Method for quantitatively characterizing donor and acceptor distribution of active layer of organic photovoltaic cell

Abstract

The invention discloses a method for quantitatively characterizing donor and acceptor distribution of an active layer of an organic photovoltaic cell, and the method comprises the steps: scanning the active layer of the organic photovoltaic cell through a Raman microscope, extracting a gray intensity value, and obtaining a Raman Mapping gray-scale map; defining a threshold value for the Raman Mapping grey-scale map, comparing the grey-scale intensity value with the threshold value, distinguishing the comparison result into different colors which respectively represent a region in which a donor material is dominated, a region in which an acceptor material is dominated and a region in which the donor and the acceptor are blended, and synthesizing a color image; and converting the regions with different colors into numerical information, and counting the number of pixel points in different regions, thereby realizing quantitative characterization of donor and acceptor distribution of the active layer of the organic photovoltaic cell. According to the method, quantitative analysis on the distribution of the donor and the acceptor materials in the organic photovoltaic cell is realized, the interaction of the donor and the acceptor materials in the active layer is conveniently and deeply understood, and a scientific basis is provided for improving the performance of the organic photovoltaic cell.

Description

Technical Field

[0001] The present invention belongs to the technical field of organic photovoltaic cells, and relates to a method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell. Background Art

[0002] The core structure of an organic photovoltaic cell is the active layer, which is usually composed of donor and acceptor materials. Donor materials are usually organic semiconductors with high electrical conductivity, capable of absorbing photons and exciting electrons, while acceptor materials are responsible for capturing these electrons and promoting their flow. The donor and acceptor materials in the active layer need to form a uniform distribution at the microscale to achieve efficient generation and separation of electron-hole pairs. In addition, the active layer usually contains blend phases of the donor and acceptor, and these blend phases directly affect the charge transport path and efficiency at the microstructural level. The morphology of the active layer is crucial for device performance, not only determining the efficiency of light absorption and charge separation, but also being closely related to key performance indicators such as the photovoltaic conversion efficiency and stability of the battery. Therefore, accurately understanding and controlling the distribution and morphology of each component in the active layer is one of the key factors for improving the performance of organic photovoltaic cells.

[0003] Traditionally, researchers have used characterization techniques such as atomic force microscopy (AFM), transmission electron microscopy (TEM), grazing incidence wide-angle X-ray scattering (GIWAXS), and grazing incidence small-angle X-ray scattering (GISAXS) to observe and analyze the morphology and structure of the active layer of organic photovoltaic cells. These techniques can provide information such as the morphology, microstructure, and crystal orientation of the material surface.

[0004] However, although the above techniques have made significant progress in characterizing the morphology and structure of materials, they mainly focus on qualitative analysis and cannot accurately provide quantitative information on the spatial distribution and interaction of donors, acceptors, and blend phases in the active layer. For example, AFM and TEM can reveal the relative distribution of donor and acceptor materials, but cannot accurately describe the interaction between these materials at the microscale and its specific impact on device performance. Although GIWAXS and GISAXS techniques can analyze the crystallization characteristics and large-scale arrangement of thin films, they still have limitations in accurately distributing the donor-acceptor phase at the nanoscale. Therefore, these methods cannot provide accurate quantitative data, limiting the comprehensive understanding of the performance of photovoltaic cells and unable to provide in-depth guidance for material design and device optimization.

[0005] Controlling the distribution of donors, acceptors, and blend phases in organic photovoltaic cells is a key factor in improving device performance. The reasonable distribution of donors and acceptors not only affects the light absorption efficiency but also the generation and separation efficiency of excitons. In an ideal situation, donor and acceptor materials should form a certain nano-scale distribution structure, which can maximize the charge separation region and reduce charge recombination, thereby improving the photoelectric conversion efficiency. In addition, the morphology and distribution of the blend phase also have an important impact on the path and rate of charge transport. Especially at the thin film interface, the interaction relationship between donor and acceptor materials determines the charge migration efficiency. Therefore, precisely controlling the distribution of these materials, especially their interactions and interface characteristics, is one of the keys to enhancing device performance. Summary of the Invention

[0006] The present invention aims to solve the key technical problem of the difficulty in quantitatively calculating the donor phase, acceptor phase, and blend phase in the active layer of organic photovoltaic cells. The present invention provides a method for quantitatively characterizing the donor-acceptor distribution in the active layer of organic photovoltaic cells, and the technical solution adopted is:

[0007] A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell, comprising the steps of:

[0008] S1. Scanning the active layer of the organic photovoltaic cell with a Raman microscope, extracting the gray intensity values, and obtaining a Raman Mapping gray-scale image;

[0009] S2. Defining a threshold for the Raman Mapping gray-scale image, comparing the gray intensity values with the threshold, and setting the comparison results as different colors according to the RGB color mode, respectively representing the regions dominated by donor materials, the regions dominated by acceptor materials, and the regions of donor-acceptor blends, and synthesizing a color image;

[0010] S3. Converting the regions represented by different colors into numerical information, counting the number of pixel points in different regions, and realizing the quantitative characterization of the donor-acceptor distribution in the active layer of the organic photovoltaic cell.

[0011] In one embodiment of the present invention, the step S1 includes:

[0012] S11. Scanning the active layer of the organic photovoltaic cell with a Raman microscope, obtaining Raman spectral data of each tiny region, and obtaining a Raman image of donor materials and a Raman image of acceptor materials;

[0013] S12. Adjusting the size of the Raman image of the donor materials or the Raman image of the acceptor materials to obtain Raman images with consistent pixel correspondences;

[0014] S13. Grayscale process the Raman images with consistent pixel correspondences, extract the grayscale intensity values respectively, and obtain the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material.

[0015] In one embodiment of the present invention, the step S11 includes: the Raman microscope detects and resolves different Raman characteristic peaks of the donor and acceptor materials, and extracts the characteristic signals related to the donor and acceptor materials;

[0016] The characteristic signals include the displacement, peak intensity, and width of the Raman peaks, and the distribution status of the donor and acceptor materials in different regions is reflected by the characteristic signals.

[0017] In one embodiment of the present invention, in S1, the laser wavelength of the Raman microscope is set to 532 nm, and the scanning resolution is set to 0.1 μm.

[0018] In one embodiment of the present invention, in the step S2, the comparison result is set to different colors according to the RGB color mode.

[0019] In one embodiment of the present invention, the step S2 includes:

[0020] S21. Define a threshold for the Raman Mapping grayscale image;

[0021] S22. Use red, blue, and yellow to distinguish the comparison results. If it is judged that "the grayscale values of the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material are both less than the threshold" holds, it is the donor-acceptor blend region and is marked as yellow; if it is judged that "the grayscale values of the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material are both less than the threshold" does not hold, and "the grayscale value of the Raman Mapping grayscale image of the donor material is greater than the grayscale value of the Raman Mapping grayscale image of the acceptor material" holds, it represents the donor phase and is marked as red; if it does not hold, it represents the acceptor phase and is marked as blue;

[0022] S23. Synthesize a color image.

[0023] In one embodiment of the present invention, the step S2 further includes: S24. Perform quantitative analysis on the Raman Mapping grayscale image using an image processing algorithm.

[0024] In one embodiment of the present invention, the step S4 includes:

[0025] Apply a segmentation algorithm to extract the regions of the donor and acceptor materials, and calculate the Raman signal intensity of each region;

[0026] Analyze the spatial distribution of the Raman signal, and extract the distribution density, uniformity, and relative concentration of the donor material and the acceptor material;

[0027] Use image processing tools for processing, including smoothing, denoising, and edge detection operations.

[0028] In one embodiment of the present invention, the range of the threshold is 0.1 - 0.5.

[0029] In one embodiment of the present invention, the step S3 includes:

[0030] S31. Convert the regions represented by different colors into integer values;

[0031] S32. Count the number of pixel points in the regions corresponding to the integer values, and characterize the donor-acceptor distribution of the active layer of the organic photovoltaic cell through the number of pixel points.

[0032] Advantages of the present invention:

[0033] The method for quantitatively characterizing the donor-acceptor distribution of the active layer of an organic photovoltaic cell according to the present invention is based on Raman Mapping technology. By processing and analyzing the image of the organic photovoltaic cell sample, combined with the image processing function, using grayscale image thresholding, color synthesis, and smoothing processing technologies, to achieve quantitative analysis of the distribution of the donor and acceptor materials in the organic photovoltaic cell. It can not only help researchers deeply understand the interaction between the donor and acceptor materials in the active layer, but also reveal their influence on key performance indicators such as photoelectric conversion efficiency and stability, providing a scientific basis for improving the performance of organic photovoltaic cells, having broad application prospects and technical value, and this method provides a powerful tool for non-destructive characterization, helping to optimize the morphology of the active layer and device performance. Description of the Drawings

[0034] Figure 1 is a flowchart of the method for quantitatively characterizing the donor-acceptor distribution of the active layer of an organic photovoltaic cell provided by an embodiment of the present invention;

[0035] Figure 2 is an AFM diagram of PM6:Y6 and the blend film in the comparative example of the present invention;

[0036] Figure 3 is a TEM diagram of PM6:Y6 and the blend film in the comparative example of the present invention;

[0037] Figure 4 is a Raman spectrum diagram of PM6, Y5, and Y6 in Example 1 of the present invention.

[0038] Figure 5 It is the Raman Mapping diagram of PM6 in Example 1 of the present invention.

[0039] Figure 6 It is the Raman Mapping diagram of Y6 in Example 1 of the present invention.

[0040] Figure 7 It is the grayscale conversion diagram of PM6 in Example 1 of the present invention.

[0041] Figure 8 It is the grayscale conversion diagram of Y6 in Example 1 of the present invention.

[0042] Figure 9 In Example 1 of the present invention, it is the component distribution diagram of PM6 and Y6. The red color represents the distribution of PM6, the blue color represents the distribution of Y6, and the yellow color represents the blend phase.

[0043] Figure 10 It is the Raman Mapping diagram of PM6 in Example 2 of the present invention.

[0044] Figure 11 It is the Raman Mapping diagram of Y5 in Example 2 of the present invention.

[0045] Figure 12 It is the grayscale conversion diagram of PM6 in Example 2 of the present invention.

[0046] Figure 13 It is the grayscale conversion diagram of Y5 in Example 2 of the present invention.

[0047] Figure 14 In Example 2 of the present invention, it is the component distribution diagram of PM6 and Y5. The red color represents the distribution of PM6, the blue color represents the distribution of Y5, and the yellow color represents the blend phase. Detailed implementation manners

[0048] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0049] As a non-destructive and high-resolution analysis method, Raman Mapping technology can penetrate into the nanoscale and provide spectral data of materials at different positions. The purpose of the present invention is to provide a method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell based on Raman Mapping technology.

[0050] Referring to the attached Figure 1 , the method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to the present invention includes the following steps:

[0051] S1. Scan the active layer of the organic photovoltaic cell through a Raman microscope, extract the gray intensity values, and obtain the Raman Mapping gray-scale image;

[0052] S2. Define a threshold for the Raman Mapping gray-scale image, compare the gray intensity values with the threshold, and distinguish the comparison results into different colors, which respectively represent the regions dominated by the donor material, the regions dominated by the acceptor material, and the regions of donor-acceptor blending, and synthesize a color image;

[0053] S3. Convert the regions of different colors into numerical information, count the number of pixel points in different regions, and realize the quantitative characterization of the donor-acceptor distribution in the active layer of the organic photovoltaic cell.

[0054] By analyzing the characteristic peaks related to the donor and acceptor materials in the Raman spectrum, the present invention can accurately locate the distribution of these materials in space, and calculate quantitative parameters such as the distribution density and uniformity of each component through data processing.

[0055] First, prepare a sample of the active layer of the organic photovoltaic cell to ensure that the surface is flat and clean. If the active layer is relatively thick, it may be necessary to use a thin slice slicing technique to obtain a cross-sectional sample.

[0056] In one embodiment of the present invention, step S1 includes:

[0057] S11. Scan the active layer of the organic photovoltaic cell through a high-resolution Raman microscope, obtain the Raman spectral data of each tiny region, and obtain the Raman image of the donor material and the Raman image of the acceptor material.

[0058] Specifically, the Raman microscope can detect and distinguish different Raman characteristic peaks of the donor and acceptor materials by analyzing the spectral response of the materials. This step can provide reliable spectral basic data for subsequent quantitative analysis, ensure effective sampling of each region during the scanning process, and efficiently obtain relevant structural information.

[0059] During the scanning process, by accurately identifying the Raman characteristic peaks related to the acceptor material, the Raman spectral data of each scanning point is extracted. This process is directly obtained through a Raman spectrometer. The characteristic signals include not only information such as the displacement, peak intensity, and width of the Raman peak, but also can reflect the distribution of the acceptor material in different regions. Through an automated algorithm written in MATLAB, a large amount of Raman data can be quickly processed, and the spectral characteristics at different positions can be displayed in the form of an image, ensuring accurate capture of the distribution characteristics of the donor-acceptor materials from the microscopic level.

[0060] S12. Adjust the size of the Raman image of the donor material or the Raman image of the acceptor material to obtain Raman images with consistent pixel correspondence. Since the Raman images may have inconsistent sizes, one of the images needs to be resized to ensure consistent pixel correspondence during subsequent analysis.

[0061] The number of rows and columns of the horizontal pixel array is the same. When comparing, it is necessary to ensure that the gray values are compared at the same position. For example, in Figure A, (1, 2, 0.5) and (5, 6, 0.4); in Figure B, (1, 2, 0.8) and (5, 6, 0.1). When comparing gray values, only (1, 2, 0.5) and (1, 2, 0.8) can be compared; not (1, 2, 0.5) and (5, 6, 0.1); because they represent completely different positions. However, when the built-in function of the program extracts gray values, the dimensions may be inconsistent. For example, Figure A has (10000, 9998, 0.6), but Figure B only goes up to (9996, 9996, 0.5). Although this will not cause an obvious difference in the results because a single pixel is too small; but for the computer, logical operations cannot be performed. The dimension of (9996, 9996) needs to be expanded to (10000, 9998) through an interpolation algorithm to ensure that the logical comparison can run properly. Therefore, a process of unifying the dimensions is necessary.

[0062] S13. Grayscale the Raman images with consistent pixel correspondence, and extract the gray intensity values respectively to obtain the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material.

[0063] The array format is (x, y, gray value), where x and y are the coordinates of the position on the scanning area respectively. For example, (1, 3, 0.6) means that the gray value at the position (1, 3) in the image is 0.6.

[0064] Since Raman Mapping images are generally color images containing signals of multiple wavelengths, each image is converted into a grayscale image to facilitate the extraction of the gray intensity values. The converted grayscale image will be helpful for subsequent quantitative analysis, such as the comparison and statistics of material distribution. Each pixel value of the grayscale image represents the signal intensity of that area.

[0065] To ensure that the Raman spectral data of each tiny area on the battery surface can be accurately obtained, the laser wavelength of the Raman microscope is set to 532 nm, and the scanning resolution is set to 0.1 μm.

[0066] In an embodiment of the present invention, step S2 includes:

[0067] S21. To facilitate the distinction of the distribution of different materials, a threshold is defined for the Raman Mapping grayscale image, and the range of the threshold is 0.1 to 0.5. Defining the threshold is an empirical value, roughly between 0.1 and 0.5. However, the difference in this threshold only affects the statistical value of the number of pixels, and does not affect the law. For example: when the threshold is 0.1, the number of donor bulk pixels is 782560, and the number of acceptor bulk pixels is 569360; when the threshold is 0.5, the number of donor bulk pixels is 56924, and the number of acceptor bulk pixels is 24156. Whether the threshold is 0.1 or 0.5, the fact that the number of donor pixels is greater than the number of acceptor bulk pixels is certain. Generally, this kind of quantitative analysis is not to determine a precise value, but to reflect a law through digital quantification. In this regard, the method of the present invention is feasible.

[0068] S22. By comparing the grayscale intensity value with the threshold, the comparison result is set to different colors according to the RGB three-color mode. Specifically, in an embodiment of the present invention, the comparison result is distinguished by red, blue, and yellow. If it is determined that "the grayscale values of the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material are both less than the threshold" holds, it is the donor-acceptor blend region and is marked as yellow; if it is determined that "the grayscale values of the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material are both less than the threshold" does not hold, and "the grayscale value of the Raman Mapping grayscale image of the donor material is greater than the grayscale value of the Raman Mapping grayscale image of the acceptor material" holds, it represents the donor phase and is marked as red; if it does not hold, it represents the acceptor phase and is marked as blue. Among them, the colors of each region can be flexibly set according to the RGB three-color mode, and this setting method is also covered by the protection scope of the present invention.

[0069] The comparison result is marked with colors in the figure. The specific steps: First step, if the grayscale values of the Raman Mapping grayscale image of the donor material and the Raman Mapping grayscale image of the acceptor material are both less than the threshold, it is marked with yellow, indicating the blend phase. If the first step is not satisfied, it is divided into two cases. If the grayscale value of the Raman Mapping grayscale image of the donor material is greater than the grayscale value of the Raman Mapping grayscale image of the acceptor material, it is the donor and is marked with red; if the grayscale value of the Raman Mapping grayscale image of the donor material is less than the grayscale value of the Raman Mapping grayscale image of the acceptor material, it is the acceptor and is marked with blue. The threshold is only used for the first-step comparison. In fact, the threshold is for judging the blend phase.

[0070] S23. Synthesize a color image.

[0071] S24. Use an image processing algorithm to perform quantitative analysis on the Raman Mapping grayscale image.

[0072] The built-in image processing toolbox in MATLAB provides a variety of functions that can perform operations such as smoothing, denoising, and edge detection on scanned images to improve the accuracy of data. To improve the visualization effect of the image and reduce noise, the synthetic image is smoothed. Through the Gaussian filter, the boundaries can be smoothed, thus obtaining a clearer and more continuous material distribution image. This step helps to improve the accuracy of subsequent analysis and avoid errors caused by noise points or irregular color distributions.

[0073] In particular, although the image processing of the present invention is implemented through the computer MATLAB software program, it should not be limited to the MATLAB software. Within the scope of the principles of the present invention, any program and operating software that can achieve the above functions are acceptable.

[0074] On this basis, a segmentation algorithm is applied to extract the regions of the donor and acceptor materials, and the Raman signal intensities of each region are calculated. Through the algorithm, the spatial distribution of the Raman signal is further analyzed, and quantitative data such as the distribution density, uniformity, and relative concentration of the donor-acceptor materials are extracted. These data can be used to generate a distribution map of the donor-acceptor materials and reveal their changes in different regions through quantitative analysis. The clustering algorithm in MATLAB can perform clustering analysis on the distribution of the materials to determine the optimal distribution pattern of the donor-acceptor phases.

[0075] In an embodiment of the present invention, step S3 includes:

[0076] S31. To facilitate statistical analysis, in the synthesized color image, the regions represented by different colors are converted into integer values. Specifically, different regions are distinguished by red, blue, and yellow. Therefore, the red region is marked as 1, the blue region is marked as 2, and the yellow region is marked as 3. In this way, the color information of the image is converted into numerical information, which is convenient for subsequent quantitative statistics.

[0077] S32. After obtaining the synthesized image represented by integers, count the number of pixel points in the regions corresponding to the integer values, and characterize the donor-acceptor distribution of the active layer of the organic photovoltaic cell through the number of pixel points. Specifically, count the number of pixel points in the red region (donor-dominated region), the number of pixel points in the blue region (acceptor-dominated region), and the number of pixel points in the yellow region (i.e., the donor-acceptor mixed region). Through these statistical results, the distribution of the materials in the active layer can be quantitatively analyzed.

[0078] The counted number of pixels will reflect the distribution ratio of different materials in the image, thus providing data support for subsequent material optimization and battery performance analysis. For example, more red pixels indicate that the distribution of the donor material PM6 is dominant, while more blue pixels indicate a larger distribution of the acceptor material Y6. The yellow area represents the blend of the donor and acceptor.

[0079] Finally, through the analysis of the above steps, a synthetic image and corresponding statistical results were obtained. On the image, researchers can visually observe the distribution of different materials in the active layer, and thus further optimize the material design. For example, if it is found that the blend phase region in some areas is too large, which may lead to charge recombination or low charge transport efficiency, then the material blend ratio can be adjusted or the coating process can be changed to optimize the battery performance.

[0080] Through the statistical results, researchers can also evaluate the impact of the blend phase content on battery performance such as the photoelectric conversion efficiency. If the proportion of the yellow area is large, it indicates that the blend phase content is too much, which is not conducive to the diffusion of excitons. On the contrary, if the red or blue area is dominant, it may indicate that the blend phase content is too little, which is not conducive to exciton dissociation. Therefore, further optimization is needed.

[0081] To further verify the effect of the present invention, the present invention also provides a comparative example and two specific embodiments.

[0082] Comparative example:

[0083] In the comparative example, the commonly used donor material PM6 and acceptor material Y6 in organic photovoltaic cells were selected for blending, and test samples were prepared at a certain mass ratio. The donor-acceptor blend solution was prepared into a thin film by spin coating and used as the active layer of the photovoltaic device.

[0084] Through the AFM (Atomic Force Microscope) image, information such as the roughness, film thickness, particle size, and morphology of the active layer can be obtained, as shown in the appendix. Figure 2 Especially when the materials are blended at different mass ratios, AFM can help observe the morphological characteristics formed by the donor and acceptor materials on the surface. For example, different phase separation characteristics may be seen, such as the particle structure of the donor material and acceptor material, and the aggregation of particles.

[0085] In this comparative example, TEM (Transmission Electron Microscope) was used to observe the internal distribution of the prepared organic photovoltaic material sample, especially the phase separation of the donor (PM6) and acceptor (Y6) materials, as shown in the appendix. Figure 3As shown. TEM images can show the distribution pattern of materials in the active layer, such as whether there is phase separation, the degree of separation, and whether a uniform blend structure is formed. Through TEM imaging, researchers can observe the size, distribution, and interfacial characteristics of different molecules or particles, thus providing certain reference for optimizing the distribution of materials.

[0086] Although both AFM and TEM can provide high-resolution images of the morphology of the active layer of organic photovoltaic materials, their main limitation is that they cannot directly quantitatively analyze the spatial distribution of donor and acceptor materials. AFM is mainly used for observing surface morphology. It can reveal information such as the roughness, particle morphology, and film thickness of materials, but it cannot distinguish the phase distribution of different materials.

[0087] Although TEM has higher spatial resolution and can provide detailed structural information inside the material, due to the limitations of its imaging method and sample preparation process, it can only qualitatively describe the phase separation of donors and acceptors and cannot quantitatively analyze their distribution density and uniformity in the active layer.

[0088] Example 1:

[0089] In this example, the commonly used donor material PM6 and acceptor material Y6 in organic photovoltaic cells were selected for blending, and test samples were prepared at a certain mass ratio.

[0090] A high-resolution Raman microscope with a laser wavelength of 532 nm was used. The surface of the battery sample was scanned at 10×10 μm with a scanning step of 0.2 μm. Raman spectral data of each scanning point were recorded, and Raman peaks of donors and acceptors were extracted.

[0091] Based on the Raman spectrogram, the characteristic peak positions of each material can be obtained, and the wavelength range of 500 - 3000 nm where the characteristic peaks are located was selected -1 , as shown in the appendix Figure 4 .

[0092] Next, the obtained Raman Mapping images were first resized, then the gray values of the grayscale images were extracted, compared with the threshold, color maps were drawn, and pixel points were counted. Counting pixel points provides a basis for quantitatively analyzing the size of the phase region.

[0093] Appendix Figure 5 is the Raman Mapping diagram of PM6, appendix Figure 6 is the Raman Mapping diagram of Y6, appendix Figure 5 and appendix Figure 6 are the original color maps collected by the device; appendix Figure 7 is the grayscale image of PM6, appendix Figure 8 is the grayscale image of Y6, appendixFigure 7 and attached Figure 8 is the grayscale image after grayscale processing. It can be seen that in the PM6:Y6 blend system, red represents the donor phase indicating the distribution of PM6, blue represents the acceptor phase indicating the distribution of Y6, and yellow represents the blend phase. Among them, the proportion of the donor phase is 45.3%, the proportion of the acceptor phase is 46.2%, and the proportion of the blend phase is 8.5%, as attached Figure 9 shown

[0094] Compared with AFM and TEM, Raman Mapping can directly distinguish donor and acceptor materials through characteristic Raman peaks and precisely display their distribution in the active layer. Different from AFM and TEM that can only qualitatively observe the morphology, Raman Mapping provides quantitative data on the material distribution and can quantify the distribution density and uniformity of donors and acceptors

[0095] Example 2:

[0096] In this example, the commonly used donor material PM6 and acceptor material Y5 in organic photovoltaic cells were selected, and a quasi-planar heterojunction was prepared by sequential deposition

[0097] A high-resolution Raman microscope with a laser wavelength of 532 nm was used. The surface of the battery sample was scanned at 8×8 μm with a scanning step of 0.2 μm. Raman spectral data of each scanning point were recorded, and Raman peaks of donors and acceptors were extracted

[0098] Based on the Raman spectrogram, the characteristic peak positions of each material can be obtained, and the wavelength range of 500 - 3000 nm where the characteristic peaks are located is selected -1 .

[0099] Next, the obtained Raman Mapping image was first resized, then the grayscale values of the grayscale image were extracted, compared with the threshold, a color map was drawn, and pixel points were counted. Counting pixel points provides a basis for quantitatively analyzing the size of the phase region

[0100] Attached Figure 10 is the Raman Mapping image of PM6, attached Figure 11 is the Raman Mapping image of Y5, attached Figure 10 and attached Figure 11 is the original color image collected by the device; attached Figure 12 is the grayscale image of PM6, attached Figure 13 is the grayscale image of Y5, attached Figure 10 and attached Figure 11 is the grayscale image after grayscale processing

[0101] It can be seen that in the double-layer system of PM6 / Y6, the red color represents the donor phase, indicating the distribution of PM6, the blue color represents the acceptor phase, indicating the distribution of Y5, and the yellow color represents the blend phase. Among them, the proportion of the donor phase is 42.5%, the proportion of the acceptor phase is 43.3%, and the proportion of the blend phase is 14.2%, as shown in the appendix Figure 14 as follows.

[0102] In summary, by analyzing the characteristic peaks related to the donor and acceptor materials in the Raman spectrum, the present invention can accurately locate the distribution of these materials in space, and calculate quantitative parameters such as the distribution density and uniformity of each component through data processing. This method can not only help researchers deeply understand the interaction between the donor and acceptor materials in the active layer, but also reveal their influence on key performance indicators such as photoelectric conversion efficiency and stability. By precisely controlling and optimizing the donor-acceptor distribution, the present invention provides a scientific basis for improving the performance of organic photovoltaic cells, and has broad application prospects and technical value.

[0103] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention by those skilled in the art within the technical scope disclosed by the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell, characterized in that, Including the steps: S1. Scanning the active layer of the organic photovoltaic cell by a Raman microscope, extracting the gray intensity values, and obtaining the Raman Mapping gray-scale image; S2. Defining a threshold for the Raman Mapping gray-scale image, comparing the gray intensity values with the threshold, classifying the comparison results into different colors, respectively representing the region dominated by the donor material, the region dominated by the acceptor material, and the region of donor-acceptor blending, and synthesizing a color image; S3. Converting the regions represented by different colors into numerical information, counting the number of pixel points in different regions, and realizing the quantitative characterization of the donor-acceptor distribution in the active layer of the organic photovoltaic cell.

2. The method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 1, wherein The step S1 includes: S11. Scanning the active layer of the organic photovoltaic cell by a Raman microscope, obtaining the Raman spectral data of each tiny region, and obtaining the Raman image of the donor material and the Raman image of the acceptor material; S12. Adjusting the size of the Raman image of the donor material or the Raman image of the acceptor material to obtain Raman images with consistent pixel correspondence; S13. Performing gray-scale processing on the Raman images with consistent pixel correspondence, respectively extracting the gray intensity values, and obtaining the Raman Mapping gray-scale image of the donor material and the Raman Mapping gray-scale image of the acceptor material.

3. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 2, wherein The step S11 includes: The Raman microscope detects and distinguishes different Raman characteristic peaks of the donor and acceptor materials, and extracts the characteristic signals related to the donor and acceptor materials; The characteristic signals include the displacement, peak intensity, and width of the Raman peak, and the distribution status of the donor and acceptor materials in different regions is reflected by the characteristic signals.

4. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 1, wherein In the step S1, the laser wavelength of the Raman microscope is set to 532 nm, and the scanning resolution is set to 0.1 μm.

5. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 1, wherein In the step S2, the comparison results are set to different colors according to the RGB color model.

6. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 1, wherein The step S2 includes: S21. Defining a threshold for the Raman Mapping gray-scale image; S22. Using red, blue, and yellow to distinguish the comparison results. If it is determined that "the gray values of the Raman Mapping gray-scale image of the donor material and the Raman Mapping gray-scale image of the acceptor material are both less than the threshold" holds, it is the donor-acceptor blending region and is marked as yellow; if it is determined that "the gray values of the Raman Mapping gray-scale image of the donor material and the Raman Mapping gray-scale image of the acceptor material are both less than the threshold" does not hold, and "the gray value of the Raman Mapping gray-scale image of the donor material is greater than the gray value of the Raman Mapping gray-scale image of the acceptor material" holds, it represents the donor phase and is marked as red; if it does not hold, it represents the acceptor phase and is marked as blue; S23. Synthesizing a color image.

7. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 6, characterized in that, The step S2 further includes: S24. Performing quantitative analysis on the Raman Mapping gray-scale image using an image processing algorithm.

8. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 7, characterized in that The step S4 includes: The application of the segmentation algorithm extracts the regions of the donor and acceptor materials, and calculates the Raman signal intensity of each region; Analyze the spatial distribution of the Raman signal, and extract the distribution density, uniformity, and relative concentration of the donor material and the acceptor material; Process using image processing tools, including smoothing, denoising, and edge detection operations.

9. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 6, characterized in that The range of the threshold is 0.1 - 0.

5.

10. A method for quantitatively characterizing the donor-acceptor distribution in the active layer of an organic photovoltaic cell according to claim 1, wherein The step S3 includes: S31. Convert the regions represented by different colors into integer values; S32. Count the number of pixel points in the regions corresponding to the integer values, and characterize the donor-acceptor distribution of the active layer of the organic photovoltaic cell through the number of pixel points.

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