Method for analyzing fibrosis degree of adhesive

Abstract

The embodiment of the invention provides a method for analyzing the fibrosis degree of an adhesive. The method comprises the following steps: S1, preparing a to-be-analyzed material into a sample; s2, target equipment is used for scanning imaging equipment to characterize the surface of the sample, and a microstructure graph of the sample is obtained; s3, carrying out image processing on the micro-topography graph to enhance fiber image features in the micro-topography graph; and S4, analysis is carried out based on the microtopography graph after image processing, an analysis result is obtained, and the analysis result is used for judging the fibrosis degree of the material binder to be analyzed. According to the analysis method for the fibrosis degree of the adhesive in the embodiment of the invention, the technical problems of large measurement deviation and large test result fluctuation in the prior art are solved.

Description

Technical Field

[0001] This application relates to the technical field of adhesive fiberization degree, and more specifically, to a method for analyzing the degree of adhesive fiberization. Background Technology

[0002] In existing technologies, the specific surface area change method is commonly used to indirectly assess the degree of fibrillation. However, this method cannot resolve details of fiber morphology, such as breakage and agglomeration, and is significantly affected by the specific surface area of ​​conductive agents (e.g., carbon black has a specific surface area of ​​60-1200 μm²). 2 / g), leading to measurement deviation. In addition, the two-dimensional image color difference ratio method is also commonly used. It is a method and means based on the difference in surface optical color / grayscale. It is easily affected by the color and reflective effect of other materials (such as active materials and conductive carbon), the bright and dark areas of the two-dimensional image, the agglomeration of powder, etc., resulting in a very high false signal and huge fluctuations in the results.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a method for analyzing the degree of fiberization of adhesives, in order to at least solve the technical problems of large measurement deviation and large fluctuations in test results.

[0005] According to one aspect of the embodiments of this application, a method for analyzing the degree of fiberization of adhesive is provided, comprising: step S1, preparing a sample of the material to be analyzed; step S2, characterizing the surface of the sample using a target device scanning imaging device to obtain a microscopic morphology image of the sample; step S3, performing image processing on the microscopic morphology image to enhance the fiber image features in the microscopic morphology image; and step S4, performing analysis based on the image-processed microscopic morphology image to obtain analysis results, wherein the analysis results are used to determine the degree of fiberization of the adhesive in the material to be analyzed.

[0006] Further, in step S4, analysis is performed based on the microscopic morphology image after image processing to obtain analysis results, including the following steps: Step S41, based on the microscopic morphology image after image processing, determine the fiber size parameters of multiple fiber regions, including the major axis length and minor axis length; Step S42, based on the fiber size parameters of multiple fiber regions, determine the aspect ratio of multiple fiber regions; Step S43, based on the aspect ratio of multiple fiber regions, determine the average aspect ratio; Step S44, based on the average aspect ratio, perform analysis to obtain analysis results.

[0007] Further, in step S4, the analysis is performed based on the microscopic morphology image after image processing to obtain the analysis results, including the following steps: Step S45, based on the microscopic morphology image after image processing, the coverage rate is determined, and the coverage rate is used to characterize the ratio of the area of ​​the fiber region to the area of ​​the selected analysis region; Step S46, the analysis is performed based on the coverage rate to obtain the analysis results.

[0008] Further, in step S3, image processing is performed on the micro-morphology image, including the following steps: step S31, converting the micro-morphology image into a target grayscale image; step S32, filtering the target grayscale image; step S33, thresholding the filtered target grayscale image to obtain a target binary image, where the white image of the target binary image represents the fiber and the black image of the target binary image represents the background.

[0009] Furthermore, in step S3, the image processing of the micro-morphology image also includes the following steps: Step S34, fiber segmentation processing is performed on the target binary image to separate the adhered fibers.

[0010] Furthermore, in step S3, before filtering the target grayscale image, the method further includes the following steps: adjusting the contrast of the target grayscale image to enhance the contrast between light and dark areas; and / or, performing noise reduction processing on the target grayscale image.

[0011] Furthermore, in step S3, the image processing of the micro-morphology image also includes the following steps: Step S35, morphological operations are used to process the target binary image to remove non-fibrous objects from the target binary image.

[0012] Furthermore, in step S31, the target grayscale image is an 8-bit grayscale image of floating-point type.

[0013] Further, the material to be analyzed includes dry-mixed and fibrous powder. In step S1, the material to be analyzed is made into a sample, including the following steps: Step S11, the dry-mixed and fibrous powder is placed in a silicone mold and epoxy resin is injected to obtain an initial sample; Step S12, the initial sample is vacuum degassed and cured; Step S13, the vacuum degassed and cured initial sample is polished with sandpaper; Step S14, the sanded initial sample is polished with diamond suspension; Step S15, the polished initial sample is spray-plated to cover the outer peripheral surface of the initial sample with a metal film to obtain the sample.

[0014] Further, the material to be analyzed includes a dry electrode sheet. In step S1, the material to be analyzed is made into a sample, including the following steps: step S16, cutting the dry electrode sheet to obtain a sample; step S17, grinding the sample using an ion beam polisher until the sample has a flat surface to obtain a sample, wherein the roughness of the flat surface is <0.1μm.

[0015] In this embodiment, by preparing the material to be analyzed into a sample, it is ensured that the analyzed sample accurately reflects the true fibrous state of the binder in the dry electrode. By using a target device scanning imaging device to characterize the surface of the sample, high-resolution images can be provided, clearly showing the microstructure and details of the binder fibers. By image processing of the micromorphology image, the image features of the fibers can be significantly enhanced, making the fiber outline clearer, which is convenient for subsequent identification and strategy. Based on the analysis of the image-processed micromorphology image, the degree of fibrousness can be determined, the quality of fibrousness can be accurately evaluated, and the subsequent optimization of the fibrousness process can be facilitated. The method for analyzing the degree of fibrousness of the binder in this embodiment can achieve accurate characterization and evaluation of the degree of fibrousness of the binder by extracting accurate and comprehensive fibrous information from the microstructure of the sample, which solves the problems of large measurement deviation and large fluctuation of test results in the prior art. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart illustrating a method for analyzing the degree of fiberization of an adhesive according to an embodiment of this application.

[0018] Figure 2 This is a flowchart illustrating an optional step in preparing a sample from the material to be analyzed, according to an embodiment of this application.

[0019] Figure 3 This is a flowchart illustrating an optional step in preparing a sample from the material to be analyzed, according to an embodiment of this application.

[0020] Figure 4 This is a step diagram illustrating an optional image processing procedure for a microscopic topography image according to an embodiment of this application;

[0021] Figure 5 This is a flowchart illustrating an optional step of analyzing a microscopic topography image based on an embodiment of this application.

[0022] Figure 6 This is a flowchart illustrating an optional step in obtaining analysis results based on a microscopic topography image after image processing, according to an embodiment of this application.

[0023] Figure 7 This is a flowchart illustrating an optional step for quantitatively assessing the degree of fibrosis of polytetrafluoroethylene binder in a dry electrode, according to an embodiment of this application.

[0024] Figure 8 This is a microscopic morphology image of a sample in an optional positive electrode process according to an embodiment of this application;

[0025] Figure 9 This is a black-and-white binary image in an optional positive electrode process according to an embodiment of this application;

[0026] Figure 10 This is a microscopic morphology image of a sample in an optional negative electrode process according to an embodiment of this application;

[0027] Figure 11 This is a black-and-white binary image in an optional negative electrode process according to an embodiment of this application;

[0028] Figure 12 This is a fiberization degree analysis diagram of an optional adhesive according to an embodiment of this application;

[0029] Figure 13 This is a visualization analysis report diagram of an optional positive dry electrode process according to an embodiment of this application;

[0030] Figure 14 This is a test result diagram of an optional positive dry electrode process according to an embodiment of this application;

[0031] Figure 15 This is a visualization report diagram of an optional dry electrode process for negative electrodes according to an embodiment of this application;

[0032] Figure 16 This is a test result diagram of an optional dry electrode process for the negative electrode according to an embodiment of this application. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] Exemplary embodiments according to this application will now be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in many different forms and should not be construed as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of this application is thorough and complete, and that the concept of these exemplary embodiments is fully conveyed to those skilled in the art. In the drawings, for clarity, the thickness of layers and regions may be exaggerated, and the same reference numerals are used to denote the same devices, and therefore their description will be omitted.

[0037] Combination Figures 1 to 6 As shown, embodiments of this application provide a method for analyzing the degree of fiberization of adhesives.

[0038] Specifically, such as Figure 1 As shown, the analytical method for the degree of fiberization of adhesives includes the following steps:

[0039] Step S1: Prepare a sample from the material to be analyzed;

[0040] Step S2: Use a scanning imaging device to characterize the surface of the sample and obtain a microscopic morphology image of the sample.

[0041] Step S3: Perform image processing on the micro-morphology image to enhance the fiber image features in the micro-morphology image;

[0042] Step S4: Analyze the microscopic morphology image after image processing to obtain the analysis results. The analysis results are used to determine the degree of fiberization of the binder in the material to be analyzed.

[0043] In step S1, the material to be analyzed is prepared into a sample to ensure that the sample being analyzed can accurately reflect the true fibrous state of the binder in the dry electrode.

[0044] In step S2, the surface of the sample is characterized using a scanning imaging device, which can obtain high-resolution images that clearly show the microstructure and details of the adhesive fibers, such as the shape, size, and distribution of the fibers. This can reduce measurement deviations and fluctuations in test results, and help improve the accuracy of assessing the degree of fiberization of the adhesive.

[0045] Preferably, the scanning imaging device is a SEM (Scanning Electron Microscope). Using SEM for characterization can provide high-resolution images that clearly show the microstructure and details of the adhesive fibers, such as fiber morphology, size, and distribution. This can reduce measurement deviations and fluctuations in test results, and is crucial for evaluating the degree of fiberization of the adhesive.

[0046] In step S3, image processing is performed on the micro-morphology image to significantly enhance the image features of the fiber, making the fiber outline clearer and facilitating subsequent identification and strategies.

[0047] Specifically, in step S3, image processing may include methods such as converting the image into a data type and adjusting the contrast.

[0048] Specifically, in step S4, based on the microscopic morphology image after image processing, data indicators such as the coverage and average length ratio of the adhesive after fiberization can be obtained. These data indicators directly reflect the degree of fiberization and can be used to evaluate the quality of fiberization and optimize the fiberization process.

[0049] Based on steps S1-S4, by preparing the material to be analyzed into a sample, it is ensured that the analyzed sample can accurately reflect the true fibrous state of the binder in the dry electrode. By using a scanning imaging device to characterize the surface of the sample, high-resolution images can be provided, clearly showing the microstructure and details of the binder fibers. By image processing of the micromorphology image, the image features of the fibers can be significantly enhanced, making the fiber outline clearer, which is convenient for subsequent identification and strategy. Based on the analysis of the image-processed micromorphology image, the degree of fibrousness can be determined, the quality of fibrousness can be accurately evaluated, and the subsequent optimization of the fibrousness process can be facilitated. The method for analyzing the degree of fibrousness of the binder in this embodiment can achieve accurate characterization and evaluation of the degree of fibrousness of the binder by extracting accurate and comprehensive fibrousness information from the microstructure of the sample, which solves the problems of large measurement deviation and large fluctuation of test results in the prior art.

[0050] Specifically, such as Figure 5 As shown, in step S4, analysis is performed based on the microscopic morphology image after image processing to obtain the analysis results, including the following steps:

[0051] Step S41: Based on the microscopic morphology image after image processing, determine the fiber size parameters of multiple fiber regions, including the major axis length and the minor axis length;

[0052] Step S42: Determine the aspect ratio of multiple fiber regions based on the fiber size parameters of multiple fiber regions;

[0053] Step S43: Determine the average aspect ratio based on the aspect ratios of multiple fiber regions;

[0054] Step S44: Analyze based on the average aspect ratio to obtain the analysis results.

[0055] In step S41, by measuring the long axis length and short axis length of the fiber, specific dimensional information of the fiber can be obtained, including length, width, and length ratio, which helps to quantify the morphological characteristics of the fiber and provides direct data support for the degree of fiberization. At the same time, the measurement results of fiber size parameters help to optimize the dry electrode fabrication process.

[0056] In step S42, the aspect ratio of the fiber region, i.e. the ratio of the fiber length to the diameter, is one of the important parameters for measuring the structural characteristics of the fiber. By calculating the aspect ratio of multiple fiber regions, the degree of fiberization can be analyzed more precisely, which makes it easier to understand the uniformity and diversity of fiber distribution.

[0057] In step S43, by determining the average length ratio of the fiber regions, the overall effect of the fiberization process can be reflected, thereby determining whether the fiberization of the adhesive has reached the expected level. At the same time, the average length-to-diameter ratio can be used to assess the consistency and uniformity of fiberization between different batches, thereby optimizing the fiberization process.

[0058] In step S44, the average aspect ratio provides a unified and objective evaluation standard for the degree of adhesive fiberization, avoiding evaluation bias caused by the randomness of samples and promoting the standardization of process parameters.

[0059] Based on steps S41-S44, through the analysis of micromorphology images and precise strategies for fiber size parameters, the analysis results obtained based on the average length ratio can quantitatively evaluate the degree of fiberization of dry electrode binders, which helps to optimize dry electrode technology and improve solid-state battery performance.

[0060] Specifically, such as Figure 6 As shown, in step S4, the analysis is performed based on the microscopic morphology image after image processing to obtain the analysis results, and the following steps are also included:

[0061] Step S45: Based on the microscopic morphology image after image processing, determine the coverage rate, which is used to characterize the ratio of the area of ​​the fiber region to the area of ​​the selected analysis region.

[0062] Step S46: Perform analysis based on coverage to obtain analysis results.

[0063] In step S45, by determining the coverage rate, the ratio of the fiber area to the selected analysis area can be characterized. The coverage rate reflects the density of fiber distribution in the dry electrode and can determine the coverage area of ​​the adhesive forming the fiber network.

[0064] In step S46, coverage is a key indicator for evaluating the density of binder fibers in the electrode material. By analyzing the coverage, the electrochemical performance of the electrode can be determined, thereby allowing for precise control of key parameters in the fiberization process.

[0065] Based on steps S45-S46, by analyzing the coverage data, the formation of the fiber network in the dry electrode can be determined, thereby optimizing the process parameters for fiber distribution.

[0066] Furthermore, such as Figure 4 As shown, in step S3, image processing is performed on the microscopic topography image, including the following steps:

[0067] Step S31: Convert the microscopic morphology image into a target grayscale image;

[0068] Step S32: Filter the target grayscale image;

[0069] Step S33: Threshold segmentation is performed on the filtered target grayscale image to obtain a target binary image. The white image of the target binary image represents the fiber, and the black image of the target binary image represents the background.

[0070] In step S31, converting the micro-topography image into a grayscale image can remove interference from color information, enhance the contrast in the image, and make the difference between the fiber region and the background more obvious, which is convenient for subsequent image processing.

[0071] In step S32, filtering the target grayscale image can effectively smooth the image, reduce noise in the original image, and make the fiber features clearer, which is convenient for subsequent analysis and measurement.

[0072] In step S33, the image is converted into a black and white target binary image by threshold segmentation, where white represents the fiber region and black represents the background, so that the fiber and non-fiber regions are clearly separated visually, which facilitates the subsequent accurate analysis of fiber features.

[0073] Furthermore, in step S3, the image processing of the microscopic morphology image also includes the following steps:

[0074] Step S34: Perform fiber segmentation processing on the target binary image to separate the adhered fibers.

[0075] In step S34, the separated fibers are obtained by performing fiber segmentation processing on the target binary image, which can more accurately identify and measure the length, width and shape of a single fiber, highlight the boundary of a single fiber, and facilitate the assessment of the degree of fiberization.

[0076] Optionally, in step S3, before filtering the target grayscale image, the method further includes the following steps:

[0077] Adjust the contrast of the target grayscale image to enhance the contrast between light and dark areas.

[0078] In this embodiment, by adjusting the contrast of the target grayscale image, the brightness difference between the fiber region and the background can be made more obvious, which can better highlight the fiber details, reduce the misidentification of non-fiber regions, and improve the accuracy of fiber network segmentation.

[0079] Optionally, in step S3, before filtering the target grayscale image, the method further includes the following steps:

[0080] The target grayscale image is subjected to noise reduction processing.

[0081] In this embodiment, by performing noise reduction processing on the target grayscale image, random noise in the image can be reduced, as well as electronic noise and scanning non-uniformity commonly found in scanning imaging, making the details of the fibers clearer.

[0082] Optionally, in step S3, image processing of the microscopic morphology image further includes the following steps:

[0083] Step S35: Morphological operations are used to process the target binary image to remove non-fiber objects from the target binary image.

[0084] In step S35, the target binary image is processed by morphological operations, which can accurately distinguish and remove parts of the image that do not belong to the adhesive fiber, avoid object identification of non-fiber objects, and thus ensure the accuracy of measurement data of the fiber area.

[0085] Preferably, in step S31, the target grayscale image is an 8-bit grayscale image of floating-point type.

[0086] In step S31, the floating-point 8-bit grayscale image can represent the grayscale values ​​of different objects in the image more precisely than the integer type. It can more accurately distinguish the grayscale differences between fibers, background and other materials. The floating-point data type can capture these subtle grayscale differences, which is helpful for subsequent thresholding.

[0087] Specifically, the materials to be analyzed include dry-mixed and fibroinated powders, such as... Figure 2 As shown, in step S1, the material to be analyzed is prepared into a sample, including the following steps:

[0088] Step S11: Place the dry-mixed and fibrous powder into a silicone mold and inject epoxy resin to obtain an initial sample;

[0089] Step S12: The initial sample is degassed and cured under vacuum;

[0090] Step S13: Use sandpaper to polish the initial sample after vacuum degassing and curing;

[0091] Step S14: Polish the initial sample after sanding with diamond suspension.

[0092] Step S15: Spray-coating the polished initial sample to cover the outer peripheral surface of the initial sample with a metal film, thereby obtaining the sample.

[0093] In step S11, the epoxy resin cures to form a hard resin matrix, which can uniformly fix the electrode powder and prevent the powder from moving during subsequent detection, ensuring the integrity and stability of the sample during SEM observation. By placing the dry-mixed and fiberized powder in a silicone mold and injecting epoxy resin, the sample becomes more robust and damage during sample preparation is reduced.

[0094] In step S12, by vacuum degassing and curing the initial sample, air bubbles generated during the mixing of resin materials and powders can be effectively removed, preventing the formation of pores in the cured sample and ensuring the smoothness of the sample surface.

[0095] In step S13, the polishing process can remove the uneven parts and impurities on the sample surface, ensuring the flatness of the sample surface and obtaining a clear fiber structure image.

[0096] In step S14, the initial sample after sanding is polished by using a diamond suspension. The diamond suspension contains extremely fine diamond particles, which can finely smooth out the tiny imperfections on the sample surface, achieve higher surface smoothness, and improve the clarity of the fiber structure.

[0097] In step S15, the initial sample after polishing is spray-coated to cover the outer peripheral surface of the initial sample with a metal film. The enhanced conductivity of the metal film helps to improve the contrast and resolution of the SEM image, making the difference between the fiber structure and other materials more obvious, which facilitates subsequent image processing and fiberization degree analysis.

[0098] Based on steps S11-S15, the material is prepared into a sample through the above steps, which improves the overall stability of the sample, provides a clean and flat sample surface, improves the clarity and contrast of the image, and improves the accuracy and efficiency of fibrosis degree analysis.

[0099] Specifically, the materials to be analyzed include dry-processed electrode sheets, such as... Figure 3 As shown, in step S1, the material to be analyzed is prepared into a sample, including the following steps:

[0100] Step S16: Cut the dry electrode sheet to obtain a sample;

[0101] Step S17: Grind the sample using an ion beam polisher until the sample has a flat surface to obtain the sample, wherein the roughness of the flat surface is <0.1μm.

[0102] In step S16, by precisely cutting the dry electrode sheet, samples with consistent dimensions can be prepared. Samples with consistent dimensions obtained after cutting are more convenient for quantitative analysis.

[0103] In step S17, the sample is polished using an ion beam polisher until it has a flat surface. When the roughness of the flat surface is <0.1μm, the image distortion caused by surface unevenness can be greatly reduced. At the same time, ion beam polishing is a non-contact micromachining technology that will not cause mechanical damage to the sample and can better protect the fiber network.

[0104] The embodiments of this application also provide a preferred embodiment of a method for quantitatively analyzing the degree of fiberization of dry electrodes.

[0105] Solid-state batteries, as a core technology of next-generation batteries, have significant development potential. Dry electrode technology, as a solvent-free and drying-free process, has become a crucial link in solid-state battery manufacturing. Especially in systems based on PTFE (Polytetrafluoroethylene) binders, PTFE exhibits fibrous properties under shear-induced stress, forming a three-dimensional network that holds the active material together. The degree of fibrosis directly affects the key performance characteristics of the electrode.

[0106] 1) Electronic conductivity: The fibrillation process constructs a continuous three-dimensional network of the conductive agent. Insufficient fibrillation results in an incomplete conductive network, obstructed electronic conduction paths, and increased internal resistance. Excessive fibrillation, while potentially leading to a denser conductive network, may damage the active material particles or cause excessive binder encapsulation, potentially increasing contact resistance or reducing the effective conductive area. Characterizing the degree of fibrillation is crucial for understanding and optimizing electronic conduction.

[0107] 2) Ion Transport: The network structure formed by fibrosis also determines the pore structure of the electrode (porosity, pore size distribution, tortuosity). Appropriate fibrosis can form open channels that are conducive to ion transport. Insufficient fibrosis may lead to low porosity and pore blockage; excessive fibrosis may make the structure too dense, which also hinders ion diffusion. Characterizing the degree of fibrosis is the basis for optimizing the ion transport path.

[0108] 3) Mechanical Strength and Flexibility: PTFE and other binders are drawn into long, thin fibers and intertwined with active materials and conductive agents, forming a structure similar to "reinforced concrete." This structure endows dry-process electrodes with excellent self-support, flexibility, and mechanical strength. The degree of fiberization directly affects the morphology, distribution, and interlocking degree of the binder fibers, thus determining the electrode sheet's peel strength, bending resistance, and resistance to powdering during pressing. Characterizing the degree of fiberization is a prerequisite for ensuring the mechanical integrity and processing performance of the electrode.

[0109] 4) Contact and Utilization of Active Materials: Good fibrillation ensures that the active material particles are fully encapsulated and effectively connected by the conductive network, while being moderately fixed by the binder fibers. Insufficient fibrillation leads to poor contact and low utilization; excessive fibrillation may result in an overly thick binder coating, hindering contact between the active material and the electrolyte. Characterizing the degree of fibrillation helps assess the electrochemical accessibility of the active material.

[0110] 5) Electrode Uniformity and Consistency: The fibrosis process needs to occur uniformly in the mixture. Insufficient or excessive fibrosis in certain areas will lead to uneven electrode microstructure, resulting in uneven current distribution, localized stress concentration, and inconsistent performance degradation, which seriously affects the cycle life and safety of the battery. Characterizing the degree of fibrosis (especially its uniformity) is key to ensuring batch-to-batch consistency and macroscopic performance uniformity of the electrodes.

[0111] 6) Interface stability: The bonding strength between the electrode and the current collector is also affected by the degree and morphology of the adhesive fiber at the interface. A good interfacial fiber structure can provide stronger bonding force.

[0112] Specifically, embodiments of this application relate to a method for quantitatively evaluating the degree of fibrosis of polytetrafluoroethylene (PTFE) binder in dry electrodes using SEM imaging combined with image processing algorithms. This method accurately characterizes the coverage and average size of PTFE fibers, providing a reference for dry electrode processes and the development of solid-state batteries. Figure 7 As shown, steps S71-S75 are included:

[0113] Step S71: After dry mixing and fiberization, random samples are taken to prepare characterization samples with smooth surfaces (surface roughness < 0.1 μm).

[0114] Optionally, the embodiments of this application can also be used to characterize the binder fibrosis on the cross-section of the dry electrode after the dry self-supporting film is formed or after the self-supporting film is combined with the current collector. Using an ion beam mill to prepare a flat characterization plane on the electrode cross-section can also characterize the binder fibrosis under different rolling and composite processes, assisting in the optimization of dry electrode process parameters. To avoid sampling errors, at least three samples of the powder prepared under each process condition need to be taken for testing and analysis.

[0115] Step S72: Use SEM to characterize the sample surface and obtain the microstructure of the binder fiberized powder.

[0116] Step S73: Import the microstructure image of the fiberized binder powder into image processing software for image processing; specifically, the following method is used:

[0117] (1) Convert the image to floating-point grayscale. Figure 8 bit, after preserving the original counting accuracy, sets the spatial scale.

[0118] Specifically, if the image contrast is low, you can increase the contrast to make the light and dark areas stand out more, making it easier to distinguish fibers from the background. Be careful to use this method moderately and avoid overdoing it to prevent introducing noise. You can also perform noise reduction on the image. Slight noise reduction can help smooth out some conductive agents, such as fine particles of carbon black SP (Super-P, a commonly used conductive additive), but it may blur the delicate PTFE fibers, so use it with caution.

[0119] (2) Enhance fiber characteristics.

[0120] Specifically, a bandpass filter can be used to filter out the characteristics of active materials and conductive agents while retaining the fiber signal; filters can also be used to help heal small breaks in the fiber.

[0121] (3) Threshold segmentation of the image.

[0122] Specifically, use Threshold to process the data. Drag the slider to select the area displayed in red, ensuring it contains only fiber networks. You can try different thresholding algorithms, such as Default, Huang, Intermodes, Moments, etc., and choose the one that best captures fibers while excluding large clumps of active material (usually the most difficult to exclude due to similar grayscale) and a large amount of SP. After adjusting, click Apply in the Threshold window to generate a binary image with only black and white, where white represents the selected fibers and black represents the background.

[0123] Specifically, if the grayscale values ​​of fibrous binders and active / conductive agents overlap, morphological operations are needed to remove non-fibrous objects based on their shape and size. This can be done using the Analyze Particles function, where you can check Display results, Summarize, and Exclude on edges in the pop-up window. You can also set a critical size (pixel^2) lower limit. By estimating and setting an area upper limit—greater than the size of the fiber segment but less than the size of the active material and conductive carbon—you can delete all objects larger than the area of ​​the fiber cluster.

[0124] (4) Separate adhesions.

[0125] Specifically, watershed processing is used to separate objects that are in contact with each other, which helps to separate fibers that are stuck to the edges of the active material; the processed image is then obtained and output.

[0126] Step S74: Analyze the coverage and average size of the adhesive in the fibrous state;

[0127] Specifically, the images processed by S3 undergo data processing, and the output data analysis includes two indicators: coverage and average size. In "Set Measurements," select "Area," "Standard deviation," "Shapedescriptors," and "Fit ellipse"; use the "Analyze Particles" function, selecting options such as "Display results," "Summarize," and "Add to Manager"; click OK, and the image processing software will automatically identify and measure each continuous white area. Alternatively, you can use "Measure" to detect the coverage of the adhesive after fiberization; the output %Area value represents the coverage of the adhesive after fiberization.

[0128] The average aspect ratio of the fiberized binder is calculated using the output data such as Major (major axis length) and Minor (minor axis width). The calculation formula is: Average aspect ratio = Major / Minor.

[0129] Specifically, the image processing software can identify the Major and Minor of all fibrous materials, thus obtaining the aspect ratio of all fibrous materials. This patent primarily analyzes the degree of fiberization of the adhesive through the average aspect ratio, and the degree of fiberization analysis is as follows: Figure 12 As shown.

[0130] Step S75: Output the analysis report.

[0131] Specifically, the process for evaluating the degree of fibrosis of the PTFE binder in the dry electrode process is as follows.

[0132] 1) Take 0.5g of the dry-mixed and fiberized powder (NCM:PTFE:SP = 96.5:2.5:1) into a silicone mold, and inject low-shrinkage epoxy resin. Avoid vibration during sample preparation to prevent density gradients. After vacuum degassing and curing, polish the sample stepwise with sandpaper (1200 grit → 4000 grit), and polish to a mirror finish using diamond suspension (0.25μm). Sputter the sample using an ion sputtering instrument to coat it with a 5-10nm thick metal film (such as gold or platinum). This greatly enhances the surface conductivity of the sample, eliminates the charging effect, and increases the secondary electron emission rate, resulting in brighter images with richer details. Finally, use conductive tape to fix the sample on the SEM platform for subsequent detection. To avoid sampling errors, at least three samples of the powder prepared under each process condition should be taken for analysis. NCM stands for Nickel Cobalt Manganese, a commonly used cathode material for lithium-ion batteries.

[0133] 2) The sample was characterized using SEM. The equipment parameters were set as follows: accelerating voltage 5kV; beam current selection: Small; working distance: 7mm; beam spot size: 1μm; pixel resolution: 1μm / pixel; counting time: 30ms / pixel. The microstructure images of the sample were obtained and exported, as shown below. Figure 8 As shown.

[0134] 3) Open the resulting .tiff file using "File > Open". Figure 8 Import it into image processing software.

[0135] Specifically, the image is converted to an 8-bit floating-point grayscale image; the image contrast is adjusted using "Adjust > Brightness / Contrast"; features of NCM and SP materials are filtered out using "Process > FFT > Bandpass Filter," retaining the signal of the fibrous binder; and thresholding is performed using "Adjust > Threshold." The thresholding algorithm "Default" is selected, and the slider is dragged until the highlighted area contains all the fiber networks and is free from interference from other materials. The final result is a binary image with only black and white, where white represents the selected fibers and black represents the background, as shown below. Figure 9 As shown.

[0136] 4) Conduct a fiberization degree analysis.

[0137] Select the entire image, and in "Set Measurements", check "Area", "Standard deviation", "Shape descriptors", and "Fit ellipse". Then use "Analyze > Measure" to obtain the fibrous coverage %Area, Major, and Minor values ​​for the entire characterization area. Calculate the average aspect ratio using the formula: Average Aspect Ratio = Major / Minor.

[0138] 5) The final output is a visual report. Figure 8 , Figure 9 and Figure 13 As shown.

[0139] 6) In order to establish the correlation chain of "microstructure-macro performance", the dry electrode powder prepared above is made into a self-supporting film and combined with the current collector, and then combined with graphite, silicon-carbon negative electrode sheet and sulfide electrolyte to finally make a 1Ah small soft pack solid battery.

[0140] Specifically, the fabricated electrode (the electrode after being combined with the self-supporting film) was subjected to film tensile strength (transverse and longitudinal), peel strength, and film resistance tests; the fabricated small pouch solid-state battery was subjected to electrochemical tests; the test results are as follows: Figure 14 As shown.

[0141] Furthermore, the following conclusions can be drawn: Under these dry electrode process conditions, the mixing effect is good, the conductive agent distribution is highly uniform, and the membrane resistance is at a low level. The degree of fiberization is also excellent, reaching an ideal state, with a coverage area of ​​7.813% and an average fiber aspect ratio of 2.888. This indicates that the binder can effectively encapsulate the active and conductive carbon materials, providing high membrane tensile strength and peel strength. At 0.05C, the initial efficiency of the battery is close to 91%. And after 100 cycles at 1C, the battery capacity retention is approximately 92%.

[0142] Specifically, the degree of fibrosis of the PTFE binder in the dry electrode process for the negative electrode is evaluated as follows.

[0143] 1) Take 0.5g of the dry-mixed and fiberized powder (lithium titanate:PTFE:SP = 97:2:1) into a silicone mold, and inject low-shrinkage epoxy resin. Avoid vibration during sample preparation to prevent density gradients. After vacuum degassing and curing, polish the sample stepwise with sandpaper (1200 grit → 4000 grit), and polish to a mirror finish using diamond suspension (0.25μm). Sputter the sample using an ion sputtering instrument to coat it with a 5-10nm thick carbon film. This enhances the surface conductivity of the sample, eliminates the charging effect, and increases the secondary electron emission rate, resulting in brighter images with richer details. Finally, use conductive tape to fix the sample on the SEM platform for subsequent detection.

[0144] 2) The sample was characterized using SEM. The equipment parameters were set as follows: accelerating voltage 3kV; beam current selected Extra Small; working distance 5mm; beam spot size 1μm; pixel resolution 1μm / pixel; counting time 30ms / pixel. The microstructure images of the sample were obtained and exported, as shown below. Figure 10 As shown.

[0145] 3) Open the resulting .tiff file using "File > Open". Figure 10 Import it into image processing software.

[0146] Specifically, the image is converted to an 8-bit floating-point grayscale image; the image contrast is adjusted using "Adjust > Brightness / Contrast"; features of lithium titanate and SP are filtered out using "Process > FFT > Bandpass Filter," retaining the signal of the fibrous binder; thresholding is performed using "Adjust > Threshold." The thresholding algorithm "Huang" is selected, and the slider is dragged until the red area contains all the fiber network elements without interference from other materials. The final result is a binary image with only black and white, where white represents the selected fiber and black represents the background, as shown below. Figure 11 As shown.

[0147] 4) Conduct a fiberization degree analysis.

[0148] Specifically, select the entire image, and in "Set Measurements," check "Area," "Standard Deviation," "Shape Descriptors," and "Fit Ellipse." Then, use "Analyze > Measure" to obtain the fibrous coverage percentages (%Area, Major, and Minor) for the entire characterization area. The average aspect ratio is then calculated using the formula: Average Aspect Ratio = Major / Minor.

[0149] 5) The final output is a visual report. Figure 10 , Figure 11 and Figure 15 As shown.

[0150] 6) To establish the correlation between "microstructure and macroscopic performance", the dry electrode powder prepared above was fabricated into a self-supporting film and combined with a current collector. Combined with a ternary cathode electrode and a sulfide electrolyte, a 1Ah small pouch solid-state battery was finally fabricated.

[0151] Specifically, the fabricated electrode (the electrode after being combined with the self-supporting film) was subjected to film tensile strength (transverse and longitudinal), peel strength, and film resistance tests; the fabricated 1Ah small pouch solid-state battery was subjected to electrochemical tests; the test results are as follows: Figure 16 As shown.

[0152] Furthermore, it can be concluded that under this dry electrode process, the mixing effect is good, the conductive agent distribution is highly uniform, the film resistance is at a low level, and the degree of fiberization is ideal, with a coverage rate of %Area of ​​4.942% and an average fiber aspect ratio of 3.315. This indicates that the binder can encapsulate the active material and conductive carbon material. At 0.05C, the battery's initial efficiency is close to 93%, and at 1C for 1000 cycles, the battery capacity retention rate is around 95%.

[0153] The calculation scheme adopted in this application has the following effects:

[0154] 1) Direct observation: SEM can provide clear images at nanometer resolution, allowing you to directly "see" the morphology, size, and distribution of the binder fibers.

[0155] 2) The analysis results are true and reliable: SEM can effectively distinguish between binder fibers, active material particles and conductive agents (such as carbon black), which greatly avoids misjudgment caused by carbon black agglomeration and surface shadows; image processing software can also effectively avoid interference from active substances and conductive agents, and the results are highly convincing.

[0156] 3) Large-area characterization: It can analyze areas of hundreds of micrometers square, which is more representative of the overall mixed state than TEM (Transmission Electron Microscope).

[0157] 4) In-depth quantification: Advanced spatial statistical analysis is performed through image analysis software, going beyond human observation and simple average values, to provide multi-dimensional objective and quantitative uniformity indicators (coverage and average size of the binder after fiberization).

[0158] 5) It can establish a direct correlation between "process-structure-performance": the quantitative results can be directly used to optimize process parameters (speed, time, additives, etc.) in the dry electrode fiberization process and to evaluate the effects of different fiberization equipment.

[0159] 6) It can establish a correlation chain between "microstructure and macro performance": the analysis results can be linked with electrode performance and battery electrochemical performance data.

[0160] 7) Non-destructive (relative): Analysis of prepared powder or roller-pressed film electrode does not destroy the original mixed structure and can also characterize the uniformity of powder distribution after roller pressing.

[0161] 8) High versatility: The method is applicable to various solid-state battery systems (oxide, sulfide, polymer electrolyte) and various positive and negative electrode formulations.

[0162] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0163] In addition to the above, it should be noted that the terms "one embodiment," "another embodiment," and "embodiment" used in this specification refer to specific features, structures, or characteristics described in connection with that embodiment, which are included in at least one embodiment described in the general description of this application. The appearance of the same expression in multiple places in the specification does not necessarily refer to the same embodiment. Furthermore, when a specific feature, structure, or characteristic is described in connection with any embodiment, the intention is to suggest that implementing such a feature, structure, or characteristic in conjunction with other embodiments also falls within the scope of this invention.

[0164] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0169] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for analyzing the degree of fiberization in adhesives, characterized in that, include: Step S1: Prepare a sample from the material to be analyzed; Step S2: The surface of the sample is characterized using a scanning imaging device to obtain a microscopic morphology image of the sample. Step S3: Perform image processing on the micro-morphology image to enhance the fiber image features in the micro-morphology image; Step S4: Analyze the microstructure image after image processing to obtain analysis results. The analysis results are used to determine the degree of fiberization of the binder in the material to be analyzed.

2. The method for analyzing the degree of fiberization of adhesives according to claim 1, characterized in that, In step S4, the microscopic morphology image after image processing is analyzed to obtain the analysis results, including the following steps: Step S41: Based on the microscopic topography image after image processing, determine the fiber size parameters of multiple fiber regions, including the major axis length and the minor axis length; Step S42: Determine the aspect ratio of the multiple fiber regions based on the fiber size parameters of the multiple fiber regions; Step S43: Determine the average aspect ratio based on the aspect ratios of the multiple fiber regions; Step S44: Analyze based on the average aspect ratio to obtain the analysis results.

3. The method for analyzing the degree of fiberization of adhesives according to claim 2, characterized in that, In step S4, the microscopic morphology image after image processing is analyzed to obtain the analysis results, including the following steps: Step S45: Based on the micro-morphology image after image processing, determine the coverage rate, which is used to characterize the ratio of the area of ​​the fiber region within the selected analysis region to the area of ​​the selected analysis region. Step S46: Perform analysis based on the coverage rate to obtain the analysis results.

4. The method for analyzing the degree of fiberization of adhesives according to any one of claims 1-3, characterized in that, In step S3, the microscopic topography image is processed, including the following steps: Step S31: Convert the microscopic morphology image into a target grayscale image; Step S32: Filter the target grayscale image; Step S33: Threshold segmentation is performed on the filtered target grayscale image to obtain a target binary image, wherein the white image of the target binary image represents fibers and the black image of the target binary image represents the background.

5. The method for analyzing the degree of fiberization of adhesives according to claim 4, characterized in that, In step S3, image processing of the microscopic topography image is performed, which further includes the following steps: Step S34: Perform fiber segmentation processing on the target binary image to separate the adhered fibers.

6. The method for analyzing the degree of fiberization of adhesives according to claim 4, characterized in that, In step S3, before filtering the target grayscale image, the method further includes the following steps: The target grayscale image is subjected to contrast adjustment to enhance the contrast between light and dark areas; and / or, The target grayscale image is subjected to noise reduction processing.

7. The method for analyzing the degree of fiberization of adhesives according to claim 4, characterized in that, In step S3, image processing of the microscopic topography image is performed, which further includes the following steps: Step S35: Morphological operations are used to process the target binary image to remove non-fiber objects from the target binary image.

8. The method for analyzing the degree of fiberization of adhesives according to claim 4, characterized in that, In step S31, the target grayscale image is an 8-bit grayscale image of floating-point type.

9. The method for analyzing the degree of fiberization of adhesives according to claim 1, characterized in that, The material to be analyzed includes powder after dry mixing and fibrosis. In step S1, the material to be analyzed is prepared into a sample, including the following steps: Step S11: Place the dry-mixed and fiberized powder into a silicone mold and inject epoxy resin to obtain an initial sample; Step S12: The initial sample is degassed and cured under vacuum; Step S13: Use sandpaper to polish the initial sample after vacuum degassing and curing; Step S14: Polish the initial sample after sanding with diamond suspension. Step S15: Perform a spray coating process on the polished initial sample to cover the outer peripheral surface of the initial sample with a metal film, thereby obtaining the sample.

10. The method for analyzing the degree of fiberization of adhesives according to claim 1, characterized in that, The material to be analyzed includes dry electrode sheets. In step S1, the material to be analyzed is prepared into a sample, including the following steps: Step S16: Cut the dry electrode sheet to obtain a sample; Step S17: Grind the sample using an ion beam polisher until the sample has a flat surface to obtain the sample, wherein the roughness of the flat surface is <0.1μm.

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