Carbon black, and method for assessing or selecting carbon black

Abstract

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.　(i): X=a / b (ii): Y=4πA / P2 (iii): Z=A / Ac [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.]

Description

Carbon black and methods for evaluating or sorting carbon black

[0001] The present disclosure relates to carbon black and methods for evaluating or sorting carbon black.

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

[0003] Japanese Patent Application Laid-Open No. 2009-035598

[0004] In recent years, the demand for secondary batteries has increased, and further improvements in the properties of conductive materials for secondary batteries are being sought.

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

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

[10] : [1] Carbon black including a plurality of aggregated particles formed by aggregation of a plurality of primary particles, wherein when the plurality of aggregated particles are divided into first aggregated particles having an X value of less than 0.588 calculated by the following formula (i), second aggregated particles having an X value of 0.588 or more and a Y value of 0.833 or more calculated by the following formula (ii), third aggregated particles having 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 calculated by the following formula (iii), and 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, a proportion of the fourth 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 41% or more. 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), and the projected area of ​​the agglomerated particles is A (μm ２ ), the perimeter of the agglomerated particles is P (μm), and the area within the envelope of the agglomerated particles is Ac (μm ２ However, the image processing system includes at least one processor, which acquires a first grayscale original image showing the plurality of agglomerated particles captured by a scanning electron microscope, performs a first binarization process on the first original image based on a Gaussian mixture model to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region, generates from the first binary image a partial image that is a part of the first binary image and shows some of the plurality of agglomerated particles, converts pixel values ​​of each pixel of the partial image into pixel values ​​of each pixel of the first original image that correspond to each pixel of the partial image to generate a second original image, and performs a second binarization process on the second original image based on a Gaussian mixture model to generate the measurement binary image that distinguishes between a foreground region consisting of some of the agglomerated particles and a background region other than the foreground region. [2] The carbon black according to [1], wherein the ratio of the third agglomerated particles to the total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles is 17% or less. [3] The carbon black according to [1] or [2], wherein the oil absorption is 150 mL / 100 g or more and 400 mL / 100 g or less. [4] The carbon black according to [1] or [2], wherein the BET specific surface area is 35 m or less. ２ / g or more 400m ２[1] The carbon black according to any one of [1] to [3], having a densitometric average molecular weight of 0.01g or less. [5] A composition for an electrode, comprising the carbon black according to any one of [1] to [4] and an active material capable of absorbing and desorbing lithium ions. [6] An electrode, comprising the composition for an electrode according to [5]. [7] A secondary battery, comprising the electrode according to [6]. [8] A method for evaluating or sorting carbon black including a plurality of aggregate particles each formed by aggregation of a plurality of primary particles, the method comprising: obtaining a binary image for measurement generated by an image processing system; and dividing the binary image for measurement into first aggregate particles having an X value calculated by the following formula (i) of less than 0.588, second aggregate particles having an X value of 0.588 or more and a Y value calculated by the following formula (ii) of 0.833 or more, third aggregate particles having an X value of 0.588 or more, a Y value of less than 0.833, and a Z value calculated by the following formula (iii) of 0.77 or more, and fourth aggregate 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, and calculating a ratio of the fourth aggregate particles to a total number of the first aggregate particles, the second aggregate particles, the third aggregate particles, and the fourth aggregate particles. acquiring a first original image showing the plurality of agglomerated particles, performing a first binarization process on the first original image to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region; generating a second original image showing some of the plurality of agglomerated particles based on the first original image and the first binary image, the second original image having pixel values ​​equal to the pixel values ​​of the pixels of the first original image that correspond to the pixels of the second original image; and performing a second binarization process on the second original image to generate the measurement binary image that distinguishes between a foreground region consisting of the portion of the agglomerated particles and a background region other than the foreground region.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 agglutinated particles is a (μm), the maximum Feret diameter of the agglutinated particles is b (μm), and the projected area of ​​the agglutinated particles is A (μm ２ ), the perimeter of the agglomerated particles is P (μm), and the area within the envelope of the agglomerated particles is Ac (μm ２ ).] [9] The method according to [8], wherein the first original image is a grayscale image, and the at least one processor performs the first binarization process based on a Gaussian mixture model, and performs the second binarization process based on the Gaussian mixture model.

[10] The method according to [8] or [9], wherein the at least one processor generates, from the first binary image, a partial image that is a part of the first binary image and shows some of the agglutinated particles among the plurality of agglutinated particles, and converts pixel values ​​of each pixel of the partial image into pixel values ​​of each pixel of the first original image that correspond to each pixel of the partial image, to generate the second original image.

[11] The method according to any one of [8] to

[10] , wherein the first original image is an image obtained by capturing an image with a scanning electron microscope.

[0007] The present disclosure provides carbon black that is suitable as a conductive material for secondary batteries and that can form electrodes with lower resistance values. The present disclosure also provides a method for evaluating or selecting carbon black that is suitable as a conductive material for secondary batteries.

[0008] 1 is a diagram showing an example of the functional configuration of an image processing system; FIG. 2 is a diagram showing a general hardware configuration of a computer that can function as an image processing system; FIG. 3 is a flowchart showing an example of processing in the image processing system; FIG. 4 is a diagram showing an example of a first original image; FIG. 5 is a diagram showing an example of a histogram of pixel values ​​in the first original image; FIG. 6 is a diagram showing an example of first binarization processing; FIG. 7 is a flowchart showing an example of a method for generating a second original image; and FIG. 8 is a diagram showing an example of a method for generating a second original image.

[0009] Hereinafter, embodiments of 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 designated by the same reference numerals, and redundant description will be omitted.

[0010] The method according to the present disclosure is a method for evaluating or sorting carbon black based on a binary image for measurement generated by an image processing system described below. Carbon black includes a plurality of aggregate particles, each formed by aggregation of a plurality of primary particles. The method according to the present disclosure may be a method for classifying aggregate particles from the binary image for measurement based on an X value calculated by formula (i), a Y value calculated by formula (ii), and a Z value calculated by formula (iii), and evaluating or sorting carbon black based on the proportion of classified aggregate particles. 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), and the projected area of ​​the agglomerated particles is A (μm ２ ), the perimeter of the agglomerated particle is P (μm), and the area within the envelope of the agglomerated particle is Ac (μm ２ )

[0011] The agglomerated particles may be classified into first agglomerated particles having an X value of less than 0.588, second agglomerated particles having an X value of 0.588 or more and a Y value of 0.833 or more, third agglomerated particles having 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, and fourth agglomerated 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. In this case, the method according to the present disclosure may be a method of determining the proportion of fourth agglomerated particles relative to the total number of first agglomerated particles, second agglomerated particles, third agglomerated particles, and fourth agglomerated particles, and evaluating or selecting carbon black based on this proportion. A high proportion of fourth agglomerated particles makes it easier to obtain an electrode with a lower resistance value. Therefore, by selecting carbon black based on the fourth agglomeration proportion, it is possible to easily select carbon black that is more likely to form an electrode with a lower resistance value. Furthermore, by evaluating the carbon black based on the fourth aggregation 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 particles are agglomerated particles having an X value of less than 0.588 in formula (i). Here, the X value indicates the aspect ratio of the agglomerated particles. The larger the difference between the major axis and the minor axis, the smaller the X value. Since the first agglomerated particles have an X value of less than 0.588, they can be said to be agglomerated particles having a shape close to a linear shape.

[0013] The second agglomerated particles have 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 particles, 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 particles have an X value of 0.588 or more and a Y value of 0.833 or more, they can be said to be agglomerated particles having a shape that is close to a sphere.

[0014] The third agglomerated particles have 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 agglomerated particles to the area within an envelope when an envelope is wrapped around the agglomerated particles, and it can be said that the larger the Z value, the less branching there is in the agglomerated particles. Because the third agglomerated particles have 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, they can be said to be agglomerated particles having a shape close to an ellipsoid.

[0015] The fourth agglomerated particles have 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 agglomerated particles to the area within an envelope when an envelope is wrapped around the agglomerated particles, and the smaller the Z value, the more branched the agglomerated particles are. Since the fourth agglomerated particles have 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, they can be said to be highly branched, branched agglomerated particles.

[0016] By classifying carbon black based on a binary measurement image generated by an image processing system described below, the classification clearly reflects the characteristics of the carbon black. That is, in the method according to the present disclosure, agglomerated particles are classified based on X, Y, and Z values ​​based on a binary measurement image generated by an image processing system described below, and carbon black is sorted based on the proportion of classified agglomerated particles, thereby easily selecting carbon black having the desired characteristics. Furthermore, by evaluating carbon black based on the proportion of classified agglomerated particles, carbon black can be easily evaluated.

[0017] Carbon blacks vary greatly in the shape of aggregated particles due to differences in the frequency of collision of primary particles resulting from differences in thermal history during synthesis (for example, thermal history resulting from thermal decomposition and combustion reaction of fuel oil, thermal decomposition and combustion reaction of raw materials, rapid cooling by a cooling medium, and reaction termination).

[0018] In a preferred embodiment of the carbon black, the total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles (N １ +N ２ +N ３ +N４ ) to the fourth agglomerated particles (N ４ / (N １ +N ２ +N ３ +N ４ )) is 41% or more, preferably 43% or more, more preferably 45% or more, and even more preferably 47% or more.

[0019] The above ratio (N ４ / (N １ +N ２ +N ３ +N ４ )) may be, for example, 60% or less, 58% or less, 56% or less, or 54% or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the above ratio (N ４ / (N １ +N ２ +N ３ +N ４ )) can be, for example, 41% to 60%, 41% to 58%, 41% to 56%, 41% to 54%, 43% to 60%, 43% to 58%, 43% to 56%, 43% to 54%, 45% to 60%, 45% to 58%, 45% to 56%, 45% to 54%, 47% to 60%, 47% to 58%, 47% to 56%, or 47% to 54%.

[0020] The total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles (N １ +N ２ +N ３ +N ４ ) to the ratio of the first agglomerated particles (N １ / (N １ +N ２ +N ３ +N ４ )) may be, for example, 30% or more, 33% or more, 36% or more, or 39% or more. This tends to make it easier to obtain an electrode with a lower resistance value. １ / (N １ +N ２ +N ３ +N ４)) may be, for example, 54% or less, 52% or less, 50% or less, or 48% or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the above ratio (N １ / (N １ +N ２ +N ３ +N ４ )) can be, for example, 30% to 54%, 30% to 52%, 30% to 50%, 30% to 48%, 33% to 54%, 33% to 52%, 33% to 50%, 33% to 48%, 36% to 54%, 36% to 52%, 36% to 50%, 36% to 48%, 39% to 54%, 39% to 52%, 39% to 50%, or 39% to 48%.

[0021] The total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles (N １ +N ２ +N ３ +N ４ ) to the ratio of the second agglomerated particles (N ２ / (N １ +N ２ +N ３ +N ４ )) may be, for example, 2% or less, 1% or less, 0.5% or less, 0.1% or less, or even 0%, which tends to make it easier to obtain an electrode with a lower resistance value.

[0022] The total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles, and the fourth agglomerated particles (N １ +N ２ +N ３ +N ４ ) to the third agglomerated particles (N ３ / (N １ +N ２ +N ３ +N ４ )) may be, for example, 0.1% or more, 0.5% or more, 1% or more, or 2% or more. This tends to make it easier to obtain an electrode with a lower resistance value. ３ / (N １ +N ２ +N ３ +N ４)) may be, for example, 17% or less, 14% or less, 11% or less, or 8% or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the above ratio (N ３ / (N １ +N ２ +N ３ +N ４ )) can be, for example, 0.1% to 17%, 0.1% to 14%, 0.1% to 11%, 0.1% to 8%, 0.5% to 17%, 0.5% to 14%, 0.5% to 11%, 0.5% to 8%, 1% to 17%, 1% to 14%, 1% to 11%, 1% to 8%, 2% to 17%, 2% to 14%, 2% to 11%, or 2% to 8%.

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

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

[0025] The oil absorption capacity of the carbon black may be, for example, 150 mL / 100 g or more, 180 mL / 100 g or more, 210 mL / 100 g or more, or 240 mL / 100 g or more. This tends to make it easier to obtain an electrode with a lower resistance value. The oil absorption capacity of the carbon black may be, for example, 400 mL / 100 g or less, 380 mL / 100 g or less, 350 mL / 100 g or less, or 320 mL / 100 g or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the oil absorption of the carbon black may be, for example, 150 to 400 mL / 100g, 150 to 380 mL / 100g, 150 to 350 mL / 100g, 150 to 320 mL / 100g, 180 to 400 mL / 100g, 180 to 380 mL / 100g, 180 to 350 mL / 100g, 180 to 320 mL / 100g, 210 to 400 mL / 100g, 210 to 380 mL / 100g, 210 to 350 mL / 100g, 210 to 320 mL / 100g, 240 to 400 mL / 100g, 240 to 380 mL / 100g, 240 to 350 mL / 100g, or 240 to 320 mL / 100g.

[0026] The oil absorption of carbon black is a value measured using DBP (dibutyl phthalate) as the oil according to the method described in JIS K6221, Method B, and converted into a value corresponding to JIS K6217-4:2008 according to the following formula (a): DBP absorption = (A - 10.974) / 0.7833 (a) (where A represents the value of DBP absorption measured according to the method described in JIS K6221, Method B).

[0027] The BET specific surface area of ​​carbon black is, for example, 35 m ２ / g or more, and ２ / g or more, 90m ２ / g or more or 120m ２ / g or more. This tends to make it easier to obtain an electrode with a lower resistance value. The BET specific surface area of ​​the carbon black may be, for example, 400 m ２ / g or less, and ２ / g or less, 320m ２ / g or less or 280m ２ / g or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the BET specific surface area of ​​the carbon black is, for example, 35 to 400 m ２ / g, 35-360m ２ / g, 35-320m ２ / g, 35-280m ２ / g, 60-400m ２ / g, 60-360m ２ / g, 60-320m ２ / g, 60-280m ２ / g, 90-400m ２ / g, 90-360m ２ / g, 90-320m ２ / g, 90-280m ２ / g, 120-400m ２ / g, 120-360m ２ / g, 120-320m ２ / g, or 120 to 280 m ２ / g.

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

[0029] The primary particle size (average particle size of primary particles) of the carbon black may be, for example, 15 nm or more, 17 nm or more, 19 nm or more, or 21 nm or more. This tends to make it easier to obtain an electrode with a lower resistance value. Furthermore, the primary particle size (average particle size of primary particles) of the carbon black may be, for example, 55 nm or less, 50 nm or less, 45 nm or less, or 40 nm or less. This tends to make it easier to obtain an electrode with a lower resistance value. That is, the primary particle size (average particle size of primary particles) of the carbon black may be, for example, 15 to 55 nm, 15 to 50 nm, 15 to 45 nm, 15 to 40 nm, 17 to 55 nm, 17 to 50 nm, 17 to 45 nm, 17 to 40 nm, 19 to 55 nm, 19 to 50 nm, 19 to 45 nm, 19 to 40 nm, 21 to 55 nm, 21 to 50 nm, 21 to 45 nm, or 12 to 40 nm.

[0030] The primary particle size (average particle size of primary particles) of carbon black can be evaluated using an image processing system described below.

[0031] In the present disclosure, an image processing system includes at least one processor. The at least one processor (1) acquires a first original image showing a plurality of agglomerated particles, (2) performs a first binarization process on the first original image to generate a first binary image that distinguishes a foreground region consisting of the agglomerated particles from a background region other than the foreground region, (3) generates a second original image based on the first original image and the first binary image, the second original image showing a portion of the agglomerated particles, the pixel values ​​of the second original image being the pixel values ​​of the pixels of the first original image corresponding to the pixels of the second original image, and (4) performs a second binarization process on the second original image to generate a measurement binary image that distinguishes the foreground region consisting of a portion of the agglomerated particles from a background region other than the foreground region. This image processing system allows efficient evaluation or sorting of carbon black by classifying the agglomerated particles as described above.

[0032] In the present disclosure, at least one processor of the image processing system may (1') acquire a first grayscale original image showing a plurality of agglomerated particles captured by a scanning electron microscope, (2') perform a first binarization process on the first original image based on a Gaussian mixture model to generate a first binary image that distinguishes a foreground region consisting of the plurality of agglomerated particles from a background region other than the foreground region, (3-1') generate a partial image from the first binary image that is a portion of the first binary image and shows some of the plurality of agglomerated particles, (3-2') convert the pixel values ​​of each pixel of the partial image to the pixel values ​​of each pixel of the first original image corresponding to each pixel of the partial image to generate a second original image, and (4') perform a second binarization process on the second original image based on a Gaussian mixture model to generate a measurement binary image that distinguishes a foreground region consisting of some of the agglomerated particles from a background region other than the foreground region. Such an image processing system may enable more accurate classification of the agglomerated particles and more effectively evaluate or sort carbon black based on the above-mentioned numerical ranges.

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

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

[0035] Agglomerated particles are particles formed by the aggregation of multiple primary particles. Primary particles are particles that cannot be further divided. In other words, primary particles are the smallest particle unit. As an example, agglomerated particles are formed from multiple carbon black particles and are used as a conductive agent in lithium-ion batteries. In this example, the smaller the particle size of the carbon black, the more parallel circuits formed by the carbon black, so that even a small amount of carbon black forms many conductive paths. In other words, in this example, by evaluating the particle size of the carbon black using an image processing system, the user can understand the performance of the agglomerated particles as a conductive agent. As an example, the particle size of this carbon black is approximately 22 to 26 nm.

[0036] To evaluate the shape of the primary particles, the computer system may calculate shape parameters related to the shape of the primary particles based on shape parameters related to the shape of the agglomerate particles. For example, the shape parameters of the agglomerate particles include area, perimeter, perimeter of the convex hull, area of ​​the convex hull, length, width, equivalent circle diameter, circularity, area circularity, envelopment degree, area within the envelope, area envelopment degree, aspect ratio (ratio of the minimum Feret diameter to the maximum Feret diameter), and elongation. For example, the shape parameter of the primary particles includes the average particle size of the primary particles. The computer system may evaluate the number of primary particles contained in the agglomerate particles, the morphology of the agglomerate particles, the electron microscope surface area, the fractal dimension of the entire agglomerate particles, and the like. As described above, an image processing system may perform such calculations and evaluations.

[0037] The original image is obtained by capturing an image using an imaging device such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM). As an example, when an SEM is used, an original image showing agglomerated particles is obtained by the following procedure. First, a user prepares a sample by dispersing a plurality of agglomerated particles in a solvent such as chloroform. Next, the user drops the prepared sample onto a substrate and dries the sample. Then, an image of the agglomerated particles on the substrate is captured by an imaging device, thereby obtaining an original image showing the agglomerated particles. When an SEM is used, unevenness in the background region of the original image is suppressed.

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

[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 performs binarization processing on the first original image 211 and the second original image 231. The binarization processing refers to converting the pixel value of each pixel in an image into a binary value. By performing the binarization processing on the original images, a binary image is generated that distinguishes between a foreground region made of aggregated particles and a background region other than the foreground region. The binarization processing unit 12 performs the binarization processing on the first original image 211 to generate a first binary image 221, and performs the 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 a portion of the plurality of aggregate particles in first original image 211. The pixel value of each pixel in second original image 231 is the pixel value of each pixel in first original image 211 corresponding to that pixel in second original image 231.

[0043] Evaluation unit 14 is a functional module that evaluates the shape of aggregate particles or primary particles. Evaluation unit 14 acquires second binary image 241 and calculates shape parameters of any aggregate particle. Evaluation unit 14 may then evaluate the shape of the primary particles that form the any aggregate particle based on the calculated shape parameters.

[0044] FIG. 2 is a diagram showing a typical hardware configuration of a computer 100 that can function as the image processing system 10. For example, the computer 100 includes a processor 101, a main memory unit 102, an auxiliary memory 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 memory unit 103 is composed of, for example, a hard disk or flash memory, and generally stores larger amounts of data than the main memory unit 102. The auxiliary memory 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 speakers.

[0045] Each functional module of the image processing system 10 is realized by loading a program 110 onto the processor 101 or the main memory unit 102 and having the processor 101 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 in accordance with the program 110, and reads and writes data from and to the main memory unit 102 or the auxiliary memory unit 103. Each functional element of the image processing system 10 is realized by this processing. Data or a database required for processing may be stored in the main memory unit 102 or the auxiliary memory unit 103.

[0046] The program 110 corresponds to an image processing program. The program 110 may be provided in a state where it is 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 may be configured with one or more computers. When multiple computers are used, the image processing system 10 is configured by connecting these computers to each other via a communication network.

[0048] [Images] A description will be given of various images processed by the image processing system 10. 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] Second original image 231 shows some of the multiple aggregate particles in first original image 211. In other words, second original image 231 can be said to be an image that captures a portion of the coverage area of ​​first original image 211. Each second original image 231 may show one aggregate particle or two or more aggregate particles. If first original image 211 shows N aggregate particles, generation unit 13 may generate multiple second original images 231 so that all of the N aggregate particles are shown in any of second original images 231. If each second original image 231 shows one aggregate particle, N second original images 231 are generated. Generation unit 13 may generate multiple second original images 231 so that some of the N aggregate particles are shown in any of second original images 231. In this case, the remaining aggregate particles are not shown in any of second original images 231.

[0050] The one or more agglomerated particles shown in the corresponding second original image 231 and second binary image 241 are the same.

[0051] [System Operation] The operation of the image processing system 10 and an example image processing method will be described with reference to Fig. 3. Fig. 3 is a flowchart showing an example of 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 the first original image 211. Alternatively, the image acquisition unit 11 may acquire the first original image 211 input by a 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, which corresponds to black, and 255, which corresponds 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 using a SEM. SEM images can be acquired with high contrast and are therefore suitable for binarization processing.

[0053] For 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 1.50 to 3.00 [nm / pixel]. For 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. As shown in FIG.

[0055] Returning to FIG. 3 , in step S12, the binarization processing unit 12 performs a first binarization process on the first original image 211 to generate a first binary image that distinguishes between a foreground region consisting of a plurality of aggregated particles and a background region other than the foreground region. The binarization processing unit 12 performs the 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 Gaussian mixture model. The Gaussian mixture model will be described in detail 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, the histograms in the foreground and background regions each form a mountain-shaped distribution. In Fig. 5, the histogram in the background region is shown as the base of the mountain-shaped distribution. The Gaussian mixture model assumes that each mountain-shaped distribution follows a Gaussian distribution, and calculates the pixel value x ｉ (i = 0 to 255) occurrence frequency F(x ｉ ) is calculated as a weighted linear sum of multiple Gaussian distributions, where x ０ ~x ２５５ correspond to grayscale pixel values ​​of 0 to 255. The binarization processing unit 12 calculates the frequency of occurrence F(x ｉ ) is calculated. Here, the Gaussian distribution is f １ (x ｉ ), f ２ (x ｉ ) f １ (x ｉ ) corresponds to a Gaussian distribution in the foreground region, and f ２ (x ｉ ) corresponds to a Gaussian distribution in the background region. １ (x ｉ ) is the average of μ １ and f １ (x ｉ ) variance is σ １ ２ f ２ (x ｉ ) is the average of μ ２ and f ２ (x ｉ ) variance is σ ２ ２ Also, f １ (x ｉ ) weight is w １ Let f ２ (x ｉ ) weight is w ２ Let's say. １ , w ２ are all real numbers greater than or equal to 0, and w １ , w ２ The sum is 1.

[0057]

[0058] Then, the binarization processing unit 12 converts each pixel value into a binary value 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) using the following formula (2).

[0059]

[0060] Then, the binarization processing unit 12 calculates the parameter w that maximizes the log likelihood lnL shown in the above formula (2). １ , w ２ , μ １ , σ １ , μ ２ , σ ２ The binarization processing unit 12 estimates the value of f based on an expectation-maximization algorithm (EM algorithm) or the like. The binarization processing unit 12 calculates a threshold T based on the estimated parameters. As an example, the binarization processing unit 12 calculates a threshold T based on two Gaussian distributions f １ (x ｉ ), f ２ (x ｉ ) is calculated as a threshold value T. Alternatively, the binarization processing unit 12 calculates a pixel value at the intersection of μ １ and μ ２ The average value of these is calculated as a threshold value T. Then, the binarization processing unit 12 performs the first binarization process by converting pixel values ​​that are less than the calculated threshold value T to 0 and pixel values ​​that are equal to or greater than the threshold value 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, particularly when it is an SEM image, the binarization processing unit 12 can generate the first binary image with higher accuracy based on the Gaussian mixture model.

[0061] Alternatively, the first binarization process may be a process based on Otsu's binarization method, which divides a histogram into two classes, calculates the degree of separation between the two classes, and calculates the pixel value at which the degree of separation is maximum as a threshold value T. Alternatively, the first binarization process may be a process that performs binarization based on a threshold value T input by a user.

[0062] 6 is a diagram showing an example of first binarization processing by binarization processing unit 12. First original image 211 shown in this example is a grayscale SEM image. First binary image 221 is an image after first binarization processing has been performed, and the pixel value of each pixel in first binary image 221 is 0 or 255. In first binary image 221, the shape of agglomerated particles 300 can be more clearly distinguished than in first original image 211.

[0063] 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. 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 represents some of the multiple aggregate particles. The generation unit 13 may generate a partial image representing one aggregate particle for the first binary image 221, or may generate partial images representing two or more aggregate particles. As an example, the generation unit 13 may generate a plurality of partial images representing one aggregate particle for the first binary image 221. In this example, the number of partial images corresponding to the number of aggregate particles represented in the first binary image 221 is obtained from the first binary image 221. Note that the generation unit 13 may determine that small-sized aggregate particles are noise and not generate partial images representing the aggregate particles.

[0065] In step S132, the generation unit 13 converts the pixel values ​​of each pixel in 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 values ​​of each pixel in the partial image into the pixel values ​​of the corresponding pixels in the first original image 211 to generate a second original image 231. The pixel values ​​of each pixel in the second original image are the pixel values ​​of the pixels in the first original image corresponding to each pixel in the second original image. The second original images are generated from the partial images. Therefore, the second original images represent some of the multiple aggregate particles, and for example, each second original image represents one aggregate particle.

[0066] 8 is a diagram showing an example of a method for generating a second original image, showing an enlarged portion of the first binary image 221. In this example, the generator 13 generates 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, the generation unit 13 first refers to the first original image and the first binary image to identify a region in the first original image that corresponds to a region in the first binary image that shows some of the aggregated particles. 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 generating 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 consisting 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 through the second binarization process. Like the first binarization process, the second binarization process may be a process based on a Gaussian mixture model or Otsu's binarization method. Alternatively, the second binarization process may be a process that performs binarization based on a threshold T input by the user. The binarization method used for the second binarization process may be the same as or different from that used for the first binarization process.

[0069] Even if the same binarization method is used in the first and second binarization processes, the results of the first and second binarization processes for a given agglomerated particle may differ from each other. This is because the histograms of pixel values ​​in the first and second original images may differ from each other. The first original image 211 captures a relatively large number of agglomerated particles, and the range or distribution of pixel values ​​among the individual agglomerated particles may differ from each other. On the other hand, the second original image captures only a portion of the agglomerated particles. Therefore, the histograms of pixel values ​​across the entire image differ between the first and second original images. Due to the difference in histograms, the threshold T in the binarization processes also differ. Therefore, the results of the first and second binarization processes for a given agglomerated particle may differ from each other.

[0070] In step S15, evaluation unit 14 evaluates the shape of at least one of the aggregate particles represented by one or more second binary images 241. For example, evaluation unit 14 evaluates the minimum Feret diameter (μm), the maximum Feret diameter (μm), the projected area (μm), and the like of each aggregate particle. ２ ), perimeter (μm) and envelope area (μm ２ ) to evaluate.

[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 agglomerated particles as a shape parameter. The evaluation unit 14 may calculate the average particle size of the primary particles forming each of the agglomerated particles based on the perimeter. For example, the evaluation unit 14 calculates the average particle size of the primary particles forming the agglomerated particle based on the area and perimeter of the agglomerated particle. For example, the evaluation unit 14 calculates the average particle size of the primary particles in accordance with ASTM D3849. In this example, the evaluation unit 14 calculates the average particle size of the primary particles using the following formula (3). The evaluation unit 14 may also calculate the number of primary particles forming one agglomerated particle using the following formula (4). In formulas (3) and (4), the perimeter of the agglomerated particle is P, and the area of ​​the agglomerated particle is A. α is the agglomeration coefficient, dp is the average particle size of the primary particles, and n is the number of primary particles. If the calculated value of α is less than 0.4, the evaluation unit 14 sets the value of α to 0.4.

[0073]

[0074]

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

[0076] [Modifications] The technology according to the present disclosure has been described in detail above based on various examples. However, the present disclosure is not limited to the above examples. The technology according to the present disclosure can be modified in various ways without departing from the gist thereof.

[0077] The processing steps 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 the steps may be executed in a different order. Furthermore, any two or more of the above steps may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be executed in addition to the above steps.

[0078] In the present disclosure, when comparing the magnitude of two numerical values, either of the two criteria "greater than or equal to" and "greater than" may be used, or either of the two criteria "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 Lth process" or an expression corresponding thereto indicates a concept including a case where the entity executing the L processes from the first process to the Lth process, i.e., the processor, changes midway. In other words, this expression indicates a concept including both a case where all of the L processes are executed by the same processor and a case where the processor changes among the L processes according to an arbitrary policy.

[0080] [Additional Notes] As can be seen from the various examples above, the image processing system according to the present disclosure includes the following aspects. Furthermore, the method according to the present disclosure may include the following image processing method. Furthermore, the image processing system according to the present disclosure may be executed by the following image processing program. (Supplementary Note 1) An image processing system comprising at least one processor, wherein the at least one processor acquires a first original image showing a plurality of agglomerated particles each formed by aggregating a plurality of primary particles, performs a first binarization process on the first original image to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region, generates a second original image showing some of the plurality of agglomerated particles based on the first original image and the first binary image, the second original image having pixel values ​​that are the pixel values ​​of the pixels of the first original image corresponding to the pixels of the second original image, and performs a second binarization process on the second original image to generate a second binary image that distinguishes between a foreground region consisting of the some of the agglomerated particles and a background region other than the foreground region. (Supplementary Note 2) The image processing system according to Supplementary Note 1, wherein the first original image is a grayscale image, and the at least one processor performs the first binarization process based on a Gaussian mixture model, and performs the second binarization process based on the Gaussian mixture model. (Supplementary Note 3) The image processing system according to Supplementary Note 1 or 2, wherein the at least one processor generates, from the first binary image, a partial image that is a part of the first binary image and shows some of the agglutination particles of the plurality of agglutination particles, and converts pixel values ​​of each pixel of the partial image into pixel values ​​of each pixel of the first original image that correspond to each pixel of the partial image, to generate the second original image. (Supplementary Note 4) The image processing system according to any one of Supplements 1 to 3, wherein the at least one processor generates the second original image that shows one of the agglutination particles of the plurality of agglutination particles as the some of the agglutination particles.(Supplementary Note 5) The image processing system according to any one of Supplementary Notes 1 to 4, wherein the at least one processor calculates, for each of the some of the aggregate particles in the second binary image, an average particle size of the plurality of primary particles forming the aggregate particle based on a perimeter of the aggregate particle. (Supplementary Note 6) The image processing system according to any one of Supplementary Notes 1 to 5, wherein the first original image is an image obtained by capturing an image using a scanning electron microscope. (Supplementary Note 7) An image processing method executed by an image processing system having at least one processor, comprising: a step of acquiring a first original image showing a plurality of agglomerated particles each formed by agglomeration of a plurality of primary particles; a step of performing a first binarization process on the first original image to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region; a step of generating a second original image showing some of the plurality of agglomerated particles based on the first original image and the first binary image, the second original image having a pixel value that is equal to the pixel value of each pixel of the first original image corresponding to each pixel of the second original image; and a step of performing a second binarization process on the second original image to generate a second binary image that distinguishes between a foreground region consisting of the some of the agglomerated particles and a background region other than the foreground region. (Supplementary Note 8) An image processing program that causes a computer to execute the steps of: acquiring a first original image that shows a plurality of agglomerated particles each formed by aggregating a plurality of primary particles; performing a first binarization process on the first original image to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region; generating a second original image that shows some of the plurality of agglomerated particles based on the first original image and the first binary image, the second original image having pixel values ​​that are the pixel values ​​of the pixels of the first original image that correspond to the pixels of the second original image; and performing a second binarization process on the second original image to generate a second binary image that distinguishes between a foreground region consisting of the some of the agglomerated particles and a background region other than the foreground region.

[0081] According to Supplements 1, 7, and 8, a first binary image showing a plurality of agglomerated particles is obtained by a first binarization process. Then, a second original image showing a portion of the plurality of agglomerated particles is generated based on the first original image and the first binary image, where each pixel value 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 portion of the agglomerated particles. In the first binarization process, the appearance of each agglomerated particle may affect the appearance of the plurality of agglomerated particles, and this influence may be reflected in the first binary image. By performing the second binarization process while focusing on a portion of the agglomerated particles, the influence of the appearance may be suppressed, allowing the shape of the agglomerated particles to be more accurately identified. Using this second binary image, the shapes of the agglomerated particles and primary particles may be evaluated with high precision.

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

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

[0084] According to Supplementary Note 4, in the second binarization process, the appearance of one aggregated particle is not affected by other aggregated particles, so the shape of the one aggregated particle can be identified more accurately, and as a result, the shape of the primary particles can be evaluated with higher accuracy.

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

[0086] According to Supplementary Note 6, since the contrast of each of the first original image and the second original image is high, it is possible to generate a first binary image and a second binary image in which the shape of the agglomerated particles is more accurately specified. As a result, it is possible to evaluate the shape of the primary particles with higher accuracy.

[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 secondary batteries. Furthermore, 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 secondary batteries.

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

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

[0090] The active material is, for example, lithium cobalt oxide (LiCoO ２ ), lithium nickel oxide (LiNiO ２ ), layered lithium manganese oxide (LiMnO ２ ), a composite oxide containing multiple transition metals, LiMn ｘ Ni ｙ Co ｚ O ２ Layered compounds such as (x+y+z=1, 0≦y<1, 0≦z<1, 0≦x<1); Li １＋ｘ Mn ２－ｘ O ４(x represents 0 to 0.33), Li １＋ｘ Mn ２－ｘ－ｙ M ｙ O ４ (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, provided that 2-x-y>0.) LiMnO ３ , LiMn ２ O ３ , LiMnO ２ , LiMn ２－ｘ M ｘ O ２ (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), Li ２ Mn ３ MO ８ manganese compounds such as copper-lithium oxide (Li ２ CuO ２ ); Iron-lithium oxide (LiFe ３ O ４ ); LiFePO ４ , LiMnPO ４ , LiMnFePO ４ , Li ２ MPO ４ olivine-based compounds such as F (M represents at least one metal selected from the group consisting of Co, Ni, Fe, Cr, and Zn); LiV ３ O ８ , V ２ O ５ , Cu ２ V ２ O ７ Vanadium oxides such as; disulfide compounds; Li ２ MSiO ４ (M represents at least one metal selected from the group consisting of Co, Ni, Fe, Cr, Zn, and Ta); ２ MO ３ ・LiMO ２ (M represents at least one metal selected from the group consisting of Mn, Co, Ni, Fe, Cr and Zn), Fe２ (MoO ４ ) ３ , Li ２ Examples include S, S, etc.

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

[0092] In the secondary battery, the electrode that does not contain the electrode composition and the configuration other than the electrode are not particularly limited, and electrodes and configurations in known secondary batteries can be used without particular limitations.

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

[0094] The present disclosure will be described in more detail below with reference to examples, but the present disclosure is not limited to these examples.

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

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

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

[0098] (c) Obtaining a Binary Image for Measurement A measurement sample was obtained by dropping a dispersion of carbon black in chloroform onto a substrate and drying it. A grayscale SEM image (first original image) of the measurement sample was obtained, and a binarization process was performed based on a Gaussian mixture model to obtain a first binary image. A partial image showing a portion of the aggregated particles was generated from the first binary image, and the pixel values ​​of each pixel in the partial image were converted to 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. A binarization process was performed based on the Gaussian mixture model on the second original image 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), the maximum Feret diameter b (μm), and the projected area A (μm ２ ), peripheral length P (μm), area inside the envelope Ac (μm ２ ) was calculated, and the agglomerated particles were classified into first agglomerated particles, second agglomerated particles, third agglomerated particles, and fourth agglomerated particles based on the X, Y, and Z values ​​of formulas (i) to (iii). 2,000 randomly selected agglomerated particles were subjected to the above classification, and the proportion of each agglomerated particle type 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, and LiNi was used as an active material. ０．５ Mn ０．３ Co ０．２ O ２ (manufactured by Umicore, product name: TX10), polyvinylidene fluoride (manufactured by Arkema, product name: HSV900, hereinafter referred to as PVdF) as a binder, any one of carbon blacks CB1 to CB6 as a conductive material, and polyvinyl alcohol (manufactured by Denka, product name: B05, hereinafter referred to as polyvinyl alcohol) as a dispersant were prepared. ０．５Mn ０．３ Co ０．２ O ２ The solid content was 98% by mass, the solid content was 2% by mass, the solid content was 1% by mass, and the solid content was 0.1% by mass. The mixture was then weighed and mixed, and NMP was added to the mixture so that the solid content was 68% by mass. Using a planetary centrifugal mixer (Thinky Corporation, Awatori Rentaro ARV-310), the mixture was mixed until uniform, resulting in a dispersion of an electrode composition (positive electrode composition). The prepared electrode composition dispersion was formed into a film on a 15 μm thick aluminum foil (manufactured by UACJ Corporation) using an applicator, and the film was pre-dried at 105 ° C. for one hour by placing it in a dryer. Next, the film was pressed with a linear pressure of 200 kg / cm using a roll press, and the thickness of the film containing the 15 μm thick aluminum foil was adjusted to 80 μm. To remove volatile components, the film was dried in a vacuum at 170 ° C. for 3 hours to obtain a positive electrode.

[0101] (b) Electrode Evaluation The prepared positive electrode was cut into a disk shape with a diameter of 14 mm, and sandwiched between SUS304 flat electrodes on both sides. Using an electrochemical measurement system (Solatron Corporation, Function Generator 1260 and Potentiogalvanostat 1287), AC impedance was measured 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]

[0103] The results shown in Table 1 confirm that when agglomerated particles are classified based on the binary image for measurement generated by the image processing system according to the present disclosure, there is a negative correlation between the proportion of fourth agglomerated particles and the resistance value of the electrode. In particular, it was confirmed that an electrode having a lower resistance value can be formed by using carbon black with a proportion of fourth agglomerated particles of 41% or more.

[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 including a plurality of agglomerated particles each formed by agglomeration of a plurality of primary particles, wherein when the plurality of agglomerated particles are divided into first agglomerated particles having an X value of less than 0.588 calculated by the following formula (i), second agglomerated particles having the X value of 0.588 or more and a Y value of 0.833 or more calculated by the following formula (ii), third agglomerated particles having the X value of 0.588 or more, the Y value of less than 0.833 and a Z value of 0.77 or more calculated by the following formula (iii), and fourth agglomerated particles 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, a 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 is 41% or more. X=a / b (i) Y=4πA / P ２ ... (ii) Z = A / Ac ... (iii) [wherein, in a binary image for measurement generated by an image processing system, the minimum Feret diameter of the agglutinate particles is a (μm), the maximum Feret diameter of the agglutinate particles is b (μm), and the projected area of ​​the agglutinate particles is A (μm ２ ), the perimeter of the agglomerated particle is P (μm), and the envelope area of ​​the agglomerated particle is Ac (μm ２ ) where the image processing system includes at least one processor, and the at least one processor acquires a first grayscale original image showing the plurality of agglutinate particles captured by a scanning electron microscope, performs a first binarization process on the first original image based on a Gaussian mixture model to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglutinate particles and a background region other than the foreground region, generates from the first binary image a partial image that is a part of the first binary image and shows some of the plurality of agglutinate particles, converts pixel values ​​of each pixel of the partial image into pixel values ​​of each pixel of the first original image corresponding to each pixel of the partial image to generate a second original image, and performs a second binarization process on the second original image based on a Gaussian mixture model to generate the measurement binary image that distinguishes between a foreground region consisting of the some of the agglutinate particles and a background region other than the foreground region.] 2. The carbon black according to claim 1, wherein a ratio of the third agglomerate particles to the total number of the first agglomerate particles, the second agglomerate particles, the third agglomerate particles, and the fourth agglomerate particles is 17% or less.

3. The carbon black according to claim 1, having an oil absorption of 150 mL / 100 g or more and 400 mL / 100 g or less.

4. BET specific surface area is 35m ２ / g or more 400m ２ 2. The carbon black according to claim 1, wherein the molecular weight of the carbon black is 1 / g or less.

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

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

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

8. A method for evaluating or selecting carbon black including a plurality of agglomerated particles each formed by aggregation of a plurality of primary particles, comprising: a step of obtaining a binary image for measurement generated by an image processing system; and a step of calculating a ratio of the fourth agglomerated particles to a total number of the first agglomerated particles, the second agglomerated particles, the third agglomerated particles and the fourth agglomerated particles when the binary image for measurement is divided into first agglomerated particles having an X value of less than 0.588 calculated by the following formula (i), second agglomerated particles having an X value of 0.588 or more and a Y value of 0.833 or more calculated by the following formula (ii), third agglomerated particles having 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 calculated by the following formula (iii), and fourth agglomerated 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. and evaluating or sorting the carbon black based on the ratio, wherein the image processing system includes at least one processor, wherein the at least one processor: acquires a first original image showing the plurality of agglomerated particles, performs a first binarization process on the first original image to generate a first binary image that distinguishes between a foreground region consisting of the plurality of agglomerated particles and a background region other than the foreground region, generates a second original image showing a portion of the plurality of agglomerated particles based on the first original image and the first binary image, the second original image being a pixel value of each pixel of the second original image that corresponds to each pixel of the second original image, and performs a second binarization process on the second original image to generate the measurement binary image that distinguishes between a foreground region consisting of the portion of the agglomerated particles and a background region other than the foreground region. 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 agglutinate particles is a (μm), the maximum Feret diameter of the agglutinate particles is b (μm), and the projected area of ​​the agglutinate particles is A (μm ２ ), the perimeter of the agglomerated particle is P (μm), and the envelope area of ​​the agglomerated particle is Ac (μm ２ ).

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

10. The method according to claim 8 or 9, wherein 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 the part of the plurality of agglutinate particles, and converts pixel values ​​of each pixel of the partial image into pixel values ​​of each pixel of the first original image corresponding to each pixel of the partial image, to generate the second original image.

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

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