Carbon black and method for assessing or selecting carbon black

Abstract

To provide carbon black which is suitable as a conductive material for secondary batteries and enables formation of an electrode having a lower resistance value.SOLUTION: This carbon black includes a plurality of aggregated particles each formed through aggregation of a plurality of primary particles. When the plurality of aggregated particles are classified using an X-value, a Y-value, and a Z-value, the proportion of fourth aggregated particles having an X-value of 0.588 or more, a Y-value of less than 0.833, and a Z-value of less than 0.77 is 41% or more. X=a / b (i), Y=4πA / P2 (ii), and Z=A / Ac (iii) [In the formulae, a (μm) represents the minimum Feret diameter; b (μm) represents the maximum Feret diameter; A (μm2) represents the projection area; P (μm) represents the perimeter length; and Ac (μm2) represents an envelope internal area in a binary image for measurement in an image processing system].SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to carbon black and a method for evaluating or selecting carbon black.

Background Art

[0002] Conventionally, carbon black has been used as a conductive material or the like, and the development of carbon black having various characteristic values has been studied (for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the demand for secondary batteries has been increasing, and further improvement in characteristics is required for conductive materials for secondary batteries.

[0005] One object of the present disclosure is to provide carbon black that is suitable as a conductive material for secondary batteries and can form an electrode with a lower resistance value. Another object of the present disclosure is to provide a method capable of evaluating or selecting carbon black suitable as a conductive material for secondary batteries.

Means for Solving the Problems

[0006] The present disclosure relates to, for example, the following [1] to

[10] . [1] Carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, When the plurality of aggregated particles are classified into a first aggregated particle having an X value obtained by the following formula (i) of less than 0.588, a second aggregated particle having an X value of 0.588 or more and a Y value obtained by the following formula (ii) of 0.833 or more, a third aggregated particle having an X value of 0.588 or more, a Y value of less than 0.833, and a Z value obtained by the following formula (iii) of 0.77 or more, and a fourth aggregated particle having an X value of 0.588 or more, a Y value of less than 0.833, and a Z value of less than 0.77, the ratio of the fourth aggregated particle to the total number of the first aggregated particle, the second aggregated particle, the third aggregated particle and the fourth aggregated particle is 41% or more, carbon black. X = a / b …(i) Y = 4πA / P 2 …(ii) Z = A / Ac …(iii) [In the formula, in the measurement binary image generated by the image processing system, the minimum Feret diameter of the aggregated particle is a (μm), the maximum Feret diameter of the aggregated particle is b (μm), the projected area of the aggregated particle is A (μm 2 ), the perimeter of the aggregated particle is P (μm), and the area of the inner surface of the envelope of the aggregated particle is Ac (μm 2 ). However, the image processing system comprises at least one processor, the at least one processor acquires a first grayscale original image showing the plurality of aggregated particles, captured by a scanning electron microscope, performs a first binarization process on the first original image based on a mixture Gaussian model to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region, generates a partial image that is a part of the first binary image and shows some of the plurality of aggregated particles from the first binary image, converts the pixel value of each pixel of the partial image into the pixel value of each pixel of the first original image corresponding to each pixel of the partial image to generate a second original image, Performing a second binarization process on the second original image based on a mixture Gaussian model to generate the binary image for measurement that distinguishes the foreground region composed of some of the aggregated particles from the background region outside the foreground region. It is an image processing system. [2] The ratio of the third aggregated particles to the total number of the first aggregated particles, the second aggregated particles, the third aggregated particles and the fourth aggregated particles is 17% or less. The carbon black according to [1]. [3] The oil absorption amount is 150 mL / 100 g or more and 400 mL / 100 g or less. The carbon black according to [1] or [2]. [4] The BET specific surface area is 35 m 2 / g or more and 400 m 2 / g or less. The carbon black according to any one of [1] to [3]. [5] An electrode composition containing the carbon black according to any one of [1] to [4] and an active material capable of occluding and releasing lithium ions. [6] An electrode containing the electrode composition according to [5]. [7] A secondary battery including the electrode according to [6]. [8] A method for evaluating or selecting carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, Obtaining a binary image for measurement generated by an image processing system; From the binary image for measurement, a first aggregated particle with an X value obtained by the following formula (i) less than 0.588, a second aggregated particle with the X value of 0.588 or more and a Y value obtained by the following formula (ii) of 0.833 or more, a third aggregated particle with the X value of 0.588 or more, the Y value less than 0.833, and a Z value obtained by the following formula (iii) of 0.77 or more, and a fourth aggregated particle with the X value of 0.588 or more, the Y value less than 0.833, and the Z value less than 0.77. When classified into these, the step of obtaining the ratio of the fourth aggregated particle to the total number of the first aggregated particle, the second aggregated particle, the third aggregated particle, and the fourth aggregated particle The step of evaluating or selecting the carbon black based on the ratio Including The image processing system Is provided with at least one processor The at least one processor Obtains a first original image showing the plurality of aggregated particles Performs a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region Based on the first original image and the first binary image, a second original image showing some of the plurality of aggregated particles, wherein the pixel value of each pixel of the second original image is the pixel value of each pixel of the first original image corresponding to each pixel of the second original image, and generates the second original image Performs a second binarization process on the second original image to generate the binary image for measurement that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region An image processing system Method X = a / b …(i) Y = 4πA / P 2 …(ii) Z = A / Ac …(iii) [wherein, in the binary image for measurement generated by the image processing system, the minimum Feret diameter of the agglomerated particles is a (μm), the maximum Feret diameter of the agglomerated particles is b (μm), the projected area of the agglomerated particles is A (μm 2 ), the perimeter of the agglomerated particles is P (μm), and the inner area of the envelope of the agglomerated particles is Ac (μm 2 ).] [9] The first original image is a grayscale image, and the at least one processor performs the first binarization process based on a mixture Gaussian model, and performs the second binarization process based on the mixture Gaussian model, The method according to [8].

[10] The at least one processor generates a partial image that is a part of the first binary image and shows a part of the plurality of agglomerated particles from the first binary image, converts the pixel value of each pixel of the partial image into the pixel value of each pixel of the first original image corresponding to each pixel of the partial image to generate the second original image, The method according to [8] or [9].

[11] The first original image is an image obtained by imaging with a scanning electron microscope, The method according to any one of [8] to

[10] .

Advantages of the Invention

[0007] According to the present disclosure, carbon black suitable as a conductive material for a secondary battery and capable of forming an electrode with a lower resistance value is provided. Further, according to the present disclosure, there is provided a method capable of evaluating or selecting carbon black suitable as a conductive material for a secondary battery.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

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Figure 8

Embodiments for Carrying Out the Invention

[0009] Hereinafter, embodiments in the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant descriptions are omitted.

[0010] The method according to the present disclosure is a method for evaluating or selecting carbon black based on a measurement binary image generated by an image processing system described later. Carbon black includes a plurality of aggregated particles each formed by aggregation of a plurality of primary particles. The method according to the present disclosure may be a method for classifying aggregated particles based on an X value obtained by formula (i), a Y value obtained by formula (ii), and a Z value obtained by formula (iii) from a measurement binary image, and evaluating or selecting carbon black based on the ratio of the classified aggregated particles. X = a / b …(i) Y = 4πA / P 2 …(ii) Z = A / Ac …(iii) [In the formula, in the measurement binary image generated by the image processing system, the minimum Feret diameter of the aggregated particle is a (μm), the maximum Feret diameter of the aggregated particle is b (μm), and the projected area of the aggregated particle is A (μm 2) The perimeter of the agglomerated particles is P (μm), and the inner area of the envelope of the agglomerated particles is Ac (μm 2 ).]

[0011] The agglomerated particles may be classified into a first agglomerated particle with an X value less than 0.588, a second agglomerated particle with an X value of 0.588 or more and a Y value of 0.833 or more, a third agglomerated particle with an X value of 0.588 or more, a Y value less than 0.833 and a Z value of 0.77 or more, and a fourth agglomerated particle with an X value of 0.588 or more, a Y value less than 0.833 and a Z value less than 0.77. At this time, the method according to the present disclosure may be a method of obtaining the ratio of the fourth agglomerated particles to the total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles and the fourth agglomerated particles, and evaluating or selecting carbon black based on the ratio. When the ratio of the fourth agglomerated particles is large, it is easier to obtain an electrode with a lower resistance value. Therefore, by selecting carbon black based on the fourth agglomeration ratio, it is possible to easily select carbon black that is likely to form an electrode with a lower resistance value. Further, by evaluating carbon black based on the fourth agglomeration ratio, it is possible to predict the resistance value of an electrode formed from the carbon black and easily evaluate whether the carbon black is useful as a conductive material for a secondary battery.

[0012] The first agglomerated particle is an agglomerated particle having an X value of less than 0.588 in the formula (i). Here, the X value indicates the aspect ratio of the agglomerated particle. The larger the difference between the major axis and the minor axis, the smaller the X value. Since the first agglomerated particle has an X value of less than 0.588, it can be said that the agglomerated particle has a shape close to a linear shape.

[0013] The second agglomerated particle is an agglomerated particle having an X value of 0.588 or more and a Y value of 0.833 or more. Here, the Y value is an index of the complexity of the agglomerated particle, and it can be said that the closer the Y value is to 1, the closer the shape is to a perfect circle. Since the second agglomerated particle has an X value of 0.588 or more and a Y value of 0.833 or more, it can be said that the agglomerated particle has a shape close to a spherical shape.

[0014] The third aggregated particle is an aggregated particle with an X value of 0.588 or more, a Y value of less than 0.833, and a Z value of 0.77 or more. Here, the Z value is the ratio of the projected area of the aggregated particle to the area within the envelope when the envelope is wrapped around the aggregated particle. The larger the Z value, the fewer the branches of the aggregated particle. Since the third aggregated particle has an X value of 0.588 or more, a Y value of less than 0.833, and a Z value of 0.77 or more, it can be said to be an aggregated particle having a shape close to an ellipsoid.

[0015] The fourth aggregated particle is an aggregated particle with an X value of 0.588 or more, a Y value of less than 0.833, and a Z value of less than 0.77. Here, the Z value is the ratio of the projected area of the aggregated particle to the area within the envelope when the envelope is wrapped around the aggregated particle. The smaller the Z value, the more branched the aggregated particle is. Since the fourth aggregated particle has an X value of 0.588 or more, a Y value of less than 0.833, and a Z value of less than 0.77, it can be said to be a branched aggregated particle with many branches.

[0016] By classifying carbon black based on the binary measurement image generated by the image processing system described below, the classification will significantly reflect the characteristics of the carbon black. That is, in the method according to the present disclosure, the aggregated particles are classified according to the X value, Y value, and Z value based on the binary measurement image generated by the image processing system described below, and the carbon black is selected based on the ratio of the classified aggregated particles, whereby the carbon black having the desired characteristics can be easily selected. Further, by evaluating the carbon black based on the ratio of the classified aggregated particles, the carbon black can be easily evaluated.

[0017] Carbon black varies greatly in the shape of aggregated particles, etc., depending on differences in the collision frequency of primary particles based on differences in the thermal history during synthesis (for example, thermal history caused by thermal decomposition and combustion reactions of fuel oil, thermal decomposition and combustion reactions of raw materials, rapid cooling by a cooling medium, and reaction termination).

[0018] In a preferred embodiment of the carbon black, the ratio of the fourth aggregated particles (N4 / (N1 + N2 + N3 + N4)) to the total number of the first aggregated particles, the second aggregated particles, the third aggregated particles and the fourth aggregated particles (N1 + N2 + N3 + N4) is 41% or more, preferably 43% or more, more preferably 45% or more, and still more preferably 47% or more.

[0019] The above ratio (N4 / (N1 + N2 + N3 + N4)) may be, for example, 60% or less, 58% or less, 56% or less, or 54% or less. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0020] The ratio of the first aggregated particles (N1 / (N1 + N2 + N3 + N4)) to the total number of the first aggregated particles, the second aggregated particles, the third aggregated particles and the fourth aggregated particles (N1 + N2 + N3 + N4) may be, for example, 30% or more, 33% or more, 36% or more, or 39% or more. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained. Also, the above ratio (N1 / (N1 + N2 + N3 + N4)) may be, for example, 54% or less, 52% or less, 50% or less, or 48% or less. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0021] The ratio of the second aggregated particles (N2 / (N1 + N2 + N3 + N4)) to the total number of the first aggregated particles, the second aggregated particles, the third aggregated particles and the fourth aggregated particles (N1 + N2 + N3 + N4) may be, for example, 2% or less, 1% or less, 0.5% or less, 0.1% or less, or even 0%. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0022] The ratio of the third aggregated particles (N3 / (N1 + N2 + N3 + N4)) to the total number of the first aggregated particles, the second aggregated particles, the third aggregated particles and the fourth aggregated particles (N1 + N2 + N3 + N4) may be, for example, 0.1% or more, and may also be 0.5% or more, 1% or more, or 2% or more. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained. Further, the above ratio (N3 / (N1 + N2 + N3 + N4)) may be, for example, 17% or less, and may also be 14% or less, 11% or less, or 8% or less. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0023] In the method according to the present disclosure, carbon black may be evaluated or selected based on the above numerical range.

[0024] The carbon black in the present disclosure is preferably acetylene black.

[0025] The oil absorption amount of the carbon black may be, for example, 150 mL / 100 g or more, and may also be 180 mL / 100 g or more, 210 mL / 100 g or more, or 240 mL / 100 g or more. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained. Further, the oil absorption amount of the carbon black may be, for example, 400 mL / 100 g or less, and may also be 380 mL / 100 g or less, 350 mL / 100 g or less, or 320 mL / 100 g or less. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0026] The oil absorption amount of the carbon black indicates a value obtained by converting the value measured by the method described in Method B of JIS K6221 using DBP (dibutyl phthalate) as the oil into a value equivalent to JIS K6217-4:2008 by the following formula (a). DBP absorption amount = (A - 10.974) / 0.7833 …(a) [In the formula, A indicates the value of the DBP absorption amount measured by the method described in Method B of JIS K6221.]

[0027] The BET specific surface area of the carbon black may be, for example, 35 m 2 / g or more, and may be 60 m 2 / g or more, 90 m 2 / g or more, or 120 m 2 / g or more. Thereby, an electrode with a lower resistance value tends to be easily obtained. Also, the BET specific surface area of the carbon black may be, for example, 400 m 2 / g or less, and may be 360 m 2 / g or less, 320 m 2 / g or less, or 280 m 2 / g or less. Thereby, an electrode with a lower resistance value tends to be easily obtained.

[0028] Note that the BET specific surface area of the carbon black is measured by the B method, the single-point nitrogen adsorption method described in JIS K 6217-2.

[0029] The primary particle diameter (average particle diameter of primary particles) of the carbon black may be, for example, 15 nm or more, and may be 17 nm or more, 19 nm or more, or 21 nm or more. Thereby, an electrode with a lower resistance value tends to be easily obtained. Also, the primary particle diameter (average particle diameter of primary particles) of the carbon black may be, for example, 55 nm or less, and may be 50 nm or less, 45 nm or less, or 40 nm or less. Thereby, an electrode with a lower resistance value tends to be easily obtained.

[0030] Note that the primary particle diameter (average particle diameter of primary particles) of the carbon black can be evaluated by the image processing system described later.

[0031] In the present disclosure, the image processing system includes at least one processor. The at least one processor: (1) acquires a first original image showing a plurality of aggregated particles; (2) performs a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region; (3) based on the first original image and the first binary image, generates a second original image showing some of the plurality of aggregated particles, where the pixel value of each pixel in the second original image is the pixel value of the corresponding pixel in the first original image of the second original image; (4) performs a second binarization process on the second original image to generate a binary image for measurement that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region. According to such an image processing system, carbon black can be efficiently evaluated or sorted by the above-described classification of the aggregated particles.

[0032] In the present disclosure, at least one processor of the image processing system: (1’) acquires a grayscale first original image showing a plurality of aggregated particles imaged by a scanning electron microscope; (2’) performs a first binarization process on the first original image based on a mixture Gaussian model to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region; (3-1’) generates a partial image that is a part of the first binary image and shows some of the plurality of aggregated particles from the first binary image; (3-2’) converts the pixel value of each pixel in the partial image into the pixel value of the corresponding pixel in the first original image of the partial image to generate a second original image; (4’) performs a second binarization process on the second original image based on a mixture Gaussian model to generate a binary image for measurement that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region. In such an image processing system, the above-described classification of the aggregated particles becomes more accurate, and carbon black can be more effectively evaluated or sorted based on the above-described numerical range.

[0033] Hereinafter, the image processing system of the present disclosure will be described in detail.

[0034] [Overview of the System] The image processing system according to the present disclosure is a computer system that executes image processing on an original image showing aggregated particles. In one example, the image processed by the image processing system is used to evaluate the shape of the aggregated particles. In another example, the image processed by the image processing system is used to evaluate the shape of the primary particles forming the aggregated particles. The evaluation may be performed by the image processing system or by a computer system separate from the image processing system. "Evaluating the shape" means a process including at least calculating a value related to the shape.

[0035] Aggregated particles refer to particles formed by aggregation of a plurality of primary particles. Primary particles refer to particles that cannot be divided further. That is, primary particles refer to the minimum unit of particles. As an example, the aggregated particles are formed from a plurality of carbon blacks and are used as a conductive agent in a lithium-ion battery. In this example, the smaller the particle size of the carbon black, the more parallel circuits are formed by the carbon black, so that a large number of conductive paths are formed even with a small amount of carbon black. That is, in this example, by evaluating the particle size of the carbon black by the image processing system, the user can grasp the performance of the aggregated particles as a conductive agent. As an example, the particle size of this carbon black is about 22 to 26 nm.

[0036] The computer system may calculate the shape parameters related to the primary particles based on the shape parameters related to the shape of the aggregated particles in order to evaluate the shape of the primary particles. As an example, the shape parameters of the aggregated particles are area, perimeter, perimeter of the convex hull, area of the convex hull, length, width, equivalent circle diameter, circularity, area circularity, envelope degree, area inside the envelope line, area envelope degree, aspect ratio (ratio of the minimum Feret diameter to the maximum Feret diameter), elongation, etc. As an example, the shape parameters of the primary particles are the average particle diameter of the primary particles, etc. The computer system may evaluate the number of primary particles contained in the aggregated particles, the morphology of the aggregated particles, the electron microscope surface area, the fractal dimension of the entire aggregated particles, etc. As described above, the image processing system may perform such calculations and evaluations.

[0037] The original image can be obtained by imaging with an imaging device such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM). As an example, when using SEM, the original image showing the aggregated particles can be obtained by the following procedure. First, the user disperses a plurality of aggregated particles in a solvent such as chloroform to prepare a sample. Next, the user drops the prepared sample onto a substrate and dries the sample. Then, by imaging the aggregated particles on the substrate with an imaging device, an original image showing the aggregated particles is obtained. When using SEM, the occurrence of unevenness in the background region of the original image is suppressed.

[0038] [Configuration of the System] FIG. 1 is a diagram showing the functional configuration of an image processing system 10 according to an example. The image processing system 10 includes, as functional elements, an image acquisition unit 11, a binarization processing unit 12, a generation unit 13, and an evaluation unit 14.

[0039] The image acquisition unit 11 is a functional module that acquires a first original image 211 obtained by an imaging device. The first original image 211 shows a plurality of aggregated particles.

[0040] The binarization processing unit 12 is a functional module that executes binarization processing on the first original image 211 and the second original image 231. Binarization processing refers to converting the pixel value of each pixel in an image into a binary value. By executing binarization processing on the original image, a binary image is generated that distinguishes the foreground region composed of aggregated particles from the background region outside the foreground region. The binarization processing unit 12 executes binarization processing on the first original image 211 to generate a first binary image 221, and executes binarization processing on the second original image 231 to generate a second binary image 241.

[0041] The generation unit 13 is a functional module that generates one or more second original images 231 based on the first original image 211 and the first binary image 221.

[0042] Each second original image 231 shows some of the aggregated particles in the first original image 211. The pixel value of each pixel in the second original image 231 is the pixel value of each pixel in the first original image 211 corresponding to the respective pixel in the second original image 231.

[0043] The evaluation unit 14 is a functional module that evaluates the shape of aggregated particles or primary particles. The evaluation unit 14 acquires the second binary image 241 and calculates the shape parameters of an arbitrary aggregated particle. Then, the evaluation unit 14 may evaluate the shape of the primary particles forming the arbitrary aggregated particle based on the calculated shape parameters.

[0044] FIG. 2 is a diagram showing a general hardware configuration of a computer 100 that can function as an image processing system 10. For example, the computer 100 includes a processor 101, a main memory unit 102, an auxiliary storage unit 103, a communication control unit 104, an input device 105, and an output device 106. The processor 101 executes an operating system and application programs. The main memory unit 102 is composed of, for example, ROM and RAM. The auxiliary storage unit 103 is composed of, for example, a hard disk or a flash memory, and generally stores a larger amount of data than the main memory unit 102. The auxiliary storage unit 103 non-temporarily stores a program 110 for causing at least one computer to function as the image processing system 10. The communication control unit 104 is composed of, for example, a network card or a wireless communication module. The input device 105 is composed of, for example, a keyboard, a mouse, a touch panel, etc. The output device 106 is composed of, for example, a monitor and a speaker.

[0045] Each functional module of the image processing system 10 is realized by causing the processor 101 or the main memory unit 102 to load the program 110 and causing the processor 101 to execute the program 110. The program 110 includes code for realizing each functional element of the image processing system 10. The processor 101 operates the communication control unit 104, the input device 105, or the output device 106 according to the program 110, and reads and writes data in the main memory unit 102 or the auxiliary storage unit 103. Each functional element of the image processing system 10 is realized by this processing. Data or databases necessary for the processing may be stored in the main memory unit 102 or the auxiliary storage unit 103.

[0046] The program 110 corresponds to an image processing program. The program 110 may be provided after being non-temporarily recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the program 110 may be provided via a communication network as a data signal superimposed on a carrier wave.

[0047] The image processing system 10 can be configured by one or more computers. When a plurality of computers are used, the image processing system 10 is configured by connecting these computers to each other via a communication network.

[0048] [Image] Various images processed by the image processing system 10 will be described. The resolution of each of the first binary image 221, the second original image 231, and the second binary image 241 is the same as the resolution of the first original image 211.

[0049] The second original image 231 shows some of the aggregated particles in the first original image 211. In other words, it can be said that the second original image 231 is an image that captures a part of the subject range of the first original image 211. Each individual second original image 231 may show one aggregated particle or two or more aggregated particles. If the first original image 211 shows N aggregated particles, the generation unit 13 may generate a plurality of second original images 231 such that all of the N aggregated particles are shown by one of the second original images 231. When each second original image 231 shows one aggregated particle, N second original images 231 are generated. The generation unit 13 may also generate a plurality of second original images 231 such that some of the N aggregated particles are shown by one of the second original images 231. In this case, the remaining aggregated particles are not shown by any of the second original images 231.

[0050] Between the corresponding second original image 231 and the second binary image 241, the one or more aggregated particles shown are the same.

[0051] [System Operation] With reference to FIG. 3, the operation of the image processing system 10 will be described, and an image processing method according to an example will also be described. FIG. 3 is a flowchart showing an example of the processing in the image processing system 10 as a processing flow S1.

[0052] In step S11, the image acquisition unit 11 acquires the first original image 211. The image acquisition unit 11 may receive the first original image 211 from an imaging device or another computer. Alternatively, the image acquisition unit 11 may access a given database or file system to read out the first original image 211. Alternatively, the image acquisition unit 11 may acquire the first original image 211 input by the user of the image processing system 10. The acquired first original image 211 is a grayscale image. In a grayscale image, the pixel value of each pixel is an integer value between 0 corresponding to black and 255 corresponding to white. The image acquisition unit 11 may acquire the first original image 211 by converting a color image into a grayscale image. In one example, the image acquisition unit 11 acquires an SEM image obtained by imaging with an SEM. Since the SEM image can be acquired with high contrast, it is suitable for binarization processing.

[0053] As an example, the resolution of the first original image 211 is 2.48 [nm / pixel]. The resolution of the first original image 211 may be 1.00 to 5.00 [nm / pixel], or may be 1.50 to 3.00 [nm / pixel]. As an example, the first original image 211 may be an image captured at a magnification of 20,000 times.

[0054] FIG. 4 is a diagram showing an example of the first original image 211.

[0055] Returning to FIG. 3, in step S12, the binarization processing unit 12 performs a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of a plurality of aggregated particles from a background region other than the foreground region. The binarization processing unit 12 performs a first binarization process on the first original image 211 to generate a first binary image 221. As an example, the first binarization process is a process based on a mixture Gaussian model. The details of the mixture Gaussian model will be described with reference to FIG. 5. FIG. 5 is a diagram showing an example of a histogram of pixel values in the first original image 211.

[0056] As shown in FIG. 5, in the first original image which is a grayscale image, histograms in the foreground region and the background region respectively form mountain-shaped distributions. In FIG. 5, the histogram in the background region is shown as the base part of the mountain-shaped distribution. The mixture Gaussian model assumes that each mountain-shaped distribution follows a Gaussian distribution, and then calculates the occurrence frequency F(x i (i = 0 to 255)) of each pixel value x i ) as a weighted linear sum of a plurality of Gaussian distributions. Here, x0 to x 255 respectively correspond to 0 to 255 which are grayscale pixel values. The binarization processing unit 12 calculates the occurrence frequency F(x i ) according to the following formula (1) based on the mixture Gaussian model. Here, let the Gaussian distributions be f1(x i ), f2(x i ). f1(x i ) corresponds to the Gaussian distribution in the foreground region, and f2(x i ) corresponds to the Gaussian distribution in the background region. The mean of f1(x i ) is μ1, and the variance of f1(x i ) is σ1 2 . The mean of f2(x i ) is μ2, and the variance of f2(x i ) is σ2 2 . Also, let the weight of f1(x i ) be w1, and the weight of f2(x i ) be w2. Both w1 and w2 are real numbers greater than or equal to 0, and the sum of w1 and w2 is 1.

[0057]

Equation

[0058] Then, the binarization processing unit 12 converts each pixel value into binary based on a certain threshold T. As an example, the binarization processing unit 12 converts each pixel value into either 0 or 255 based on the threshold T. As an example, the binarization processing unit 12 calculates the threshold T as follows. First, the binarization processing unit 12 calculates the log-likelihood lnL of the above formula (1) according to the following formula (2).

[0059]

Number

[0060] Then, the binarization processing unit 12 estimates the values of the parameters w1, w2, μ1, σ1, μ2, σ2 that maximize the log-likelihood lnL shown in the above formula (2) based on the EM algorithm (expectation-maximization algorithm) or the like. The binarization processing unit 12 calculates the threshold T based on the estimated parameters. As an example, the binarization processing unit 12 calculates the pixel value at the intersection of the two Gaussian distributions f1(x i ), f2(x i ) as the threshold T. Alternatively, the binarization processing unit 12 calculates the average value of μ1 and μ2 as the threshold T. Then, the binarization processing unit 12 executes the first binarization process by converting the pixel values less than the calculated threshold T to 0 and the pixel values greater than or equal to the threshold T to 255. In a grayscale image, each distribution forming the histogram of pixel values follows a Gaussian distribution or has a shape close to a Gaussian distribution. Therefore, when the first original image is a grayscale image, especially when it is a SEM image, the binarization processing unit 12 can generate the first binary image more accurately based on the mixture Gaussian model.

[0061] Alternatively, the first binarization process may be a process based on Otsu's binarization method. Otsu's binarization process refers to a process of dividing the histogram into two classes and calculating the separation degree between the two classes, and calculating the pixel value at which the separation degree is maximized as the threshold T. Alternatively, the first binarization process may be a process of performing binarization based on the threshold T input by the user.

[0062] FIG. 6 is a diagram showing an example of the first binarization process by the binarization processing unit 12. The first original image 211 shown in this example is a grayscale SEM image. The first binary image 221 is an image after the first binarization process is executed, and the pixel value of each pixel in the first binary image 221 is 0 or 255. In the first binary image 221, the shape of the agglomerated particles 300 is more clearly distinguishable than in the first original image 211.

[0063] Returning to FIG. 3, in step S13, the generation unit 13 generates one or more second original images based on the first original image and the first binary image. The generation unit 13 generates one or more second original images 231 based on the first original image 211 and the first binary image 221. The details of step S13 will be described with reference to FIG. 7. FIG. 7 is a flowchart showing an example of a method for generating a second original image.

[0064] In step S131, the generation unit 13 generates one or more partial images from the first binary image 221. Each partial image is a part of the first binary image and shows some of the aggregated particles among the plurality of aggregated particles. The generation unit 13 may generate a partial image showing one aggregated particle or a partial image showing two or more aggregated particles for the first binary image 221. As an example, the generation unit 13 may generate a plurality of partial images showing one aggregated particle for the first binary image 221. In this example, the number of partial images corresponding to the number of aggregated particles shown in the first binary image 221 is obtained from the first binary image 221. Note that the generation unit 13 may not generate a partial image showing such an aggregated particle after determining that an aggregated particle with a small size is noise.

[0065] In step S132, the generation unit 13 converts the pixel values of each pixel for one or more partial images to generate one or more second original images. For each of the one or more partial images obtained from the first binary image 221, the generation unit 13 converts the pixel value of each pixel of the partial image to the pixel value of the pixel in the first original image 211 corresponding to the pixel, to generate the second original image 231. The pixel value of each pixel of the second original image is the pixel value of the pixel in the first original image corresponding to each pixel of the second original image. The second original image is generated from the partial image. Therefore, the second original image shows some of the aggregated particles among the plurality of aggregated particles. For example, each second original image shows one aggregated particle.

[0066] FIG. 8 is a diagram showing an example of a method for generating a second original image, and shows an enlarged view of a part of the first binary image 221. In this example, the generation unit 13 shows two partial images 251 and two second original images 231 corresponding to the partial regions 211c and 211d of the first binary image 221.

[0067] Alternatively, in step S13, the generation unit 13 may directly generate one or more second original images from the first original image 211. In this case, first, the generation unit 13 refers to the first original image and the first binary image, and identifies a region in the first original image corresponding to a region indicating some aggregated particles in the first binary image. Then, the generation unit 13 cuts out the identified region from the first original image to generate the second original image 231. In this way, the generation unit 13 can generate one or more second original images from the first original image without going through the generation of partial images.

[0068] Returning to FIG. 3, in step S14, the binarization processing unit 12 performs a second binarization process on each of the one or more second original images to generate one or more second binary images that distinguish a foreground region composed of aggregated particles from a background region other than the foreground region. The binarization processing unit 12 generates one or more second binary images 241 from the one or more second original images 231 by the second binarization process. The second binarization process may be a process based on a mixture Gaussian model or a process based on Otsu's binarization method, similar to the first binarization process. Alternatively, the second binarization process may be a process of performing binarization based on a threshold value T input by the user. The binarization method used for the second binarization process may be the same as or different from the first binarization process.

[0069] Even if the same binarization is adopted in the first binarization process and the second binarization process, the results of the first binarization process and the second binarization process for a single aggregated particle can be different from each other. This is because the histograms of the pixel values of the first original image and the second original image can be different from each other. The first original image 211 captures relatively many aggregated particles, and the range or distribution of pixel values can be different between individual aggregated particles. On the other hand, the second original image captures some of the aggregated particles. Therefore, the histogram of pixel values across the entire image is different between the first original image and the second original image. Due to the difference in the histograms, the threshold value T in the binarization process is also different. Therefore, the results of the first binarization process and the second binarization process for a single aggregated particle can be different from each other.

[0070] In step S15, the evaluation unit 14 evaluates the shape of at least one aggregated particle among the aggregated particles indicated by one or more second binary images 241. For example, for each aggregated particle, the evaluation unit 14 evaluates the minimum Feret diameter (μm), the maximum Feret diameter (μm), the projected area (μm 2 ), the perimeter length (μm), and the area inside the envelope (μm 2 ).

[0071] The evaluation unit 14 outputs the evaluation result. The evaluation unit 14 may display the processing result on a display device, may store the processing result in a predetermined storage device such as a memory or a database, or may transmit the processing result to another computer system.

[0072] The evaluation unit 14 may calculate the perimeter of each of some of the aggregated particles as a shape parameter. For each of the some of the aggregated particles, the evaluation unit 14 may calculate the average particle size of the plurality of primary particles forming the aggregated particle based on the perimeter. As an example, the evaluation unit 14 calculates the average particle size of the plurality of primary particles forming the aggregated particle based on the area of the aggregated particle and the perimeter of the aggregated particle. As an example, the evaluation unit 14 calculates the average particle size of the primary particles in accordance with the standard of ASTM D3849. In this example, the evaluation unit 14 calculates the average particle size of the primary particles by the following formula (3). The evaluation unit 14 may calculate the number of primary particles forming one aggregated particle by the following formula (4). In formulas (3) and (4), let the perimeter of the aggregated particle be P and the area of the aggregated particle be A. α is an aggregation coefficient, dp is the average particle size of the primary particles, and n is the number of primary particles. Note that when the calculated value of α is less than 0.4, the evaluation unit 14 sets the value of α to 0.4.

[0073] [Number]

[0074] [Number]

[0075] The evaluation unit 14 may execute the above processing for each of two or more types of shape parameters.

[0076] [Modification Example] As described above, the technology according to the present disclosure has been described in detail based on various examples thereof. However, the present disclosure is not limited to the above examples. Various modifications are possible for the technology according to the present disclosure without departing from the gist thereof.

[0077] The processing procedures of the method executed by at least one processor are not limited to the above examples. For example, some of the above steps may be omitted, or each step may be executed in a different order. Also, any two or more of the above steps may be combined, or a part of a step may be modified or deleted. Alternatively, other steps may be executed in addition to each of the above steps.

[0078] In the comparison of the magnitude relationship between two numerical values in the present disclosure, either of the two criteria of "greater than or equal to" and "greater than" may be used, and either of the two criteria of "less than or equal to" and "less than" may be used.

[0079] In the present disclosure, the expression "at least one processor executes a first process, executes a second process,... executes an L-th process." or a corresponding expression indicates a concept including the case where the execution subject of the L processes from the first process to the L-th process, that is, the processor, changes in the middle. That is, this expression indicates a concept including both the case where all of the L processes are executed by the same processor and the case where the processor changes in an arbitrary manner in the L processes.

[0080] [Appendix] As can be understood from the various examples above, the image processing system in the present disclosure includes the following aspects. Also, the method according to the present disclosure may include the following image processing methods. Further, the image processing system in the present disclosure may be executed by the following image processing program. (Appendix 1) comprising at least one processor, wherein the at least one processor acquires a first original image showing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, performs a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region other than the foreground region, Based on the first original image and the first binary image, generate a second original image showing some of the plurality of aggregated particles, where the pixel value of each pixel in the second original image is the pixel value of the corresponding pixel in the first original image of the second original image. Execute a second binarization process on the second original image to generate a second binary image that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region. Image processing system. (Appendix 2) The first original image is a grayscale image. The at least one processor Execute the first binarization process based on a mixture Gaussian model. Execute the second binarization process based on the mixture Gaussian model. The image processing system according to Appendix 1. (Appendix 3) The at least one processor Generate a partial image from the first binary image, where the partial image is a part of the first binary image and shows some of the plurality of aggregated particles. Convert the pixel value of each pixel in the partial image to the pixel value of the corresponding pixel in the first original image of the partial image to generate the second original image. The image processing system according to Appendix 1 or 2. (Appendix 4) The at least one processor generates the second original image showing one of the plurality of aggregated particles as some of the aggregated particles. The image processing system according to any one of Appendices 1 to 3. (Appendix 5) For each of the some aggregated particles in the second binary image, the at least one processor calculates the average particle size of the plurality of primary particles forming the aggregated particle based on the perimeter length of the aggregated particle. The image processing system according to any one of Appendices 1 to 4. (Appendix 6) The first original image is an image obtained by imaging with a scanning electron microscope. The image processing system according to any one of Appendices 1 to 5. (Appendix 7) An image processing method executed by an image processing system including at least one processor, acquiring a first original image showing a plurality of aggregated particles respectively formed by aggregation of a plurality of primary particles; performing a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region; generating a second original image showing some of the plurality of aggregated particles based on the first original image and the first binary image, wherein a pixel value of each pixel of the second original image is a pixel value of each pixel of the first original image corresponding to each pixel of the second original image; performing a second binarization process on the second original image to generate a second binary image that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region; An image processing method including the above. (Appendix 8) acquiring a first original image showing a plurality of aggregated particles respectively formed by aggregation of a plurality of primary particles; performing a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region; generating a second original image showing some of the plurality of aggregated particles based on the first original image and the first binary image, wherein a pixel value of each pixel of the second original image is a pixel value of each pixel of the first original image corresponding to each pixel of the second original image; performing a second binarization process on the second original image to generate a second binary image that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region; An image processing program for causing a computer to execute the above.

[0081] According to Supplementary Notes 1, 7, and 8, a first binary image showing a plurality of aggregated particles is obtained by a first binarization process. Then, based on the first original image and the first binary image, a second original image showing some of the aggregated particles among the plurality of aggregated particles is generated, and the pixel value of each pixel of the second original image is the pixel value of each pixel of the first original image. A second binarization process is performed on this second original image to generate a second binary image for the some of the aggregated particles. In the first binarization process, the appearance of individual aggregated particles may affect each other among the plurality of aggregated particles, and the influence may be reflected in the first binary image. By performing the second binarization process focusing on some of the aggregated particles, such influence of the appearance is suppressed, and the shape of the aggregated particles can be specified more accurately. By using this second binary image, it becomes possible to evaluate the shapes of the aggregated particles and the primary particles with high accuracy.

[0082] According to Supplementary Note 2, based on the first original image which is a grayscale image, a second original image which is also a grayscale image is generated. By performing a first binarization process based on a mixture Gaussian model on the first original image which is a grayscale image, a first binary image in which a foreground region composed of a plurality of aggregated particles and a background region other than the foreground region are accurately distinguished is generated. By using this first binary image, after accurately discriminating some of the aggregated particles among the plurality of aggregated particles, a second original image is generated. Then, by performing a second binarization process based on a mixture Gaussian model on the second original image which is a grayscale image, a second binary image in which a foreground region composed of some of the aggregated particles and a background region other than the foreground region are more accurately distinguished is generated. By using this second binary image, it becomes possible to evaluate the shapes of the aggregated particles and the primary particles with higher accuracy.

[0083] According to Supplementary Note 3, since in the first binary image, a foreground region composed of a plurality of aggregated particles and a background region other than the foreground region are distinguished, a partial image can be easily generated from the first binary image. By converting the pixel value of each pixel of this partial image, a second original image can be easily generated.

[0084] According to Supplementary Note 4, during the second binarization process, since the appearance of one aggregated particle is not affected by other aggregated particles, the shape of the one aggregated particle can be specified more accurately. As a result, the shape of the primary particles can be evaluated with higher precision.

[0085] According to Supplementary Note 5, based on the perimeter lengths of some aggregated particles whose shapes are accurately specified in the second binary image, the average particle size of the plurality of primary particles forming the some aggregated particles can be accurately calculated.

[0086] According to Supplementary Note 6, since the contrast of each of the first original image and the second original image is high, the first binary image and the second binary image in which the shapes of the aggregated particles are specified more accurately can be generated. As a result, the shape of the primary particles can be evaluated with higher precision.

[0087] The method according to the present disclosure may be a method for selecting carbon black suitable as a conductive material, or may be a method for selecting carbon black suitable as a conductive material for a secondary battery. Further, the method according to the present disclosure may be a method for evaluating the performance of carbon black as a conductive material, or may be a method for evaluating the performance of carbon black as a conductive material for a secondary battery.

[0088] Carbon black can be used, for example, as a composition for an electrode including carbon black and an active material capable of occluding and releasing lithium ions.

[0089] As the active material in the composition for an electrode, known active materials can be used without particular limitation.

[0090] Examples of the active material include lithium cobaltate (LiCoO2), lithium nickelate (LiNiO2), layered lithium manganate (LiMnO2), LiMn which is a composite oxide containing a plurality of transition metals x Ni y Co z O2 (x + y + z = 1, 0 ≤ y < 1, 0 ≤ z < 1, 0 ≤ x < 1), and other layered compounds; Li1+x Mn 2-x O4 (x represents 0 to 0.33.), Li 1+x Mn 2-x-y M y O4 (M represents at least one metal selected from the group consisting of Ni, Co, Cr, Cu, Fe, Al, and Mg, x represents 0 to 0.33, and y represents 0 to 1.0. However, 2 - x - y > 0.), LiMnO3, LiMn2O3, LiMnO2, LiMn 2-x M x O2 (M represents at least one metal selected from the group consisting of Co, Ni, Fe, Cr, Zn, and Ta, and x represents 0.01 to 0.1.), Li2Mn3MO8 (M is at least one metal selected from Co, Ni, Fe, Cr, Zn)), etc. manganese - based compounds; Copper - lithium oxide (Li2CuO2); Iron - lithium oxide (LiFe3O4); LiFePO4, LiMnPO4, LiMnFePO4, Li2MPO4F (M represents at least one metal selected from the group consisting of Co, Ni, Fe, Cr, and Zn.), etc. olivine - type compounds; Vanadium oxides such as LiV3O8, V2O5, Cu2V2O7; Disulfide compounds; Silicate - type compounds such as Li2MSiO4 (M represents at least one metal selected from the group consisting of Co, Ni, Fe, Cr, Zn, and Ta.); Li2MO3·LiMO2 (M represents at least one metal selected from the group consisting of Mn, Co, Ni, Fe, Cr, and Zn), Fe2(MoO4)3, Li2S, S, etc. are included.

[0091] The above electrode composition may be used, for example, as an electrode containing the above electrode composition or as a secondary battery equipped with such an electrode. In the secondary battery, the positive electrode and / or the negative electrode may contain the above electrode composition, and it is preferable that the positive electrode contains the above electrode composition.

[0092] In a secondary battery, the electrodes that do not contain the above electrode composition and the components other than the electrodes are not particularly limited, and the electrodes and components in known secondary batteries can be used without particular limitation.

[0093] As described above, the preferred embodiments of the present disclosure have been explained, but the present disclosure is not limited to the above embodiments.

Examples

[0094] Hereinafter, the present disclosure will be described in more detail by way of examples, but the present disclosure is not limited to these examples.

[0095] (Test Examples 1 to 6) (1) Characteristics of Carbon Black For carbon blacks CB1 to CB6, the oil absorption amount and the BET specific surface area were measured by the following methods. Also, for carbon blacks CB1 to CB6, measurement binary images were obtained by the following methods. From the obtained measurement binary images, the ratios of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles were determined by the following methods. The results are shown in Table 1.

[0096] (a) Measurement of Oil Absorption Amount The oil absorption amount of the carbon black was measured by the method described in Method B of JIS K6221 using DBP (dibutyl phthalate) as the oil. The obtained value of the DBP absorption amount was converted to a value equivalent to JIS K6217-4:2008 by the following formula (a). DBP absorption amount = (A - 10.974) / 0.7833 …(a) [In the formula, A represents the value of the DBP absorption amount measured by the method described in Method B of JIS K6221.]

[0097] (b) Measurement of BET Specific Surface Area The BET specific surface area of the carbon black was measured by the BET single-point method under the condition of relative pressure p / p0 = 0.30 ± 0.04 in accordance with Method B of JIS K 6217-2 using nitrogen as the adsorption gas.

[0098] (c) Acquisition of Binary Image for Measurement A dispersion obtained by dispersing carbon black in chloroform was dropped onto a substrate and dried to obtain a sample for measurement. For the sample for measurement, a grayscale SEM image (first original image) was acquired, and binarization processing was performed based on a mixture Gaussian model to obtain a first binary image. From the first binary image, a partial image showing a part of the aggregated particles was generated, and the pixel value of each pixel in the partial image was converted into the pixel value of each pixel in the first original image corresponding to each pixel in the partial image to generate a second original image. Binarization processing was performed on the second original image based on a mixture Gaussian model to obtain a binary image for measurement.

[0099] (d) Classification of Aggregated Particles From the binary image for measurement generated in (c) above, the minimum Feret diameter a (μm), maximum Feret diameter b (μm), projected area A (μm 2 ), perimeter length P (μm), and area of the inner envelope Ac (μm 2 ) of the aggregated particles were determined, and the aggregated particles were classified into any one of the first aggregated particles, second aggregated particles, third aggregated particles, and fourth aggregated particles based on the X value, Y value, and Z value of formulas (i) to (iii). The above classification was performed on 2000 randomly selected aggregated particles, and the ratio of each aggregated particle was determined. The results are shown in Table 1.

[0100] (2) Evaluation of Carbon Black (a) Preparation of Positive Electrode N-methyl-2-pyrrolidone (manufactured by Kanto Chemical Co., Ltd., hereinafter referred to as NMP) was used as a solvent, LiNi 0.5 Mn 0.3 Co 0.2 O2 (manufactured by Umicore, product name: TX10) was used as an active material, polyvinylidene fluoride (manufactured by Arkema, product name: HSV900, hereinafter referred to as PVdF) was used as a binder, any one of carbon blacks CB1 to CB6 was used as a conductive material, and polyvinyl alcohol (manufactured by Denka Co., Ltd., product name: B05, hereinafter referred to as polyvinyl alcohol) was used as a dispersant. LiNi 0.5 Mn 0.3 Co 0.2Weigh and mix so that O₂ is 98% by mass of the solid content, PVdF is 2% by mass of the solid content, carbon black is 1% by mass of the solid content, and polyvinyl alcohol is 0.1% by mass of the solid content. Add NMP to this mixture so that the solid content is 68% by mass, and use a planetary mixer (manufactured by Shinchi Co., Ltd., Awatori Rentaro ARV - 310) to mix until uniform, obtaining a dispersion of the electrode composition (positive electrode composition). The prepared dispersion of the electrode composition was formed into a film on an aluminum foil (manufactured by UACJ Corporation) with a thickness of 15 μm using an applicator, and left standing in a dryer for preliminary drying at 105 °C for one hour. Next, press with a roll press at a linear pressure of 200 kg / cm² to prepare a film containing an aluminum foil with a thickness of 15 μm and a thickness of 80 μm. To remove the volatile components, vacuum dry at 170 °C for 3 hours to obtain a positive electrode.

[0101] (b) Electrode evaluation The fabricated positive electrode was cut into a disk shape with a diameter of 14 mm, and while being sandwiched between SUS304 flat electrodes on the front and back, an electrochemical measurement system (manufactured by Solartron, Function Generator 1260 and Potentiostat Galvanostat 1287) was used to measure the AC impedance at an amplitude voltage of 10 mV and a frequency range of 0.1 Hz to 1 MHz, and the intersection with the X - axis of the Cole - Cole plot was taken as the resistance value. The results are shown in Table 1.

[0102]

Table 1

[0103] From the results shown in Table 1, when aggregating particles were classified based on the measurement binary image generated by the image processing system according to the present disclosure, it was confirmed that there was a negative correlation between the ratio of the fourth aggregating particles and the resistance value of the electrode. In particular, it was confirmed that an electrode with a lower resistance value could be formed by carbon black with a ratio of the fourth aggregating particles of 41% or more.

Explanation of symbols

[0104] 10…Image processing system, 11…Image acquisition unit, 12…Binarization processing unit, 13…Generation unit, 14…Evaluation unit, 211…First original image, 221…First binary image, 231…Second original image, 241…Second binary image, 251…Partial image.

Claims

1. Carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, When the plurality of aggregated particles are classified into a first aggregated particle having an X value obtained by the following formula (i) of less than 0.588, a second aggregated particle having the X value of 0.588 or more and a Y value obtained by the following formula (ii) of 0.833 or more, a third aggregated particle having the X value of 0.588 or more, the Y value of less than 0.833, and a Z value obtained by the following formula (iii) of 0.77 or more, and a fourth aggregated particle having the X value of 0.588 or more, the Y value of less than 0.833, and the Z value of less than 0.77, the ratio of the fourth aggregated particle to the total number of the first aggregated particle, the second aggregated particle, the third aggregated particle and the fourth aggregated particle is 41% or more. Carbon black. X = a / b …(i) Y = 4πA / P ２ …(ii) Z = A / Ac …(iii) [In the formula, in the measurement binary image generated by the image processing system, the minimum Feret diameter of the aggregated particle is a (μm), the maximum Feret diameter of the aggregated particle is b (μm), the projected area of the aggregated particle is A (μm ２ ), the perimeter of the aggregated particle is P (μm), and the area of the inner surface of the envelope of the aggregated particle is Ac (μm ２ ). However, the image processing system includes at least one processor, the at least one processor acquires a first original gray-scale image showing the plurality of aggregated particles imaged by a scanning electron microscope, executes a first binarization process on the first original image based on a mixture Gaussian model to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region, generates a partial image that is a part of the first binary image and shows some of the plurality of aggregated particles from the first binary image, Convert the pixel values of each pixel in the partial image into the pixel values of each pixel in the first original image corresponding to each pixel in the partial image to generate a second original image. Perform a second binarization process on the second original image based on a mixture Gaussian model to generate the binary image for measurement that distinguishes the foreground region composed of some of the aggregated particles from the background region outside the foreground region. It is an image processing system. ]

2. The ratio of the third aggregated particle to the total number of the first aggregated particle, the second aggregated particle, the third aggregated particle and the fourth aggregated particle is 17% or less. The carbon black according to claim 1.

3. The oil absorption amount is 150 mL / 100 g or more and 400 mL / 100 g or less. The carbon black according to claim 1.

4. The BET specific surface area is 35 m ２ / g or more and 400 m ２ / g or less. The carbon black according to claim 1.

5. An electrode composition containing the carbon black according to any one of claims 1 to 4 and an active material capable of occluding and releasing lithium ions.

6. An electrode containing the electrode composition according to claim 5.

7. A secondary battery including the electrode according to claim 6.

8. A method for evaluating or selecting carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, The step of obtaining a binary image for measurement generated by an image processing system, From the binary image for measurement, a first aggregated particle with an X value obtained by the following formula (i) less than 0.588, a second aggregated particle with the X value of 0.588 or more and a Y value obtained by the following formula (ii) of 0.833 or more, a third aggregated particle with the X value of 0.588 or more, the Y value of less than 0.833 and a Z value obtained by the following formula (iii) of 0.77 or more, and a fourth aggregated particle with the X value of 0.588 or more, the Y value of less than 0.833 and the Z value of less than 0.

77. When classified into these, a step of obtaining the ratio of the fourth aggregated particle to the total number of the first aggregated particle, the second aggregated particle, the third aggregated particle and the fourth aggregated particle A step of evaluating or selecting the carbon black based on the ratio Including The image processing system Is provided with at least one processor The at least one processor Obtains a first original image showing the plurality of aggregated particles Executes a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region composed of the plurality of aggregated particles from a background region outside the foreground region Based on the first original image and the first binary image, a second original image showing some of the plurality of aggregated particles, wherein the pixel value of each pixel of the second original image is the pixel value of each pixel of the first original image corresponding to each pixel of the second original image, and the second original image is generated Executes a second binarization process on the second original image to generate the binary image for measurement that distinguishes a foreground region composed of some of the aggregated particles from a background region outside the foreground region An image processing system Method X = a / b … (i) Y = 4πA / P ２ … (ii) Z = A / Ac … (iii) [wherein, in the binary image for measurement generated by the image processing system, the minimum Feret diameter of the agglomerated particles is a (μm), the maximum Feret diameter of the agglomerated particles is b (μm), the projected area of the agglomerated particles is A (μm ２ ), the perimeter of the agglomerated particles is P (μm), and the inner area of the envelope of the agglomerated particles is Ac (μm ２ ).]

9. The first original image is a grayscale image, The at least one processor performs the first binarization process based on a mixture Gaussian model, and performs the second binarization process based on the mixture Gaussian model, The method according to claim 8.

10. The at least one processor generates a partial image from the first binary image, the partial image being a part of the first binary image and showing some of the plurality of agglomerated particles, converts the pixel value of each pixel of the partial image into the pixel value of each pixel of the first original image corresponding to each pixel of the partial image to generate the second original image, The method according to claim 8 or 9.

11. The first original image is an image obtained by imaging with a scanning electron microscope, The method according to claim 8 or 9.

Images