Method, system and program for evaluating the crosslinking state of crosslinked rubber

Abstract

To provide an evaluation method, evaluation system and evaluation program of a cross-linked state of cross-linked rubber which exactly grasp the cross-linked state for an actual network structure.SOLUTION: An evaluation method of a cross-linked state of cross-linked rubber acquires image data 30 of an ultra-thin section 20 of cross-linked rubber fixed by a network structure swelled by a transmission electron microscope 2, acquires line image data 35 in which a molecular chain constituting the network structure in the image data 30 is specified and displayed by performing data processing of the image data 30 by a calculation device 3, calculates a branch point 38 and a branch length in the molecular chain of the line image data 30, and evaluates the cross-linked state on the basis of the calculated number of branch points 38 and branch length.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method, an evaluation system, and an evaluation program for evaluating the crosslinking state of crosslinked rubber, and more particularly to a method, an evaluation system, and an evaluation program for evaluating the crosslinking state of crosslinked rubber that can more thoroughly evaluate the crosslinking state of crosslinked rubber with respect to the network structure of the crosslinked rubber based on image data acquired by a transmission electron microscope. [Background technology]

[0002] One method proposed for evaluating the network structure of crosslinked rubber involves obtaining a three-dimensional image of an ultrathin slice of crosslinked rubber by computer tomography using a transmission electron microscope, and then using the median diameter of particle diameters obtained by approximating voids in the network structure to spheres from the obtained three-dimensional image (see Patent Document 1).Patent Document 1 also discloses, as a comparative example, a method in which a two-dimensional image of an ultrathin slice of crosslinked rubber is obtained by a transmission electron microscope, and then using the median diameter obtained by approximating voids in the network structure to circles from the obtained two-dimensional image.

[0003] The invention disclosed in Patent Document 1 approximates the size of the mesh using the outer diameter of the inscribed sphere relative to the mesh in a three-dimensional image, and the diameter of the inscribed circle relative to the mesh in a two-dimensional image. However, even if the inscribed sphere and inscribed circle have the same outer diameter, the lengths of the molecular chains that make up the mesh differ, resulting in different mesh shapes. Therefore, there is room for improvement in order to more faithfully grasp the crosslinking state of the actual mesh structure. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-15022 Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide a method, an evaluation system and an evaluation program for evaluating the crosslinking state of crosslinked rubber, which can grasp the crosslinking state more faithfully to the actual network structure. [Means for solving the problem]

[0006] The method for evaluating the crosslinked state of crosslinked rubber of the present invention, which achieves the above-mentioned object, is characterized in that image data of an ultrathin section of crosslinked rubber in which a swollen network structure has been fixed is obtained by a transmission electron microscope, and in the method for evaluating the crosslinked state of crosslinked rubber using the obtained image data, the image data is processed by a computing device to obtain line image data in which the molecular chains that make up the network structure in the image data are identified and displayed, branch points in the molecular chains of this line image data and the branch lengths from these branch points to the branch ends are calculated, and the crosslinked state is evaluated based on the calculated number of branch points and the branch lengths.

[0007] The system for evaluating the crosslinked state of crosslinked rubber of the present invention, which achieves the above-mentioned object, comprises a transmission electron microscope that acquires image data of an ultrathin section of crosslinked rubber in which a swollen network structure has been fixed, and an arithmetic device that evaluates the crosslinked state of crosslinked rubber using the image data acquired by this transmission electron microscope, and is characterized in that the arithmetic device is configured to perform data processing to acquire line image data that identifies and displays the molecular chains that make up the network structure in the image data, data processing to calculate branch points in the molecular chains of the line image data and the branch lengths from these branch points to the branch ends, and data processing to evaluate the crosslinked state based on the calculated number of branch points and the branch lengths.

[0008] The evaluation program for the crosslinked state of crosslinked rubber of the present invention, which achieves the above-mentioned object, is a program for evaluating the crosslinked state of crosslinked rubber, which has an arithmetic device to which image data of an ultrathin section of crosslinked rubber in which a swollen network structure has been fixed, obtained by a transmission electron microscope, is input, and the program evaluates the crosslinked state of crosslinked rubber, and is characterized in that the arithmetic device executes the following steps: acquiring line image data in which the molecular chains that make up the network structure in the image data are identified and displayed; calculating branch points in the molecular chains of the line image data and the branch lengths from these branch points to the branch ends; and evaluating the crosslinked state based on the calculated number of branch points and the branch lengths. [Effects of the Invention]

[0009] According to the present invention, the number of branch points and the branch length obtained from the line image data are indexes that more specifically indicate the state of the network structure of the crosslinked rubber. Therefore, using the number of branch points and the branch length is advantageous in grasping the crosslinked state more faithfully to the actual network structure. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a configuration diagram illustrating an embodiment of a system for evaluating the crosslinking state of a crosslinked rubber. [Figure 2] FIG. 1 is a flow chart illustrating the procedure of an embodiment of a method and program for evaluating the crosslinking state of a crosslinked rubber. [Figure 3] FIG. 2 is an explanatory diagram illustrating image data acquired by a transmission electron microscope. [Figure 4] 4 is an explanatory diagram illustrating separated image data obtained by processing the image data of FIG. 3. FIG. [Figure 5] 5 is an explanatory diagram illustrating leveled image data obtained by processing the separated image data of FIG. 4. FIG. [Figure 6] 6 is an explanatory diagram illustrating an example of line-converted image data obtained by processing the leveled image data of FIG. 5. FIG. [Figure 7] 7 is an explanatory diagram illustrating an enlarged example of line image data in the line-converted image data of FIG. 6; FIG. [Figure 8] FIG. 1 is a graph illustrating branch lengths and their frequencies obtained in the examples. [Figure 9] FIG. 1 is a graph illustrating the correlation between median branch length and crosslink density used in the examples. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, the method, system and program for evaluating the crosslinked state of a crosslinked rubber of the present invention will be described based on the embodiments shown in the drawings.

[0012] The evaluation system 1 illustrated in FIG. 1 includes a transmission electron microscope 2 and a computing device 3. The computing device 3 can be any of various known computers. The computing device 3 includes a central processing unit (CPU) 4, a main storage unit (memory) 5, an auxiliary storage unit (e.g., HDD) 6, an input unit (keyboard, mouse) 7, and an output unit (display) 8. An evaluation program 10 is installed in the auxiliary storage unit 6 of the computing device 3.

[0013] Various known transmission electron microscopes can be used as the transmission electron microscope 2. The transmission electron microscope 2 irradiates an ultrathin section 20 of crosslinked rubber in which a swollen network structure has been fixed with an electron beam, and acquires image data 30 that enables observation of the spatial distribution of electron transmittance inside the ultrathin section 20 from the intensity of the transmitted electron beam. The image data 30 acquired by the transmission electron microscope 2 is input to the calculation device 3 and stored in the auxiliary storage unit 6. The transmission electron microscope 2 needs only to be able to photograph the ultrathin section 20 and acquire two-dimensional image data as the image data 30, but it may also acquire three-dimensional image data of the ultrathin section 20 as the image data 30.

[0014] When the evaluation program 10 is started and executed by the input unit 7, the calculation device 3 executes each data processing instructed by the evaluation program 10. Then, by executing each data processing, the calculation device 3 calculates an evaluation result of the crosslinking state of the crosslinked rubber and outputs it to the output unit 8.

[0015] After the evaluation program 10 is started, initial settings including selection of a filter to be used in each process and selection of an evaluation result are performed by the input unit 7. After the initial settings are completed, the evaluation program 10 causes the arithmetic unit 3 to execute each process in accordance with the initial settings.

[0016] 2 shows an example of the evaluation method and the procedure executed by the evaluation program 10. First, an ultrathin section 20 is prepared (S110), and image data 30 of the ultrathin section 20 is acquired by a transmission electron microscope 2 (S120). Next, the evaluation program 10 is started and executed, causing the arithmetic unit 3 to execute each procedure (S130 to S180). Finally, the evaluation result of the crosslinked state of the crosslinked rubber is output by the output unit 8, and the process ends. The contents of each step (S110) to (S180) are described in detail below.

[0017] In the step (S110) of preparing the ultrathin section 20, a test piece of crosslinked rubber is prepared and cut out from the test piece to form the ultrathin section 20. The test piece of crosslinked rubber is prepared by swelling the crosslinked rubber with a polymerizable monomer and then polymerizing the polymerizable monomer in the presence of a polymerization initiator, so that the network structure swollen to a saturated state is fixed.

[0018] The rubber component of crosslinked rubber can be any rubber component that can be crosslinked with a vulcanizing agent, such as natural rubber (NR) or synthetic rubbers such as styrene-butadiene rubber (SBR) or butadiene rubber (BR). The vulcanizing agent (crosslinking agent) can be any agent that can crosslink the molecular chains of the rubber component, which represent the linear polymer, such as organic peroxides or sulfur. Crosslinked rubber may also contain rubber compounding agents other than the vulcanizing agent, such as vulcanization accelerators, antioxidants, flame retardants, reinforcing materials, and colorants.

[0019] The polymerizable monomer used in preparing the test specimen may be any monomer capable of swelling the crosslinked rubber to a saturated state and polymerizing in the presence of the crosslinked rubber, such as methyl methacrylate, styrene monomer, silicone, etc. The polymerization initiator may be any monomer capable of generating radicals by heat, light, or oxidation-reduction, such as benzoyl peroxide, peroxide, triethylborane, etc.

[0020] The thickness of the ultrathin section 20 is, for example, 50 nm to 300 nm. When a two-dimensional image is obtained using the transmission electron microscope 2, the thickness of the ultrathin section 20 is preferably 50 nm to 100 nm, and when a three-dimensional image is obtained, the thickness of the ultrathin section 20 is preferably 100 nm to 300 nm.

[0021] The ultrathin section 20 can also be stained with a staining agent. Examples of staining agents include osmium tetroxide and iodine. The staining agent is preferably one that stains the molecular chains of the crosslinked rubber but does not stain the polymerizable monomer.

[0022] In the step (S120) of acquiring image data 30, the ultrathin section 20 is photographed using the transmission electron microscope 2 to acquire image data 30 that allows observation of the spatial distribution of electron transmittance within the ultrathin section 20. The image data 30 illustrated in FIG. 3 has dark areas 31 and bright areas 32 within a square whose side length is L [nm] (a variable that corresponds to the magnification of the transmission electron microscope 2). The dark areas 31 are areas with low electron transmittance due to the presence of molecular chains that form a network structure and crosslinking points where these molecular chains are bonded to each other. The bright areas 32 are areas in the image data 30 other than the dark areas 31, and are areas with high electron transmittance due to the presence of an embedding resin that is mainly formed by the polymerization of polymerizable monomers.

[0023] In the step (S130) of separating the image data 30 into dark areas 31 and light areas 32, the arithmetic device 3 executes data processing to separate and binarize the dark areas 31 and light areas 32 appearing in the image data 30. The separated image data 33 shown in Fig. 4 is obtained by separating and binarizing the dark areas 31 and light areas 32, with white areas representing the dark areas 31 and black areas representing the light areas 32.

[0024] In this step, it is preferable to remove noise from the image data 30 and correct the brightness of the image data 30 before separating the dark areas 31 and the bright areas 32. However, depending on the noise conditions and brightness unevenness conditions of the image data 30, noise removal and brightness unevenness correction may be omitted, and it is preferable to make the data processing of noise removal and brightness correction selectable as appropriate.

[0025] As a method for removing noise, various known noise removal methods can be used. Examples of such methods include a method using a noise removal filter (e.g., a Gaussian filter), a method using expansion and contraction processing, and a method using a noise removal model created by machine learning. As a method for correcting brightness, known shading correction methods can be used. Examples of such methods include a method using the cosine fourth power law and a method using white reference data.

[0026] Various known binarization methods can be used for region separation and binarization. Typical binarization methods include threshold-based binarization, region extraction binarization (e.g., active contour model), and binarization using a region separation model created by machine learning. When using threshold-based binarization, a predetermined threshold value may be used, or a value automatically determined using Otsu's binarization method may be used. Alternatively, the bright area 32 may be divided into multiple regions, separated into three or more regions including the dark area 31, and the dark area 31 may be binarized from the other image regions.

[0027] In the step of smoothing the contours of the dark portions 31 (S140), the arithmetic unit 3 performs data processing to smooth the contours of all dark portions 31 captured in the separated image data 33. The smoothed image data 34 illustrated in FIG. 5 is obtained by smoothing the contours of the dark portions 31 in the separated image data 33 illustrated in FIG. 4. In the smoothed image data 34 illustrated in FIG. 5, the contours of the dark portions 31 have been smoothed, and converted from a complex shape to a simpler shape, compared to the portion illustrated in FIG. 4. Smoothing the contours of the dark portions 31 means replacing the contours with a simpler shape when they are complex. Even if this image data processing to smooth the contours of the dark portions 31 is omitted, the next image data processing to thin the dark portions 31 can be performed; however, it is preferable to perform this processing to reduce the computational load required for the image data processing to thin the dark portions 31.

[0028] Examples of edge smoothing techniques include techniques similar to those used for noise removal. While noise removal and noise smoothing are generally considered to be the same in image processing, in this embodiment they are defined as different data processing techniques. Noise removal is performed on image data 30 acquired by a transmission electron microscope 2, with the objective of removing noise from the image data 30. Contour smoothing, on the other hand, is performed on separated image data 33 that has been binarized through region separation, with the objective of smoothing the contours of dark areas 31 in the separated image data 33. For example, if the same filter is used for noise removal and contour smoothing, the kernel coefficients of the filter are different. The number of kernels of the filter used for noise removal is set to a value appropriate for removing noise from the image data 30, while the number of kernels of the filter used for contour smoothing is set to a value appropriate for smoothing the contours of dark areas 31 in the separated image data 33.

[0029] In the step (S150) of thinning the dark portions 31, the calculation device 3 executes data processing to thin all of the dark portions 31 in the leveled image data 34. The line-converted image data 36 illustrated in Fig. 6 is obtained by performing image data processing for thinning on all of the dark portions 31 appearing in the leveled image data 34. The line image data 35 obtained by thinning the dark portions 31 represents the dark portions 31 with center lines, and is data in which the skeleton of the dark portions 31 is identified. Because the dark portions 31 are areas where molecular chains and crosslinking points of a network structure exist, the line image data 35 identifies and displays the molecular chains that make up the network structure and the crosslinking points where these molecular chains are bonded to each other.

[0030] Various known algorithms can be used for thinning, and representative examples include the Hilditch algorithm, Tamura's method, Nagendraprasad-Wang-Gupta's algorithm, and Zhang-Suen's algorithm.

[0031] In the step (S160) of extracting the branch end 37 and the branch point 38 shown in the enlarged explanatory diagram of line image data 35 illustrated in Fig. 7, the calculation device 3 executes data processing to extract the branch end 37 and the branch point 38 in the line image data 35. For ease of viewing, the gradation of the line image data 35 and other parts is inverted in Fig. 7. The branch end 37 and the branch point 38 may be extracted simultaneously, or the branch end 37 and the branch point 38 may be extracted separately.

[0032] Various known methods for extracting feature points can be used as a method for simultaneously extracting them. Examples of such methods include a method using an extraction model based on machine learning, a method that determines the connectivity of eight pixels neighboring a pixel with a predetermined gradation value, and Hough transform. A known edge extraction method can be used as a method for extracting only the branch end 37. An example of such a method is a method that uses a differential filter that extracts edges, such as a Laplacian filter. A known template matching method can be used to extract the branch point 38. An example of the multiple template images is an n×n pixel two-gradation image in which the number of gradation connections representing line segments is three or more.

[0033] As shown in the examples, various known methods can be used to extract the branch end 37 and the branch point 38 from the line image data 35, but it is preferable to extract the branch end 37 and the branch point 38 separately. The branch end 37 and the branch point 38 each have different characteristics. Therefore, by extracting them using separate methods specialized for extracting their respective characteristics, the computational load can be reduced compared to a method that extracts them simultaneously, which is advantageous in shortening the time required for computation.

[0034] In the step (S170) of calculating the number of branch points 38 and the branch length, the calculation device 3 executes data processing to calculate the number of branch points 38 and the branch length in the line image data 35 that are present in large numbers in the line converted image data 36. The number of branch points 38 is the total number of branch points 38 in the line converted image data 36. The branch length is the line segment length (extension length) from the branch point 38 to the branch end 37.

[0035] As a method for calculating the branch length, various known methods for calculating the length of a line segment can be used. Typical examples of such methods include a method of calculating the length of a line segment from the branch point 38 to the branch end 37 by connecting the centers of the pixels constituting the area from the branch point 38 to the branch end 37, and a method of calculating the length of a line segment from the branch point 38 to the branch end 37 by an approximation function. It is preferable that the branch length is the length of the line segment from the branch point 38 to the branch end 37 (extension length), but to simplify the calculation process, it can also be the straight-line distance between the branch point 38 and the branch end 37.

[0036] In the step (S180) of evaluating the crosslinked state, the calculation device 3 executes data processing to evaluate the crosslinked state based on the number of branch points 38 and the branch length. Indicators for evaluating the crosslinked state include, for example, crosslink density and crosslink uniformity (crosslink density distribution). The crosslink density increases as the number of crosslink points increases, and decreases as the mesh size increases. The uniformity of crosslinks decreases as the mesh size varies more. The physical properties of crosslinked rubber are affected by the crosslink density and the degree of crosslink uniformity. The number of branch points 38 can be considered to correspond to the number of crosslink points, and the branch length can be considered to correspond to the mesh size. Therefore, it is possible to evaluate the crosslinked state (crosslink density and crosslink uniformity) of the crosslinked rubber based on the calculated number of branch points 38 and branch length.

[0037] Specifically, to evaluate the crosslink density, the median of the branch lengths in the line image data 35 present in large numbers in the line converted image data 36 is calculated based on the number of branch points 38 and the branch lengths, and the median is used as the evaluation index. Instead of the median of the branch lengths, the average of the branch lengths may be used as the evaluation index. However, the median can grasp the crosslinking state more accurately.

[0038] To evaluate the uniformity of crosslinking, a dispersion index that represents the dispersion state of the branch lengths is calculated based on the number of branch points 38 and the branch lengths, and the dispersion index is used as an evaluation index. This dispersion index uses so-called arithmetic dispersion, standard deviation, etc. The crosslinking state may be evaluated using both the evaluation index for crosslink density and the evaluation index for crosslink uniformity described above.

[0039] Crosslinking of crosslinked rubber can be classified as chemical crosslinking or physical crosslinking, but physical crosslinking is a bond formed by weak physical forces other than covalent bonds. The area between branch points 38 is often a specific indication of the entanglement of molecular chains caused by this physical crosslinking. Therefore, the length between adjacent branch points 38 does not necessarily have to be used as an evaluation index of the crosslinking state, but it can be used as needed.

[0040] The calculation of the number of branch points 38 and branch length can be performed under the same standardized conditions (using the same magnification, size, and number of image data 30). By setting the same conditions in this way, the crosslinking state of various test specimens can be simply compared. If the magnification, size, and number of image data 30 used differ depending on the test specimen, the number of branch points 38 per unit area and branch length are calculated to evaluate the crosslinking state.

[0041] Furthermore, when the relationship between the evaluation index for crosslink density (median branch length) described above and the crosslink density determined by known measurement methods (e.g., equilibrium swelling method and swelling compression method) was examined, it was found that there is a high correlation between the two. Therefore, it is advisable to obtain this correlation in advance using a large number of test pieces. Then, using a test piece of the crosslinked rubber to be evaluated, the number of branch points 38 and the branch length are calculated using the procedure described above. The crosslink density of the crosslinked rubber to be evaluated can be estimated by using the median branch length calculated from the number of branch points 38 and the branch length and the previously determined correlation described above.

[0042] As described above, according to this embodiment, by focusing on the dark areas 31 where the molecular chains and crosslinking points that make up the network are present in the image data 30 obtained using the transmission electron microscope 2, it becomes possible to grasp the differences in the lengths of the molecular chains that make up the network and the complex shape of the network. Specifically, the number of branch points and branch lengths obtained from the line image data 35 represent the bonds between the molecular chains that make up the network structure and the size of the network, and are indicators that more specifically show the state of the network structure of the crosslinked rubber. This is advantageous for grasping the crosslinking state more faithfully to the actual network structure, and it also becomes possible to improve the accuracy of evaluation of the crosslinking state of crosslinked rubber using methods that use the image data 30.

[0043] Although the embodiments of the present invention have been described above, the method, system, and program for evaluating the crosslinked state of crosslinked rubber of the present invention are not limited to specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention.

[0044] The image data 30 is not limited to two-dimensional image data, but may be three-dimensional image data. When three-dimensional image data is used as the image data 30, the methods and filters exemplified above may be compatible with three-dimensional data. Since three-dimensional image data has a larger amount of data than two-dimensional image data, the accuracy of the evaluation of the cross-linking state is higher. On the other hand, a complex method must be used to acquire three-dimensional image data, which is more time-consuming than acquiring two-dimensional image data. Furthermore, data processing of three-dimensional image data requires a larger amount of calculations than data processing of two-dimensional image data. In other words, using two-dimensional image data as the image data 30 results in a simpler evaluation method.

[0045] The evaluation program 10 may have one of the exemplified methods and filters, but may also have multiple methods and filters. When multiple methods and filters are included, it is preferable to configure the evaluation program 10 so that a desired method or filter can be selected from the multiple methods and filters by initial settings at the time of startup. Similarly, it is preferable to configure the evaluation program 10 so that an evaluation index calculated based on the number and branch length of branch points 38 and evaluation content of the crosslinking state corresponding to the evaluation index can be selected by initial settings at the time of startup.

[0046] The evaluation index calculated based on the number and branch length of branch points 38 can be at least one of the median (average) of branch length and a dispersion index that indicates the dispersion state of branch length. Using both of these as evaluation indexes results in a quantitative evaluation that takes into account both the crosslink density and the crosslink uniformity of the crosslinked rubber.

[0047] In the above evaluation method, the crosslinking state may be evaluated using only one ultrathin section 20. However, it is preferable to prepare multiple ultrathin sections 20 from one test specimen and evaluate the crosslinking state based on the total number of branch points 38 and the total branch length in all image data 30 obtained from each ultrathin section 20. In this way, using multiple image data 30 is advantageous in reducing the variability in the data on the number of branch points 38 and the branch length due to differences in the collection positions of the ultrathin sections 20 on the test specimen. When collecting the ultrathin sections 20, it is preferable to set the magnification of the transmission electron microscope 2 lower than when acquiring the image data 30, identify a position exhibiting a representative crosslinking state of the test specimen (a more uniform crosslinking state), and collect the ultrathin sections from that position. [Example]

[0048] The crosslinked rubber to be evaluated was a test piece of crosslinked rubber obtained by vulcanizing butadiene rubber with sulfur. It was swollen in methyl methacrylate for 48 hours. 1 wt / % benzoyl peroxide was added to the test piece, and it was left at room temperature for 3 hours, then heated at 60°C for 24 hours. Five 70 nm ultrathin sections 20 were then prepared from the test piece using an ultramicrotome and stained with osmium tetroxide. Next, the transmission electron microscope 2 was set to an accelerating voltage of 200 kV and a magnification of 60,000x, and the ultrathin sections 20 were photographed. Image data 30 of each ultrathin section 20 was obtained, for a total of five image data 30.

[0049] The calculation device 3 performed Gaussian filtering on each image data 30 to remove noise and shading correction to correct brightness unevenness. Next, the image data 30 was separated into regions using Otsu's binarization and converted into separated image data 33. Next, the separated image data 33 was subjected to contour approximation processing and converted into leveled image data 34. Next, the leveled image data 34 was subjected to thinning processing using the Zhang-Suen algorithm and converted into line-converted image data 36 containing multiple line image data 35. Next, an edge extraction filter was applied to the line-converted image data 36 to extract branch ends 37 in the line image data 35. Next, template matching was performed on the line-converted image data 36 to extract branch points 38 in the line image data 35. Next, the number of branch points 38 in the line image data 35 and the branch lengths were calculated from the line-converted image data 36.

[0050] The graph showing the branch length and its frequency shown in Fig. 8 was created based on the number of branch points 38 and branch lengths obtained from five pieces of image data 30. If the median branch length is calculated from the data in Fig. 8, it can be used as an evaluation index for evaluating the crosslinking state.

[0051] Figure 9 was created by preparing multiple different crosslinked rubbers and based on the median branch length obtained by the same method as in the Examples and the crosslink density determined by a known measurement method (equilibrium swelling method). The black dots in the figure are plotted at the positions corresponding to the crosslink density and median branch length of each crosslinked rubber prepared. The straight line obtained by approximating this group of plotted black dots shows the correlation between the median branch length and the crosslink density. As shown in Figure 9, it can be seen that there is a high correlation between the crosslink density determined by the known measurement method and the median branch length. [Explanation of symbols]

[0052] 1. Rating System 2. Transmission electron microscope 3 Computing device 10. Evaluation Program 20 Ultrathin sections 30 Image data 31 Dark part 32 Akabe 35 Linear image data 37 Branch Termination 38 Branching Point

Claims

1. A method for evaluating the crosslinking state of a crosslinked rubber, comprising obtaining image data of an ultrathin section of a crosslinked rubber in which a swollen network structure is fixed by a transmission electron microscope, and using the obtained image data, A method for evaluating the crosslinked state of crosslinked rubber, characterized in that the image data is processed by a computing device to obtain line image data in which molecular chains that make up the network structure in the image data are identified and displayed, branch points in the molecular chains of this line image data and branch lengths from these branch points to branch ends are calculated, and the crosslinked state is evaluated based on the calculated number of branch points and the branch lengths.

2. 2. The method for evaluating the crosslinked state of a crosslinked rubber according to claim 1, further comprising calculating a median of the branch lengths based on the calculated number of branch points and the calculated branch lengths, and using the median as an index for evaluating the crosslinked state.

3. 3. The method for evaluating the crosslinked state of a crosslinked rubber according to claim 1 or 2, wherein a dispersion index representing a dispersion state of the branch lengths is calculated based on the calculated number of branch points and the branch lengths, and the dispersion index is used as an index for evaluating the crosslinked state.

4. The method for evaluating the crosslinked state of a crosslinked rubber according to any one of claims 1 to 3, further comprising: obtaining a correlation between the calculated number of branch points and the branch length and the crosslink density of the crosslinked rubber; and estimating the crosslink density of the crosslinked rubber to be evaluated using the calculated number of branch points and the branch length using the crosslinked rubber to be evaluated and the correlation known in advance.

5. A system for evaluating the crosslinked state of crosslinked rubber, comprising: a transmission electron microscope for acquiring image data of an ultrathin section of crosslinked rubber in which a swollen network structure is fixed; and a computing device for evaluating the crosslinked state of the crosslinked rubber using the image data acquired by the transmission electron microscope, The calculation device is configured to perform data processing to acquire line image data in which the molecular chains that make up the network structure in the image data are identified and displayed, data processing to calculate branch points in the molecular chains of the line image data and branch lengths from these branch points to branch ends, and data processing to evaluate the crosslinked state based on the calculated number of branch points and the branch lengths.

6. A program for evaluating the crosslinked state of crosslinked rubber, which causes a computing device to evaluate the crosslinked state of crosslinked rubber, to which image data of an ultrathin section of crosslinked rubber in which a swollen network structure is fixed, obtained by a transmission electron microscope, is input, A program for evaluating the crosslinked state of crosslinked rubber, characterized by having the arithmetic device execute the following steps: acquiring line image data in which the molecular chains that make up the network structure in the image data are identified and displayed; calculating branch points in the molecular chains of the line image data and branch lengths from these branch points to branch ends; and evaluating the crosslinked state based on the calculated number of branch points and the branch lengths.

Images