Method for analyzing the porosity distribution of porous structures

Abstract

According to one embodiment of the present invention, there is provided a method for analyzing the pore distribution of a porous structure, comprising the steps of observing a cross-section of the porous structure using a scanning electron microscope (SEM) to obtain an original image of the cross-section of the porous structure, and quantifying the pore distribution of the obtained original image using a Voronoi diagram. According to the method for analyzing pore distribution, the pore distribution of a porous structure can be quantitatively analyzed.

Description

[Technical Field]

[0001] This application claims priority based on Korean Patent Application No. 10-2022-0113080, filed on September 6, 2022, and all contents disclosed in the specification and drawings of said application are incorporated herein by reference.

[0002] The present invention relates to a method for analyzing the pore distribution of a porous structure. [Background technology]

[0003] Interest in energy storage technologies has been growing recently. As applications expand to include mobile phones, video cameras, laptops, and even electric vehicles, research and development efforts in electrochemical devices are becoming increasingly concrete. Electrochemical devices are one of the most noteworthy fields from this perspective, and among them, efforts to develop rechargeable secondary batteries are attracting particular attention.

[0004] Among the rechargeable batteries currently in use, lithium-ion batteries, developed in the early 1990s, are attracting attention for their advantages over conventional batteries such as Ni-MH, including a higher operating voltage and higher energy density.

[0005] A lithium secondary battery consists of a positive electrode, a negative electrode, an electrolyte, and a separator. Among these components, the required characteristics of the separator are to electrically insulate the positive and negative electrodes while increasing ionic conductivity by enhancing lithium ion permeability based on high porosity. Polyolefin materials such as polyethylene (PE) and polypropylene (PP) are commonly used as polymer substrates for separators because they are advantageous for pore formation and have excellent chemical resistance, mechanical properties, and thermal properties.

[0006] The required characteristics for separators used in lithium-ion secondary batteries include excellent air permeability, low thermal shrinkage, and high puncture strength. However, with the development of high-capacity and high-power batteries, there is a constant demand for excellent air permeability.

[0007] Such separators can generally be applied in the form of a porous polymer substrate manufactured by kneading a polymer such as polyolefin with a diluent, then extruding and stretching it to form a film, and finally extracting the diluent using a solvent to form pores. Alternatively, they may be applied in the form of a composite separator in which an organic-inorganic composite porous layer containing a binder polymer and inorganic particles is provided on at least one side of such a porous polymer substrate.

[0008] In this case, the porous polymer substrate used as a separator for secondary batteries can have its physical properties adjusted in various ways depending on the manufacturing process conditions. In particular, the structure and distribution of the pores in the porous polymer substrate, which serve as pathways for lithium ions, can also be varied in various ways depending on the process conditions.

[0009] In the past, qualitative evaluation methods involving visual inspection of microscopic images were the mainstream method for confirming the pore distribution of porous structures, such as porous polymer substrates. However, there were significant difficulties in deriving quantitative criteria from qualitative criteria.

[0010] For this reason, to quantify the stomatal distribution of porous structures, a method has been developed that uses Euclidean distance measurement to quantify the degree of stomatal distribution from images measured by scanning electron microscopy (SEM). However, this method has the problem that the distance between stomata is affected by the stomatal diameter. Furthermore, it has the problem of producing inaccurate results, such as the stomatal distribution being biased to one side, unlike the population distribution measured in a single image. [Overview of the project] [Problems that the invention aims to solve]

[0011] Therefore, an object of the present invention is to provide a method capable of more quantitatively analyzing the pore distribution of a porous structure.

Means for Solving the Problems

[0012] In order to achieve the above object, in one aspect of the present invention, a method for analyzing the pore distribution of a porous structure according to the following aspect is provided.

[0013] A method for analyzing the pore distribution of a porous structure according to the first aspect includes a step of observing a cross-section of the porous structure with a scanning electron microscope (SEM) to obtain an original image of the cross-section of the porous structure, and a step of quantifying the pore distribution using a Voronoi diagram for the obtained original image.

[0014] According to a second aspect, in the first aspect, the step of quantifying the pore distribution includes a process of converting the original image into a Voronoi diagram area image using the Voronoi diagram method.

[0015] According to a third aspect, in the second aspect, in the process of converting into the Voronoi diagram area image, the original image is first converted into a binary image (2-D binary image) by region-dividing the original image into pixels indicating a plurality of structure regions adjacent to or spanning the pixels of the original image and pixels indicating pore regions formed between the plurality of structure regions, and then the binary image is converted into a Voronoi diagram area image using the Voronoi diagram method.

[0016] According to a fourth aspect, in the second or third aspect, the step of quantifying the pore distribution includes obtaining correlation data between the pore area of the original image and the Voronoi diagram area corresponding to each pore of the original image, and analyzing the pore distribution of the porous structure using the correlation data.

[0017] According to the fifth aspect, in any one of the second to fourth aspects, the step of quantifying the pore distribution is characterized by including creating a graph with the pore area of ​​the original image as the x-axis and the area of ​​the Voronoi diagram as the y-axis, plotting the data relating to the pore area of ​​the original image and the corresponding area of ​​the Voronoi diagram on the graph, and analyzing the pore distribution of the porous structure using the graph.

[0018] According to the sixth aspect, in any one of the second to fifth aspects, the pore area of ​​the original image is calculated using the pixels of the original image.

[0019] According to the seventh aspect, in any one of the third to fifth aspects, the pore area of ​​the original image is calculated using the pore area of ​​the binary image.

[0020] According to the eighth aspect, in the seventh aspect, the pore area of ​​the binary image is calculated using the pixels of the binary image.

[0021] According to the ninth aspect, the method further includes, in any one aspect of the fourth to eighth aspects, obtaining the correlation data for various cross-sections of the porous structure and applying a Bayesian population estimation method to estimate the porosity distribution of the porous structure. [Effects of the Invention]

[0022] According to one embodiment of the present invention, the pore distribution of a porous structure can be analyzed. In particular, in the present invention, the pore distribution of a porous structure can be quantitatively analyzed from scanning electron microscope (SEM) images.

[0023] Specifically, according to one embodiment of the present invention, it is possible to analyze the pore diameter and pore location in a porous structure. Furthermore, it is possible to analyze the degree of dispersion of pores and the degree of bias in the location of pores within the entire porous structure.

[0024] In particular, according to one embodiment of the present invention, the distribution of stomata can be analyzed without being affected by the stomata diameter.

[0025] The drawings accompanying this specification illustrate preferred embodiments of the present invention and, together with the aforementioned description of the invention, serve to further illustrate the technical idea of ​​the present invention; therefore, the present invention is not to be construed as being limited only to what is shown in the drawings. On the other hand, the shapes, sizes, scales, or ratios of elements in the drawings included herein may be exaggerated to emphasize a clearer explanation. [Brief explanation of the drawing]

[0026] [Figure 1] This is the original scanning electron microscope (SEM) image from Example 1. [Figure 2] This shows a Voronoi diagram in the binary image of Example 1. [Figure 3] This is a visualization of the area in the Voronoi diagram of Example 1. [Figure 4] This is a visualization of the area in the Voronoi diagram of Example 1. [Figure 5] This is the original scanning electron microscope (SEM) image from Example 2. [Figure 6] The Voronoi diagram is shown in the binary image of Example 2. [Figure 7] This is a visualization of the area in the Voronoi diagram of Example 2. [Figure 8] This is a visualization of the area in the Voronoi diagram of Example 2. [Figure 9] Example 1 shows the correlation between the pore area and the Voronoi area in a binary image. [Figure 10] Example 2 shows the correlation between the pore area and the Voronoi area in a binary image. [Modes for carrying out the invention]

[0027] The present invention will be described in detail below with reference to the attached drawings. The terms and words used in this specification and in the claims are not to be interpreted in a manner limited to their usual or dictionary meanings, but rather in a manner and concept corresponding to the technical idea of ​​the present invention, in accordance with the principle that the inventor himself may appropriately define the concepts of terms in order to best describe the invention.

[0028] Therefore, the embodiments described herein and the configurations shown in the drawings represent only one of the most preferred embodiments of the present invention and do not represent the entire technical concept of the invention. It should be understood that there are various equivalents and modifications that can be substituted for these at the time of filing this application.

[0029] Furthermore, throughout the specification, when a part is described as "include, comprised," "have," or "equipped with" a certain component, this does not mean that other components are excluded, but rather that other components may be further included, unless otherwise specified.

[0030] Throughout this specification, the phrase "A and / or B" means "A or B or both."

[0031] In this specification, a Voronoi diagram may refer to a plane divided into sets of points that are closest to a given point.

[0032] In this specification, the unit of Voronoi area or pore area in scanning electron microscope (SEM) images is, unless otherwise specified, pixel. 2 Let's assume that.

[0033] A method for analyzing the pore distribution of a porous structure according to one aspect of the present invention includes the steps of: observing a cross-section of the porous structure with a scanning electron microscope (SEM) to obtain a raw image of the cross-section of the porous structure; and quantifying the pore distribution of the obtained raw image using a Voronoi diagram.

[0034] Conventionally, attempts have been made to analyze the pore distribution of porous structures using permeability and electrical resistance, but even with the same permeability and electrical resistance, the actual pore distribution sometimes differed. Furthermore, attempts have been made to analyze pore distribution using the Euclidean distance transformation method, but it has been impossible to control the influence of pore diameter or the distance between pores on the analysis of the overall pore distribution.

[0035] The pore distribution analysis method according to the present invention utilizes Voronoi diagrams to provide more accurate results for analyzing the pore distribution of porous structures, and which reflect the actual pore distribution. Here, the pore distribution includes pore diameter and pore location, and the pore location may represent the degree of dispersion and / or bias of pores in the porous structure.

[0036] One embodiment of the present invention may include the step of preparing a porous structure in order to analyze the pore distribution of the porous structure.

[0037] Here, the porous structure to be analyzed is not limited to any particular material; any structure with a large number of pores can be used. For example, the porous structure can be a porous polymer substrate such as a filter or nonwoven fabric, and specifically, it can be a porous polymer substrate contained in a separator for secondary batteries.

[0038] The porous polymer substrate refers to the separator fabric before the organic-inorganic composite porous layer is formed in a separator having an organic-inorganic composite porous layer. The porous polymer substrate may include fiber regions and pore regions, and may consist of a plurality of polymer fibers arranged parallel to or across each other, with a plurality of pores formed between the plurality of polymer fibers.

[0039] The porous polymer substrate may specifically be a porous polymer film substrate or a porous polymer nonwoven fabric substrate.

[0040] The porous polymer film substrate may be a porous polymer film made of polyolefin such as polyethylene or polypropylene, and such a polyolefin porous polymer film substrate exhibits a shutdown function at temperatures of, for example, 80 to 130°C.

[0041] In this case, the polyolefin porous polymer film can be formed from polymers such as polyethylene (including high-density polyethylene, linear low-density polyethylene, low-density polyethylene, and ultra-high molecular weight polyethylene), polyolefin polymers (including polypropylene, polybutylene, and polypentene) individually or by mixing two or more of these polymers.

[0042] In this case, the polyolefin porous polymer film can be formed from polymers such as polyethylene (including high-density polyethylene, linear low-density polyethylene, low-density polyethylene, and ultra-high molecular weight polyethylene), polyolefin polymers (including polypropylene, polybutylene, and polypentene) individually or by mixing two or more of these polymers.

[0043] Furthermore, the porous polymer film substrate may be manufactured by forming it into a film using various polymers other than polyolefins, such as polyester. Moreover, the porous polymer film substrate may be formed in a structure in which two or more film layers are laminated, and each film layer may be formed from the aforementioned polymers such as polyolefins and polyester, either individually or as a mixture of two or more of these polymers.

[0044] Furthermore, the porous polymer film substrate and the porous nonwoven fabric substrate can be formed from polymers such as polyolefins, polyesters (polyethylene terephthalate, polybutylene terephthalate, polyethylene naphthalate, polyacetal, polyamide, polycarbonate, polyimide, polyetheretherketone, polyethersulfone, polyphenylene oxide, polyphenylenesulfide), etc., either individually or as mixtures thereof.

[0045] The thickness of the porous polymer substrate is not particularly limited, but more specifically, it is 1 to 100 μm, and more specifically, 5 to 50 μm. The pore diameter and porosity of the pores present in the porous polymer substrate are also not particularly limited, but may be 0.01 to 50 μm and 10 to 95%, respectively.

[0046] As the porous polymer substrate, the porous polyolefin described above is particularly usable, in which case the polyolefin may include polyethylene; polypropylene; polybutylene; polypentene; polyhexene; polyoctene; copolymers of two or more of ethylene, propylene, butene, pentene, 4-methylpentene, hexene, and octene; or mixtures thereof.

[0047] In particular, examples of polyethylene include low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE), and high-density polyethylene (HDPE). Among these, high-density polyethylene, which has a high degree of crystallinity and a high melting point, is the most commonly used.

[0048] In one embodiment of the present invention, the weight-average molecular weight of the polyolefin may be 200,000 to 1,500,000, or 220,000 to 1,000,000, or 250,000 to 800,000. In the present invention, by using a high molecular weight polyolefin having a weight-average molecular weight of 200,000 to 1,000,000 as a starting material for separator production, it is possible to obtain a separator with excellent strength and heat resistance while ensuring the uniformity and film-forming processability of the separator.

[0049] Next, the process includes observing the prepared porous structure with a scanning electron microscope (SEM) to obtain an original image of the porous polymer substrate.

[0050] According to one embodiment of the present invention, an image of the porous polymer substrate can be obtained by scanning electron microscope (SEM) at a magnification of 20,000 under conditions of 5kV and 10uA.

[0051] The process then includes a step of quantifying the stomatal distribution of the obtained original image using a Voronoi diagram.

[0052] In one embodiment of the present invention, the step of quantifying the stomatal distribution may include the process of converting the original image into a Voronoi diagram area image using the Voronoi diagram method.

[0053] The process of converting to a Voronoi area image can be performed using the original image itself. Alternatively, the original image can be converted into a binary image (2-D binary image) by dividing the pixels of the original image into pixels representing multiple adjacent or overlapping structural regions and pixels representing pore regions formed between the multiple structural regions, and then the binary image can be converted into a Voronoi area image using the Voronoi diagram method. In a binary image, it is easier to distinguish between structural regions and pore regions compared to the original image, so it can be converted to a Voronoi area image with even greater accuracy.

[0054] Converting the aforementioned original image or binary image into a Voronoi area image can be done using techniques well known in the industry. For example, points can be plotted in the original image or binary image at positions corresponding to each pore region, and the set of points closest to a specific point can be connected to convert it into a Voronoi area image.

[0055] In one embodiment of the present invention, the step of converting the original image of a porous structure into a binary image may be performed by applying a thresholding method to the original image of the structure obtained by observation with a scanning electron microscope (SEM). The image converted to a binary image makes it possible to further clearly observe the porosity and non-porosity parts in the original image of the structure obtained by observation with a scanning electron microscope (SEM).

[0056] The aforementioned binarization method may be whole-area binarization, Otsu thresholding, or adaptive thresholding.

[0057] The aforementioned whole-area binarization method is a method in which, after setting a threshold, pixels that exceed the threshold are processed as black, and those that do not exceed the threshold are processed as white.

[0058] The Otsu binarization method described above is a method for creating a binary image using Otsu's algorithm. When converting to a binary image, Otsu's algorithm arbitrarily sets a threshold to divide pixels into two categories and repeatedly calculates the distribution of brightness and darkness between the two categories. After this, it selects the threshold that results in the most uniform distribution of brightness and darkness between the two categories from all possible cases. The advantage of Otsu's algorithm is that it automatically finds the optimal threshold.

[0059] The adaptive binarization method described above divides an image into various regions and then uses only the surrounding pixel values ​​to determine the threshold for converting it to a binary image. When the original image has multiple background colors or a variety of hues, and it is difficult to create a vivid binary image using a single threshold, the adaptive binarization method can be used to create a binary image of superior quality.

[0060] In one embodiment of the present invention, the step of quantifying the pore distribution may include obtaining correlation data between the pore area of ​​the original image and the Voronoi diagram area corresponding to each pore area of ​​the original image, and analyzing the pore distribution of the porous structure based on the degree to which the data is dispersed.

[0061] For example, a graph can be created with the pore area of ​​the original image as the x-axis and the Voronoi diagram area as the y-axis. Data relating to each pore area of ​​the original image and the corresponding Voronoi diagram area can then be plotted on the graph, and the pore distribution of a porous structure can be analyzed based on the degree to which the data is dispersed in the graph. However, there are no restrictions on the method of plotting the correlation data; for example, the method and format of the graph can be freely chosen.

[0062] More specifically, a) the x-axis can be either the area of ​​pores measured in the original image obtained by observing a cross-section of a porous structure with a scanning electron microscope (SEM), or the area of ​​pores measured in the image obtained by converting the original image to a binary image, and b) the original image obtained by observing a cross-section of a porous structure with a scanning electron microscope (SEM), or the original image converted to a binary image, can be illustrated as a Voronoi diagram, and the correlation can be plotted on a graph with the Voronoi area measured in the illustrated Voronoi diagram as the y-axis. For example, if the Voronoi area of ​​a particular pore is 500 and the area measured in the binary image is 100, a graph can be drawn with the Voronoi area on the y-axis and the area measured in the binary image on the x-axis, and the particular pore can be associated with the coordinates (100, 500).

[0063] In this way, the pore distribution in porous structures can be analyzed using values ​​that map pore-related values ​​to a graph. For example, the degree to which pore-related values ​​are dispersed on the graph can be used to determine the degree to which pore locations are dispersed in a porous structure. Alternatively, the pore location distribution can be analyzed using Voronoi area values ​​that are proportional to the distance between each pore, obtained using a Voronoi diagram. For example, the pore location distribution can be analyzed by using the fact that the Voronoi area is small when the distance between pores is short, and large when the distance between pores is long.

[0064] In one embodiment of the present invention, the pore area of ​​the original image can be calculated using the pixels of the original image itself. Alternatively, the pore area calculated using the pixels of a binary image obtained by converting the original image can be used as the pore area of ​​the original image. The pore area may also be converted to the actual size using the observation conditions of a scanning electron microscope (SEM) used to observe the porous structure.

[0065] One embodiment of the present invention may further include the step of obtaining the aforementioned correlation data for various cross-sections of a porous structure and applying Bayesian estimation to estimate the pore distribution of the porous structure. For example, various cross-sections of a porous structure are observed using a scanning electron microscope (SEM) to obtain raw images. Then, the correlation between the Voronoi diagram area of ​​each pore in the obtained raw images and the area of ​​each pore in the raw images is calculated as a graph several times for each cross-section, and Bayesian population estimation is applied to estimate the pore distribution of the porous structure.

[0066] When an image obtained by scanning electron microscopy (SEM) represents only a portion of the entire stomatal structure, Bayesian estimation can be used to analyze the stomatal distribution of the entire stomatal structure without obtaining images of all parts of the entire structure. The porous polymer substrate targeted by the pore distribution analysis of the present invention can be applied as a separator for secondary batteries on its own, but in fields where further thermal stability is required, it can be manufactured as a separator with enhanced stability by including an organic-inorganic composite porous layer on at least one side.

[0067] Thus, a separator whose stability is enhanced by including an organic-inorganic composite porous layer on at least one side is located on at least one side of a porous polymer substrate and includes an organic-inorganic composite porous layer comprising a large number of inorganic particles and a binder polymer located on part or all of the surface of the inorganic particles to connect and fix the inorganic particles together.

[0068] As the binder polymer, polymers commonly used in this industry for forming organic-inorganic composite porous layers can be used. In particular, polymers with a glass transition temperature (Tg) of -200 to 200°C can be used because they can improve the mechanical properties such as flexibility and elasticity of the final organic-inorganic composite porous layer. Such binder polymers effectively fulfill the role of a binder, linking and stably fixing inorganic particles together, thereby contributing to preventing a decrease in the mechanical properties of the separator into which the organic-inorganic composite porous layer is introduced.

[0069] Furthermore, while the binder polymer does not necessarily need to have ionic conductivity, using a polymer with ionic conductivity can further improve the performance of the electrochemical element. Therefore, a binder polymer with the highest possible dielectric constant can be used. In fact, since the degree of dissociation of salt in the electrolyte depends on the dielectric constant of the electrolyte solvent, the higher the dielectric constant of the binder polymer, the better the degree of dissociation of salt in the electrolyte can be. Such binder polymers with dielectric constants in the range of 1.0 to 100 (measurement frequency = 1 kHz) can be used, and in particular, they may be 10 or higher.

[0070] In addition to the functions described above, the binder polymer may have the characteristic of exhibiting a high degree of swelling of the electrolyte due to gelation during impregnation into the liquid electrolyte. As a result, the solubility index of the binder polymer, i.e., the Hildebrand solubility parameter, is 15-45 MPa. 1 / 2 or 15-25 MPa 1 / 2 and 30-45 MPa 1 / 2 This is within the range. Therefore, hydrophilic polymers with even more polar groups can be used more extensively than hydrophobic polymers such as polyolefins. This is because the solubility index is 15 MPa. 1 / 2 Less than 45 MPa 1 / 2 This is because if the temperature exceeds a certain level, it may become difficult for the battery to swell using the liquid electrolyte typically used in batteries.

[0071] Non-restrictive examples of such binder polymers include polyvinylidene fluoride-co-hexafluoropropylene, polyvinylidene fluoride-co-trichloroethylene, polymethyl methacrylate, polybutylacrylate, polybutyl methacrylate, polyacrylonitrile, polyvinylpyrrolidone, polyvinylacetate, polyethylene-co-vinylacetate, polyethylene oxide, polyarylate, cellulose acetate, cellulose acetate butyrate, and cellulose acetate propionate. Examples include, but are not limited to, propionate, cyanoethyl pullulan, cyanoethyl polyvinyl alcohol, cyanoethylcellulose, cyanoethylsucrose, pullulan, and carboxyl methyl cellulose.

[0072] Non-restrictive examples of the inorganic particles include high dielectric constant inorganic particles having a dielectric constant of 5 or more, more specifically 10 or more, inorganic particles having lithium ion transport capability, or mixtures thereof.

[0073] Non-limiting examples of the inorganic particles having a dielectric constant of 5 or more include BaTiO3, Pb(Zr,Ti)O3 (PZT), Pb 1-x La x Zr 1-y Ti y O3 (PLZT), Pb(Mg3Nb 2 / 3 )O3-PbTiO3 (PMN-PT), hafnia (HfO2), SrTiO3, SnO2, CeO2, MgO, NiO, CaO, ZnO, ZrO2, Y2O3, Al2O3, TiO 2、 SiC, AlO(OH), Al2O3·H2O, or mixtures thereof, and the like.

[0074] As used herein, the "inorganic particles having lithium ion conductivity" refer to inorganic particles that contain lithium element, do not store lithium, and have a function of moving lithium ions. Non-limiting examples of the inorganic particles having lithium ion conductivity include lithium phosphate (Li3PO4), lithium titanium phosphate (Li x Ti y (PO4)3, 0 < x < 2, 0 < y < 3), lithium aluminum titanium phosphate (Li x Al y Ti z (PO4)3, 0 < x < 2, 0 < y < 1, 0 < z < 3), (LiAlTiP) x O y -based glass (glass) (0 < x < 4, 0 < y < 13), lithium lanthanum titanate (Li x La y TiO3, 0 < x < 2, 0 < y < 3), Li 3.25 Ge 0.25 P 0.75 S4 and the like, such as lithium germanium thiophosphate (Li x Ge y P z S w , 0 < x < 4, 0 < y < 1, 0 < z < 1, 0 < w < 5), lithium nitride such as Li3N (Li x N y, where 0 < x < 4 and 0 < y < 2), SiS2-based glasses such as Li3PO4-Li2S-SiS2 (Li x Si y S z , where 0 < x < 3, 0 < y < 2, and 0 < z < 4), P2S5-based glasses such as LiI-Li2S-P2S5 (Li x P y S z , where 0 < x < 3, 0 < y < 3, and 0 < z < 7), or mixtures thereof, etc.

[0075] According to an embodiment of the present invention, the organic-inorganic composite porous layer can be an oil-based coating layer using an organic slurry or an aqueous coating layer using an aqueous slurry. Among them, in the case of an aqueous coating layer, it is advantageous for coating thin films and is even more advantageous in terms of reducing the resistance of the separator.

[0076] A separator with enhanced stability, having an organic-inorganic composite porous layer on at least one side of a porous polymer substrate, can be specifically manufactured as follows.

[0077] First, to form the organic-inorganic composite porous layer, a binder polymer is dissolved or dispersed in a dispersion medium, and then inorganic particles are added and dispersed to prepare a composition (slurry) for forming the organic-inorganic composite porous layer. The inorganic particles can be added in a crushed state having a predetermined average particle size in advance, or after adding the inorganic particles to a mixture of the binder polymer and the dispersion medium, the inorganic particles can be crushed and dispersed while controlling them to have a predetermined average particle size using a ball mill method or the like.

[0078] The composition for forming the organic-inorganic composite porous layer is coated onto the porous polymer substrate. The coating method is not particularly limited, but it is preferable to use the slot coating method or the deep coating method. The slot coating method is a method in which the composition supplied through a slot die is applied to the entire surface of the substrate, and the thickness of the coating layer can be adjusted according to the flow rate supplied from a metering pump. The deep coating method is a method in which the substrate is coated by immersing it in a tank containing the composition, and the thickness of the coating layer can be adjusted according to the concentration of the composition and the speed at which the substrate is removed from the composition tank, and for more accurate control of the coating thickness, it can be weighed after immersion using a Meyer bar or the like.

[0079] In this way, by drying a porous polymer substrate coated with a composition for forming an organic-inorganic composite porous layer using a dryer such as an oven, an organic-inorganic composite porous layer is formed on at least one side of the porous polymer substrate.

[0080] In one embodiment of the present invention, the binder polymer can cause inorganic particles to adhere to each other (i.e., the binder polymer connects and fixes the inorganic particles together) so that they can maintain a state in which they are bound together, and the binder polymer can maintain a state in which the inorganic particles and the porous polymer substrate are bound together. The inorganic particles of the organic-inorganic composite porous layer can form interstitial volumes in a state in which they are substantially in contact with each other, and in this case, the interstitial volumes refer to the spaces limited by the inorganic particles that are substantially in contact in a closed-packed ordensely packed structure. The interstitial volumes between the inorganic particles become empty spaces and can form pores in the organic-inorganic composite porous layer.

[0081] Non-restrictive examples of dispersion media used in this case include one compound or a mixture of two or more compounds selected from acetone, tetrahydrofuran, methylene chloride, chloroform, dimethylformamide, N-methyl-2-pyrrolidone, methyl ethyl ketone, cyclohexane, methanol, ethanol, isopropyl alcohol, propanol, and water.

[0082] Furthermore, in one embodiment of the present invention, after coating the porous polymer substrate with the composition for forming the organic-inorganic composite porous layer, the solvent can be removed by drying it at a temperature of 20-70°C or 23-60°C under relative humidity of 30-80% or 50-80% for 1 minute to 2 hours, 5 minutes to 1 hour, or 10 minutes to 1 hour to produce a separator with enhanced stability that ultimately contains an organic-inorganic composite porous layer on at least one side.

[0083] When the method for analyzing the pore distribution of a porous structure according to one embodiment of the present invention is applied to analyze the pore distribution of a porous polymer substrate for a secondary battery separator, the results of analyzing the pore distribution in the porous polymer substrate state can ultimately be perfectly correlated with the pore distribution characteristics of a separator equipped with an organic-inorganic composite porous layer.

[0084] The present invention will be described in detail below with reference to examples. However, the examples of the present invention can be modified into various other forms, and the scope of the present invention should not be construed as being limited to the examples detailed below. The examples of the present invention are provided to give a more complete explanation of the present invention to a person of average skill in the art.

[0085] Example 1 The air permeability is 60 sec / 100cc, and the average pore size is 105 pixels. 2 A porous polyethylene film (manufactured by LG Chem, South Korea) was prepared. The average pore size was measured using the pixels of a scanning electron microscope (SEM) image as a reference.

[0086] The polyethylene was observed using a scanning electron microscope (SEM) (manufacturer: Hitachi High-Technologies Corporation, product name: S-4800) under conditions of 5kV, 10uA, and 20,000 magnification to obtain the original image of the polyethylene (Figure 1).

[0087] Next, the original image in Figure 1 was divided into pixels representing multiple polymer regions that are adjacent to or span across pixels, and pixels representing pore regions formed between the multiple polymers to obtain a binary image. A Voronoi diagram was then applied to the obtained binary image to convert it into a Voronoi diagram area image (Figure 2).

[0088] Figures 3 and 4 visualize the area in the Voronoi diagram of Example 1. Figure 3 shows the Voronoi diagram with colors assigned according to the size of each area. The size of the area can be calculated using pixels in a binary image as a guide and converted to the actual size using scanning electron microscope (SEM) imaging conditions. Figure 4 shows that the average area of ​​the Voronoi diagram calculated using pixels is 513.4 pixels. 2 (Standard deviation: 296.3) indicates that there are 536 stomata.

[0089] Example 2 The air permeability is 64 sec / 100cc, and the average pore size is 45 pixels. 2 We prepared a polyethylene porous film (manufactured by LG Chem of South Korea). The polyethylene was observed using a scanning electron microscope (SEM) (manufacturer: Hitachi High-Technologies Corporation, product name: S-4800) under conditions of 5kV, 10uA, and 20,000 magnification to obtain the original image of the polyethylene (Figure 5).

[0090] Next, the original image in Figure 5 was divided into pixels representing multiple polymer regions that are adjacent to or span across pixels, and pixels representing pore regions formed between the multiple polymers to obtain a binary image. A Voronoi diagram was then applied to the obtained binary image to convert it into a Voronoi diagram area image (Figure 6).

[0091] Figures 7 and 8 visualize the area in the Voronoi diagram of Example 2. Figure 7 shows the Voronoi diagram with colors assigned according to the size of each area. The size of the area is calculated using pixels in a scanning electron microscope (SEM) as a guide, and can be converted to the actual size using the SEM imaging conditions. Figure 8 shows that the average area of ​​the Voronoi diagram calculated using pixels is 263.3 pixels. 2 (Standard deviation: 120.9) indicates that the number of stomata is 1045.

[0092] In Examples 1 and 2, the average Voronoi plot area in Example 2 is even smaller than the average Voronoi plot area in Example 1, and the standard deviation is also smaller in Example 2. Since the Voronoi plot area is proportional to the distance between stomata, it can be confirmed that the stomatal distribution in Example 2 is even more uniform than that in Example 1. The scanning electron microscope (SEM) images in Figures 1 and 5 also confirm that the distance between stomata in Example 1 is even greater.

[0093] The average pore diameter calculated using pixels in scanning electron microscope (SEM) images was 105 in Example 1 and 45 in Example 2. Although the pore diameter is larger in Example 1, the distribution of pore locations is more uniform in Example 2, confirming that the air permeability of Example 2 is even better.

[0094] Figures 9 and 10 show graphs of the original images of the cross-sections of the porous films of Example 1 and Example 2, converted to binary images, with the pore area on the x-axis and the Voronoi area on the y-axis.

[0095] Referring to Figures 9 and 10, it can be seen that both the pore area and Voronoi area of ​​the data plotted in Figure 9 are large. In other words, the pore area and distance between pores in Example 1 are even larger than those in Example 2. This is the same result that can be confirmed from the scanning electron microscope (SEM) images of Example 1 and Example 2 in Figures 1 and 5.

[0096] As described above, the present invention has been explained with limited embodiments and drawings, but it goes without saying that the present invention is not limited thereto, and various modifications and variations are possible within the equivalent scope of the technical concept and claims of the present invention by persons with ordinary skill in the art to which the present invention belongs.

Claims

1. The steps include: observing the cross-section of a porous structure using a scanning electron microscope (SEM) to obtain a raw image of the cross-section of the porous structure; The process involves quantifying the stomatal distribution of the obtained raw image using a Voronoi diagram, Includes, The step of quantifying the stomatal distribution is: The process includes converting the original image into a Voronoi diagram area image using the Voronoi diagram method, The step of quantifying the stomatal distribution is: Correlation data is obtained between the pore area of ​​the original image and the Voronoi diagram area corresponding to each pore in the original image. A method for analyzing the pore distribution of a porous structure, comprising analyzing the pore distribution of the porous structure using the aforementioned correlation data.

2. In the process of converting to the aforementioned Voronoi diagram area image, A method for analyzing the pore distribution of a porous structure according to claim 1, comprising: converting the original image into a binary image (2-D binary image) in which pixels of the original image are divided into pixels indicating a plurality of structural regions that are adjacent to or span across pixels of the original image, and pixels indicating pore regions formed between the plurality of structural regions; and then converting the binary image into a Voronoi area image using the Voronoi diagram method.

3. The step of quantifying the stomatal distribution is: A graph is created with the stomatal area of ​​the original image as the x-axis and the area of ​​the Voronoi diagram as the y-axis. A method for analyzing the pore distribution of a porous structure according to claim 1, comprising plotting data relating to the pore area of ​​the original image and the corresponding area of ​​the Voronoi diagram on the graph, and analyzing the pore distribution of the porous structure using the graph.

4. The method for analyzing the pore distribution of a porous structure according to any one of claims 1 to 3, wherein the pore area of ​​the original image is calculated using the pixels of the original image.

5. The method for analyzing the pore distribution of a porous structure according to claim 2, wherein the pore area of ​​the original image is calculated using the pore area of ​​the binary image.

6. The method for analyzing the pore distribution of a porous structure according to claim 5, wherein the pore area of ​​the binary image is calculated using the pixels of the binary image.

7. The method for analyzing the pore distribution of a porous structure according to claim 1, further comprising the step of obtaining the correlation data for various cross-sections of the porous structure and applying a Bayesian population estimation method to estimate the pore distribution of the porous structure.

Images