Method for representing distribution of third component in ternary organic photovoltaic cell

Abstract

The invention relates to a method for representing longitudinal distribution and transverse distribution of a third component in an active layer of a ternary organic photovoltaic cell. For longitudinal distribution characterization, a dynamic X-ray photoelectron spectroscopy (DXPS) technology is adopted, element XPS intensities of different depths are obtained through multiple times of etching-collection, a ratio equation is constructed and converted into an optimal model for solving, the molecular number ratio of a donor to an acceptor to a third component is obtained, and then the relative molecular number (RNM) concept is introduced for analysis. The method can solve the longitudinal characterization problem of the third component which does not contain characteristic elements. For transverse distribution, a Raman Mapping imaging technology is combined with a computer image processing technology, and a multispectral color recognition and processing technology is developed. According to the method, threshold comparison and gray value comparison are performed by extracting gray values of Raman Mapping gray-scale maps of samples, so that distribution of the third components in the XY transverse direction is determined, different phase regions can be marked as different colors by means of color marking and pixel statistics, the distribution of the third components is quantitatively represented, and the detection accuracy is improved. Therefore, the accuracy of characterization of the transverse distribution of the third component is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of solar cells, and more particularly to a method for characterizing the distribution of a third component in a ternary organic photovoltaic cell. Background Art

[0002] Organic photovoltaic cells (OPVs) have attracted widespread attention due to their advantages such as low cost, flexibility, and large-scale fabrication. To address the problem of insufficient light absorption in the active layer of binary OPVs, ternary organic photovoltaic cells (T-OPVs) are prepared by adding a third component with a complementary absorption range to the donor and acceptor materials. This not only broadens the absorption spectrum of the active layer and improves its light absorption capacity, but also offers advantages such as simple preparation process and wide application range.

[0003] To date, the working mechanisms of T-OPVs include charge transfer, energy transfer, parallel structure and alloy mechanism. Although the working processes of different mechanisms are inconsistent, they all have special requirements for the distribution of the third component in the active layer. For example, the charge transfer mechanism requires the third component to be distributed at the donor-acceptor interface to form a better energy level cascade structure to promote charge transfer. The energy transfer mechanism requires the energy donor to be distributed in the phase region of the energy acceptor to improve the energy transfer efficiency (E FRET ). It can be seen that characterizing the distribution of the third component in the active layer is of great significance for studying the structure-activity relationship of the active layer and further realizing high-performance OPVs.

[0004] However, in the current morphological research of T-OPVs, commonly used characterization techniques, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), can provide information on the microstructure of the material, but it is still difficult to accurately detect the precise distribution of the third component at the nanoscale. Although the emergence of dynamic X-ray photoelectron spectroscopy (DXPS) has solved the problem of longitudinal distribution characterization of third components containing characteristic elements to a certain extent, the longitudinal distribution characterization of third components without characteristic elements is still at a stage that requires further exploration. More importantly, current research has not yet achieved the characterization of the lateral distribution of each component in the active layer.

[0005] In summary, the shortcomings of T-OPVs in characterizing the distribution of the third component have seriously restricted its further development and optimization, making it difficult for researchers to deeply understand the relationship between the distribution of the third component and device performance, and unable to specifically improve the OPVs device structure and preparation process. Summary of the Invention

[0006] The object of the present invention is to provide a method for characterizing the longitudinal and lateral distributions of the third component in the active layer of T-OPVs.

[0007] The technical means adopted by the present invention to characterize the longitudinal distribution is DXPS; the technical means to characterize the transverse distribution is Raman Mapping imaging technology.

[0008] The DXPS longitudinal characterization method adopted in the present invention can solve the longitudinal characterization of the third component that does not contain characteristic elements. The technical solution is as follows:

[0009] S1: After several cycles of “etch-collect-etch-collect”, the XPS intensity I of several elements i (i = 1, 2, 3…) in the active layer at different depths is obtained. i ;

[0010] S2: According to the elemental composition of the donor, acceptor and third component, the number of molecules of the donor, acceptor and third component are the unknown numbers x, y and z respectively. i Construct several ratio equations of the following form and form a system of equations:

[0011]

[0012] In the above formula, i and j both represent atom i and atom j, i x ,i y and i z are the number of atoms of element i in the donor, acceptor and third component molecules respectively; j x ,j y and j z are the number of j atoms in the donor, acceptor and third component molecules respectively; S i and S i is the atomic sensitivity coefficient (available for inspection);

[0013] S3: Convert the equations in step S2 into an optimization model; at the same time, to make the solution physically meaningful, set x, y, and z as constraints that are all greater than or equal to 0. For example, the following optimization problem:

[0014]

[0015] S4: Solve the above optimization problem. Since the equations are ratio equations, the number of molecules of the donor, acceptor, and third component obtained is not the actual number of molecules, but the ratio of their molecular numbers. Because x, y, and z are multiplied by the same coefficient, the optimal solution still holds.

[0016] S5: Substitute the molecular ratio of the donor, acceptor and third component obtained in step S4 into the XPS intensity I of any element k k The calculation formula is further transformed:

[0017]

[0018] Where A and B are unknown constants, f is the X-ray flux incident on the sample (constant), and V is the volume (constant);

[0019] S6: Introduce the concept of relative number of molecules (RNM), let,

[0020] RNM(donor)=B*x

[0021] RNM(receptor)=B*y

[0022] RNM (third component) = B*z

[0023] It is important to note that RNM is not the absolute number of molecules of the donor, acceptor, and third component. Instead, it is the relative number of molecules, taking into account the constants f and V. However, since the absolute number of molecules of the donor, acceptor, and third component differs from their RNM values by the same constant factor (B), this does not affect the analysis of the component distribution within the active layer.

[0024] The lateral characterization technology used in this invention combines Raman Mapping imaging technology with computer image processing technology to develop a multispectral color recognition and processing technology. The technical solution is as follows:

[0025] S1: Measure the Raman spectrum of a certain area of the sample and draw the corresponding Raman Mapping based on the Raman Shift of the single-component donor, acceptor and third-component characteristics;

[0026] S2: Export the grayscale image (also called black and white image) on the test software;

[0027] S3: A computer program is used to normalize the grayscale image and extract its grayscale values, with white representing large values and black representing small values. The extracted grayscale values are then normalized. Normalization is performed to facilitate grayscale value comparisons in the next step; normalization allows for comparisons between different images. The grayscale array for the donor Raman mapping is designated GrayD, the acceptor Raman mapping is designated GrayA, and the third component Raman mapping is designated GrayT.

[0028] S4: Define a threshold threshold(th);

[0029] S5: Compare the gray values to determine the component distribution. In the first step, if Gray T is greater than or equal to th, it is a third component-rich region; in the second step, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a mixed phase of the third component and the donor, that is, the third component is distributed in the donor phase; otherwise, it is distributed in the acceptor phase. If Gray T in the first step is less than th, it is a donor phase or an acceptor phase; similarly, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a donor phase; otherwise, it is an acceptor phase.

[0030] S6: For the convenience of intuitive observation and analysis, we can mark "Gray T is greater than or equal to th and Gray D is greater than or equal to Gray A" as color 1, "Gray T is greater than or equal to th and Gray D is less than Gray A" as color 2, "Gray T is less than th and Gray D is greater than or equal to Gray A" as color 3, and "Gray T is less than th and Gray D is less than Gray A" as color 4. Then colors 1-4 represent the third component and donor blend phase, the third component and acceptor blend phase, and the donor phase and acceptor phase, respectively;

[0031] S7: Counting the number of pixels of different colors and their percentages can reflect the percentage of the corresponding phase area.

[0032] In particular, the DXPS test method in the longitudinal characterization S1 and the Raman Mapping test method in the transverse characterization S1 are the same as the currently used methods. The present invention does not require any special settings for the DXPS and Raman Mapping parameters.

[0033] Specifically, S3 to S6 are completed on a computer, and the program flow chart is shown in the attached figure. Figure 1 shown.

[0034] In particular, the optimization model for characterizing the longitudinal distribution of the present invention is applicable to both ternary systems without characteristic elements and ternary systems with characteristic elements.

[0035] Preferably, the range of th mentioned in step S4 of the lateral characterization technical means is 0.5-0.9.

[0036] In particular, the th mentioned in step S4 of the horizontal characterization technique will only affect the pixel count statistics mentioned in step 6 but will not affect the final conclusion, as shown in the attached figure. Figure 2 As shown in the figure, although the th value has changed and the number of pixels in the corresponding phase region where IBC-F is distributed in PM6 or Y6 has also changed, the fact that "there are more IBC-F distributed in PM6 than in Y6" remains unchanged.

[0037] In particular, the present invention has no special requirements on the structure, solution concentration, spin coating speed, solvent type, annealing process, material component content ratio, etc. of the T-OPVs active layer.

[0038] In particular, the longitudinal distribution described in the present invention does not impose any restrictions on the thickness of the active layer, DXPS etching parameters, etc.; the lateral distribution characterization does not impose any restrictions on the size of the scanning area.

[0039] In particular, the color setting of step S6 in the multispectral color recognition and processing technology in the lateral characterization of the present invention can be specifically selected according to the needs of the implementer.

[0040] In particular, the optimization model for solving the longitudinal characterization and the image processing for the transverse characterization mentioned in the present invention are both implemented by computer programs. We implemented this operation using the Mathematica program, and screenshots of the Mathematica program used to solve the optimization model for the longitudinal characterization and the multispectral color recognition and processing technology for the transverse characterization in Example 1 are attached in the accompanying figure.

[0041] The advantages of the present invention are:

[0042] (1) An optimization model was provided to obtain the longitudinal distribution of the third component without characteristic elements in the film, thus improving the DXPS longitudinal distribution analysis method for T-OPVs.

[0043] (2) A lateral characterization technique based on Raman Mapping imaging technology was provided. By combining the Raman Mapping images obtained from the test with computer image processing technology, a multispectral color recognition and processing technology was developed to achieve the lateral characterization of the distribution of the third component in the active layer of T-OPVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flowchart of the multi-spectral color recognition and processing technology program for the lateral characterization of the third component of the present invention.

[0045] Figure 2 This is a relationship diagram between the threshold value th and the number of pixels in step S4 of the multispectral color recognition and processing technology of the present invention.

[0046] Figure 3 DXPS graphs of (a) F element, (b) N element and (c) S element in the longitudinal characterization of Comparative Example 1 and Example 1 of the present invention.

[0047] Figure 4These are the Raman spectra of (a) single-component IBC-F, PM6 and Y6 under 532nm incident light in the lateral characterization of Comparative Example 1 and Example 1 of the present invention; and the Raman Mapping color images of (b) IBC-F, (c) PM6 and (d) Y6 under 532nm incident light.

[0048] Figure 5 DXPS diagrams of (a) F element, (b) N element and (c) S element in the longitudinal characterization of Comparative Example 2 and Example 2 of the present invention.

[0049] Figure 6 These are the Raman Mapping color images of (a) IBC-F, (b) PM6 and (c) Y6 in the lateral characterization of Comparative Example 2 and Example 2 of the present invention under 532nm incident light.

[0050] Figure 7 DXPS graphs of (a) F element, (b) N element and (c) O element in the longitudinal characterization of Comparative Example 3 and Example 3 of the present invention.

[0051] Figure 8 These are the Raman spectra of (a) single-component IBC-F, PM6 and PY-DT under 532nm incident light in the lateral characterization of Comparative Example 3 and Example 3 of the present invention; and the Raman Mapping color images of (b) IBC-F, (c) PM6 and (d) PY-DT under 532nm incident light.

[0052] Figure 9 These are the RNM values at different depths of the film obtained by longitudinal characterization in Example 1 of the present invention.

[0053] Figure 10 This is a Raman Mapping grayscale image of the lateral characterization of Example 1 of the present invention.

[0054] Figure 11 These are (a) a multispectral color recognition and processing redrawn component distribution diagram (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), (b) a multispectral color recognition and processing redrawn component distribution diagram (black represents the donor or acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), and (c) the proportion of each phase region (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor) obtained by the lateral characterization of Example 1 of the present invention.

[0055] Figure 12 These are the RNM values at different depths of the film obtained by longitudinal characterization in Example 2 of the present invention.

[0056] Figure 13 This is a Raman Mapping grayscale image of the lateral characterization of Example 2 of the present invention.

[0057] Figure 14 These are (a) a multispectral color recognition and processing redrawn component distribution diagram (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), (b) a multispectral color recognition and processing redrawn component distribution diagram (black represents the donor or acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), and (c) the proportion of each phase region (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor) obtained by the lateral characterization of Example 2 of the present invention.

[0058] Figure 15 These are the RNM values at different depths of the film obtained by longitudinal characterization in Example 3 of the present invention.

[0059] Figure 16 This is a Raman Mapping grayscale image of the lateral characterization of Example 3 of the present invention.

[0060] Figure 17 These are (a) a multispectral color recognition and processing redrawn component distribution diagram (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), (b) a multispectral color recognition and processing redrawn component distribution diagram (black represents the donor or the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor), and (c) the proportion of each phase region (red represents the donor, blue represents the acceptor, yellow represents the third component distributed in the donor, and cyan represents the third component distributed in the acceptor) obtained by the lateral characterization of Example 3 of the present invention.

[0061] Figure 18 This is a screenshot of part of the Mathematica program used to solve the optimization model in the longitudinal characterization of Example 1 of the present invention.

[0062] Figure 19 This is a screenshot of part of the Mathematica program used in the multispectral color recognition and processing technology in the lateral characterization of Example 1 of the present invention.

[0063] Figure 20 It is an abstract of the specification of the present invention. DETAILED DESCRIPTION

[0064] The specific implementation method of the present invention will be further described in detail below with reference to the accompanying drawings. It is particularly noted that if no specific conditions are indicated in the following examples, they are all carried out according to conventional conditions or conditions recommended by the manufacturer.

[0065] Comparative Example 1

[0066] In this comparative example 1, the donor of the system is PM6, the acceptor is Y6, and the third component is IBC-F.

[0067] Specifically, the structural formula of PM6 is:

[0068]

[0069] Specifically, the structural formula of Y6 is:

[0070]

[0071] Specifically, the structural formula of IBC-F is:

[0072]

[0073] Specifically, according to the preparation method of bulk heterojunction (BHJ) active layer, a thin film with BHJ structure was prepared by one-time deposition using CF as solvent and annealed at 100°C for 10 min.

[0074] The longitudinal distribution characteristics adopted in Comparative Example 1 are as follows:

[0075] The DXPS test method is as follows: The DXPS test parameters are set to 1.5 minutes per etching, and the XPS intensity of the three elements F, N, and S is collected. After eight "etch-and-collect" cycles, the active layer film is completely etched. XPS signals of F, N, and S are obtained at different depths.

[0076] Draw the two-dimensional images of the XPS signals of the three elements F, N, and S, as shown in the attached figure. Figure 3 shown.

[0077] Since the third component IBC-F lacks characteristic elements, the longitudinal distribution information of the third component cannot be directly obtained through the changes in the element XPS intensity.

[0078] The lateral distribution characterization adopted in Comparative Example 1 is as follows:

[0079] Test the Raman spectra of single components PM6, Y6 and IBC-F, as shown in the attached Figure 4 (a) The characteristic Raman shifts of PM6, Y6 and IBC-F were 2900 cm -1 , 2210cm -1 and 1605cm -1 ;

[0080] Set the scanning step size to 0.2μm, the scanning area to 12μm×12μm, the laser power to 0.1%, and scan the Raman Mapping information of an area on the sample;

[0081] Based on the characteristic Raman Shift of PM6, Y6 and IBC-F, the Raman Mapping of each component was drawn, as shown in the attached figure. Figure 4 (bd)

[0082] Attachment Figure 4 In (bd), red, green, and blue respectively indicate that the Raman intensity decreases gradually. Therefore, if the red area accounts for a large proportion in a specific region, it indicates that the content of the corresponding substance in this region is relatively high; conversely, if the blue area accounts for a large proportion, it indicates that the content of the substance is relatively low.

[0083] However, the Raman Mapping diagram obtained by direct measurement can only reflect the distribution of a certain substance. Figure 4 As shown in (b), the red area indicates the IBC-F-rich area, followed by the green area, and the blue area indicates the area with less IBC-F, but the distribution of PM6 and Y6 is unknown. Figure 4 As shown in (c), the red area indicates the PM6-rich area, followed by the green area, and the blue area indicates the area with less PM6. However, the distribution of IBC-F and Y6 is unknown. Figure 4 The same is true for (d), where the distribution of Y6 is known, but the distribution of IBC-F and PM6 is unknown.

[0084] Comparative Example 2

[0085] In this comparative example 2, the donor of the system is PM6, the acceptor is Y6, and the third component is IBC-F.

[0086] Specifically, the molecular structure formula is the same as that of Comparative Example 1.

[0087] Specifically, according to the quasi-planar heterojunction (PPHJ) active layer preparation method, the PM6:IBC-F donor layer was first prepared using CB as the solvent. Then, the Y6 acceptor layer was prepared by spin-coating a Y6 solution using CF as the solvent. After that, the Y6 acceptor layer was annealed at 90°C for 10 minutes.

[0088] The longitudinal distribution characteristics adopted in Comparative Example 2 are as follows:

[0089] The DXPS test method is as follows: DXPS test parameters are set to 1.5min per etching, and XPS signals of the three elements F, N, and S are collected. After eight "etch-and-collect" cycles, the active layer film is completely etched. XPS signals of F, N, and S at different depths are obtained.

[0090] Draw the two-dimensional images of the XPS signals of the three elements F, N, and S, as shown in the attached figure. Figure 5 shown.

[0091] Since the third component IBC-F lacks characteristic elements, the longitudinal distribution information of the third component cannot be directly obtained through the changes in the element XPS intensity.

[0092] The lateral distribution characterization adopted in Comparative Example 2 is as follows:

[0093] Test the Raman spectra of single components PM6, Y6 and IBC-F, as shown in the attached Figure 4 (a) The characteristic Raman shifts of PM6, Y6 and IBC-F were 2900 cm -1 , 2210cm -1 and 1605cm -1 ;

[0094] Set the scanning step size to 0.3μm, the scanning area to 18μm×18μm, the laser power to 0.1%, and scan the Raman Mapping information of an area on the sample;

[0095] Based on the characteristic Raman Shift of PM6, Y6 and IBC-F, the Raman Mapping of each component was drawn, as shown in the attached figure. Figure 6 (ac) shown;

[0096] Attachment Figure 6 In (ac), red, green, and blue respectively indicate gradually decreasing intensities. Therefore, if the red area accounts for a large proportion in a specific region, it indicates that the content of the corresponding substance in that region is relatively high; conversely, if the blue area accounts for a large proportion, it indicates that the content of the substance is relatively low.

[0097] However, the Raman Mapping diagram obtained by direct measurement can only reflect the distribution of a certain substance. Figure 6 As shown in (a), the red area indicates the IBC-F enriched area, followed by the green area, and the blue area indicates the area with less IBC-F, but the distribution of PM6 and Y6 is unknown. Figure 6 As shown in (b), the red area indicates the PM6-rich area, followed by the green area, and the blue area indicates the area with less PM6, but the distribution of IBC-F and Y6 is unknown. Figure 6 The same is true for (c), where the distribution of Y6 is known, but the distribution of IBC-F and PM6 is unknown.

[0098] Comparative Example 3

[0099] In this comparative example 1, the donor of the system is PM6, the acceptor is PY-DT, and the third component is IBC-F.

[0100] Specifically, the structural formula of PM6 is:

[0101]

[0102] Specifically, the structural formula of PY-DT is:

[0103]

[0104] Specifically, the structural formula of IBC-F is:

[0105]

[0106] Specifically, according to the preparation method of bulk heterojunction (BHJ) active layer, a thin film with BHJ structure was prepared by one-time deposition using CF as solvent; no annealing treatment was performed.

[0107] The longitudinal distribution characterization scheme adopted in Comparative Example 3 is as follows:

[0108] The DXPS test method is as follows: The DXPS test parameters are set to 2.5 minutes per etching, and XPS signals of the three elements F, N, and O are collected. After seven "etch-and-collect" cycles, the active layer film is completely etched. XPS signals of the three elements N, S, and F are obtained at different depths.

[0109] Draw the two-dimensional images of the XPS signals of the three elements F, N, and O, as shown in the attached figure. Figure 7 shown.

[0110] Since the third component IBC-F lacks characteristic elements, the longitudinal distribution information of the third component cannot be directly obtained through the changes in the element XPS intensity.

[0111] The lateral distribution characterization adopted in Comparative Example 3 is as follows:

[0112] The Raman spectra of single components PM6, PY-DT and IBC-F were tested as shown in the attached figure. The characteristic Raman shifts of PM6, PY-DT and IBC-F were 2900 cm -1 , 2168cm -1 and 1605cm -1 .

[0113] Set the scan step size to 0.2 μm, the scan area to 12 μm × 12 μm, the laser power to 0.2%, and scan the Raman Mapping information of an area of the sample;

[0114] Based on the characteristic Raman Shift of PM6, PY-DT and IBC-F, the Raman Mapping of each component was drawn, as shown in the attached figure. Figure 8(a)

[0115] Attachment Figure 8 In (a), red, green, and blue indicate gradually decreasing intensities. Therefore, if the red area accounts for a large proportion in a specific region, it indicates that the content of the corresponding substance in that region is relatively high; conversely, if the blue area accounts for a large proportion, it indicates that the content of the substance is relatively low.

[0116] However, the Raman Mapping diagram obtained by direct measurement can only reflect the distribution of a certain substance. Figure 8 As shown in (b), the red area indicates the IBC-F-rich area, followed by the green area, and the blue area indicates the area with less IBC-F, but the distribution of PM6 and Y6 is unknown. Figure 8 As shown in (c), the red area indicates the PM6-rich area, followed by the green area, and the blue area indicates the area with less PM6. However, the distribution of IBC-F and PY-DT is unknown. Figure 8 The same is true for (d), where the distribution of PY-DT is known, but the distribution of IBC-F and PM6 is unknown.

[0117] Example 1

[0118] The films used in Example 1 and Comparative Example 1 are the same.

[0119] The longitudinal distribution characterization scheme adopted in this embodiment 1 is as follows:

[0120] The DXPS test method is consistent with the longitudinal distribution characterization used in Comparative Example 1, and the results are shown in the attached figure. Figure 3 shown.

[0121] According to the molecular structures of PM6, Y6, and IBC-F, each PM6 molecule has 2 Fs and 8 Ss; each Y6 molecule has 4 Fs, 8 Ns, and 5 Ss; and each IBC-F molecule has 4 Fs, 2 Ns, and Ss. Let x, y, and z be the number of PM6, Y6, and IBC-F molecules, respectively. Therefore, we can conclude that:

[0122]

[0123] The above equations are transformed into the following optimization problem:

[0124]

[0125] Solving the above optimization problem, we get 8 groups of (x, y, z) at different longitudinal positions. The solutions at different depths of the film are:

[0126] Depth(nm) x y z 6.5 2 2.15 0.0775 20.0 2 2.26 0.0807 31.1 2 2.03 0.0887 44.5 2 2.10 0.0791 55.8 2 1.96 0.0843 70.2 2 1.98 0.0879 81.5 2 2.10 0.0993 94.5 2 1.77 0.0777

[0127] Substitute the (x, y, z) obtained in the above steps into the XPS intensity I of the N element. N :

[0128]

[0129] Next, calculate the relative number of molecules (RNM), let:

[0130] RNM(PM6)=B*x

[0131] RNM(Y6)=B*y

[0132] RNM(IBC-F)=B*z

[0133] The calculation results at different depths of the film are shown in the following table:

[0134] Depth(nm) <![CDATA[I N ]]> B RNM(PM6) RNM(Y6) RNM(IBC-F) 6.5 11574.36 1587.91 3176 3414 123 20.0 11776.8 1537.16 3074 3474 124 31.1 11110.68 1611.33 3223 3271 143 44.5 12067.44 1694.28 3389 3558 134 55.8 11767.56 1767.85 3536 3465 149 70.2 11098.92 1650.00 3300 3267 145 81.5 10712.52 1500.47 3001 3151 149 94.5 10696.56 1776.77 3554 3149 138

[0135] Plot the RNM values of PM6, Y6 and IBC-F at different depths, as shown in the attached figure. Figure 9 shown.

[0136] Obviously, the longitudinal distribution of IBC-F without special elements was known, while the longitudinal characterization of Comparative Example 1 was not possible.

[0137] The lateral distribution characterization scheme adopted in this embodiment 1 is as follows:

[0138] The Raman Mapping test method is consistent with the lateral distribution characterization used in Comparative Example 1. The obtained Raman Mapping color map is shown in the attached figure. Figure 4 (cd) shown;

[0139] Export the grayscale image on the test software, as shown in the attached Figure 10 As shown;

[0140] The grayscale image is dimensionalized and its grayscale values are extracted using a computer program to form a grayscale array. White represents a large value, black represents a small value, and the extracted grayscale values are normalized. The grayscale array of the donor Raman mapping is denoted as Gray D, the acceptor Raman mapping is denoted as Gray A, and the third component Raman mapping is denoted as Gray T.

[0141] Define a threshold th as 0.65;

[0142] Compare the gray values point by point to determine the component distribution. In the first step, if Gray T is greater than or equal to 0.65, it is a third component-rich area; in the second step, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a mixed phase of the third component and the donor, that is, the third component is distributed in the donor phase; otherwise, it is distributed in the acceptor phase. If Gray T in the first step is less than 0.65, it is a donor phase or an acceptor phase. Similarly, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a donor phase; otherwise, it is an acceptor phase.

[0143] The blend phase of the third component and the donor is drawn as yellow (color 1), the blend phase of the third component and the receptor is drawn as cyan (color 2), the donor phase is drawn as red (color 3), and the receptor phase is drawn as blue (color 4). Figure 11 (a)

[0144] In order to more clearly distinguish the distribution of the third component, the donor phase and the acceptor phase are drawn in black (that is, colors 3 and 4 are set to black at the same time), and the other colors remain unchanged. Figure 11 (b)

[0145] Count the number of pixels of the corresponding color and their percentage, that is, the percentage of the corresponding phase area. The results are shown in the attached Figure 11 (c) shown.

[0146] Obviously, the lateral distribution of the third component in the film is quantitatively characterized, and the third component is evenly distributed in the donor phase and the acceptor phase (6.7% vs. 6.2%); while the lateral characterization of Comparative Example 1 not only makes it difficult to derive the lateral distribution of the third component, but also makes it impossible to achieve quantitative characterization.

[0147] Example 2

[0148] The films used in Example 2 and Comparative Example 2 are the same.

[0149] The longitudinal distribution characterization scheme adopted in this embodiment 2 is as follows:

[0150] The DXPS test method is consistent with the longitudinal distribution characterization adopted in Comparative Example 2, and the results are shown in the attached figure. Figure 5 shown.

[0151] According to the molecular structures of PM6, Y6, and IBC-F, each PM6 molecule has 2 Fs and 8 Ss; each Y6 molecule has 4 Fs, 8 Ns, and 5 Ss; and each IBC-F molecule has 4 Fs, 2 Ns, and Ss. Let x, y, and z be the number of PM6, Y6, and IBC-F molecules, respectively. Therefore, we can conclude that:

[0152]

[0153] The above equations are transformed into the following optimization problem:

[0154]

[0155] Solving the above optimization problem, we get 8 groups of (x, y, z) at different longitudinal positions. The solutions at different depths of the film are:

[0156] Depth(nm) x y z 6.5 2 6.00 0.161 20.0 2 3.00 0.167 31.1 2 2.10 0.158 44.5 2 2.17 0.166 55.8 2 2.01 0.165 70.2 2 2.14 0.156 81.5 2 1.10 0.167 94.5 2 0.69 0.159

[0157] Substitute the (x, y, z) obtained in the above steps into the XPS intensity I of the N element. N :

[0158]

[0159] Next, calculate the relative number of molecules (RNM), let:

[0160] RNM(PM6)=B*x

[0161] RNM(Y6)=B*y

[0162] RNM(IBC-F)=B*z

[0163] The calculation results at different depths of the film are shown in the following table:

[0164] Depth(nm) <![CDATA[I N ]]> B RNM(PM6) RNM(Y6) RNM(IBC-F) 6.5 13324.08 656.50 1313 3940 106 20.0 11531.52 1128.50 2257 3385 188 31.1 10019.52 1394.00 2788 2927 220 44.5 10053.96 1353.00 2706 2936 225 55.8 10077.48 1462.50 2925 2939 241 70.2 10048.08 1372.50 2745 2937 214 81.5 7843.08 2045.00 4090 2249 341 94.5 6293.28 2567.50 5135 1770 408

[0165] Plot the RNM values of PM6, Y6 and IBC-F at different depths, as shown in the attached figure. Figure 12 shown.

[0166] Obviously, the longitudinal distribution of IBC-F without special elements was known, while the longitudinal characterization of Comparative Example 2 was not possible.

[0167] The lateral distribution characterization scheme adopted in this embodiment 2 is as follows:

[0168] The Raman Mapping test method is consistent with the lateral distribution characterization used in Comparative Example 2. The obtained Raman Mapping color map is shown in the attached figure. Figure 6 As shown;

[0169] Export the grayscale image on the test software, as shown in the attached Figure 13 As shown;

[0170] The grayscale image is dimensionalized and its grayscale values are extracted using a computer program to form a grayscale array. White represents a large value, black represents a small value, and the extracted grayscale values are normalized. The grayscale array of the donor Raman mapping is denoted as Gray D, the acceptor Raman mapping is denoted as Gray A, and the third component Raman mapping is denoted as Gray T.

[0171] Define a threshold th as 0.65;

[0172] Compare the gray values point by point to determine the component distribution. In the first step, if Gray T is greater than or equal to 0.65, it is a third component-rich area; in the second step, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a mixed phase of the third component and the donor, that is, the third component is distributed in the donor phase; otherwise, it is distributed in the acceptor phase. If Gray T in the first step is less than 0.65, it is a donor phase or an acceptor phase. Similarly, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a donor phase; otherwise, it is an acceptor phase.

[0173] The blend phase of the third component and the donor is drawn as yellow (color 1), the blend phase of the third component and the receptor is drawn as cyan (color 2), the donor phase is drawn as red (color 3), and the receptor phase is drawn as blue (color 4). Figure 14 (a)

[0174] In order to more clearly distinguish the distribution of the third component, the donor phase and the acceptor phase are drawn in black (that is, colors 3 and 4 are set to black at the same time), and the other colors remain unchanged. Figure 14 (b)

[0175] Count the number of pixels of the corresponding color and their percentage, that is, the percentage of the corresponding phase area. The results are shown in the attached Figure 14 (c) shown.

[0176] Obviously, the lateral distribution of the third component in the film is quantitatively characterized, and the third component tends to be distributed in the donor phase rather than the acceptor phase (4.4% vs 1.6%); while the lateral characterization of Comparative Example 2 is not only difficult to derive the distribution of the third component, but also cannot achieve quantitative characterization.

[0177] Example 3

[0178] The films used in Example 3 and Comparative Example 3 are the same.

[0179] The longitudinal distribution characterization scheme adopted in this embodiment 3 is as follows:

[0180] The DXPS test method is consistent with the longitudinal distribution characterization used in Comparative Example 3, and the results are shown in the attached figure. Figure 7 shown.

[0181] According to the molecular structures of PM6, Y6, and IBC-F, each PM6 molecule has 2 F and 2 O atoms; each PY-DT molecule has 4 F atoms, 8 N atoms, and 2 O atoms; and each IBC-F molecule has 4 F atoms, 2 N atoms, and 4 O atoms. Let x, y, and z be the number of PM6, Y6, and IBC-F molecules, respectively. Therefore, we can conclude that:

[0182]

[0183] The above equations are transformed into the following optimization problem:

[0184]

[0185] Solving the above optimization problem, we get 8 groups of (x, y, z) at different longitudinal positions. The solutions at different depths of the film are:

[0186] Depth(nm) x y z 7.0 2 2.99 0.476 21.3 2 2.66 0.403 35.5 2 2.73 0.371 49.8 2 2.94 0.485 64.0 2 2.85 0.430 78.3 2 2.93 0.526 92.5 2 2.80 0.447

[0187] Substitute the (x, y, z) obtained in the above steps into the XPS intensity I of the F element F :

[0188]

[0189] Next, calculate the relative number of molecules (RNM), let:

[0190] RNM(PM6)=B*x

[0191] RNM(PY-DT)=B*y

[0192] RNM(IBC-F)=B*z

[0193] The calculation results at different depths of the film are shown in the following table:

[0194] Depth(nm) <![CDATA[I N ]]> B RNM(PM6) RNM(Y6) RNM(IBC-F) 7.0 108081.9 18301.28 36603 54678 8719 21.3 110893.2 19766.47 39533 52510 7957 35.5 107463.2 19597.14 39194 53537 7269 49.8 109454.5 18424.85 36850 54212 8939 64.0 108257.3 18919.89 37840 54016 8144 78.3 111919.5 18338.34 36677 53682 9642 92.5 110334.3 19063.86 38127 53352 8519

[0195] Plot the RNM values of PM6, PY-DT and IBC-F at different depths, as shown in the attached figure. Figure 15 shown.

[0196] Obviously, the longitudinal distribution of IBC-F without special elements was known, while the longitudinal characterization of Comparative Example 2 was not possible.

[0197] The lateral distribution characterization scheme adopted in this embodiment 2 is as follows:

[0198] The Raman Mapping test method is consistent with the lateral distribution characterization used in Comparative Example 2. The obtained Raman Mapping color map is shown in the attached figure. Figure 8 (bd)

[0199] Export the grayscale image on the test software, as shown in the attached Figure 16 As shown;

[0200] The grayscale image is dimensionalized and its grayscale values are extracted using a computer program to form a grayscale array. White represents a large value, black represents a small value, and the extracted grayscale values are normalized. The grayscale array of the donor Raman mapping is denoted as Gray D, the acceptor Raman mapping is denoted as Gray A, and the third component Raman mapping is denoted as Gray T.

[0201] Define a threshold th as 0.8;

[0202] Compare the gray values point by point to determine the component distribution. In the first step, if Gray T is greater than or equal to 0.8, it is a third component-rich area; in the second step, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a mixed phase of the third component and the donor, that is, the third component is distributed in the donor phase; otherwise, it is distributed in the acceptor phase. If Gray T in the first step is less than 0.8, it is a donor phase or an acceptor phase. Similarly, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a donor phase; otherwise, it is an acceptor phase.

[0203] The blend phase of the third component and the donor is drawn as yellow (color 1), the blend phase of the third component and the receptor is drawn as cyan (color 2), the donor phase is drawn as red (color 3), and the receptor phase is drawn as blue (color 4). Figure 17 (a)

[0204] In order to more clearly distinguish the distribution of the third component, the donor phase and the acceptor phase are drawn in black (i.e., colors 3 and 4 are set to black at the same time), and the other colors remain unchanged. Figure 17 (b)

[0205] Count the number of pixels of the corresponding color and their percentage, that is, the percentage of the corresponding phase area. The results are shown in the attached Figure 17 (c) shown.

[0206] Obviously, the lateral distribution of the third component in the film is quantitatively characterized, and the third component is evenly distributed in the donor phase and the acceptor phase (12.6% vs. 11.8%); while the lateral characterization of Comparative Example 3 is not only difficult to derive the distribution of the third component, but also cannot achieve quantitative characterization.

[0207] The embodiments described above provide a detailed description of the technical solutions of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not limited to the present invention. Any modifications or supplements made within the scope of the principles of the present invention should be included in the protection scope of the present invention. In particular, the optimization model solution of the longitudinal characterization of the present invention, i.e., step S4 in the longitudinal characterization, and the image processing process of the transverse characterization, i.e., steps S3-S7 in the transverse characterization, are both implemented through the computer Mathematica software program, but should not be limited to Mathematica software. Within the scope of the principles of the present invention, any program and operating software used to achieve the above functions are covered by the protection scope of the present invention.

[0208] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for characterizing the longitudinal and lateral distribution of a third component in an active layer of a ternary organic photovoltaic cell, characterized in that: The following steps are involved: The technical means adopted by the present invention to characterize the longitudinal distribution is DXPS; the technical means to characterize the transverse distribution is Raman Mapping imaging technology. The DXPS longitudinal characterization method adopted in the present invention can solve the longitudinal characterization of the third component that does not contain characteristic elements. The technical solution is as follows: S1: After several cycles of "etch-collect-etch-collect", the XPS intensity I of several elements i (i = 1, 2, 3...) in the active layer at different depths is obtained. i ; S2: According to the elemental composition of the donor, acceptor and third component, the number of molecules of the donor, acceptor and third component are the unknown numbers x, y and z respectively. i Construct several ratio equations of the following form and form a system of equations: In the above formula, i and j both represent atom i and atom j, i x ,i y and i z are the number of atoms of element i in the donor, acceptor and third component molecules respectively; j x ,j y and j z are the number of j atoms in the donor, acceptor and third component molecules respectively; S i and S i is the atomic sensitivity coefficient (available for inspection); S3: Convert the equations described in step S2 into an optimization model; at the same time, to make the solution physically meaningful, set x, y, and z as constraints that are all greater than or equal to 0. For example, the following optimization problem: S4: Solve the above optimization problem. Since the equations are ratio equations, the number of molecules of the donor, acceptor, and third component obtained is not the actual number of molecules, but the ratio of their molecular numbers. Because x, y, and z are multiplied by the same coefficient, the optimal solution still holds. S5: Substitute the molecular ratio of the donor, acceptor and third component obtained in step S4 into the XPS intensity I of any element k k The calculation formula is further transformed: Where A and B are unknown constants, f is the X-ray flux incident on the sample (constant), and V is the volume (constant); S6: Introduce the concept of relative number of molecules (RNM), let, RNM(donor)=B*x RNM(receptor)=B*y RNM (third component) = B*z It is important to note that RNM is not the absolute number of molecules of the donor, acceptor, and third component. Instead, it is the relative number of molecules, taking into account the constants f and V. However, since the absolute number of molecules of the donor, acceptor, and third component differs from their RNM values by the same constant factor (B), this does not affect the analysis of the component distribution within the active layer. The lateral characterization technology used in this invention combines Raman Mapping imaging technology with computer image processing technology to develop a multispectral color recognition and processing technology. The technical solution is as follows: S1: Test the Raman spectrum of a certain area of the sample and draw the corresponding Raman Mapping based on the RamanShift characteristics of the single-component donor, acceptor and third component; S2: Export the grayscale image (also called black and white image) on the test software; S3: The grayscale image is dimensionalized using a computer program and its grayscale values are extracted. White represents a large value, and black represents a small value. The extracted grayscale values are then normalized. Normalization is performed to facilitate grayscale value comparison in the next step; dimensionality unification allows for comparison between different images. The grayscale array of the donor Raman mapping is denoted as Gray D, the grayscale array of the acceptor Raman mapping is denoted as Gray A, and the grayscale array of the third component Raman mapping is denoted as Gray T. S4: Define a threshold threshold(th); S5: Compare grayscale values to determine component distribution. In the first step, if Gray T is greater than or equal to th, the region is enriched in the third component. In the second step, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, the region is a blend of the third component and the donor, meaning the third component is distributed in the donor phase. Otherwise, the region is distributed in the acceptor phase. If Gray T in the first step is less than th, the region is either a donor phase or an acceptor phase. Similarly, compare Gray D and Gray A. If Gray D is greater than or equal to Gray A, it is a donor phase; otherwise, it is an acceptor phase. S6: For the convenience of intuitive observation and analysis, we can mark "Gray T greater than or equal to th and Gray D greater than or equal to Gray A" as color 1, "Gray T greater than or equal to th and Gray D less than Gray A" as color 2, "Gray T less than th and Gray D greater than or equal to Gray A" as color 3, and "Gray T less than th and Gray D less than Gray A" as color 4. Then colors 1-4 represent the blended phase of the third component and the donor, the blended phase of the third component and the acceptor, and the donor phase and the acceptor phase, respectively. S7: Counting the number of pixels of different colors and their percentages can reflect the percentage of the corresponding phase area.

2. The method according to claim 1, characterized in that There are no specific requirements for the material types, component content ratios, spin coating conditions, doctor blade coating conditions, annealing conditions, active layer structure, active layer film thickness, etc. of the T-OPVs active layer film.

3. The method for characterizing the longitudinal and lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: The DXPS technique employed in the longitudinal distribution characterization step can address the longitudinal characterization of third components lacking characteristic elements. This technique transcends the traditional characterization methods' reliance on elemental signatures. Through comprehensive analysis of multiple elements and the construction of mathematical models, it effectively characterizes the longitudinal distribution of third components lacking characteristic elements.

4. The method for characterizing the longitudinal and lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: The lateral distribution characterization utilizes a novel multispectral color recognition and processing technology. By processing and analyzing Raman Mapping grayscale images, the lateral distribution of the third component within the active layer of T-OPVs can be visually presented and quantitatively compared. This innovative technology combines Raman Mapping imaging with computer image processing techniques, utilizing color labeling and pixel statistics to visually and quantitatively visualize the distribution of the third component within the active layer.

5. In the longitudinal distribution characterization step S1, the etching gas used in the etching process is argon gas, which can be ionized into argon ions in the etching chamber. Under the accelerating electric field, argon ions collide with the film at high speed, causing atoms to be sputtered out from the lattice, thereby achieving layer-by-layer removal of the film. The single etching time is 1-10 min. The chemical properties of argon gas are stable, which can generate large kinetic energy and the stability of argon ions. It can ensure that the etching depth is accurately controlled at the nanometer level within the single etching time of 1-10 min, providing a guarantee for the subsequent accurate acquisition of XPS intensity data at different depths. The X-ray flux f and volume V are determined by the instrument itself, which cannot be known. However, in each test, f and V of different elements are the same and are constants, which provides a theoretical basis for incorporating them into B in the next step. The XPS sampling elements should be determined according to the actual situation of the material. Generally, the form of the equation group described in S2 should be simple.

6. In the longitudinal characterization step S2 according to claim 1, it is characterized in that The XPS principle establishes a system of ratio equations rather than simple equations, allowing the elimination of constant but unknown terms. Too many equations in a system can lead to overdetermination, while too few can lead to indetermination, both of which can result in large errors. Typically, the number of equations is 2-4.

7. In the longitudinal characterization step S3 according to claim 1, it is characterized in that The objective function of the optimization problem is the sum of the squares of the ratio equations, which can minimize the error of the results of all equations at the same time.

8. In the longitudinal characterization step S4 according to claim 1, it is characterized in that Solving optimization problems can be achieved with the help of computer programs, which greatly improves computing efficiency.

9. In the longitudinal characterization step S5 according to claim 1, it is characterized in that The element k is chosen to simplify the calculation formula of B. At the same time, incorporating the unknown constants f and V into the calculation of B facilitates the calculation without affecting the final analysis conclusion.

10. The longitudinal characterization step S6 according to claim 1, characterized in that Compared with the weight percentage commonly used in DXPS, the RNM value can more intuitively reflect the distribution amount of the third component.

11. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In step S1, there are no special requirements for the laser wavelength, laser power, scanning area size, and step size of Raman Mapping. However, in order to accurately obtain the Raman spectrum of a certain area of the sample, the instrument used to test the Raman spectrum is usually required to have good resolution, and its scanning area size and scanning step size ratio can reach at least 20:

1. The laser wavelength and power should be set so that the Raman signal is not obviously "swamped" by miscellaneous peaks; at the same time, the test software can directly export the grayscale image.

12. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In the S3 step, the computer program samples the grayscale values of the pixels of the original image, takes the maximum number of pixels as the reference number, and generates new pixels from the grayscale image with insufficient number of pixels through an interpolation algorithm. The above-mentioned original image sampling and grayscale value extraction are to obtain the grayscale information of each pixel and form a grayscale value array through an image data conversion program. The programs mentioned here include but are not limited to: Mathematic's ImageData function, Python's Pillow and OpenCV libraries, MATLAB's imread function, etc. The returned array elements are real numbers between 0 and 1, 0 represents black, 1 represents white, and the intermediate values represent different degrees of gray. Normalization is achieved through the following formula: Here x norm is the normalized value, x is the original grayscale data, x min is the minimum value in the grayscale value array, x max The maximum value in the grayscale value array.

13. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In step S4, the threshold (th) is selected based on the definition of the "third component-enriched region." Setting th too low can select interfering data, causing the donor or acceptor phase to be mistakenly identified as the third component. Setting th too high can also lead to over-screening, ignoring third component-enriched regions. Optimally, th is in the range of 0.5-0.9 to ensure effective differentiation of different component distribution regions.

14. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In step S5, the grayscale, which has been converted to digital form, is compared using a relational operator ("≥") to accurately determine the distribution of different components. The multi-step judgment is implemented by looping and nesting conditional judgment instructions. Specifically, if "Gray T ≥ th" is first determined to be true, then "Gray D ≥ Gray A" is determined to be true, indicating that the third component is distributed in the donor; if not, it is in the acceptor. If "Gray T ≥ th" is first determined to be false, then "Gray D is greater" is determined to be true, indicating that the third component is distributed in the donor; if not, it is in the acceptor.

15. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In step S6, the color mark is set using the RGB triplet of the corresponding software program, for example, (1,0,0) is red, (0,0,1) is blue, and (1,1,0) is yellow. The user can freely set the color by setting different RGB values as needed.

16. The multispectral color recognition and processing method for characterizing the lateral distribution of the third component in the active layer of T-OPVs according to claim 1, characterized in that: In step S7, pixel count is performed using a built-in function. The principle is to compare the RGB of the color to be counted with the RGB of the entire image point by point ("=" or "≠"). If the RGB is the same, the number of pixels is increased by one, and so on. Ultimately, the number and percentage of pixels corresponding to the corresponding color, i.e., the percentage of the corresponding phase area, can be obtained to achieve accurate quantitative comparison of the distribution of the third component. The above-mentioned built-in functions include but are not limited to: Mathematica's Count function, Python's Counter function, and MATLAB's Count function.

17. The optimization model solution of the longitudinal representation, i.e., step S4 of the longitudinal representation, and the image processing process of the transverse representation, i.e., steps S3-S7 of the transverse representation, of the present invention are characterized in that: Although all of the above functions are implemented by the computer Mathematica software program, they should not be limited to Mathematica software. Within the scope of the principles of the present invention, any program and operating software that can implement the above functions are included in the protection scope of the present invention.

18. A method for characterizing the longitudinal and transverse distribution of a third component in a T-OPVs active layer, characterized in that ， The longitudinal distribution characterization method according to any one of claims 1 to 10, wherein steps S2-S6 introduce the concept of RNM by solving the optimization model to intuitively characterize the longitudinal distribution of the third component that does not contain the characteristic element; The lateral distribution characterization method described in any one of claims 1 and 11 to 15, wherein steps S5-S7 are based on Raman Mapping and combined with image processing technology to distinguish different phase regions with different colors and count the number of pixels in the corresponding color areas.

Images