Method for analyzing pore distribution in 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 to Korean Patent Application No. 10-2022-0113080 filed on September 6, 2022, the entire contents of which are incorporated herein by reference in their entirety in the specification and drawings of that application.

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

[0003] Recently, interest in energy storage technology is increasing. As the fields of application expand to include mobile phones, video cameras, and laptops, as well as the energy of electric vehicles, efforts in the research and development of electrochemical elements are becoming more and more concrete. From this perspective, electrochemical elements are the field that has attracted the most attention, and efforts in the development of rechargeable secondary batteries are the focus of attention.

[0004] Among the secondary batteries currently in use, lithium secondary batteries, developed in the early 1990s, have been attracting attention due to their advantages of higher operating voltage and higher energy density compared to conventional batteries such as Ni-MH.

[0005] Lithium secondary batteries are composed of a positive electrode, a negative electrode, an electrolyte, and a separator, and among these, the separator is required to separate the positive electrode and the negative electrode to electrically insulate them, while increasing the permeability of lithium ions based on high porosity to increase ion conductivity. Polyolefin-based materials such as polyethylene (PE) and polypropylene (PP) are commonly used as the polymer base material for separators, as they are favorable for forming pores and have excellent chemical resistance, mechanical properties, and thermal properties.

[0006] Required properties for separators for lithium secondary batteries include excellent air permeability, low thermal shrinkage, and high puncture strength. With the development of higher capacity and higher power batteries, there is a constant demand for excellent air permeability.

[0007] Such a separator may be in the form of a porous polymer substrate prepared by kneading a polymer such as polyolefin and a diluent, extruding and stretching the mixture to form a film, and then extracting the diluent using a solvent to form pores. Alternatively, the separator may be in the form of a composite separator having an organic-inorganic composite porous layer including a binder polymer and inorganic particles on at least one side of the porous polymer substrate.

[0008] In this case, the physical properties of the porous polymer substrate used in the separator for the secondary battery can be adjusted in various ways depending on the process conditions during the manufacturing process. In particular, the structure and distribution of the pores of the porous polymer substrate, which are the passages of lithium ions, can also be changed in various ways depending on the process conditions.

[0009] In the past, qualitative evaluation methods that visually check microscopic images were the mainstream 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, a method has been used to quantify the pore distribution of porous structures by using the Euclidean distance measurement method on images measured by a scanning electron microscope (SEM). However, this method has the problem that the distance between pores is affected by the pore diameter. In addition, unlike the population distribution measured in a single image, the pore distribution is biased to one side, resulting in inaccurate results. Summary of the Invention [Problem to be solved by the invention]

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

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

[0013] A method for analyzing the pore distribution of a porous structure according to a first aspect includes 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.

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

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

[0016] According to the 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 in that it includes creating a graph with the pore area of ​​the original image on the x-axis and the area of ​​the Voronoi diagram on the y-axis, plotting data regarding 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 a sixth aspect, in any one of the second to fifth aspects, the pore area of ​​the original image is calculated using pixels of the original image.

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

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

[0021] According to the 9th aspect, in any one of the 4th to 8th aspects, the method further includes a step of obtaining the correlation data for various cross sections of the porous structure and applying a Bayesian population estimation method to the correlation data to estimate the pore distribution of the porous structure. Effect of the Invention

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

[0023] Specifically, according to an embodiment of the present invention, the pore size and pore position in the porous structure can be analyzed, and the degree of dispersion of the pores in the entire porous structure and the degree of bias in the pore positions can be analyzed.

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

[0025] The drawings attached to this specification are intended to illustrate preferred embodiments of the present invention and serve to further understand the technical ideas of the present invention together with the above-mentioned contents of the invention, and therefore the present invention should not be interpreted as being limited to the matters described in the drawings. Meanwhile, the shape, size, scale, or ratio of elements in the drawings included in this specification may be exaggerated to emphasize a clearer description. [Brief description of the drawings]

[0026] [Figure 1] 1 is an original scanning electron microscope (SEM) image of Example 1. [Diagram 2] 1 shows a Voronoi diagram for the binary image of the first embodiment. [Diagram 3] 1 is a visualization of the area in the Voronoi diagram of Example 1. [Figure 4] 1 is a visualization of the area in the Voronoi diagram of Example 1. [Diagram 5] 1 is an original scanning electron microscope (SEM) image of Example 2. [Figure 6] 13 shows a Voronoi diagram for a binary image in the second embodiment. [Figure 7] 13 is a visualization of the area in the Voronoi diagram of the second embodiment. [Figure 8] 13 is a visualization of the area in the Voronoi diagram of the second embodiment. [Figure 9] 1 shows the correlation between the pore area of ​​a binary image and the Voronoi diagram area in Example 1. [Figure 10] 13 shows the correlation between the pore area of ​​a binary image and the Voronoi diagram area in Example 2. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] The present invention will be described in detail below with reference to the accompanying drawings. The terms and words used in the present specification and claims are not to be construed as being limited to their ordinary or dictionary meanings, but are to be construed as having meanings and concepts that correspond to the technical ideas of the present invention, in accordance with the principle that the inventor himself can appropriately define the concepts of terms in order to best describe the invention.

[0028] Therefore, it should be understood that the embodiment described in this specification and the configurations shown in the drawings are merely the most preferred embodiment of the present invention and do not represent the entire technical idea of ​​the present invention, and therefore there may be various equivalents and modifications that can be substituted therefor at the time of this application.

[0029] In addition, throughout the specification, when a part "includes," "comprises," "haves," or "has" a certain element, this does not mean that other elements are excluded, but that other elements may also be included, unless otherwise specified.

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

[0031] As used herein, a Voronoi diagram may refer to a division of a plane into a set of points that are closest to a particular point.

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

[0033] A method for analyzing the pore distribution of a porous structure according to one embodiment of the present invention includes 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.

[0034] In the past, there have been attempts to analyze the pore distribution of a porous structure using air permeability or electrical resistance, but even if the structure has the same air permeability or electrical resistance, the pore distribution may actually be different. Also, there have been attempts to analyze the pore distribution using the Euclidean distance transformation method, but it was not possible to control the effect that the pore diameter or the distance between pores has on the analysis of the overall pore distribution.

[0035] The pore distribution analysis method according to the present invention can provide a more accurate analysis result of the pore distribution of a porous structure that reflects the actual pore distribution by utilizing a Voronoi diagram. Here, the pore distribution includes the pore size and the pore position, and the pore position may mean the degree of dispersion of the pores in the porous structure and / or the degree of bias of the pores.

[0036] In one embodiment of the present invention, the method may include the step of preparing a porous structure for analyzing the pore distribution of the porous structure.

[0037] Here, the porous structure to be analyzed is not limited to a specific material, and any structure having a large number of pores can be used. For example, the porous structure can be a porous polymer substrate such as a filter or a nonwoven fabric, and more specifically, the porous polymer substrate can be a porous polymer substrate included in a separator for a secondary battery.

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

[0039] Specifically, the porous polymer substrate may 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 a polyolefin such as polyethylene or polypropylene. Such a polyolefin porous polymer film substrate exhibits a shutdown function at a temperature of, for example, 80 to 130°C.

[0041] In this case, the polyolefin porous polymer film can be formed from a polyolefin polymer such as polyethylene, such as high density polyethylene, linear low density polyethylene, low density polyethylene, and ultra-high molecular weight polyethylene, polypropylene, polybutylene, and polypentene, either alone or in a mixture of two or more of these.

[0042] In this case, the polyolefin porous polymer film can be formed from a polyolefin polymer such as polyethylene, such as high density polyethylene, linear low density polyethylene, low density polyethylene, and ultra-high molecular weight polyethylene, polypropylene, polybutylene, and polypentene, either alone or in a mixture of two or more of these.

[0043] In addition, the porous polymer film substrate may be manufactured by forming various polymers such as polyester, in addition to polyolefin, into a film shape. Furthermore, the porous polymer film substrate may be formed into a structure in which two or more film layers are laminated, and each film layer may be formed from the above-mentioned polymers such as polyolefin and polyester alone or a mixture of two or more of them.

[0044] Furthermore, the porous polymer film substrate and the porous nonwoven fabric substrate may be formed from a polymer such as polyolefin, polyester (polyethylene terephthalate, polybutylene terephthalate, polyethylene naphthalate, polyacetal, polyamide, polycarbonate, polyimide, polyether ether ketone, polyether sulfone, polyphenylene oxide, polyphenylene sulfide, etc., either alone or in combination.

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

[0046] As the porous polymer substrate, a porous polyolefin may be particularly used, and in this case, the polyolefin may include polyethylene; polypropylene; polybutylene; polypentene; polyhexene; polyoctene; a copolymer of two or more of ethylene, propylene, butene, pentene, 4-methylpentene, hexene, and octene; or a mixture thereof.

[0047] In particular, examples of the 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 of the resin, can be mainly 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 producing a separator, it is possible to finally obtain a separator having excellent strength and heat resistance while ensuring the uniformity and film-forming processability of the separator.

[0049] Then, the method 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, the porous polymeric substrate can be imaged using a scanning electron microscope (SEM) at 5 kV and 10 uA at a magnification of 20,000.

[0051] The method further includes a step of quantifying pore distribution in the obtained original image using a Voronoi diagram.

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

[0053] The process of converting into the Voronoi diagram area image may be performed using the original image itself. Alternatively, the original image may be converted into a binary image (2-D binary image) in which the pixels of the original image are divided into pixels representing a plurality of adjacent or overlapping structure regions and pixels representing pore regions formed between the plurality of structure regions, and the binary image may then be converted into the Voronoi diagram area image using a Voronoi diagram method. Since it is easier to distinguish between structure regions and pore regions in a binary image than in an original image, the conversion into the Voronoi diagram area image may be more accurate.

[0054] The original image or the binary image can be converted into a Voronoi diagram area image by a technique well known in the art. For example, in the original image or the binary image, points are plotted at positions corresponding to each pore region, and a set of points that are closest to a specific point are connected to convert into a Voronoi diagram area image.

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

[0056] The thresholding method can be global thresholding, Otsu thresholding, or adaptive thresholding.

[0057] The full-range binarization method is a method in which a threshold value is determined, and if a pixel value exceeds the threshold value, the pixel value is processed as black, and if the pixel value does not exceed the threshold value, the pixel value is processed as white.

[0058] The Otsu binarization method uses the Otsu algorithm to create a binary image. When converting to a binary image, the Otsu algorithm repeatedly determines a threshold value to divide pixels into two categories and finds the distribution of light and dark in the two categories. After this, it selects the threshold value that gives the most uniform distribution of light and dark in the two categories among all the cases. The Otsu algorithm has the advantage of automatically finding the optimal threshold value.

[0059] The adaptive thresholding method is a method for dividing an image into various regions, and then calculating a threshold value for converting the image into a binary image by using only the pixel values ​​around the regions. 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 value, the adaptive thresholding method can be used to create a binary image of excellent 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 may be created with the pore area of ​​the original image on the x-axis and the Voronoi diagram area on the y-axis, and data relating to each pore area of ​​the original image and the corresponding Voronoi diagram area may be plotted on the graph, and the pore distribution of the porous structure may be analyzed based on the degree to which data is dispersed on the graph. However, the method of plotting the correlation data is not limited in any way, and for example, the method and format of the graph may be freely adopted.

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

[0063] In this way, the pore distribution in the porous structure can be analyzed by the values ​​corresponding to the pore values ​​on the graph. For example, the degree to which the pore positions are dispersed in the porous structure can be determined by using the degree to which the pore values ​​are dispersed on the graph. Alternatively, the pore position distribution can be analyzed by using a Voronoi area value proportional to the distance between each pore obtained by using a Voronoi diagram. For example, the pore position distribution can be analyzed by using the fact that if the distance between pores is short, the Voronoi area becomes small, and if the distance between pores is long, the Voronoi area becomes large.

[0064] In one embodiment of the present invention, the pore area of ​​the original image may be calculated using the pixels of the original image itself. Alternatively, the pore area of ​​the original image may be calculated using the pixels of a binary image converted from the original image. The pore area may be converted to an actual size using the observation conditions of a scanning electron microscope (SEM) under which the porous structure was observed.

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

[0066] When an image obtained by observation using a scanning electron microscope (SEM) is only a portion of the entire pore structure, Bayesian estimation can be used to analyze the pore distribution of the entire pore structure without obtaining images of all parts of the entire pore structure. The porous polymer substrate that is the subject of the pore distribution analysis of the present invention can be used as a separator for a secondary battery by itself, 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] In this way, the separator having enhanced stability by including an organic-inorganic composite porous layer on at least one side thereof includes an organic-inorganic composite porous layer located on at least one side of a porous polymer substrate, the organic-inorganic composite porous layer including a plurality of inorganic particles and a binder polymer located on some or all of the surfaces of the inorganic particles to connect and fix the inorganic particles to each other.

[0068] The binder polymer may be a polymer commonly used in the art for forming an organic-inorganic composite porous layer. In particular, a polymer having a glass transition temperature (Tg) of -200 to 200°C may be used, because it is possible to improve mechanical properties such as flexibility and elasticity of the finally formed organic-inorganic composite porous layer. Such a binder polymer effectively plays a role of a binder that connects and stably fixes inorganic particles to each other, thereby contributing to preventing deterioration of the mechanical properties of the separator in which the organic-inorganic composite porous layer is introduced.

[0069] The binder polymer does not necessarily have to have ion conductivity, but when a polymer having ion conductivity is used, the performance of the electrochemical element can be further improved. Therefore, the binder polymer can have as high a dielectric constant as possible. In fact, since the degree of dissociation of salt in an electrolyte depends on the dielectric constant of the electrolyte solvent, the higher the dielectric constant of the binder polymer, the more the degree of dissociation of salt in the electrolyte can be improved. The dielectric constant of such a binder polymer can be in the range of 1.0 to 100 (measurement frequency = 1 kHz), and can be particularly 10 or more.

[0070] In addition to the above-mentioned functions, the binder polymer may have a feature that it can exhibit a high degree of swelling in the liquid electrolyte by being gelled when it is immersed in the liquid electrolyte. Therefore, the solubility index of the binder polymer, i.e., Hildebrand solubility parameter, is 15 to 45 MPa. 1 / 2 Or 15 to 25 MPa 1 / 2 and 30 to 45 MPa 1 / 2 Therefore, hydrophilic polymers having more polar groups can be used more widely than hydrophobic polymers such as polyolefins. 1 / 2 Less than 45MPa 1 / 2 If it exceeds this limit, there is a risk that it may become difficult to swell with a liquid electrolyte for a normal battery.

[0071] Non-limiting examples of such binder polymers include polyvinylidene fluoride-co-hexafluoropropylene, polyvinylidene fluoride-co-trichloroethylene, polymethylmethacrylate, polybutylacrylate, polybutylmethacrylate, polyacrylonitrile, polyvinylpyrrolidone, polyvinylacetate, ethylene-vinylacetate copolymer, polyethylene oxide, polyarylate, cellulose acetate, cellulose acetate butyrate, and cellulose acetate propionate. Examples of suitable cellulose acetate copolymers include, but are not limited to, cyanoethyl propionate, cyanoethyl pullulan, cyanoethyl polyvinyl alcohol, cyanoethyl cellulose, cyanoethyl sucrose, pullulan, and carboxyl methyl cellulose.

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

[0073] Non-limiting examples of 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、 Examples include SiC, AlO(OH), Al2O3·H2O, or mixtures of these.

[0074] In the present specification, the term "inorganic particles having lithium ion transfer ability" refers to inorganic particles that contain lithium element but do not store lithium and have the function of transferring lithium ions. Non-limiting examples of inorganic particles having lithium ion transfer ability include lithium phosphate (Li3PO4), lithium titanium phosphate (Li x Ti y (PO4)3, 0 <x<2, 0<y<3)、リチウムアルミニウムチタンホスフェート(Li x Al y Ti z (PO4)3, 0 <x<2, 0<y<1, 0<z<3)、14Li2O-9Al2O3-38TiO2-39P2O5などの(LiAlTiP) x O y Glass (0 <x<4, 0<y<13)、リチウムランタンチタネート(Li x La y TiO3, 0 <x<2, 0<y<3)、Li 3.25 Ge 0.25 P 0.75 Lithium germanium thiophosphates such as S4 (Li x Ge y P z S w , 0 <x<4, 0<y<1, 0<z<1, 0<w<5)、Li3Nなどのリチウムニトリド(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 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 on the porous polymer substrate. The coating method is not particularly limited, but it is preferable to use a slot coating method or a 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. In addition, the deep coating method is a method in which the substrate is immersed in a tank containing the composition to coat it, 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. After immersion, the substrate can be post-weighed using a Mayer bar or the like for more accurate control of the coating thickness.

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

[0080] In one embodiment of the present invention, the binder polymer may adhere the inorganic particles to each other (i.e., the binder polymer connects and fixes the inorganic particles to each other) so that the inorganic particles can be maintained in a state of being bound to each other, and the binder polymer may maintain the inorganic particles and the porous polymer substrate in a state of being bound to each other. The inorganic particles of the organic-inorganic composite porous layer may form an interstitial volume in a state of being substantially in contact with each other, and the interstitial volume here means a space limited by the inorganic particles that are substantially in contact in a packed structure of the inorganic particles (closed packed or densely packed). The interstitial volume between the inorganic particles may become an empty space and form pores of the organic-inorganic composite porous layer.

[0081] Non-limiting examples of the dispersion medium used in this case include any 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] In addition, in one embodiment of the present invention, the composition for forming the organic-inorganic composite porous layer is coated onto the porous polymer substrate, and then the composition is dried at a temperature of 20 to 70°C or 23 to 60°C under conditions of a relative humidity of 30 to 80%, or 50 to 80%, for 1 minute to 2 hours, or 5 minutes to 1 hour, or 10 minutes to 1 hour to remove the solvent, thereby finally producing a separator having an organic-inorganic composite porous layer on at least one side and having enhanced stability.

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

[0084] The present invention will be described in detail below with reference to examples. However, the examples according to 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 described below. The examples of the present invention are provided to more completely explain the present invention to those having average knowledge in the art.

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

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

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

[0088] 3 and 4 are visualizations of the areas in the Voronoi diagram of Example 1. In FIG. 3, the Voronoi diagram is colored according to the size of each area of ​​the Voronoi diagram. The size of the area is calculated using the pixels in the binary image as a guide, and can be converted to the actual size using the shooting conditions of a scanning electron microscope (SEM). In FIG. 4, the average value of the area of ​​the Voronoi diagram calculated using pixels is 513.4 pixels. 2 (standard deviation: 296.3), indicating that the number of stomata is 536.

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

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

[0091] 7 and 8 are visualizations of areas in the Voronoi diagram of Example 2. In FIG. 7, the Voronoi diagram is colored 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 imaging conditions of the scanning electron microscope (SEM). FIG. 8 shows that the average area of ​​the Voronoi diagram calculated using pixels is 263.3 pixels. 2 (standard deviation: 120.9), indicating that the number of stomata is 1045.

[0092] In Examples 1 and 2, the average value of the Voronoi diagram area in Example 2 is smaller than the average value of the Voronoi diagram area in Example 1, and the standard deviation is also smaller in Example 2. Since the Voronoi diagram area is proportional to the distance between pores, it can be seen that the pore distribution in Example 2 is more uniform than the pore distribution in Example 1. It can also be seen from the scanning electron microscope (SEM) images of Figures 1 and 5 that the distance between pores in Example 1 is greater.

[0093] The average pore size of the pores calculated using pixels in the scanning electron microscope (SEM) image was 105 in Example 1 and 45 in Example 2, which is larger in Example 1, but the distribution of the pore positions is more uniform in Example 2, confirming that the breathability of Example 2 is even better.

[0094] 9 and 10 are graphs showing the pore area of ​​the binary image converted from the original images of the cross sections of the porous films of Examples 1 and 2 on the x-axis and the Voronoi diagram area on the y-axis.

[0095] 9 and 10, it can be seen that the pore area and Voronoi diagram area of ​​the data plotted in Fig. 9 are both large. That is, it can be seen that the pore area and the distance between the pores in Example 1 are larger than the pore area and the distance between the pores in Example 2. This is the same result as can be seen from Figs. 1 and 5, which are scanning electron microscope (SEM) images of Examples 1 and 2.

[0096] As described above, the present invention has been described using limited embodiments and drawings, but the present invention is not limited to these, and it goes without saying that various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present invention belongs within the technical spirit of the present invention and the equivalent scope of the claims.

Claims

1. 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; A step of quantifying pore distribution on the obtained raw image using a Voronoi diagram; A method for analyzing the pore distribution of a porous structure, comprising:

2. The step of quantifying the pore distribution includes: The method for analyzing pore distribution of a porous structure according to claim 1, further comprising a step of converting the original image into a Voronoi diagram area image using a Voronoi diagram method.

3. In the process of converting the Voronoi diagram area image, 3. The method for analyzing pore distribution in a porous structure according to claim 2, further comprising converting the original image into a binary image (2-D binary image) in which pixels of the original image are divided into pixels representing a plurality of structure regions adjacent to or spanning each other and pixels representing pore regions formed between the plurality of structure regions, and then converting the binary image into a Voronoi diagram area image using a Voronoi diagram method.

4. The step of quantifying the pore distribution includes: obtaining correlation data between a pore area of ​​the original image and a Voronoi diagram area corresponding to each pore of the original image; The method for analyzing the pore distribution of a porous structure according to claim 2, further comprising analyzing the pore distribution of the porous structure using the correlation data.

5. The step of quantifying the pore distribution includes: creating a graph with the pore area of ​​the original image on the x-axis and the area of ​​the Voronoi diagram on the y-axis; The method for analyzing the pore distribution of a porous structure according to claim 2, further 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.

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

7. The method for analyzing pore distribution of a porous structure according to claim 3 , wherein the pore area of ​​the original image is calculated based on the pore area of ​​the binary image.

8. The method for analyzing pore distribution of a porous structure according to claim 7, wherein the pore area of ​​the binary image is calculated using pixels of the binary image.

9. The method for analyzing the pore distribution of a porous structure as described in claim 4, 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 the correlation data to estimate the pore distribution of the porous structure.

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