in a sample, overcoming the drawbacks of manual judgment, and realizing the quantitative analysis of silicon deposition uniformity.

[0006] To achieve the above objectives, the present invention provides a method for detecting the uniformity of silicon deposition in a sample to be tested, the method comprising:

[0007] The sample to be tested, containing silicon-carbon material, is placed on the sample stage of a field emission scanning electron microscope.

[0008] Adjust the field emission scanning electron microscope to a preset magnification, and under the preset magnification condition, acquire a preset number of images of the first sample to be tested from different regions;

[0009] The first image of the sample to be tested is input into image analysis software for brightness adjustment to obtain the second image of the sample to be tested.

[0010] The image of the second sample to be tested is binarized to obtain images of silicon-carbon particles and interparticle gaps.

[0011] The number of silicon carbon particles in each of the silicon carbon particle images is counted, and the area of ​​each silicon carbon particle is calculated to obtain a statistical image of silicon carbon particles.

[0012] Each statistical image of silicon-carbon particles is mapped to each second sample image to be tested, and feature values ​​are extracted from the gray-level distribution histogram of each second sample image to calculate the disorder within the particles in the sample to be tested.

[0013] The particle gap image and the second sample image to be tested are processed to obtain the third sample image to be tested;

[0014] Feature values ​​are extracted from the grayscale distribution histogram of each of the third sample images to be tested, and the deposition uniformity between particles in the sample to be tested is calculated.

[0015] Preferably, the preset magnification is 1000 or 2000.

[0016] Preferably, the preset quantity is at least 10.

[0017] Preferably, before adjusting the field emission scanning electron microscope to a preset magnification, the method further includes:

[0018] The sample to be tested was subjected to argon ion polishing.

[0019] Preferably, the image analysis software is ImageJ, and the step of inputting the first sample image to be detected into the image analysis software for brightness adjustment specifically includes:

[0020] Obtain the standard average grayscale value of a standard grayscale image;

[0021] Calculate the average grayscale value of each first image of the sample to be tested;

[0022] The brightness of the first sample image to be tested is adjusted according to the standard average gray value so that the average gray value of the first sample image to be tested is equal to the standard average gray value, thereby obtaining the second sample image to be tested.

[0023] More preferably, the standard average gray value is 90.

[0024] Preferably, the binarization processing of the second sample image to be detected specifically includes:

[0025] The silicon-carbon particles and interparticle gaps in the second sample image to be tested are identified and distinguished, and then input into a preset model for training to obtain a trained model; wherein, the number of silicon-carbon particles is at least 4, and the number of interparticle gaps is at least 4.

[0026] Using the trained model, each image in the second sample image to be detected is traversed, judged, and distinguished, and then binarized.

[0027] Preferably, the size range of the silicon-carbon particles counted is 0.5μm-50μm; the sphericity of the surface area ranges from 0.01 to 1; and the total area of ​​the counted silicon-carbon particles accounts for more than 70% of the total area of ​​silicon-carbon particles in each silicon-carbon particle image.

[0028] Preferably, the step of extracting feature values ​​from the grayscale distribution histogram of each second sample image to calculate the disorder within particles in the sample to be tested specifically includes:

[0029] Analyze the grayscale distribution histogram of each silicon-carbon particle region in each second image of the sample to be tested.

[0030] Feature values ​​are extracted from the gray-level distribution histogram; the feature values ​​include the average standard deviation of the gray levels.

[0031] The total area of ​​silicon carbon particles in each of the aforementioned silicon carbon particle statistical images is calculated, and the area of ​​each silicon carbon particle is normalized to the total area to obtain a weight.

[0032] The disorder within particles in the sample to be tested is calculated based on the weights and the average standard deviation of grayscale.

[0033] Preferably, the step of extracting feature values ​​from the grayscale distribution histogram of each of the third sample images to be tested and calculating the deposition uniformity between particles in the sample to be tested specifically includes:

[0034] The grayscale distribution histogram of each third sample image to be tested is fitted with multiple peaks using Origin software to extract the feature values ​​of each peak; the feature values ​​of each peak include peak position, peak area, half-peak width, and fitting coefficient.

[0035] Based on the peak position, peak area, half-peak width, and fitting coefficient, the deposition uniformity among particles in the sample to be tested is calculated.

[0036] This invention provides a method for detecting the uniformity of silicon deposition in a sample. By intelligently identifying and analyzing particles in an image, it efficiently distinguishes silicon-carbon particles from the gaps between them, improving the selection rate of silicon-carbon particles in the sample and avoiding the inefficiency, inaccuracy, and subjectivity of manual selection. Feature values ​​are extracted from the grayscale distribution histogram to calculate the disorder within the particles in the sample. Furthermore, by removing the background from the original image and fitting the grayscale distribution histogram to extract feature values, the deposition uniformity between particles is calculated. This achieves a more standardized and objective quantitative analysis of silicon deposition uniformity. Attached Figure Description

[0037] Figure 1 A flowchart of a method for detecting the uniformity of silicon deposition in a sample to be tested, provided in an embodiment of the present invention;

[0038] Figure 2 A backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 1 of the present invention;

[0039] Figure 3 This is a backscattering electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 2 of the present invention;

[0040] Figure 4 This is a backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 3 of the present invention;

[0041] Figure 5 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 1 of the present invention.

[0042] Figure 6 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 2 of the present invention.

[0043] Figure 7 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 3 of the present invention.

[0044] Figure 8 The grayscale distribution histograms of the third silicon-carbon negative electrode images provided in Embodiment 1 and Comparative Example 1 of the present invention are shown below.

[0045] Figure 9 This is a backscattering electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 4 of the present invention;

[0046] Figure 10 This is a backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 5 of the present invention;

[0047] Figure 11 This is a backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 6 of the present invention;

[0048] Figure 12 A backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 7 of the present invention;

[0049] Figure 13 A backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 8 of the present invention;

[0050] Figure 14 A backscattered electron microscope image of the first silicon-carbon negative electrode sheet provided in Embodiment 9 of the present invention;

[0051] Figure 15 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 4 of the present invention.

[0052] Figure 16 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 5 of the present invention.

[0053] Figure 17 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 6 of the present invention.

[0054] Figure 18 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 7 of the present invention.

[0055] Figure 19 The grayscale distribution histogram and its fitting diagram are shown for the third silicon-carbon negative electrode image provided in Embodiment 8 of the present invention.

[0056] Figure 20 The grayscale distribution histogram and its fitting diagram of the third silicon-carbon negative electrode image provided in Embodiment 9 of the present invention;

[0057] Figure 21 The graph shows the correlation and fitting of specific capacity and intraparticle disorder U0 in Examples 4-9 of the present invention.

[0058] Figure 22 The graph shows the correlation and fitting of specific capacity and interparticle deposition uniformity U1 in Examples 4-9 of this invention.

[0059] Figure 23 The graph shows the correlation and fitting of specific capacity and deposition uniformity U2 between particles in Examples 4-9 of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0061] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0062] This invention provides a method for detecting the uniformity of silicon deposition in a sample, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0063] Step 110: Place the sample to be tested containing silicon-carbon material on the field emission scanning electron microscope sample stage;

[0064] Specifically, the field emission scanning electron microscope (FET) in this application has a backscattered electron detector, thus possessing backscattered electron imaging technology. Backscattered electron imaging is a microscopic analysis technique capable of reflecting information about the internal structure of materials. The grayscale differences in its images mainly originate from the differences in atomic numbers of different elements within the material. Therefore, by analyzing the grayscale changes in the images, the uniformity of silicon deposition in the sample can be determined, which is of great significance for evaluating the quality and performance of materials.

[0065] Step 120: Adjust the field emission scanning electron microscope to the preset magnification, and under the preset magnification condition, acquire a preset number of images of the first sample to be tested from different regions;

[0066] Specifically, the preset magnification can be 1000 or 2000. At this preset magnification, it can be ensured that each captured image contains 100-200 micrometer-sized particles. The preset quantity can be at least 10, meaning that at least 10 different regions of the sample to be tested are collected to obtain the first image of the sample to be tested. Each first image of the sample to be tested can be labeled Ai, where i is a positive integer. For the 10 different regions, i would be 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10.

[0067] To expose the silicon-carbon material in the sample to be tested on the surface of the sample, so as to facilitate subsequent analysis of the internal and external parts of the silicon-carbon particles, as a preferred option, the first sample to be tested will be subjected to argon ion polishing before performing step 120.

[0068] Step 130: Input the first image of the sample to be tested into the image analysis software for brightness adjustment to obtain the second image of the sample to be tested;

[0069] Specifically, in this application, the image analysis software can be implemented using ImageJ software. During the field emission scanning electron microscope (FET) imaging process, the average grayscale value of the captured images is inconsistent due to factors such as the conductivity of the sample state, i.e., the grayscale value range is not uniform. This method mainly analyzes the image grayscale using the backscattered electron imaging principle of FET, requiring a unified median range of grayscale values ​​so that the grayscale histogram falls within the same interval. Using a standard grayscale image for correction allows control over the grayscale distribution range of the sample image to be tested, facilitating the subsequent uniformity calculation and definition of silicon, silicon-carbon, and carbon materials. Otherwise, the extracted particle grayscale feature values ​​will have significant differences in peak positions, making definition difficult. Therefore, this step first requires obtaining the standard average grayscale value of the standard grayscale image. In this example, the grayscale range of the standard grayscale image is (0, 181), and the standard average grayscale value is 90. Then, the average grayscale value of each first sample image to be tested is calculated. Finally, based on the standard average grayscale value, the brightness of the first sample image to be tested is adjusted so that the average grayscale value of the first sample image to be tested equals the standard average grayscale value, thus obtaining the second sample image to be tested. This process can be performed in batches by running Macro programming code.

[0070] Each second sample image to be tested is labeled Bi, where i is a positive integer.

[0071] Step 140: Binarize the image of the second sample to be tested to obtain an image of silicon-carbon particles and an image of the interparticle gaps.

[0072] Specifically, firstly, the Trainable Weka Segmentation intelligent algorithm in ImageJ can be used to identify and distinguish the silicon-carbon particles and interparticle gaps in the second sample image. This information is then input into a pre-set model for training several times (1-2 times in this application) to obtain a trained model. The Trainable Weka Segmentation intelligent algorithm specifically requires the selection of parameters such as Gaussian blur, Sobel filter, Hessian matrix, differential Gaussian, and membrane projection for calculation. The number of silicon-carbon particles must be at least four. It should be noted that the silicon-carbon particles here refer to clearly defined silicon-carbon particles of varying brightness. The number of interparticle gaps must be at least four.

[0073] Secondly, using the trained model, we iterate through and distinguish each image in the second sample image to be detected, and then perform binarization on each image.

[0074] In this process, each image of a silicon-carbon particle is labeled as class1-i, and each image of the interparticle gaps is labeled as class2-i. In other words, after binarization, each image of the second sample to be tested is labeled as two images: class1-i and class2-i. Binarization improves the recognition of silicon-carbon particles with different grayscale values ​​in the second sample image, which is beneficial for subsequent analysis.

[0075] This step uses intelligent algorithms in ImageJ to distinguish between silicon carbide particles and interparticle gaps, thereby achieving intelligent identification of silicon carbide particles and interparticle gaps and improving the selection rate of particles in the sample to be tested.

[0076] Step 150: Count the number of silicon carbide particles in each silicon carbide particle image and calculate the area of ​​each silicon carbide particle to obtain a statistical image of silicon carbide particles.

[0077] Specifically, before performing step 150, the method also includes setting a scale in each second sample image to determine the actual length of the distance between pixels in the second sample image. Therefore, the area of ​​each silicon carbide particle is the actual area, and no secondary conversion is required.

[0078] More specifically, the Analyze Particles algorithm can be used to count the number of silicon carbide particles in the silicon carbide particle image. The size range of the silicon carbide particles counted can be 0.5μm-50μm, preferably 20μm-30μm. The sphericity of the surface area can range from 0.01 to 1. The total area of ​​the counted silicon carbide particles should account for 70% or more of the total area of ​​silicon carbide particles in each image. The counted silicon carbide particles are recorded in the region of interest manager, and each image is labeled as class3-i.

[0079] Step 160: Map each statistical image of silicon-carbon particles to each second sample image to be tested, extract feature values ​​from the gray-scale distribution histogram of each second sample image to be tested, and calculate the disorder within the particles in the sample to be tested.

[0080] Specifically, the second sample image Bi is used as the original image. Each silicon carbide particle statistical image class3-i records the particle information and number of each silicon carbide particle. Therefore, when mapping each silicon carbide particle statistical image class3-i to each second sample image Bi, the silicon carbide particles in class3-i can be marked as new regions of interest (ROIs) in the same position in Bi.

[0081] The following sub-steps can be used to achieve the specific results:

[0082] Step 161: Calculate the grayscale distribution histogram of the region where each silicon-carbon particle is located in each second image of the sample to be tested;

[0083] Step 162: Extract feature values ​​from the gray-scale distribution histogram;

[0084] Specifically, the grayscale distribution histogram data is exported to a CVS file. Starting with silicon carbide particle number 1, feature values ​​are calculated and extracted from this histogram data. This process is repeated until the current silicon carbide particle number is the final number. If it is not the final number, the number is incremented by 1 and the statistical analysis continues until all silicon carbide particle numbers for each class 3-i particle are read. Feature values ​​may include the grayscale mean standard deviation.

[0085] Step 163: Calculate the total area of ​​silicon carbide particles in each statistical image of silicon carbide particles, and normalize the area of ​​each silicon carbide particle with the total area to obtain the weight.

[0086] Step 164: Calculate the disorder within particles in the sample to be tested based on the weights and the average standard deviation of the grayscale.

[0087] Specifically, the higher the value of the disorder within a particle, the greater the non-uniformity within the particle. The formula for calculating the disorder within a particle is as follows:

[0088]

[0089] Where U0 represents the intraparticle disorder, n represents the number of silicon-carbon particles in the ROI region of each image, i represents the silicon-carbon particle number currently involved in the calculation, and A i Let б be the normalized area of ​​silicon-carbon particle numbered i. i denoted as the average standard deviation of the grayscale value of silicon-carbon particle numbered i.

[0090] It should be noted that U0 here is actually the average value of the disorder within the particles in the sample to be tested.

[0091] Step 170: Process the particle gap image and the second sample image to obtain the third sample image.

[0092] Specifically, the subtract algorithm under Image Calculator is used for processing. The second sample image to be detected is defined as Image 1, and the image of the particle gap is defined as Image 2, resulting in the third sample image to be detected. In other words, the third sample image to be detected is actually the image with the background removed, which improves the fitting coefficient when performing multi-peak fitting in the subsequent process. Each third sample image to be detected is labeled Ci.

[0093] Step 180: Extract feature values ​​from the grayscale distribution histogram of each third sample image to be tested, and calculate the deposition uniformity between particles in the sample to be tested.

[0094] Specifically, the portion of the grayscale distribution histogram with a grayscale range of [2, 220] is exported to a CSV file. This can then be performed using the following sub-steps:

[0095] Step 181: Perform multi-peak fitting on the grayscale distribution histogram of each third sample image to be tested using Origin software, and extract the feature values ​​of each peak.

[0096] In Origin software, the fitting function can be the Gaussian function. The characteristic values ​​of each peak include peak position, peak area, full width at half maximum (FWHM), and fitting coefficient. This enables the digital extraction of image information of silicon-carbon particles in the sample, which is beneficial for subsequent quantitative analysis.

[0097] Step 182: Calculate the deposition uniformity between particles in the sample to be tested based on the peak position, peak area, half-peak width, and fitting coefficient.

[0098] Specifically, the deposition uniformity between particles can be calculated using the following two methods, thus enabling quantitative analysis of the sample to be tested.

[0099] The calculation formula for the first method is as follows:

[0100]

[0101] Where U1 is the deposition uniformity between particles, x SiC A is the weighted value of the grayscale value of silicon-carbon particles, where 'a' is the weighted value of the region of interest (ROI) of the silicon-carbon particles. SiC w represents the normalized area of ​​the fitted peak in the gray-scale histogram of silicon-carbon particles. SiC x represents the full width at half maximum (FWHM) of the fitted peak in the grayscale histogram of silicon-carbon particles; Si A is a weighted value of the grayscale value of silicon particles. Si w represents the normalized area of ​​the fitted peak in the grayscale histogram of silicon particles. Si x is the full width at half maximum (FWHM) of the fitted peak in the grayscale histogram of silicon particles; C A is a weighted value of the grayscale value of carbon particles. C w is the normalized area of ​​the fitted peak of the carbon particle grayscale histogram. C is the half-width at half-maximum (WHM) of the fitted peak in the grayscale histogram of carbon particles.

[0102] It should be noted that the aforementioned weighting values ​​are primarily determined using images captured by a field emission scanning electron microscope (FET) and then processed by backscattered electron imaging (BSO). The grayscale values ​​are related to the elemental ordinal numbers, with silicon particles having the highest grayscale value, followed by silicon-carbon particles, which in turn have the highest grayscale value. The grayscale distribution histogram shows that characteristic peaks appear at grayscale values ​​of 40-50, 60-90, and 100-120. According to the principles of BSO, these characteristic peaks correspond one-to-one with the characteristic peaks of carbon particles, silicon-carbon particles, and silicon particles, respectively. Therefore, the weighted grayscale value for carbon particles is defined as 50, for silicon-carbon particles as 80, and for silicon particles as 100. The normalized area A mentioned above… SiC A Si and A C The ratio of the peak area of ​​the corresponding characteristic peak to the total area of ​​the gray-level distribution histogram is given by the peak area and half-width (W). SiC w Si w C This is the value read directly from the fitting function result report when performing feature peak fitting using the Gauss function.

[0103] The calculation formula for the second method is as follows:

[0104] U2=H SiC / H C

[0105] Where U2 is the deposition uniformity between particles, H SiC H represents the peak height of the fitted peak in the grayscale histogram of silicon-carbon particles. C This represents the peak height of the fitted peak in the grayscale histogram of carbon particles.

[0106] In summary, the present invention provides a method for detecting silicon deposition uniformity in a sample. By intelligently identifying and analyzing particles in an image, it efficiently distinguishes silicon-carbon particles from interparticle gaps, improving the selection rate of silicon-carbon particles in the sample and avoiding the inefficiency, inaccuracy, and subjectivity of manual selection. Furthermore, by extracting feature values ​​from a grayscale distribution histogram to calculate the disorder within particles in the sample, and by removing the background from the original image and fitting the grayscale distribution histogram to extract feature values, it calculates the deposition uniformity between particles. This achieves a more standardized and objective quantitative analysis of silicon deposition uniformity.

[0107] To better understand the technical solution provided by the present invention, the specific process of the above detection method is further explained below using a silicon-carbon negative electrode sheet as an example of the sample to be tested.

[0108] Example 1

[0109] The first step is to prepare silicon-carbon negative electrode sheets.

[0110] First, the biomass porous hard carbon was pulverized to obtain a porous carbon matrix of 9 μm.

[0111] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 500°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0112] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 45wt%.

[0113] The silicon content was determined as follows: First, 0.1 g (accurate to 0.1 mg) of silicon-carbon particle anode material was accurately weighed into a capped polypropylene plastic quantitative bottle, and 3 mL of aqua regia and 1 mL of HF were added. The volume ratio of HCl to HNO3 in the aqua regia was 3:1. The solution was heated to 95°C and digested in a sealed container. After the digestion solution became clear, the quantitative bottle was removed, cooled, and diluted to 50 mL for later use. The silicon content was then detected using inductively coupled plasma atomic emission spectrometry (ICP-AES). Silicon content detection is a conventional technique in this field and will not be described in detail. The determination of silicon content in the following examples is the same as in Example 1 and will not be repeated.

[0114] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0115] The second step is to place the aforementioned silicon-carbon negative electrode sheet under a vacuum of 10... -5 Argon ion polishing was performed under Pa conditions, and the sample was placed on the field emission scanning electron microscope stage.

[0116] The third step is to adjust the magnification of the field emission scanning electron microscope to 1000x so that the silicon-carbon particles in the silicon-carbon anode sheet are clearly visible. Ten images of the first silicon-carbon anode sheet in different regions are collected and denoted as Ai, where i is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.

[0117] The fourth step is to import Ai into ImageJ, run Macro programming code to batch process the images, and adjust the brightness so that the average gray value of Ai is equal to the standard average gray value, thus obtaining the second silicon-carbon negative electrode image, denoted as Bi.

[0118] The fifth step involves using the Trainable Weka Segmentation intelligent algorithm to intelligently identify silicon-carbon particles and interparticle gaps in B-1. Four silicon-carbon particles are randomly selected in B-1 and labeled as class1, and four interparticle gaps are labeled as class2. The model is then trained once using a preset model to obtain a trained model. The trained model is then used to traverse and distinguish silicon-carbon particles and interparticle gaps in other Bi samples. The binarized silicon-carbon particle image class1 is saved as image class1-i, and the binarized interparticle gap image class2 is saved as image class2-i.

[0119] The sixth step is to count the number of silicon carbide particles in each silicon carbide particle image class1-i, calculate the area of ​​each silicon carbide particle, obtain a silicon carbide particle statistical image, denoted as class3-i, and record the counted silicon carbide particles in the region of interest manager.

[0120] Step 7: Using the macro programming language, map the silicon-carbon particle statistical image class3-i to the second silicon-carbon negative electrode image Bi, and mark the region of the silicon-carbon particles in class3-i at the same position in Bi as a new region of interest.

[0121] Step 8: Calculate the grayscale histogram of silicon-carbon particles (class 1-i) in the second silicon-carbon negative electrode image Bi. Extract the average standard deviation of grayscale values ​​from the histogram. Calculate the total area of ​​silicon-carbon particles in each image. Normalize the area of ​​each silicon-carbon particle to the total area to obtain a weight. Then calculate the disorder degree within each particle in each Bi image and average it to obtain the disorder degree U within each Bi image. 0-i Then, the disorder within 10 Bi particles was averaged to obtain the disorder U0 within the silicon-carbon anode electrode particles. Among them, the size range of the silicon-carbon particles counted was 20μm-30μm, the sphericity of the surface area ranged from 0.08 to 1, and the total area of ​​silicon-carbon particles accounted for 70% of the total area of ​​silicon-carbon particles in each silicon-carbon particle image.

[0122] In the ninth step, the subtract algorithm under Image Calculator is used to define the second silicon-carbon anode image Bi as image 1 and the interparticle gap image class2-i as image 2 to obtain the third silicon-carbon anode image, which is labeled Ci.

[0123] Step 10: Obtain the grayscale distribution histogram of Ci, select the portion with grayscale range [33, 220], and export it to a CSV file. Perform multi-peak fitting on the grayscale distribution histogram using Origin software, extract the characteristic values ​​of each peak, and calculate the deposition uniformity U between particles. 1-i and U 2-i This process is repeated until all 10 images have been processed, and the average values ​​U1 and U2 are calculated.

[0124] Example 2

[0125] In the first step, during the preparation of the silicon-carbon anode sheet, after passing through silane, it is kept at 540°C for 4 hours. The silicon content in the silicon-carbon particulate anode material is 44 wt%. The remaining steps are the same as in Example 1.

[0126] Example 3

[0127] The first step is to prepare the silicon-carbon anode sheet. After passing through silane, it is kept at 600°C for 4 hours. The silicon content in the silicon-carbon particulate anode material is 42 wt%. The remaining steps are the same as in Example 1.

[0128] Example 4

[0129] The first step is to prepare silicon-carbon negative electrode sheets.

[0130] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0131] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 480°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0132] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 41 wt%.

[0133] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0134] Steps two through ten are the same as in Example 1.

[0135] Step 11: Assemble the solid-state battery and conduct tests. The specific process is as follows:

[0136] First, the silicon-carbon granular anode material, conductive additive carbon black, and binder prepared in the first step were taken in a mass ratio of 95:2:3. The binder consisted of sodium carboxymethyl cellulose and styrene-butadiene rubber in a mass ratio of 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 220 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet. Using the silicon-carbon anode electrode sheet as the electrode, lithium metal as the counter electrode, and Li2S-SiS2 as the composite solid electrolyte, without adding any electrolyte, the cells were assembled using a CR2032 coin cell battery case in an argon-atmosphere glove box and then packaged using a packaging machine. The assembled battery was placed in a 60°C oven and left to stand for 24 hours, then cooled to room temperature for electrochemical performance testing.

[0137] Secondly, constant current charge-discharge mode tests were conducted using a charge-discharge apparatus at a current density of 0.1C, with a discharge cutoff voltage of 0.005V and a charge cutoff voltage of 2V. The battery's charge specific capacity, first-cycle coulombic efficiency, and capacity retention after 300 cycles were tested. Detailed test data are shown in Table 1.

[0138] Example 5

[0139] The first step is to prepare silicon-carbon negative electrode sheets.

[0140] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0141] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is then kept at 490°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0142] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 41 wt%.

[0143] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The binder consisted of sodium carboxymethyl cellulose and styrene-butadiene rubber in a mass ratio of 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0144] Steps two through ten are the same as in Example 1.

[0145] Step 11, same as in Example 4.

[0146] Example 6

[0147] The first step is to prepare silicon-carbon negative electrode sheets.

[0148] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0149] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 500°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0150] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 42wt%.

[0151] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0152] Steps two through ten are the same as in Example 1.

[0153] Step 11, same as in Example 4.

[0154] Example 7

[0155] The first step is to prepare silicon-carbon negative electrode sheets.

[0156] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0157] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 510°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0158] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 41 wt%.

[0159] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0160] Steps two through ten are the same as in Example 1.

[0161] Step 11, same as in Example 4.

[0162] Example 8

[0163] The first step is to prepare silicon-carbon negative electrode sheets.

[0164] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0165] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 520°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0166] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 42wt%.

[0167] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0168] Steps two through ten are the same as in Example 1.

[0169] Step 11, same as in Example 4.

[0170] Example 9

[0171] The first step is to prepare silicon-carbon negative electrode sheets.

[0172] First, the biomass porous hard carbon is pulverized to obtain a 10μm porous carbon matrix.

[0173] Next, the porous carbon matrix is ​​placed on a substrate in the deposition chamber of a vapor deposition furnace. Silane is introduced into the deposition chamber using nitrogen gas with a purity of 99.99%. The mixture is kept at 530°C for 4 hours to perform vapor deposition, allowing the silane to deposit inside the porous carbon matrix, thus obtaining a silicon-carbon precursor.

[0174] Then, acetylene is introduced into the deposition chamber by nitrogen gas and kept at 540°C for 2 hours to seal the pores and form a silicon-carbon particle anode material with a nanoscale carbon coating, wherein the silicon content in the silicon-carbon particle anode material is 43wt%.

[0175] Finally, the silicon-carbon particle anode material and binder were taken in a mass ratio of 95:5. The mass ratio of sodium carboxymethyl cellulose to styrene-butadiene rubber in the binder was 1:0.8. Then, a slurry was prepared at room temperature using a pulping machine. The prepared slurry was uniformly coated onto copper foil to a thickness of 180 μm, placed in a forced-air drying oven, and dried at 55°C for 2 hours. The coated foil was then cut into circular electrode sheets with a diameter of 14 mm and placed in a vacuum drying oven, where it was vacuum-dried at 100°C for 8 hours to obtain the silicon-carbon anode electrode sheet.

[0176] Steps two through ten are the same as in Example 1.

[0177] Step 11, same as in Example 4.

[0178] Comparative Example 1

[0179] Steps one through eight are the same as in Example 1.

[0180] Step 9: Directly obtain the grayscale distribution histogram of Bi, select the portion with grayscale range [33, 220], and export it to a CSV file. Perform multi-peak fitting on the grayscale distribution histogram using Origin software, extract the characteristic values ​​of each peak, and calculate the deposition uniformity U between particles. 1-i and U 2-I This process is repeated until all 10 images have been processed, and the average values ​​U1 and U2 are calculated.

[0181] Table 1 shows the detection results obtained by the detection methods of Examples 1-9 and Comparative Example 1, as well as the electrochemical test data of the silicon-carbon negative electrode sheets prepared in Examples 4-9.

[0182]

[0183]

[0184] Table 1

[0185] Combination Figure 5-7 As shown, when performing multi-peak fitting with Origin software, the fitting coefficients for Example 1 were 0.998, Example 2 was 0.996, and Example 3 was 0.980. This indicates that the three examples, after intelligent identification of silicon-carbon particles and interparticle gaps, achieved excellent grayscale distribution histogram fitting results after a series of processing steps. The fitting peaks mainly appeared at grayscale values ​​of approximately 50, 80, and 120 (e.g., ...). Figure 7 (As shown in the image). Based on the working principle of backscattered electron imaging, these fitted peaks can be identified as carbon particles, silicon-carbon particles, and silicon particles sequentially as the gray value increases. The presence of these particles is mainly due to the uneven silicon deposition during the silane vapor deposition process. The reason for the silicon particle fitted peak may be that the uneven deposition of silicon particles during the vapor deposition process caused silicon particles to aggregate on the surface of the porous carbon matrix.

[0186] like Figure 4 As shown in Example 3, silicon enrichment is observed, and the uniformity of silicon deposition is poor; Figure 3 In Example 2 shown, the number of particles with small gray values ​​(dark chroma) is greater than that shown. Figure 2Example 1 illustrates that the uniformity of silicon deposition in Example 2 is worse than that in Example 1. Particles with small gray values, as analyzed by backscattered electron microscopy (PSEM), indicate a high content of elements with low elemental numbers; in this application, this signifies a high carbon content. For silicon-carbon materials obtained through vapor deposition, silicon should be uniformly deposited within the silicon-carbon particles, resulting in uniform color. If there are many particles with small gray values, it indicates that silicon has not been deposited or uniformly deposited within these particles, indicating poor deposition uniformity. Specifically, from the PEM images, the silicon-carbon anode sheet of Example 1 exhibits the best silicon deposition uniformity, followed by Example 2, and the silicon-carbon anode sheet of Example 3 exhibits the worst uniformity. Furthermore, as shown in Table 1, as the silicon deposition uniformity in the PEM images deteriorates, the intraparticle disorder (U0) gradually increases, while the interparticle deposition uniformity (U1 and U2) gradually decreases, demonstrating that the detection method of this application can achieve quantitative analysis of silicon deposition uniformity.

[0187] like Figure 8 As shown in the figure, Comparative Example 1 exhibits significant background signal interference in the low grayscale region, resulting in a low fitting coefficient (0.853) during multi-peak fitting. Furthermore, the deposition uniformity between particles calculated from the fitted peak data is too low, deviating significantly from the actual situation and making it unsuitable as a quantitative indicator. This is because Comparative Example 1 directly obtains the grayscale distribution histogram of Bi for calculating the deposition uniformity between particles without using a subtract algorithm to remove the background of Bi from the second silicon-carbon anode image. Therefore, when calculating the deposition uniformity between particles, the background signal is superimposed on the carbon signal, leading to an underestimation of the calculated deposition uniformity between particles.

[0188] like Figure 9-14 Examples 4-9 show samples with varying silicon deposition uniformity obtained by changing the vapor deposition temperature. Deposition uniformity analysis was performed on these samples, and the figures show that silicon deposition uniformity gradually decreases with increasing deposition temperature. Correspondingly, the silicon-carbon anode sheets obtained in the above examples were assembled into solid-state batteries and subjected to electrochemical testing. The battery's specific capacity gradually decreased with increasing deposition temperature, and its cycle performance also declined. This is due to the variation caused by differences in silicon deposition uniformity.

[0189] like Figure 15-20As shown, the fitting coefficients of the grayscale distribution histograms for Examples 4-9 were calculated to be 0.994, 0.992, 0.995, 0.992, 0.994, and 0.992, respectively; the fitting peaks mainly appeared at grayscale values ​​of approximately 60 and 100. Based on the principle of backscattered electron imaging, these two peaks can be identified as carbon particles and silicon-carbon particles, respectively. Furthermore, with increasing vapor deposition temperature, the peak height and area of ​​the characteristic peaks for carbon particles continuously increased, while those for silicon-carbon particles continuously decreased. This indicates that although the silicon content in the silicon-carbon particle anode materials of Examples 4-9 was not significantly different (41wt%-43wt%), the deposition uniformity of silicon in the porous carbon matrix decreased with increasing vapor deposition temperature. In other words, the ratio of carbon particles with low silicon deposition to silicon-carbon particles with high silicon deposition increased, which is consistent with the gradual decrease in deposition uniformity U1 and U2 among particles in Table 1. In summary, the vapor deposition temperature affects the deposition uniformity among particles.

[0190] like Figure 21-23 As shown, the linear fitting coefficient between the intraparticle disorder U0 and the specific capacity of the solid-state battery is 0.92; the linear fitting coefficient between the interparticle deposition uniformity U1 and the specific capacity of the solid-state battery is 0.89; and the linear fitting coefficient between the interparticle deposition uniformity U2 and the specific capacity of the solid-state battery is 0.93. The specific capacity of the silicon-carbon particle anode sheet is mainly provided by silicon. In Examples 4-9, when the silicon content in the silicon-carbon anode materials is similar, the difference in specific capacity mainly reflects the difference in silicon utilization. Because the worse the silicon deposition uniformity, the lower the silicon utilization and the lower the specific capacity. In this application, specific capacity is used as a test value for evaluating silicon deposition uniformity, which also realizes a quantitative measurement of silicon deposition uniformity.

[0191] Example 10

[0192] The first and second steps are the same as in Example 1.

[0193] The third step is to adjust the magnification of the field emission scanning electron microscope to 2000 times so that the silicon-carbon particles in the silicon-carbon anode sheet are clearly visible. Twelve images of the first silicon-carbon anode sheet from different regions are collected and denoted as Ai, where i is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12.

[0194] The fourth step is to import Ai into ImageJ, run Macro programming code to batch process the images, and adjust the brightness so that the average gray value of Ai is equal to the standard average gray value, thus obtaining the second silicon-carbon negative electrode image, denoted as Bi.

[0195] The fifth step involves using the Trainable Weka Segmentation intelligent algorithm to intelligently identify silicon-carbon particles and interparticle gaps in B-1. Five silicon-carbon particles are randomly selected in B-1 and labeled as class1, and five interparticle gaps are labeled as class2. The model is then trained twice using a preset model to obtain a trained model. The trained model is then used to traverse and distinguish silicon-carbon particles and interparticle gaps in other Bi samples. The binarized silicon-carbon particle image class1 is saved as image class1-i, and the binarized interparticle gap image class2 is saved as image class2-i.

[0196] Steps six and seven are the same as in Example 1.

[0197] Step 8: Calculate the grayscale histogram of silicon-carbon particles (class 1-i) in the second silicon-carbon negative electrode image Bi. Extract the average standard deviation of grayscale values ​​from the histogram. Calculate the total area of ​​silicon-carbon particles in each image. Normalize the area of ​​each silicon-carbon particle to the total area to obtain a weight. Then calculate the disorder degree within each particle in each Bi image and average it to obtain the disorder degree U within each Bi image. 0-i Then, the disorder within the 12 Bi particles was averaged to obtain the disorder U0 within the silicon-carbon anode electrode particles. Among them, the size range of the silicon-carbon particles counted ranged from 0.5μm to 30μm, the sphericity of the surface area ranged from 0.01 to 0.5, and the total area of ​​silicon-carbon particles accounted for 80% of the total area of ​​silicon-carbon particles in each silicon-carbon particle image.

[0198] Step 9 is the same as in Example 1.

[0199] Step 10: Obtain the grayscale distribution histogram of Ci, select the portion with grayscale range [2, 200], and export it to a CSV file. Perform multi-peak fitting on the grayscale distribution histogram using Origin software, extract the characteristic values ​​of each peak, and calculate the deposition uniformity U between particles. 1-i and U 2-i This process is repeated until all 12 images have been processed, and the average values ​​U1 and U2 are calculated.

[0200] Example 11

[0201] The first and second steps are the same as in Example 1.

[0202] The third step is to adjust the magnification of the field emission scanning electron microscope to 2000 times so that the silicon-carbon particles in the silicon-carbon anode sheet are clearly visible. Twelve images of the first silicon-carbon anode sheet from different regions are collected and denoted as Ai, where i is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 15.

[0203] The fourth step is to import Ai into ImageJ, run Macro programming code to batch process the images, and adjust the brightness so that the average gray value of Ai is equal to the standard average gray value, thus obtaining the second silicon-carbon negative electrode image, denoted as Bi.

[0204] The fifth step involves using the Trainable Weka Segmentation intelligent algorithm to intelligently identify silicon-carbon particles and interparticle gaps in B-1. Six silicon-carbon particles are randomly selected in B-1 and labeled as class1, and six interparticle gaps are labeled as class2. The model is then trained twice using a preset model to obtain a trained model. The trained model is then used to traverse and distinguish silicon-carbon particles and interparticle gaps in other Bi samples. The binarized silicon-carbon particle image class1 is saved as image class1-i, and the binarized interparticle gap image class2 is saved as image class2-i.

[0205] Steps six and seven are the same as in Example 1.

[0206] Step 8: Calculate the grayscale histogram of silicon-carbon particles (class 1-i) in the second silicon-carbon negative electrode image Bi. Extract the average standard deviation of grayscale values ​​from the histogram. Calculate the total area of ​​silicon-carbon particles in each image. Normalize the area of ​​each silicon-carbon particle to the total area to obtain a weight. Then calculate the disorder degree within each particle in each Bi image and average it to obtain the disorder degree U within each Bi image. 0-i Then, the disorder within 15 Bi particles was averaged to obtain the disorder U0 within the silicon-carbon anode electrode particles. Among them, the size range of the silicon-carbon particles counted ranged from 10μm to 50μm, the sphericity of the surface area ranged from 0.05 to 1, and the total area of ​​silicon-carbon particles accounted for 75% of the total area of ​​silicon-carbon particles in each silicon-carbon particle image.

[0207] Step 9 is the same as in Example 1.

[0208] Step 10: Obtain the grayscale distribution histogram of Ci, select the portion with grayscale range [50, 220], and export it to a CSV file. Perform multi-peak fitting on the grayscale distribution histogram using Origin software, extract the characteristic values ​​of each peak, and calculate the deposition uniformity U between particles. 1-i and U 2-i This process is repeated until all 15 images have been processed, and the average values ​​U1 and U2 are calculated.

[0209] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting the uniformity of silicon deposition in a sample to be tested, characterized in that, The detection method includes: The sample to be tested, containing silicon-carbon material, is placed on the sample stage of a field emission scanning electron microscope. Adjust the field emission scanning electron microscope to a preset magnification, and under the preset magnification condition, acquire a preset number of images of the first sample to be tested from different regions; The first image of the sample to be tested is input into image analysis software for brightness adjustment to obtain the second image of the sample to be tested. The image of the second sample to be tested is binarized to obtain images of silicon-carbon particles and interparticle gaps. The number of silicon carbon particles in each of the silicon carbon particle images is counted, and the area of ​​each silicon carbon particle is calculated to obtain a statistical image of silicon carbon particles. Each statistical image of silicon-carbon particles is mapped to each second sample image to be tested, and feature values ​​are extracted from the gray-level distribution histogram of each second sample image to calculate the disorder within the particles in the sample to be tested. The particle gap image and the second sample image to be tested are processed to obtain the third sample image to be tested; Feature values ​​are extracted from the grayscale distribution histogram of each of the third sample images to be tested, and the deposition uniformity between particles in the sample to be tested is calculated.

2. The detection method according to claim 1, characterized in that, The preset magnification is 1000 or 2000.

3. The detection method according to claim 1, characterized in that, The preset quantity is at least 10.

4. The detection method according to claim 1, characterized in that, Before adjusting the field emission scanning electron microscope to a preset magnification, the method further includes: The sample to be tested was subjected to argon ion polishing.

5. The detection method according to claim 1, characterized in that, The image analysis software is ImageJ. The step of inputting the first image of the sample to be detected into the image analysis software for brightness adjustment specifically includes: Obtain the standard average grayscale value of a standard grayscale image; Calculate the average grayscale value of each first image of the sample to be tested; The brightness of the first sample image to be tested is adjusted according to the standard average gray value so that the average gray value of the first sample image to be tested is equal to the standard average gray value, thereby obtaining the second sample image to be tested.

6. The detection method according to claim 5, characterized in that, The standard average grayscale value is 90.

7. The detection method according to claim 1, characterized in that, The binarization processing of the second sample image to be detected specifically includes: The silicon-carbon particles and interparticle gaps in the second sample image to be tested are identified and distinguished, and then input into a preset model for training to obtain a trained model; wherein, the number of silicon-carbon particles is at least 4, and the number of interparticle gaps is at least 4; Using the trained model, each image in the second sample image to be detected is traversed, judged, and distinguished, and then binarized.

8. The detection method according to claim 1, characterized in that, The size range of the silicon-carbon particles used for the quantity statistics is 0.5μm-50μm; the sphericity range of the surface area is 0.01-1; the total area of ​​the silicon-carbon particles used for the quantity statistics accounts for more than 70% of the total area of ​​silicon-carbon particles in each of the aforementioned silicon-carbon particle images.

9. The detection method according to claim 1, characterized in that, The step of extracting feature values ​​from the grayscale distribution histogram of each second sample image and calculating the disorder within particles in the sample includes: Analyze the grayscale distribution histogram of each silicon-carbon particle region in each second image of the sample to be tested. Feature values ​​are extracted from the gray-level distribution histogram; the feature values ​​include the average standard deviation of the gray levels. The total area of ​​silicon carbon particles in each of the aforementioned silicon carbon particle statistical images is calculated, and the area of ​​each silicon carbon particle is normalized to the total area to obtain a weight. The disorder within particles in the sample to be tested is calculated based on the weights and the average standard deviation of grayscale.

10. The detection method according to claim 1, characterized in that, The step of extracting feature values ​​from the grayscale distribution histogram of each of the third sample images to be tested and calculating the deposition uniformity between particles in the sample to be tested specifically includes: The grayscale distribution histogram of each third sample image to be tested is fitted with multiple peaks using Origin software to extract the feature values ​​of each peak; the feature values ​​of each peak include peak position, peak area, half-peak width, and fitting coefficient. Based on the peak position, peak area, half-peak width, and fitting coefficient, the deposition uniformity among particles in the sample to be tested is calculated.

Images