Method for evaluating or selecting carbon black

Abstract

To provide an evaluation method capable of easily evaluating a characteristic of a secondary battery conductive material of a carbon black, and provide a selection method capable of easily selecting the carbon black in advantageous for the secondary battery conductive material.SOLUTION: A method for evaluating or selecting a carbon black containing a plurality of aggregated particles formed by aggregating a plurality of primary particles, contains: a step of obtaining a measurement binary image to be generated by an imaging processing system; a step of calculating an average front surface area of each aggregated particle from the measurement binary image; a step of calculating a BET ratio front surface area of the carbon black; and a step of evaluating or selecting the carbon black on the basis of a ratio of the average front surface area against the BET ratio front surface area.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to 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 there is a need for a method for easily evaluating the characteristics of carbon black as a conductive material for secondary batteries and a method for easily selecting carbon black useful as a conductive material for secondary batteries.

[0005] One object of the present disclosure is to provide an evaluation method capable of easily evaluating the characteristics of carbon black as a conductive material for secondary batteries and a selection method capable of easily selecting carbon black useful as a conductive material for secondary batteries.

Means for Solving the Problems

[0006] The present disclosure relates to, for example, the following [1] to [4]. [1] A method for evaluating or selecting carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, a step of obtaining a binary image for measurement generated by an image processing system, A step of obtaining the average surface area of the aggregated particles from the binary image for measurement; A step of obtaining the BET specific surface area of the carbon black; A step of evaluating or selecting the carbon black based on the ratio of the average surface area to the BET specific surface area; including; The image processing system is equipped 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, is generated, performs a second binarization process on the second original image to generate the binary image for measurement that distinguishes a foreground region composed of the some aggregated particles from a background region outside the foreground region, An image processing system Method. [2] The first original image is a grayscale image, The at least one processor performs the first binarization process based on a mixture Gaussian model, performs the second binarization process based on the mixture Gaussian model, The method according to [1]. [3] The at least one processor generates a partial image that is a part of the first binary image and shows the some of the plurality of aggregated particles from the first binary image, The method according to [1] or [2], wherein the pixel value of each pixel of the partial image is converted 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. [4] The method according to any one of [1] to [3], wherein the first original image is an image obtained by imaging with a scanning electron microscope. [5] The method according to any one of [1] to [4], wherein the step of evaluating or selecting the carbon black is a step of predicting the oil absorption amount of the carbon black based on having a negative correlation with the ratio of the average surface area to the BET specific surface area.

Advantages of the Invention

[0007] According to the present disclosure, there are provided an evaluation method capable of easily evaluating the characteristics of carbon black as a conductive material for secondary batteries, and a selection method capable of easily selecting carbon black useful as a conductive material for secondary batteries.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Embodiments for Carrying Out the Invention

[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 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 binary image for measurement generated by an image processing system described later. Carbon black contains 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 including a step of obtaining an average surface area of the aggregated particles from the binary image for measurement, a step of obtaining a BET specific surface area of the carbon black, and a step of evaluating or selecting the carbon black based on a ratio of the average surface area to the BET specific surface area.

[0011] The above ratio shows a negative correlation with the oil absorption amount of the carbon black. When the oil absorption amount of the carbon black is within a predetermined range, electrodes with lower resistance values tend to be easily obtained. Therefore, by selecting carbon black based on the above ratio, it is possible to easily select carbon black that is likely to form electrodes with lower resistance values. Further, by evaluating carbon black based on the above 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 secondary batteries.

[0012] Note that the oil absorption amount of carbon black has drawbacks in measurement, such as requiring skill in measurement. The method according to the present disclosure can also be said to be a method for predicting the oil absorption amount of carbon black based on the above ratio. That is, the step of evaluating or selecting carbon black may be a step of predicting the oil absorption amount of carbon black from the above ratio based on the fact that the ratio of the average surface area to the BET specific surface area and the oil absorption amount of the carbon black have a negative correlation.

[0013] The negative correlation between the ratio of the average surface area to the BET specific surface area and the oil absorption of carbon black may be approximated as a linear function G. The linear function G can be expressed as y = ax, where one of the ratio of the average surface area to the BET specific surface area and the oil absorption of carbon black is x and the other is y. The obtained linear function G can be used as a calibration curve. Therefore, the step of evaluating or selecting carbon black may be a step of predicting the oil absorption of carbon black based on the ratio of the average surface area to the BET specific surface area and the previously obtained linear function G (negative correlation).

[0014] The average surface area of the agglomerated particles (hereinafter also referred to as the electron microscope surface area) is a value calculated in accordance with the standard of ASTM D3849 from the perimeter and projected area measured from the binary image for measurement for 2000 randomly selected agglomerated particles. More specifically, it is a value calculated by the following method.

[0015] (Calculation of average surface area) First, the average particle size (d p ) of the primary particles is calculated by the following formula (i). Next, the number (n) of primary particles forming one agglomerated particle is calculated by the following formula (ii). Next, the particle size surface average diameter (d sm ) is calculated by the following formula (iii). Next, the average surface area (electron microscope surface area) (EMSA) is calculated by formula (iv). In the formula, P is the perimeter of the agglomerated particle, A is the area of the agglomerated particle, and α is the agglomeration coefficient. When the calculated value of α is less than 0.4, the value of α shall be 0.4. ρ is a numerical value corresponding to the density of carbon black, and the value of ρ is 1.8 g / cm 3 shall be taken.

[0016] [Number]

[0017] [Number]

[0018] [Number]

[0019] [Number]

[0020] The above ratio (average surface area (m 2 / g) / BET specific surface area (m 2 / g)) may be, for example, 0.1 or more, and may be 0.15 or more or 0.2 or more. Also, the above ratio (average surface area (m 2 / g) / BET specific surface area (m 2 / g)) may be, for example, 1.1 or less, and may be 1.0 or less, 0.9 or less, 0.8 or less, 0.7 or less, 0.6 or less, 0.5 or less, or 0.4 or less. Thereby, the resistance value of the electrode formed from the carbon black tends to be lower.

[0021] In the method according to the present disclosure, the carbon black may be evaluated or selected based on the above numerical range.

[0022] The carbon black in the present disclosure is preferably acetylene black.

[0023] 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.

[0024] The BET specific surface area of carbon black is measured by the B method described in JIS K 6217-2, the single-point nitrogen adsorption method.

[0025] The primary particle diameter (average particle diameter of primary particles) of carbon black may be, for example, 15 nm or more, 17 nm or more, 19 nm or more, or 21 nm or more. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained. Also, the primary particle diameter (average particle diameter of primary particles) of carbon black may be, for example, 55 nm or less, 50 nm or less, 45 nm or less, or 40 nm or less. Thereby, there is a tendency that an electrode with a lower resistance value is more easily obtained.

[0026] The primary particle diameter (average particle diameter of primary particles) of carbon black can be evaluated by the image processing system described later.

[0027] 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 a 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 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; (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 selected by the above-described classification of the aggregated particles.

[0028] In the present disclosure, at least one processor of an image processing system acquires a first grayscale original image showing a plurality of aggregated particles imaged by a scanning electron microscope ((1’)), 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 a plurality of aggregated particles from a background region outside the foreground region ((2’)), 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 ((3-1’)), 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 ((3-2’)), and may perform 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 ((4’)). In such an image processing system, the correlation between the above-described ratio and the oil absorption amount becomes more accurate, and carbon black can be evaluated or selected more effectively based on the above-described numerical range.

[0029] Hereinafter, the image processing system of the present disclosure will be described in detail.

[0030] [Overview of the System] The image processing system according to the present disclosure is a computer system that performs 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.

[0031] An aggregated particle refers to a particle formed by the aggregation of a plurality of primary particles. A primary particle refers to a particle that cannot be further divided into finer particles. That is, a primary particle refers to the smallest unit of a particle. As an example, 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 formed by the carbon black, so that more 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 an image processing system, the user can grasp the performance of the aggregated particle as a conductive agent. As an example, the particle size of this carbon black is about 22 to 26 nm.

[0032] The computer system may calculate the shape parameter regarding the primary particle based on the shape parameter regarding the aggregated particle in order to evaluate the shape of the primary particle. As an example, the shape parameter of the aggregated particle is area, perimeter, perimeter of convex hull, area of convex hull, length, width, equivalent circle diameter, circularity, area circularity, envelope degree, area inside envelope, area envelope degree, aspect ratio (ratio of the minimum Feret diameter to the maximum Feret diameter), elongation, etc. As an example, the shape parameter of the primary particle is the average particle size of the primary particle, etc. The computer system may evaluate the number of primary particles contained in the aggregated particle, the morphology of the aggregated particle, the electron microscope surface area (average surface area of the aggregated particle), the fractal dimension of the entire aggregated particle, etc. As described above, the image processing system may perform such calculations and evaluations.

[0033] The original image is obtained by imaging with an imaging device such as a scanning electron microscope (SEM) or a transmission electron microscope (TEM). As an example, the original image showing aggregated particles can be obtained by the following procedure when using an SEM. 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 an SEM, the occurrence of unevenness in the background region of the original image is suppressed.

[0034] [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.

[0035] 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.

[0036] 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. Binarization processing refers to converting the pixel value of each pixel of an image into a binary value. By performing binarization processing on the original image, a binary image that distinguishes a foreground region composed of aggregated particles from a background region other than the foreground region is generated. The binarization processing unit 12 performs binarization processing on the first original image 211 to generate a first binary image 221, and performs binarization processing on the second original image 231 to generate a second binary image 241.

[0037] 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.

[0038] 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.

[0039] The evaluation unit 14 is a functional module that evaluates the shape of the aggregated particles or primary particles. The evaluation unit 14 acquires the second binary image 241 and calculates the shape parameters of any aggregated particle. Then, the evaluation unit 14 may evaluate the shape of the primary particles forming the any aggregated particle based on the calculated shape parameters.

[0040] FIG. 2 is a diagram showing a general 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 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 an application program. 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.

[0041] 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 thereon and causing the processor 101 to execute the program 110. The program 110 includes codes 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.

[0042] 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.

[0043] The image processing system 10 may be constituted by one or more computers. When a plurality of computers are used, the image processing system 10 is constituted by connecting these computers to each other via a communication network.

[0044] [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.

[0045] 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. Assuming that 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.

[0046] Between the corresponding second original image 231 and the second binary image 241, the one or more aggregated particles shown are the same.

[0047] [Operation of the System] 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.

[0048] 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 any 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 a SEM image obtained by imaging with a SEM. Since the SEM image can be acquired with high contrast, it is suitable for binarization processing.

[0049] 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.

[0050] FIG. 4 is a diagram showing an example of the first original image 211.

[0051] 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.

[0052] 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 trailing 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.

[0053]

Equation

[0054] 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).

[0055] [Number]

[0056] Then, the binarization processing unit 12 estimates the values of the parameters w1, w2, μ1, σ1, μ2, and 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 a threshold value 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 value T. Alternatively, the binarization processing unit 12 calculates the average value of μ1 and μ2 as the threshold value T. Then, the binarization processing unit 12 executes the first binarization process by converting pixel values less than the calculated threshold value T to 0 and pixel values greater than or equal to 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 more accurately based on the mixture Gaussian model.

[0057] 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 a 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 value T. Alternatively, the first binarization process may be a process of performing binarization based on a threshold value T input by the user.

[0058] 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 aggregated particles 300 is more clearly distinguishable than in the first original image 211.

[0059] 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.

[0060] 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 the aggregated particle after determining that the aggregated particle with a small size is noise.

[0061] 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. The generation unit 13 converts the pixel value of each pixel of each of the one or more partial images obtained from the first binary image 221 to the pixel value of the pixel of the first original image 211 corresponding to the pixel, and generates the second original image 231. 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. 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.

[0062] 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.

[0063] 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 to identify 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.

[0064] 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 between a foreground region composed of aggregated particles and 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.

[0065] 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 certain agglomerated particle may be different from each other. This is because the histograms of the pixel values of the first original image and the second original image may be different from each other. The first original image 211 captures relatively many agglomerated particles, and the range or distribution of pixel values may be different among individual agglomerated particles. On the other hand, the second original image captures some agglomerated particles. Therefore, the histogram of pixel values in the entire image is different between the first original image and the second original image. Due to the difference in the histograms, the threshold T in the binarization process is also different. Therefore, the results of the first binarization process and the second binarization process for a certain agglomerated particle may be different from each other.

[0066] In step S15, the evaluation unit 14 evaluates the shape of at least one agglomerated particle among the agglomerated particles indicated by one or more second binary images 241. For example, for each agglomerated 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 ).

[0067] 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.

[0068] 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.

[0069] [Number]

[0070] [Number]

[0071] The evaluation unit 14 may execute the above processing for each of two or more types of shape parameters.

[0072] [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.

[0073] 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-described steps may be omitted, or each step may be executed in a different order. Also, any two or more of the above-described 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.

[0074] 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.

[0075] In the present disclosure, the expression "at least one processor executes the first process, executes the second process,..., executes the 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.

[0076] [Appendix] As can be understood from the various examples above, the image processing system in the present disclosure includes the aspects shown below. Also, the method according to the present disclosure may include the image processing method shown below. Further, the image processing system in the present disclosure may be executed by the image processing program shown below. (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, wherein 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. Perform 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 Performs the first binarization process based on a Gaussian mixture model. Performs the second binarization process based on the Gaussian mixture 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, which 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 some of the 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 steps. (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 that causes a computer to execute the above steps.

[0077] 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, in which the pixel value of each pixel is the pixel value of each pixel of the first original image, is generated. A second binarization process is executed 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 such an influence may be reflected in the first binary image. By performing the second binarization process focusing on some of the aggregated particles, such an influence on 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.

[0078] 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.

[0079] 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.

[0080] According to Supplementary Note 4, during the second binarization process, the influence of other aggregated particles does not affect the appearance of one aggregated particle, so the shape of the one aggregated particle can be specified more accurately. As a result, it becomes possible to evaluate the shape of the primary particles with higher precision.

[0081] 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.

[0082] 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, it becomes possible to evaluate the shape of the primary particles with higher precision.

[0083] 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.

[0084] Carbon black can be used, for example, as a composition for an electrode containing carbon black and an active material capable of occluding and releasing lithium ions.

[0085] As the active material in the composition for an electrode, known active materials can be used without particular limitation.

[0086] Examples of the active material include lithium cobalt oxide (LiCoO2), lithium nickel oxide (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), etc., 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-based compounds; Vanadium oxides such as LiV3O8, V2O5, Cu2V2O7; Disulfide compounds; Silicate-based 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 exemplified.

[0087] The above electrode composition may be used, for example, as an electrode containing the above electrode composition or as a secondary battery provided with the electrode. The secondary battery may have a positive electrode and / or a negative electrode containing the above electrode composition, and it is preferable that the positive electrode contains the above electrode composition.

[0088] 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 any particular limitation.

[0089] 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

[0090] Hereinafter, the present disclosure will be described in more detail by way of examples, but the present disclosure is not limited to these examples.

[0091] (Test Examples 1 to 6) (1) Characteristics of Carbon Black For carbon blacks CB1 to CB6, the oil absorption amount and BET specific surface area were measured by the following methods. Also, for carbon blacks CB1 to CB6, binary images for measurement were obtained by the following methods. From the obtained binary images for measurement, the average surface area (electron microscope surface area) of the aggregates was determined by the following methods. The results are shown in Table 1.

[0092] (a) Measurement of Oil Absorption Amount The oil absorption amount of 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.]

[0093] (b) Measurement of BET Specific Surface Area The BET specific surface area of 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.

[0094] (c) Acquisition of Binary Image for Measurement A dispersion liquid in which carbon black was dispersed in chloroform was dropped onto a substrate and dried to obtain a measurement sample. For the measurement sample, 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 the mixture Gaussian model to obtain a binary image for measurement.

[0095] (d) Calculation of electron microscope surface area From the binary image for measurement generated in (c) above, the perimeter P (μm) and projected area A (μm 2 ) of the aggregated particles were determined, and the electron microscope surface area was calculated in accordance with the standard of ASTM D3849. More specifically, the electron microscope surface area was calculated by the method described in (Calculation of average surface area) above. By this method, the electron microscope surface area was determined from 2000 randomly selected aggregated particles. The results are shown in Table 1.

[0096] (2) Evaluation of carbon black (a) Preparation of positive electrode As the solvent, N-methyl-2-pyrrolidone (manufactured by Kanto Chemical Co., Ltd., hereinafter referred to as NMP), as the active material, LiNi 0.5 Mn 0.3 Co 0.2 O2 (manufactured by Umicore, product name: TX10), as the binder, polyvinylidene fluoride (manufactured by Arkema, product name: HSV900, hereinafter referred to as PVdF), as the conductive material, any one of carbon blacks CB1 to CB6, and as the dispersant, polyvinyl alcohol (manufactured by Denka Co., Ltd., product name: B05, hereinafter referred to as polyvinyl alcohol) were prepared respectively. 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 to obtain 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 and pre-dried at 105 °C for one hour. Next, press with a roll press at a linear pressure of 200 kg / cm² so that the thickness of the film including the 15-μm-thick aluminum foil becomes 80 μm. To remove volatile components, vacuum dry at 170 °C for 3 hours to obtain a positive electrode.

[0097] (b) Electrode evaluation The prepared positive electrode was cut out 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.

[0098]

Table 1

[0099] From the results shown in Table 1, when the electron microscope surface area (average surface area of aggregated particles) was determined 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 (-0.98 as the correlation coefficient) between the ratio of the electron microscope surface area to the BET specific surface area (ratio (S1 / S2) in Table 1) and the oil absorption amount. Also, when the above ratio was within a predetermined range, it was confirmed that the resistance value of the electrode was low. In particular, it was confirmed that an electrode having a lower resistance value could be formed by carbon black in which the above ratio (S1 / S2) was 0.1 or more and 1.1 or less.

Explanation of Symbols

[0100] 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. A method for evaluating or selecting carbon black containing a plurality of aggregated particles each formed by aggregation of a plurality of primary particles, comprising: obtaining a binary measurement image generated by an image processing system; obtaining the average surface area of the aggregated particles from the binary measurement image; obtaining the BET specific surface area of the carbon black; evaluating or selecting the carbon black based on the ratio of the average surface area to the BET specific surface area; and the image processing system comprises at least one processor, and 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, generates 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, performs a second binarization process on the second original image to generate the binary measurement image that distinguishes a foreground region composed of the some of the aggregated particles from a background region outside the foreground region, which is an image processing system, method.

2. 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. The method according to claim 1.

3. at least one of the processors generates, from the first binary image, a partial image that is a part of the first binary image and that shows some of the plurality of aggregated particles among the plurality of aggregated 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 1 or 2.

4. The first original image is an image obtained by imaging with a scanning electron microscope The method according to claim 1 or 2.

5. The step of evaluating or selecting the carbon black is a step of predicting the oil absorption amount of the carbon black based on having a negative correlation with the ratio of the average surface area to the BET specific surface area The method according to claim 1 or 2.

Images