JP2023178856A - Spatial distribution evaluation method, spatial distribution evaluation device, spatial distribution evaluation system, and spatial distribution evaluation program - Google Patents

Abstract

To provide a spatial distribution evaluation method, a spatial distribution evaluation device, a spatial distribution evaluation system, and a spatial distribution evaluation program that appropriately evaluate a distribution of filler particles.SOLUTION: The spatial distribution evaluation method includes the steps of: generating spectral data for each element contained in a sample containing filler in a resin; obtaining from the spectral data intensity of a predetermined characteristic element contained in the filler; and evaluating a distribution of the filler in the sample on the basis of the intensity of the characteristic element.SELECTED DRAWING: Figure 13

Description

Application for application of Article 30, Paragraph 2 of the Patent Act Publication date: March 1, 2020 Proceedings of the 2020 National Conference of the Institute of Electrical Engineers of Japan, page 46 Date: March 21, 2020 (Duration: Reiwa (March 21-23, 2020) 2020 National Conference of the Institute of Electrical Engineers of Japan held online

The present invention relates to a spatial distribution evaluation method, a spatial distribution evaluation device, a spatial distribution evaluation system, and a spatial distribution evaluation program.

Nanocomposite insulation materials improve various material properties such as electrical properties and thermal properties by mixing fillers, which are inorganic particles such as alumina, titania, and silica, into resin materials such as epoxy resins. Development has been underway for this purpose. Fillers are also called filler particles. In particular, in recent years, various resins have been developed to achieve desired material properties by precisely controlling the filler particle size and mixing both nanometer-order nanofillers and micrometer-order microfillers. A combination of filler and filler is being considered.

Furthermore, in recent years, functionally graded materials whose properties gradually change within a single bulk sample have been developed by applying centrifugal separators and variable casting technology to precisely gradient the distribution of fillers in resin materials. (FGM: Functionally Graded Materials) has also been developed.

In any of the above nanocomposite insulating materials, in order to obtain desired properties and improve functionality, it is important to control the distribution of the filler, and there is a strong need for a method to appropriately evaluate the distribution. For example, if filler aggregation occurs in the resin, the properties may deteriorate.

Conventionally, a scanning electron microscope (SEM) has been mainly used to evaluate the distribution of fillers in nanocomposite insulating materials. Evaluation using SEM makes it possible to simultaneously understand the local distribution and dispersion of filler in the material.

Furthermore, as a method for evaluating composite materials, a technique has been proposed in which the surface of the composite material is irradiated with an electron beam, conductive fillers are identified based on the amount of secondary electrons generated, and the state of distribution thereof is evaluated.

Japanese Patent Application Publication No. 2013-156151

However, in order to perform SEM observation, it is necessary to cut the sample into an appropriate size, embed it in resin if necessary, polish the observed cross section, and conductive treatment using a vacuum evaporation device. Various treatments are required. Therefore, it is difficult to evaluate the distribution of filler by observing with SEM for a bulk sample having a large size and complicated shape like an actual product.

Further, the technique of evaluating the distribution state of fillers by irradiating a composite material with an electron beam targets conductive fillers, and it is difficult to evaluate the distribution of fillers other than conductive fillers.

The disclosed technology has been made in view of the above, and aims to provide a spatial distribution evaluation method, a spatial distribution evaluation device, a spatial distribution evaluation system, and a spatial distribution evaluation program that appropriately evaluate the distribution of filler. do.

In one aspect of the spatial distribution evaluation method, spatial distribution evaluation apparatus, spatial distribution evaluation system, and spatial distribution evaluation program disclosed in the present application, spectral data regarding each element contained in a sample containing a filler in the resin is generated, The intensity of a predetermined characteristic element contained in the filler is obtained from the spectrum data, and the distribution of the filler in the sample is evaluated based on the intensity of the characteristic element.

In one aspect, the present invention can appropriately evaluate filler distribution.

FIG. 1 is a diagram showing a configuration example of a spatial distribution evaluation system according to an embodiment. FIG. 2 is a diagram showing a sample piece and a measurement area. FIG. 3 is a block diagram of the spatial distribution evaluation device. FIG. 4 is a plan view showing an overview of line-shaped irradiation marks on the surface of a sample piece. FIG. 5 is a cross-sectional view schematically showing a cross section of an irradiation mark on a sample piece. FIG. 6 is a diagram showing an example of spectrum data. FIG. 7 is a diagram showing an example of the correspondence between each element and the peak wavelength. FIG. 8 is a diagram showing the correlation between the number of laser irradiations and the emission intensity regarding the Al peak. FIG. 9 is a diagram showing the relationship between Al peak and Ti peak. FIG. 10 is a diagram showing the correlation between the number of laser irradiations and the Al/Ti ratio. FIG. 11 is a diagram showing the Al/Ti ratio and the estimated Al ₂ O ₃ content in each measurement area. FIG. 12 is a diagram showing the analysis results by the XRF device. FIG. 13 is a flowchart of filler distribution evaluation processing performed by the spatial distribution evaluation system according to the embodiment. FIG. 14 is a diagram showing an example of the hardware configuration of the spatial distribution evaluation device.

Embodiments of the spatial distribution evaluation method, spatial distribution evaluation device, spatial distribution evaluation system, and spatial distribution evaluation program disclosed in the present application will be described in detail below based on the drawings. Note that the following examples do not limit the spatial distribution evaluation method, spatial distribution evaluation apparatus, spatial distribution evaluation system, and spatial distribution evaluation program disclosed in the present application.

FIG. 1 is a diagram showing a configuration example of a spatial distribution evaluation system according to an embodiment. The spatial distribution evaluation system 1 evaluates the distribution of filler particles contained in a sample using laser induced breakdown spectroscopy (LIBS). LIBS is an analytical technique used to determine the elemental composition of samples.

The spatial distribution evaluation system 1 according to this embodiment includes a spatial distribution evaluation device 10, a laser light source 11, mirrors 12 and 13, a condenser lens 14, an off-axis parabolic mirror 15, a container with ventilation device 16, and a biaxial stage 17. , an optical fiber 18, a spectrometer 19, and a detector 20.

Further, the sample piece 200 is a nanocomposite insulating material used as a target for measuring the distribution of filler particles. FIG. 2 is a diagram showing a sample piece and a measurement area. The sample piece 200 has, for example, an epoxy resin as a base material, contains SiO ₂ and SrTiO ₃ as microfillers with a primary particle size of several tens of μm, and contains Al ₂ O as a nanofiller with a primary particle size of several tens of mm. Contains ₃ . Further, the size of the sample piece 200 is 140 mm x 140 mm x 8 mm thick. In this example, a case will be described in which spatial distribution evaluation is performed for nanofiller Al ₂ O ₃ .

In the sample piece 200, each of the eight regions on the front and back sides of the portions indicated by measurement areas 201 to 204 is used as a measurement region. Hereinafter, the front and back areas of the portions indicated by measurement areas 201 to 204 will be simply referred to as measurement areas 201 to 204, respectively. Further, the comparison areas 211 and 212 in the sample piece 200 are regions of a cross section of the sample piece 200, and serve as comparison points for verifying the influence of measurement points.

The laser light source 11 emits solid-state laser light excited by a semiconductor laser according to the control from the spatial distribution evaluation device 10 . The semiconductor laser excitation solid-state laser beam has, for example, a wavelength of 1064 mm, a frequency of 20 Hz, and an energy of 20 mJ. Hereinafter, the semiconductor laser-excited solid-state laser light will be simply referred to as "laser light." The laser light source 11 is controlled to irradiate the laser light 500 times to each of the measurement areas 201 to 204 in the sample piece 200 shown in FIG. 2, for example. The irradiated laser beam is reflected by mirrors 12 and 13, respectively, and enters condenser lens 14.

As the condenser lens 14, for example, a plano-convex lens with a focal length of 350 mm can be used. The condensing lens 14 condenses the laser light incident through the mirror 13 and irradiates it onto the surface of the sample piece 200 .

A sample piece 200 is placed on the biaxial stage 17 . The biaxial stage 17 is placed inside the ventilated container 16 with the sample piece 200 installed thereon. Then, the two-axis stage 17 moves the sample piece 200 in one direction at a speed of 2 mm/sec. Thereby, the biaxial stage 17 can move the irradiation position of the laser beam directed toward one point on the sample piece 200 in a line shape on the surface of the sample piece 200. Further, the biaxial stage 17 moves the sample piece 200 in a direction different from the one direction in which the sample piece 200 was moved, and then moves the sample piece 200 in one direction to move it to another line on the surface of the sample piece 200. The irradiation position of the laser beam can be moved along the line.

For example, when the two-axis stage 17 acquires 500 pieces of spectral data in each measurement area 201 to 204, five lines each containing 100 laser beam irradiation points are formed for each measurement area 201 to 204. The sample piece 200 is moved as follows.

The surface of the sample piece 200 is slightly scraped by laser beam irradiation. As a result, on the surface of the sample piece 200, the atoms present in the sample piece 200 that are in an electronically excited state are ionized and plasma is generated. When the atoms and ions in the generated plasma return to their ground state, they emit a wavelength of light unique to the element. Hereinafter, the emission of light from each element upon returning from the plasma to the ground state will be referred to as "light emission from the plasma."

The off-axis parabolic mirror 15 has a through hole. The off-axis parabolic mirror 15 has, for example, a focal length of 152.4 mm. Light emitted from plasma in the sample piece 200 is incident on the off-axis parabolic mirror 15 . The off-axis parabolic mirror 15 forms an image of the incident light on the end surface of the optical fiber 18.

For example, a bundle fiber can be used as the optical fiber 18. Optical fiber 18 is connected to spectrometer 19 . The optical fiber 18 receives at its end face an input of light in which plasma emission is imaged. The optical fiber 18 then transfers the input light to the spectrometer 19.

The spectrometer 19 is a Czerny-Turner type spectrometer. The spectrometer 19 has, for example, a gate width of 10 μs, a delay time of 0.1 μs, and a grating number of 2400 lines. The spectrometer 19 receives light input from the optical fiber 18 due to light emission from the plasma. Then, the spectroscope 19 spectrally separates the input light into wavelengths and outputs them to the detector 20 .

The detector 20 receives input of light separated by the spectrometer 19 . The detector 20 then detects the light for each wavelength and generates spectrum data. Thereafter, the detector 20 outputs the generated spectrum data to the spatial distribution evaluation device 10.

The spatial distribution evaluation device 10 calculates the ratio of the light intensity at the peak of a characteristic element contained in the filler and the light intensity at the peak of a comparison element other than the element contained in the filler from the spectrum data. Then, the spatial distribution evaluation device 10 calculates the content of the characteristic element, that is, the filler concentration, for each measurement area 201 to 204 using the obtained ratio, and evaluates the distribution of filler particles in each measurement area 201 to 204. do. The details of the spatial distribution evaluation device 10 will be explained below.

FIG. 3 is a block diagram of the spatial distribution evaluation device. As shown in FIG. 3, the spatial distribution evaluation device 10 includes an irradiation control section 101, a spectrum data reception section 102, a concentration calculation section 103, and a spatial distribution evaluation section 104. Further, the spatial distribution evaluation device 10 is connected to an input device 111 such as a keyboard and a mouse, and a display device 112 such as a monitor.

The irradiation control unit 101 controls the laser light source 11 to control the timing at which the sample piece 200 is irradiated with laser light. For example, when the sample piece 200 is moved by the two-axis stage 17 to a position where the measurement area 201 is irradiated with laser light, the irradiation control unit 101 controls the irradiation of the laser light from the laser light source 11. start. Then, the irradiation control unit 101 causes the laser light source 11 to irradiate laser light so that one line is irradiated 100 times to form five lines in accordance with the movement of the sample piece 200 by the two-axis stage 17. The irradiation control unit 101 similarly causes the laser light source 11 to irradiate the measurement areas 202 to 205 with laser light.

Here, irradiation of the sample piece 200 with laser light will be explained in detail. FIG. 4 is a plan view showing an overview of line-shaped irradiation marks on the surface of a sample piece. Moreover, FIG. 5 is a cross-sectional view showing an outline of the cross-section of the irradiation mark on the sample piece.

For example, in FIG. 4, the sample piece 200 is moved in the direction of arrow P by the biaxial stage 17. As a result of this movement, lines are formed on the surface of the sample piece 200 by laser beam irradiation.

The surface of the sample piece 200 is scraped by laser light irradiation. In this example, in order to align the shape of the irradiated surface on the sample piece 200, one line is scanned with laser light twice before measurement, and then the spectrum obtained by the third scan with laser light is used for analysis. Using. In FIG. 5, a region 231 is the portion that has been scraped by the first laser beam scan. Further, a region 232 is a portion etched by the second laser beam scan. The area 233 is the area removed by the third scan, and is the area to be analyzed and evaluated. The spot diameter 220 of the laser beam in the third scan is 250 μm. That is, the width W of this region 233 is approximately 250 μm. Further, the depth from the surface of the sample piece 200 before being scraped to the bottom surface of the region 233 is about 20 μm.

In FIG. 4, the spot diameter 220 of the laser beam in the region 233 corresponds to the width W of the region 233, which is about 250 μm. Further, since the moving speed of the sample piece 200 is 2 mm/S and the repetition frequency of laser light irradiation is 20 Hz, each point is irradiated with laser light about twice. In FIG. 4, a region 221 is a depleted portion for one irradiation, and a region 222 is a depleted portion for two irradiations.

The spectral data receiving unit 102 receives from the detector 20 spectral data of light emitted from plasma generated by irradiating the surface of the sample piece 200 with laser light. The spectrum data receiving unit 102 acquires 500 pieces of spectrum data for each of the measurement areas 201 to 204. Then, the spectral data receiving section 102 outputs 500 pieces of spectral data for each of the measurement areas 201 to 204 to the concentration calculating section 103.

FIG. 6 is a diagram showing an example of spectrum data. In FIG. 6, the horizontal axis represents the wavelength of light, and the vertical axis represents the emission intensity. FIG. 6 shows spectrum data of a sample piece 200 whose base material is an epoxy resin, which contains 50 vol% of SiO ₂ and SrTiO ₃ as microfillers, and 1 vol% of Al ₂ O ₃ as a nanofiller. The spectral data receiving section 102 outputs 500 pieces of spectral data as shown in FIG. 6 to the concentration calculating section 103.

The concentration calculating section 103 receives input of 500 pieces of spectral data for each of the measurement areas 201 to 204 from the spectral data receiving section 102. Next, the concentration calculation unit 103 selects a LIBS peak to be used for quantitative evaluation. Here, the concentration calculation unit 103 holds peak wavelength information indicating the correspondence between each element and the emission peak wavelength. FIG. 7 is a diagram showing an example of the correspondence between each element and the emission peak wavelength. For example, in the case of Ti (titanium), the concentration calculation unit 103 calculates that the wavelengths are 261.1, 264.7, 334.2, 398.2, 453.4, 484.1, and 521.0 (nm). In the case of Al (aluminum), peak wavelength information including information that the wavelength has a peak at 309.7, 394.4, and 396.1 (nm) is held. Hereinafter, the Al peak will be referred to as an Al peak, and the Ti peak will be referred to as a Ti peak.

In this example, the nanofiller is Al ₂ O ₃ and Al is not included in the other materials constituting the sample piece 200 . Therefore, by estimating the distribution of Al in the sample piece 200, the distribution of nanofiller particles can be estimated. The actual element to be detected contained in the nanofiller for estimating the distribution of the nanofiller particles to be evaluated in this way is herein referred to as a "characteristic element." Moreover, below, the distribution of nanofillers may be referred to as nanofiller distribution.

The concentration calculation unit 103 detects the emission intensity of a peak derived from Al, which is a characteristic element contained in the nanofiller, in the acquired spectrum data. Here, the concentration calculation unit 103 calculates the emission intensity of the Al peak by using the emission intensity at a wavelength of 396.2 nm as a peak that has a relatively large peak intensity and has little overlap with peaks derived from other elements other than Al. Detected as.

Here, in this embodiment, the concentration calculation unit 103 receives a designation in advance to use a peak at a wavelength of 396.2 nm as an Al peak. However, the concentration calculation unit 103 can also automatically select the peaks used for estimating the distribution of the characteristic elements. For example, the concentration calculation unit 103 uses the peak wavelength information of each element held to determine whether the peak intensity of the material constituting the sample piece 200 has little overlap with peaks originating from other elements other than the characteristic element. It is sufficient to select a relatively large peak. It is also possible to automatically select characteristic elements. For example, the concentration calculation unit 103 selects characteristic elements that have little overlap with peaks derived from other elements among the elements contained in the nanofiller, and which An element having a relatively large peak of luminescence intensity may be selected as the characteristic element.

However, in the spectrum data obtained by measurement, large variations occur in the intensity change of the Al peak for each laser irradiation. FIG. 8 is a diagram showing the correlation between the number of laser irradiations and the emission intensity regarding the Al peak. In FIG. 8, the horizontal axis represents the number of laser irradiations, and the vertical axis represents the emission intensity. As shown in FIG. 8, the emission intensity of the Al peak for each number of laser irradiations may vary greatly in the range of 18,000 to 24,000 counts. Furthermore, since there is no clear difference between the variation in the intensity change of the Al peak in the measurement areas 201 to 204 and the variation in the intensity change of the Al peak in the comparison areas 211 to 212, this variation is due to the measurement location. It can be said that it is not an influence. Therefore, in order to suppress the influence of variations in the number of laser irradiations, the concentration calculation unit 103 calculates the concentration using the ratio of the emission intensity to the peak derived from other elements contained in the sample piece 200. In the following, other elements whose ratios to the characteristic elements are determined will be referred to as "comparison elements."

Here, when calculating the ratio of emission intensities, it is desirable that the energies of the upper order of excitation of the peaks of the characteristic element and the comparative element be as close as possible. This is because the emission intensity has a strong correlation with the energy of the higher order, and when the temperature of the plasma varies, the emission intensity of the peaks of elements whose energies are close to each other in the upper order will vary to the same extent.

In this respect, there are many peaks derived from Ti as peaks close to the Al peak. FIG. 9 is a diagram showing the relationship between Al peak and Ti peak. As shown in FIG. 9, among the Ti peaks, the Ti peak with a wavelength of 398.2 nm has little overlap with other peaks, and is a peak whose upper order energy with respect to excitation of the Al peak is close. Therefore, in this example, Ti is used as a comparison element, and the wavelength of the peak used for detection of the comparison element is set to 398.2 nm.

Here, in this embodiment, the concentration calculation unit 103 receives a notification in advance that the peak at a wavelength of 398.2 nm will be used as the Ti peak. However, it is also possible for the concentration calculation unit 103 to automatically select the comparison element and the peaks used for estimating the distribution of the comparison element. For example, the concentration calculation unit 103 determines an element that has many peaks close to the peak of the characteristic element as a comparison element, and determines an element that has a small amount of overlap with other peaks among the peaks of the comparison element and also It is sufficient to select peaks having similar upper-order energies regarding excitation of the peaks.

The concentration calculation unit 103 calculates the Al/Ti ratio, which is the ratio of the emission intensity using the Ti peak at a wavelength of 398.2 nm and the Al peak at 396.3 nm. FIG. 10 is a diagram showing the correlation between the number of laser irradiations and the Al/Ti ratio. As shown in FIG. 10, by using the Al/Ti ratio, variations in values due to the number of laser irradiations are reduced.

Furthermore, when a histogram was created from the Al/Ti ratio calculated from the spectral data of the measurement areas 201 to 204 and the comparison areas 211 to 212, a shape roughly following a Gaussian distribution was obtained for each. Since the histogram of the Al/Ti ratio in each area follows a Gaussian distribution, it can be said that the average of the Al/Ti ratios can be treated as a representative value of the Al/Ti ratios. Therefore, when the average Al/Ti ratio was calculated and compared, no tendency such as an increase in the Al/Ti ratio in any of the measurement areas 201 to 204 and the comparison areas 211 to 212 was confirmed. Therefore, the variation in the average value is considered to represent the difference in the distribution state of Al ₂ O ₃ , which is the nanofiller, in the measurement areas 201 to 204.

The concentration calculation unit 103 calculates the Al/Ti ratio for all of the spectral data. Next, the concentration calculation unit 103 calculates the average Al/Ti ratio for each of the measurement areas 201 to 204. Here, in the case of light emission from plasma, the light emission intensity increases as more elements are included. That is, the emission intensity in this case can be considered as the distribution of the element per predetermined region, that is, the content rate. Furthermore, since the content rate is a concentration, the concentration calculation unit 103 can calculate the concentration from the emission intensity of the characteristic element.

Therefore, the concentration calculation unit 103 calculates the Al distribution obtained from the Al/Ti ratio for each of the measurement areas 201 to 204 as the distribution of Al ₂ O ₃ , which is a nanofiller, in each of the measurement areas 201 to 204, that is, the content rate. . FIG. 11 is a diagram showing the Al/Ti ratio and the estimated Al ₂ O ₃ content in each measurement area. The concentration calculation unit 103 calculates the Al/Ti ratio shown in FIG. 11 for each of the eight measurement areas 201 to 204, for example.

Next, the concentration calculation unit 103 calculates the total average of the Al/Ti ratio obtained from all the spectral data, and sets it as the reference value of the Al/Ti ratio when 1 vol% of Al ₂ O ₃ nanofiller is filled. do. Here, in this embodiment, the concentration calculation unit 103 uses the total average of the Al/Ti ratios in the measurement areas 201 to 204 of the sample piece 200 as the reference value of the Al/Ti ratio, but the reference value is not limited to this. . For example, if a standard sample is available, the concentration calculation unit 103 may use a reference value of the Al/Ti ratio determined based on a calibration curve using the standard sample. Furthermore, if past statistical information is available, the concentration calculation unit 103 may use the statistical information to calculate the reference value of the Al/Ti ratio.

Next, the concentration calculation unit 103 calculates the deviation between the average value of the Al/Ti ratio obtained in each of the measurement areas 201 to 204 and the reference value of the Al/Ti ratio. For example, the concentration calculation unit 103 calculates the value expressed as the estimated Al ₂ O ₃ content rate in FIG. 11 as the deviation of each of the measurement areas 201 to 204. Then, the concentration calculation unit 103 evaluates the Al ₂ O ₃ nanofiller distribution in each measurement area 201 to 204 using the calculated deviation. For example, if there is almost no bias in the deviation in each of the measurement areas 201 to 204, the concentration calculation unit 103 determines that the nanofiller is evenly distributed in each of the measurement areas 201 to 204 and the variation is small. Furthermore, if there is an area among the measurement areas 201 to 204 in which the deviation is significantly different from other areas, the concentration calculation unit 103 determines that the distribution of that area is different from the distribution of other areas and that the variation is large. Thereafter, the concentration calculation unit 103 notifies the user of the evaluation result by transmitting the evaluation result to the display device 112 and displaying it.

In this example, the concentration calculation unit 103 calculated the estimated Al ₂ O ₃ content of each of the measurement areas 201 to 204 as 0.96 to 1.04 vol%, as shown in FIG. Based on this result, the concentration calculation unit 103 estimates that the Al ₂ O ₃ nanofiller particles in the sample piece 200 have small distribution variations and are uniform in spatial distribution on the scale of the sample piece 200 . In this embodiment, the scale of the sample piece 200 ranges from the mm scale, which is the interval between lines used for measurement, to the cm scale of the entire sample piece 200.

In addition, in the evaluation of the nanofiller distribution using LIBS by the spatial distribution evaluation apparatus 10 according to the present example, the presence or absence of aggregation of the nanofiller, which is a collection of primary particles of several tens of nanometers, that is, the dispersibility of the nanofiller, It is difficult to evaluate from the perspective of spatial resolution. The evaluation of the dispersibility of the nanofiller is preferably carried out by existing SEM observation or the like.

Here, in order to verify the validity of the evaluation of the nanofiller distribution in the nanocomposite insulating material by the spatial distribution evaluation device 10 according to the present example, the same sample piece 200 was measured using an XRF (X-Ray Fluorescence) device. Analysis was carried out. The information obtained by XRF is the wt% of each detected element. Therefore, in this case as well, the Al/Ti mass ratio was calculated, and the Al ₂ O ₃ content was calculated from the deviation from the average value.

FIG. 12 is a diagram showing the analysis results by the XRF device. In measurements using an XRF device, the Al/Ti mass ratio is estimated to be 0.198 to 0.230, as shown in Figure 12, and the deviation from the average value calculated from the results of all 8 locations is within ±10%. It became. Since X-rays have characteristics that transmittance differs depending on the density of the object to be measured, and XRF has a detection depth that changes depending on the amount of filler particles, it is difficult to use it for quantitative evaluation of nanofiller distribution. . However, even if it is assumed that there are variations in detection depth for each measurement location, the range of variation in the Al/Ti mass ratio is large, but in general, the nanofiller distribution by the spatial distribution evaluation device 10 according to this embodiment is Results corresponding to the evaluation results were obtained. From this, it is considered that the evaluation results of the spatial distribution evaluation device 10 according to the present example are valid.

FIG. 13 is a flowchart of filler distribution evaluation processing by the spatial distribution evaluation system according to the embodiment. Next, with reference to FIG. 13, the flow of filler distribution evaluation processing performed by the spatial distribution evaluation system 1 will be described.

The laser light source 11 irradiates laser light under control from the irradiation control unit 101 of the spatial distribution evaluation device 10 (step S1).

The laser irradiated from the laser light source 11 enters the condenser lens 14 via the mirrors 12 and 13, is condensed, and is irradiated onto the surface of the sample piece 200. When the surface of the sample piece 200 is irradiated with a laser, atoms present in the sample piece 200 are ionized and plasma is generated. Then, when the atoms and ions of the generated plasma return to the ground state, they emit a wavelength of light unique to the element, thereby generating light emission from the plasma (step S2).

Light generated by light emission from the plasma passes through the off-axis parabolic mirror 15 and forms an image on the end face of the optical fiber 18 . The optical fiber 18 transfers the light input from the off-axis parabolic mirror 15 to the spectrometer 19 . The spectrometer 19 separates the light input from the optical fiber 18 into wavelengths and outputs the separated light to the detector 20 (step S3).

The detector 20 receives input of light separated by the spectrometer 19 . The detector 20 then detects the light for each wavelength and generates spectrum data (step S4). Thereafter, the detector 20 outputs the generated spectrum data to the spatial distribution evaluation device 10.

The irradiation control unit 101 of the spatial distribution evaluation device 10 determines whether scanning of all lines in all measurement areas 201 to 204 has been completed (step S5). If there are still scans to be executed (step S5: negative), the irradiation control unit 101 controls the laser light source 11 to perform the next scan. The evaluation process then returns to step S1.

On the other hand, if all scanning is completed (step S5: affirmative), the spectral data receiving unit 102 of the spatial distribution evaluation device 10 receives the spectral data from the detector 20 and outputs it to the concentration calculating unit 103. . The concentration calculation unit 103 acquires the emission intensity of the peak having a predetermined wavelength of the characteristic element contained in the filler to be evaluated from the acquired spectrum data (step S6).

Next, the concentration calculation unit 103 acquires the emission intensity of a peak having a specific wavelength of a predetermined comparison element other than the element included in the filler to be evaluated from the acquired spectrum data (step S7).

Next, the concentration calculation unit 103 calculates the ratio between the peak emission intensity of the characteristic element and the peak emission intensity of the comparison element (step S8).

Next, the concentration calculation unit 103 calculates the average of all calculated ratios to calculate a ratio reference value (step S9).

Next, the concentration calculation unit 103 calculates the average of the ratios for each of the measurement areas 201 to 204 (step S10).

Next, the concentration calculating unit 103 calculates the estimated filler content for each of the measurement areas 201 to 204 from the deviation between the reference value of the ratio and the average ratio of each of the measurement areas 201 to 204 (step S11). Thereafter, the concentration calculation unit 103 outputs information on the calculated estimated filler content for each of the measurement areas 201 to 204 to the spatial distribution evaluation unit 104.

The spatial distribution evaluation unit 104 receives input from the concentration calculation unit 103 of information on the estimated filler content for each of the measurement areas 201 to 204. Then, the spatial distribution evaluation unit 104 evaluates the distribution of filler particles in each of the measurement areas 201 to 204 by comparing the estimated filler content in each of the measurement areas 201 to 204 (step S12).

After that, the spatial distribution evaluation unit 104 notifies the user of the evaluation result by transmitting the evaluation result to the display device 112 and displaying it (step S13).

As explained above, the spatial distribution evaluation system according to the present example acquires spectrum data in each measurement area using LIBS. Then, the spatial distribution evaluation system calculates the ratio of the light intensity at the peak of the characteristic element contained in the filler and the light intensity at the peak of a comparison element other than the element contained in the filler from the spectral data, and uses the calculated ratio to calculate the measurement area. The content of each characteristic element, that is, the concentration of the filler, is calculated to evaluate the distribution of filler particles in each measurement area.

This makes it possible to evaluate the spatial distribution by considering the total amount of nanofiller particles contained in a certain volume as the concentration. In addition, there is no need to perform various treatments such as cutting the sample into an appropriate size, embedding it in resin as necessary, polishing the observed cross section, and making it conductive using a vacuum evaporation device. Therefore, it is possible to easily evaluate the spatial distribution of filler particles. In addition, distribution can be easily evaluated even for samples with large or complex shapes. Furthermore, by using the ratio of the two elements, it is possible to reduce fluctuations in light intensity for each measurement, making it possible to perform more accurate spatial distribution evaluation. Furthermore, by using the average value of the light intensity obtained from the measured spectrum data as the reference value for the distribution, and evaluating the distribution using the deviation from the reference value, the distribution can be evaluated even for fillers whose content rate is unknown. It becomes possible to do so.

Here, in this example, the dispersion was evaluated using the ratio of the emission intensity corresponding to the peak height of the characteristic element and the comparison element, but the concentration analyzer can also evaluate the height of the peak derived from the characteristic element. Alternatively, the dispersion may be evaluated using the area of the peak, which is the integrated value of the emission intensity at the peak, and the area ratio with the reference peak. In addition, although this example describes the case where nanofiller distribution is evaluated, the spatial distribution evaluation system according to this example can be used to evaluate dispersants (e.g., glass fibers) in microfillers and other composite materials using a similar method. It is also possible to evaluate the distribution of inclusions that have a different elemental composition from the base material, such as glass in reinforced plastics.

Furthermore, in this example, the spatial distribution was evaluated using LIBS, but it is also possible to obtain the spectrum of each element using other methods and similarly perform the spatial distribution evaluation. For example, X-ray fluorescence spectroscopy (XRF) may be used. In that case, it is possible to obtain a local elemental spectrum to some extent using an XRF microscope or portable XRF, and it is also possible to evaluate the spatial distribution using the method described above from the obtained elemental spectrum. Alternatively, Raman spectroscopy may be used. In that case, it is possible to obtain a chemical bond spectrum, and by treating the height of the chemical bond peak derived from the filler as the peak height derived from the characteristic element, it is also possible to perform spatial distribution evaluation using the method described above. It is. In this case as well, large-scale samples can be measured quickly without pretreatment.

(Hardware configuration)
FIG. 14 is a diagram showing an example of the hardware configuration of the spatial distribution evaluation device. As shown in FIG. 14, the spatial distribution evaluation device 10 includes, for example, a CPU (Central Processing Unit) 91, a memory 92, a hard disk 93, and a network interface 94. The CPU 91 is connected to a memory 92, a hard disk 93, and a network interface 94 via a bus.

The network interface 94 is an interface for communication between the spatial distribution evaluation device 10 and external devices. The network interface 94 relays communication between the CPU 91 and the detector 20, for example.

The hard disk 93 is an auxiliary storage device. The hard disk 93 stores, for example, spectrum data. Further, the hard disk 93 stores various programs including programs for realizing the functions of the irradiation control section 101, the spectrum data reception section 102, the concentration calculation section 103, and the spatial distribution evaluation section 104.

Memory 92 is a main storage device. For example, a DRAM (Dynamic Random Access Memory) can be used as the memory 92.

The CPU 91 reads various programs stored in the hard disk 93, expands them to the memory 92, and executes them. Thereby, the CPU 91 realizes the functions of the spectral data receiving section 102, the concentration calculating section 103, and the spatial distribution evaluating section 104.

1 Spatial distribution evaluation system 10 Spatial distribution evaluation device 11 Laser light source 12, 13 Mirror 14 Condensing lens 15 Off-axis parabolic mirror 16 Container with ventilation device 17 Two-axis stage 18 Optical fiber 19 Spectrometer 20 Detector 101 Irradiation control section 102 Spectrum data receiving section 103 Concentration calculation section 104 Spatial distribution evaluation section 111 Input device 112 Display device 201 to 204 Measurement area 211, 212 Comparison area

Claims (9)

Generates spectral data for each element contained in a sample containing filler in the resin,
Obtaining the intensity of a predetermined characteristic element contained in the filler from the spectral data,
A spatial distribution evaluation method, comprising: evaluating the distribution of the filler in the sample based on the intensity of the characteristic element. Irradiating the sample with a laser,
generating the spectrum data based on light emission from the plasma when the sample is turned into plasma by irradiation with the laser;
obtaining the emission intensity of the characteristic element contained in the filler from the spectral data;
The spatial distribution evaluation method according to claim 1, characterized in that the distribution of the filler in the sample is evaluated based on the intensity of the characteristic element. The spatial distribution evaluation method according to claim 1, characterized in that the distribution of the filler is evaluated by determining the concentration of the filler based on the intensity of the characteristic element. A claim characterized in that, among the peaks of the characteristic element appearing in the spectral data, the intensity of a wavelength having a peak with little overlap with other elements and having a higher peak intensity is taken as the intensity of the characteristic element. 1. The spatial distribution evaluation method described in 1. 2. The spatial distribution evaluation method according to claim 1, further comprising acquiring a reference intensity of the characteristic element and comparing the intensity of the characteristic element with the reference intensity to evaluate the distribution of the filler. Obtaining the intensity of a comparative element contained in the sample other than the element contained in the filler from the spectral data,
The spatial distribution evaluation method according to claim 1, further comprising calculating a ratio between the intensity of the characteristic element and the intensity of the comparison element, and evaluating the distribution of the filler based on the calculated ratio. a spectral data receiving unit that receives spectral data regarding each element contained in a sample containing a filler in the resin;
a concentration calculation unit that obtains the intensity of a predetermined characteristic element contained in the filler from the spectral data;
A spatial distribution evaluation device comprising: a distribution evaluation section that evaluates the distribution of the filler in the sample based on the intensity of the characteristic element acquired by the concentration calculation section. A laser light source that irradiates a sample containing filler in resin with a laser to turn it into plasma;
a spectrometer that spectrally spectra the light emitted from the plasma when the sample is turned into plasma;
a detector that detects the light separated by the spectrometer and generates spectral data;
a spectral data receiving unit that receives spectral data generated by the detector; a concentration calculation unit that obtains the emission intensity of a predetermined characteristic element contained in the filler from the spectral data; A spatial distribution evaluation system comprising: a spatial distribution evaluation device having a spatial distribution evaluation section that evaluates the distribution of the filler in a sample. Generates spectral data for each element contained in a sample containing filler in the resin,
Obtaining the intensity of a predetermined characteristic element contained in the filler from the spectral data,
A spatial distribution evaluation program characterized by causing a computer to execute a process of evaluating the distribution of the filler in the sample based on the intensity of the characteristic element.

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