JP2023085735A - Evaluation method, evaluation system and evaluation program of cross-linked state of cross-linked rubber - Google Patents

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

The present invention relates to an evaluation method, an evaluation system, and an evaluation program for the crosslinked state of a crosslinked rubber, and more specifically, based on image data acquired by a transmission electron microscope, the crosslinked state of the crosslinked rubber is evaluated with respect to the network structure of the crosslinked rubber. The present invention relates to an evaluation method, an evaluation system, and an evaluation program for the crosslinked state of crosslinked rubber that can be evaluated more fully.

As a method for evaluating the network structure of the crosslinked rubber, a three-dimensional image of an ultra-thin section of the crosslinked rubber is obtained by computer tomography using a transmission electron microscope, and the voids present in the network structure are identified from the obtained three-dimensional image. A method using the median diameter of particle diameters obtained by approximating a sphere has been proposed (see Patent Document 1). As a comparative example in Patent Document 1, a two-dimensional image of an ultra-thin section of crosslinked rubber is obtained by a transmission electron microscope, and the median diameter obtained by approximating the void part of the network structure from the obtained two-dimensional image to a circle. is also disclosed.

In the invention disclosed in Patent Document 1, the mesh size is approximated by using the outer diameter of the inscribed sphere with respect to the mesh in a three-dimensional image, and by using the diameter of the inscribed circle with respect to the mesh in a two-dimensional image. I understand that. However, even if the inscribed spheres have the same outer diameter and the inscribed circles have the same diameter, the lengths of the molecular chains forming the networks are different, and as a result, the shapes of the networks are also different. Therefore, there is room for improvement in grasping the cross-linking state more faithfully to the actual network structure.

Japanese Patent Application Laid-Open No. 2021-15022

An object of the present invention is to provide an evaluation method, an evaluation system, and an evaluation program for the crosslinked state of a crosslinked rubber that more faithfully comprehends the crosslinked state of an actual network structure.

The method for evaluating the crosslinked state of the crosslinked rubber of the present invention for achieving the above object is obtained by obtaining image data of an ultra-thin section of the crosslinked rubber in which the swollen network structure is fixed by a transmission electron microscope, and obtaining the obtained image. In the method for evaluating the crosslinked state of a crosslinked rubber using data, the image data is processed by an arithmetic device to obtain line image data in which the molecular chains constituting the network structure in the image data are specified and displayed. Then, the branch point in the molecular chain of this line image data and the branch length from this branch point to the branch end are calculated, and the crosslinked state is evaluated based on the calculated number of branch points and the branch length. It is characterized by

The system for evaluating the crosslinked state of a crosslinked rubber according to the present invention for achieving the above objects comprises a transmission electron microscope for acquiring image data of an ultra-thin section of a crosslinked rubber in which a swollen network structure is fixed, and the transmission electron microscope. A system for evaluating the crosslinked state of a crosslinked rubber comprising an arithmetic device that evaluates the crosslinked state of the crosslinked rubber using the image data obtained by a microscope, wherein the arithmetic device comprises a molecular chain that constitutes the network structure in the image data. data processing for acquiring line image data that is specified and displayed, data processing for calculating a branch point in the molecular chain of the line image data and a branch length from this branch point to the branch end, and It is characterized by performing data processing for evaluating the crosslinked state based on the number of branch points and the branch length.

In the program for evaluating the crosslinked state of the crosslinked rubber of the present invention that achieves the above objects, image data of an ultra-thin section of the crosslinked rubber in which the swollen network structure is fixed, obtained by a transmission electron microscope, is input. In the evaluation program for the crosslinked state of the crosslinked rubber that causes the computing device to evaluate the crosslinked state of the crosslinked rubber, a procedure for obtaining line image data in which the molecular chains constituting the network structure in the image data are specified and displayed; A procedure for calculating a branch point in the molecular chain of the line image data and a branch length from the branch point to the branch end, and evaluating the crosslinked state based on the calculated number of the branch points and the branch length. A procedure is executed by the computing device.

According to the present invention, the number of branch points and the branch length obtained from the line image data serve as indicators that more specifically show the state of the network structure of the crosslinked rubber. Therefore, by using the number of branch points and the branch length, it is advantageous to grasp the cross-linking state more faithfully with respect to the actual network structure.

1 is a configuration diagram illustrating an embodiment of a system for evaluating the crosslinked state of crosslinked rubber; FIG. FIG. 2 is a flowchart illustrating procedures of an embodiment of an evaluation method and an evaluation program for the crosslinked state of crosslinked rubber. FIG. 4 is an explanatory diagram illustrating image data acquired by a transmission electron microscope; 4 is an explanatory diagram illustrating separated image data obtained by data processing the image data of FIG. 3; FIG. FIG. 5 is an explanatory diagram exemplifying leveled image data obtained by data processing the separated image data of FIG. 4 ; 6 is an explanatory diagram illustrating line-converted image data obtained by data processing the leveled image data of FIG. 5; FIG. FIG. 7 is an explanatory diagram exemplifying an enlarged line image data in the line-converted image data of FIG. 6; FIG. 4 is a graph illustrating branch lengths and their frequencies obtained in Examples. FIG. 4 is a graph illustrating the correlation between the median branch length and the crosslink density used in Examples.

BEST MODE FOR CARRYING OUT THE INVENTION The evaluation method, evaluation system, and evaluation program for the crosslinked state of the crosslinked rubber of the present invention will be described below based on the embodiments shown in the drawings.

An evaluation system 1 illustrated in FIG. 1 includes a transmission electron microscope 2 and an arithmetic device 3 . Various known computers can be used as the arithmetic device 3 . The arithmetic unit 3 includes a central processing unit (CPU) 4, a main memory unit (memory) 5, an auxiliary storage unit (for example, HDD) 6, an input unit (keyboard, mouse) 7, and an output unit (display) 8. have. The evaluation program 10 is installed in the auxiliary storage section 6 of the computing device 3 .

Various known transmission electron microscopes can be used as the transmission electron microscope 2 . The transmission electron microscope 2 irradiates an ultra-thin section 20 of crosslinked rubber with a swollen network structure fixed with an electron beam, and the spatial distribution of the electron transmittance inside the ultra-thin section 20 is determined from the intensity of the transmitted electron beam. Observable image data 30 is obtained. Image data 30 acquired by the transmission electron microscope 2 is input to the arithmetic unit 3 and stored in the auxiliary storage unit 6 . The transmission electron microscope 2 may acquire two-dimensional image data as the image data 30 by photographing the ultra-thin section 20, but may acquire three-dimensional image data of the ultra-thin section 20 as the image data 30. .

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, each data processing is executed to calculate the evaluation result of the crosslinked state of the crosslinked rubber and output to the output unit 8 .

After the evaluation program 10 is activated, initial settings including selection of filters to be used in each process and selection of evaluation results are performed by the input unit 7 . After completing the initial setting, the evaluation program 10 causes the arithmetic device 3 to execute each process according to the initial setting.

FIG. 2 shows an example of the procedure executed by the evaluation method and evaluation program 10. As shown in FIG. First, an ultra-thin section 20 is prepared (S110), and image data 30 of the ultra-thin section 20 is acquired by the transmission electron microscope 2 (S120). Next, by starting and executing the evaluation program 10, the evaluation program 10 causes the arithmetic unit 3 to execute each procedure (S130 to S180). Finally, when the evaluation result of the crosslinked state of the crosslinked rubber is outputted from the output unit 8, the process ends. Details of each step from (S110) to (S180) will be described below.

In the step of preparing the ultra-thin section 20 (S110), a crosslinked rubber test piece is prepared, and the ultra-thin section 20 is cut out from the test piece. The test piece of the crosslinked rubber is a state in which the network structure swollen to a saturated state is fixed by swelling the crosslinked rubber using a polymerizable monomer and then polymerizing the polymerizable monomer in the presence of a polymerization initiator. It has become.

The rubber component of the crosslinked rubber may be any rubber component that can be crosslinked with a vulcanizing agent, and examples thereof include natural rubber (NR) and synthetic rubbers such as styrene-butadiene rubber (SBR) and butadiene rubber (BR). be. Any vulcanizing agent (crosslinking agent) may be used as long as it can crosslink the molecular chains representing the chain polymer of the rubber component, and examples thereof include organic peroxides and sulfur. The crosslinked rubber may contain rubber compounding agents other than the vulcanizing agent, such as a vulcanization accelerator, anti-aging agent, flame retardant, reinforcing material, and coloring agent.

As the polymerizable monomer used for preparing the test piece, any monomer can be used as long as it can swell the crosslinked rubber to a saturated state and polymerize in the presence of the crosslinked rubber. Examples include methyl methacrylate, styrene monomer, and silicone. be done. The polymerization initiator may be one that generates radicals by heat, light, or oxidation-reduction, and examples thereof include benzoyl peroxide, peroxide, and triethylborane.

The thickness of the ultra-thin section 20 is, for example, 50 nm to 300 nm. The thickness of the ultra-thin section 20 is preferably 50 nm to 100 nm when acquiring a two-dimensional image with the transmission electron microscope 2, and the thickness of the ultra-thin section 20 is preferably 100 nm to 300 nm when acquiring a three-dimensional image.

The ultra-thin section 20 can also be stained with a staining agent. Examples of dyes include osmium tetroxide and iodine. The dye is preferably one that dyes the molecular chains of the crosslinked rubber but does not dye the polymerizable monomer.

In the step of acquiring the image data 30 (S120), the ultra-thin section 20 is imaged by the transmission electron microscope 2 to acquire the image data 30 in which the spatial distribution of the electron transmittance inside the ultra-thin section 20 can be observed. The image data 30 illustrated in FIG. 3 has a dark portion 31 and a bright portion 32 within a square range having a side length of L [nm] (variable according to the magnification of the transmission electron microscope 2). The dark portion 31 is a portion having a low electron transmittance due to the presence of molecular chains forming a network structure and cross-linking points where the molecular chains are bonded to each other. The bright portion 32 is a portion other than the dark portion 31 in the image data 30, and is a portion having a high electron transmittance due to the presence of an embedding resin mainly formed by polymerizing a polymerizable monomer.

In the step of segmenting the dark portion 31 and the bright portion 32 of the image data 30 (S130), the dark portion 31 and the bright portion 32 appearing in the image data 30 are segmented and binarized by the arithmetic unit 3. Data processing is performed. The separated image data 33 illustrated in FIG. 4 is obtained by separating the dark portion 31 and the bright portion 32 and binarizing them.

In this step, it is preferable to remove noise from the image data 30 or correct the luminance of the image data 30 before separating the dark portion 31 and the bright portion 32 into areas. However, depending on the noise condition and luminance unevenness condition of the image data 30, noise removal and luminance unevenness correction may be omitted, and data processing for noise removal and luminance correction may be selected as appropriate.

Various known methods for removing noise can be used as the method for removing noise. Examples of such methods include a method using a noise removal filter (for example, a Gaussian filter), a method using expansion processing and contraction processing, and a method using a noise removal model created by machine learning. A known shading correction technique can be used as a technique for correcting luminance. Examples of the method include a method using the cosine fourth power law, a method using data that serves as a white reference, and the like.

Various well-known binarization techniques can be used as the area separation and binarization technique. Typical binarization methods include threshold binarization, binarization by area extraction (e.g., active contour model), and binarization using an area separation model created by machine learning. exemplified. For binarization using a threshold value, a previously grasped value may be used as the threshold value, or a value automatically determined by Otsu's binarization method may be used. Alternatively, the bright portion 32 may be divided into a plurality of regions, separated into three or more regions including the dark portion 31, and the dark portion 31 region and other image regions may be binarized.

In the step of leveling the outline of the dark portion 31 (S140), data processing for leveling the outline of the dark portion 31 is executed by the computing device 3 targeting all the dark portions 31 appearing in the separated image data 33 . The leveled image data 34 illustrated in FIG. 5 is obtained by leveling the outline of the dark portion 31 in the separated image data 33 illustrated in FIG. In the portion indicated by A in the leveled image data 34 illustrated in FIG. 5, the contour of the dark portion 31 is smoothed compared to the portion indicated by A in the separated image data 33 illustrated in FIG. It has been converted to a simpler shape. Leveling of the contour of the dark portion 31 means replacing the contour with a simple shape when the contour has a complicated shape. Even if the image data processing for leveling the outline of the dark portion 31 is omitted, the next image data processing for thinning the dark portion 31 can be executed. It is desirable to do so for reduction.

As a method for smoothing the contour, the same method as the method for removing noise is exemplified. Although noise removal and smoothing are generally regarded as the same in image processing, they are defined as different data processing in this embodiment. The purpose of the noise removal is to remove noise appearing in the image data 30 acquired by the transmission electron microscope 2 . On the other hand, the contour smoothing aims at smoothing the contour of the dark portion 31 appearing in the separated image data 33, which is binarized by segmentation. For example, if the same filter is used for noise removal and contour smoothing, the kernel coefficients in the filter are different. The number of kernels of the filter used for noise removal is set to a value suitable for removing noise from the image data 30, and the number of kernels of the filter used for smoothing the contour is set to smooth the contour of the dark portion 31 appearing in the separated image data 33. set to a suitable value.

In the step of thinning the dark portions 31 ( S<b>150 ), data processing for thinning all the dark portions 31 of the leveled image data 34 is executed by the computing device 3 . Line-converted image data 36 illustrated in FIG. 6 is obtained by subjecting all the dark portions 31 appearing in the leveled image data 34 to thinning image data processing. Line image data 35 obtained by thinning the dark portion 31 represents the dark portion 31 with a central line, and is data in which the skeleton of the dark portion 31 is specified. Since the dark portion 31 is a portion where the molecular chains and the cross-linking points of the network structure exist, the line image data 35 is displayed by specifying the molecular chains forming the network structure and the cross-linking points where the molecular chains are bonded to each other. is.

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

In the step (S160) of extracting the branch end 37 and the branch point 38 shown in an enlarged explanatory view of the line image data 35 illustrated in FIG. data processing is performed to extract the In FIG. 7, the gradations of the line image data 35 and the other parts are reversed for easy viewing. The branch end 37 and the branch point 38 may be extracted at the same time, or the branch end 37 and the branch point 38 may be extracted separately.

As a method for extracting simultaneously, various known methods for extracting feature points can be used. Examples of such methods include a method using a machine-learned extraction model, a method of determination based on the connectivity of eight pixels in the vicinity of a pixel with a predetermined gradation value, and a Hough transform. As a technique for extracting only the branch end 37, a known technique for extracting edges can be used. Examples of such methods include a method using a differential filter for extracting edges, such as a Laplacian filter. Known template matching can be used as a technique for extracting the branch point 38 . An example of the plurality of template images is a two-gradation image of n×n pixels in which the number of connected gradations representing line segments is three or more.

Various known methods can be used for extracting the branch end 37 and the branch point 38 from the line image data 35. However, the extraction of the branch end 37 and the extraction of the branch point 38 can be performed separately. is desirable. The branch end 37 and the branch point 38 have different characteristics. Therefore, by extracting each feature using a separate method that specializes in extracting each feature, the computational load can be reduced compared to a method that extracts them simultaneously, which is advantageous in shortening the time required for computation. become.

In the step of calculating the number of branch points 38 and the branch length (S170), the calculation device 3 calculates the number of branch points 38 and the branch length of the line image data 35 that exist in large numbers in the line conversion image data 36. Data processing to calculate is performed. 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 (extended length) from the branch point 38 to the branch end 37 .

As a technique for calculating the branch length, various known techniques for calculating the line segment length can be used. As a representative method, a method of calculating from the number of pixels obtained by connecting the centers of a plurality of pixels constituting from the branch point 38 to the branch end 37, or a method of calculating from an approximation function of the line segment from the branch point 38 to the branch end 37 and the like. The branch length is preferably the line segment length (extended length) from the branch point 38 to the branch end 37, but for the sake of simplification of arithmetic processing, the branch length between the branch point 38 and the branch end 37 is can also be the linear distance of .

In the step of evaluating the cross-linking state (S180), data processing for evaluating the cross-linking state based on the number of branch points 38 and the branch length is executed by the arithmetic unit 3. FIG. Examples of indices for evaluating the state of cross-linking include cross-linking density and uniformity of cross-linking (cross-linking density distribution). The crosslink density increases as the number of crosslink points increases, and decreases as the mesh size increases. The uniformity of cross-linking decreases with increasing variation in network size. The physical properties of crosslinked rubber are affected by the degree of crosslink density and crosslink uniformity. The number of branch points 38 can be considered to correspond to the number of cross-linking points and the branch length to the mesh size. Therefore, it is possible to evaluate the crosslinked state (crosslinking density and uniformity of crosslinking) of the crosslinked rubber based on the calculated number of branch points 38 and branch length.

Specifically, in order to evaluate the crosslink density, the median value of the branch lengths in the line image data 35 existing in large numbers in the line conversion image data 36 is calculated based on the number of branch points 38 and branch lengths. and use the median value as the evaluation index. Instead of the median branch length, the average branch length may be used as an evaluation index. However, the median value can accurately grasp the crosslinked state.

Further, in order to evaluate the uniformity of cross-linking, a dispersion index representing the dispersion state of branch lengths is calculated based on the number of branch points 38 and branch lengths, and the dispersion index is used as an evaluation index. This dispersion index uses so-called arithmetic dispersion, standard deviation, or the like. The crosslinked state may be evaluated using both the crosslink density evaluation index and the crosslink uniformity evaluation index described above.

Cross-linking of cross-linked rubber includes chemical cross-linking and physical cross-linking. Physical cross-linking is bonding by weak physical force other than covalent bond. The portion between the branch points 38 is often indicated by specifying the entanglement of the molecular chains due to this physical cross-linking. Therefore, it is not necessary to positively use the length of the adjacent branch points 38 as an evaluation index of the crosslinked state, but it can be used as necessary.

The number of branch points 38 and the branch length may be calculated under the same standardized conditions (the image data 30 to be used have the same magnification, size, and number of sheets). By setting the same conditions in this way, it is possible to simply compare the cross-linked states of various test pieces. When the magnification, size, and number of image data 30 to be used differ depending on the test piece, the number of branch points 38 per unit area and the branch length are calculated to evaluate the crosslinked state.

In addition, when the relationship between the crosslink density evaluation index (median branch length) described above and the crosslink density ascertained by a known measurement method (equilibrium swelling method or swelling compression method) was examined, it was found that both were high. A correlation was found. Therefore, it is preferable to obtain this correlation in advance using a large number of test pieces. Then, using the crosslinked rubber test piece to be evaluated, the number of branch points 38 and the branch length are calculated according to the procedure described above. The crosslink density of the crosslinked rubber to be evaluated can be estimated by using the median value of the branch lengths calculated from the number of branch points 38 and the branch lengths, and the above-described previously grasped correlation.

As described above, according to the present embodiment, attention is focused on the dark portion 31 in which the molecular chains forming the network and the cross-linking points are present in the image captured in the image data 30 obtained using the transmission electron microscope 2. It is possible to understand the difference in the length of the molecular chains that make up the network and the complicated shape of the network. Specifically, the number of branch points and the branch length obtained from the line image data 35 represent the bond between the molecular chains forming the network structure and the size of the network. It is an index that shows Therefore, it is advantageous to grasp the crosslinked state more faithfully to the actual network structure, and it is possible to improve the evaluation accuracy of the crosslinked state of the crosslinked rubber by the method using the image data 30 .

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

The image data 30 is not limited to two-dimensional image data, and may be three-dimensional image data. When three-dimensional image data is used as the image data 30, the methods and filters exemplified above need only be compatible with three-dimensional data. Since the three-dimensional image data has a larger amount of data than the two-dimensional image data, the evaluation accuracy of the crosslinked state is improved. On the other hand, it is necessary to use a complicated technique to acquire three-dimensional image data, which takes more time than acquiring two-dimensional image data. Further, data processing of three-dimensional image data requires a larger amount of calculation than data processing of two-dimensional image data. That is, by using two-dimensional image data as the image data 30, the evaluation method becomes simpler.

The evaluation program 10 may have one of the exemplified methods and filters, but may have a plurality of methods and filters. When a plurality of methods and filters are provided, it is preferable to configure the evaluation program 10 so that a desired method or filter can be selected from the plurality of methods and filters by initial setting when the evaluation program 10 is activated. Similarly, the evaluation index calculated based on the number of branch points 38 and the branch length and the content of evaluation of the crosslinked state in accordance with the evaluation index may be selectable by initial setting when the evaluation program 10 is activated.

As the evaluation index calculated based on the number of branch points 38 and the branch lengths, at least one of the median value (average value) of the branch lengths and the dispersion index representing the distributed state of the branch lengths can be used. If both of them are used as evaluation indices, it becomes a quantitative evaluation considering both the crosslink density and crosslink uniformity of the crosslinked rubber.

In the above evaluation method, the crosslinked state may be evaluated using only one ultrathin section 20, but a plurality of ultrathin sections 20 are prepared from one test piece, and each ultrathin section 20 It is desirable to evaluate the cross-linking state based on the total number of branch points 38 and branch lengths in all the image data 30 obtained. Using a plurality of pieces of image data 30 in this manner is advantageous in reducing variations in data on the number of branch points 38 and branch lengths due to differences in sampling positions on the test piece of the ultra-thin section 20. become. When the ultra-thin section 20 is taken, the magnification of the transmission electron microscope 2 is set to a lower magnification than when the image data 30 is acquired, and a representative crosslinked state (a more uniform crosslinked state) of the specimen is exhibited. It is recommended to identify the location where the

As the crosslinked rubber to be evaluated, a test piece of crosslinked rubber obtained by vulcanizing butadiene rubber with sulfur was used. Swelled with methyl methacrylate for 48 hours. 1 wt/% benzoyl peroxide was added to the test piece, which was allowed to stand at room temperature for 3 hours and then heated at 60°C for 24 hours. Then, five ultra-thin sections 20 of 70 nm were made from the specimen using an ultramicrotome, and stained with osmium tetroxide. Next, the transmission electron microscope 2 is set to an acceleration voltage of 200 [kV] and a magnification of 60,000 times, and the ultrathin section 20 is photographed, and the image data 30 of each ultrathin section 20 is acquired, and a total of 5 image data 30 was acquired.

The arithmetic unit 3 applied Gaussian filter processing to each image data 30 to remove noise, and applied shading correction to correct luminance unevenness. Then, the image data 30 is segmented into separated image data 33 by Otsu's binarization. Next, the separated image data 33 was subjected to contour approximation processing and converted into leveled image data 34 . Then, the leveled image data 34 was subjected to thinning processing by the Zhang-Suen algorithm, and converted into line-converted image data 36 having a plurality of 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, a branch point 38 in the line image data 35 is extracted by template matching to the line conversion image data 36 . Next, the number of branch points 38 and branch lengths in the line image data 35 were calculated from the line conversion image data 36 .

A graph showing branch lengths and their frequencies illustrated in FIG. If the median branch length is calculated from the data in FIG. 8, it can be used as an evaluation index for evaluating the crosslinked state.

FIG. 9 is prepared based on the median value of the branch length obtained by preparing a plurality of different crosslinked rubbers in the same manner as in the example, and the crosslink density determined by a known measurement method (equilibrium swelling method). It is what was done. The black dots in the figure are plotted at corresponding positions of the crosslink density and the median value of the branch length of each prepared crosslinked rubber. A straight line approximating the plotted group of black dots indicates the correlation between the median branch length and the crosslink density. As shown in FIG. 9, it can be seen that there is a high correlation between the crosslink density and the median value of the branch length as determined by a known measurement method.

1 Evaluation System 2 Transmission Electron Microscope 3 Arithmetic Device 10 Evaluation Program 20 Ultrathin Section 30 Image Data 31 Dark Section 32 Bright Section 35 Line Image Data 37 Branch End 38 Branch Point

Claims (6)

Image data of an ultra-thin section of the crosslinked rubber in which the swollen network structure is fixed is acquired by a transmission electron microscope, and the method for evaluating the crosslinked state of the crosslinked rubber using the acquired image data includes:
Acquiring line image data in which the molecular chains forming the network structure in the image data are specified and displayed by processing the image data by an arithmetic device, and branching the line image data at the molecular chains A method for evaluating the crosslinked state of a crosslinked rubber, comprising calculating a point and a branch length from the branch point to the end of the branch, and evaluating the crosslinked state based on the calculated number of branch points and the branch length. . The crosslinked state of the crosslinked rubber according to claim 1, wherein the median value of the branch length is calculated based on the calculated number of branch points and the branch length, and the median value is used as an index for evaluating the crosslinked state. evaluation method. 3. The method according to claim 1, wherein, based on the calculated number of branch points and the calculated branch lengths, a dispersion index representing the dispersion state of the branch lengths is calculated, and the dispersion index is used as an index for evaluating the crosslinked state. A method for evaluating the crosslinked state of the crosslinked rubber. A correlation between the calculated number of branch points and the branch length and the cross-linking density of the cross-linked rubber is obtained, and the number of branch points and the branch length calculated using the cross-linked rubber to be evaluated, The method for evaluating the crosslinked state of the crosslinked rubber according to any one of claims 1 to 3, wherein the crosslinked density of the crosslinked rubber to be evaluated is estimated using the previously grasped correlation. A transmission electron microscope that acquires image data of an ultra-thin section of crosslinked rubber in which the swollen network structure is fixed, and an operation that evaluates the crosslinked state of the crosslinked rubber using the image data acquired by this transmission electron microscope. In a system for evaluating the crosslinked state of a crosslinked rubber comprising a device,
The arithmetic unit performs data processing for acquiring line image data in which the molecular chains forming the network structure in the image data are specified and displayed, branch points in the molecular chains of the line image data, and branch points at the branch points. A crosslinked rubber characterized by performing data processing for calculating the branch length from to the branch end and data processing for evaluating the crosslinked state based on the calculated number of branch points and the branch length. Evaluation system for cross-linking state. A program for evaluating the cross-linked state of cross-linked rubber, which evaluates the cross-linked state of cross-linked rubber in an arithmetic device to which image data of an ultra-thin section of cross-linked rubber with a swollen network structure fixed obtained by a transmission electron microscope is input. in
a procedure for causing the arithmetic unit to acquire line image data in which the molecular chains forming the network structure in the image data are identified and displayed; branching points in the molecular chains of the line image data; A crosslinked state of a crosslinked rubber characterized by executing a procedure of calculating a branch length to a branch end and a procedure of evaluating a crosslinked state based on the calculated number of branch points and the branch length. evaluation program.

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