Automatic quantitative statistical characterization method and device for information of supported nanoparticles

Abstract

The invention relates to the technical field of image processing, in particular to an automatic quantitative statistical characterization method and device for load-type nanoparticle information, and the method comprises the steps: obtaining a microscopic image of dissolution-type load-type nanoparticles, preprocessing the microscopic image to obtain a processed image, marking the processed image to obtain the position information of each dissolution type supported nanoparticle, thereby determining the particle diameter of each dissolution type supported nanoparticle, and obtaining the metal density and relative atomic mass of the dissolution type supported nanoparticles, the pseudo cubic cell parameters of perovskite and the stoichiometric number of doped elements; and calculating the average number of atoms of the particles based on the particle diameter, the metal density and the relative atomic mass calculation formula, and calculating the average depth of the perovskite material based on the average number of atoms of the particles, the pseudo-cubic cell parameters of the perovskite and the stoichiometric number calculation formula of the doped elements. Therefore, the problems that the number of nano-particles loaded on the base material is large, and manual analysis and statistics are difficult are solved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method and device for automatic quantitative statistical characterization of loaded nanoparticle information. Background Art

[0002] Nanoparticles play a vital role in many diverse fields. For example, they are widely used in photography, catalysis, biomarkers, optoelectronics, information storage, and the preparation of magnetic ferrofluids. The microscopic morphology and distribution of supported nanoparticles are closely related to their performance. For example, metal nanoparticles in supported metal catalysts typically require an optimal particle size of less than 3 nanometers to increase the specific surface area of ​​the active metal, thereby improving their catalytic activity per unit mass.

[0003] Currently, automated quantitative analysis of supported nanoparticles focuses on particle size, area, and number density. While these two parameters are certainly valuable, they are insufficient to fully capture the microscopic morphology and distribution of the particles. Nanoparticle shape also significantly influences their performance. For example, in the benzene hydrogenation reaction, supported platinum nanoparticle catalysts with a cuboctahedral shape can produce cyclohexane and cyclohexene, while cubic particles only produce cyclohexene. The spacing between adjacent nanoparticles can reflect their tendency to sinter and can be used to investigate methods for maintaining catalyst dispersion. In addition, many other physical quantities are relevant to the performance of supported nanoparticles but remain underexplored.

[0004] Currently, no effective solution has been proposed to the problem of insufficient extraction of characteristic data of loaded nanoparticles in related technologies. Summary of the Invention

[0005] The present application provides a method and device for automatic quantitative statistical characterization of loaded nanoparticle information to solve the problem that the number of nanoparticles loaded on the matrix material is large and difficult to analyze manually.

[0006] The first aspect of the present application provides an automatic quantitative statistical characterization method for loaded nanoparticle information, comprising the following steps: obtaining a microscopic image of loaded nanoparticles of a dissolution type, preprocessing the microscopic image to obtain a processed image, and marking the processed image to obtain the position information of the loaded nanoparticles of each dissolution type; determining the particle diameter of the loaded nanoparticles of each dissolution type based on the position information of the loaded nanoparticles of each dissolution type, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type; based on the particle diameter, the metal density and the relative atomic mass, calculating the average particle atomic number using a preset formula, and based on the average particle atomic number, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element, calculating the average depth of the perovskite material using a preset formula.

[0007] Optionally, after obtaining the position information of the loaded nanoparticles of each dissolution class in the processed image, it also includes: calculating the dispersion and particle area of ​​the loaded nanoparticles of each dissolution class based on the position information of the loaded nanoparticles of each dissolution class, and obtaining the minimum circumscribed rectangle of the loaded nanoparticles of each dissolution class based on a preset convex hull strategy; calculating the rectangularity, circularity and equivalent circle radius of the loaded nanoparticles of each dissolution class according to the minimum circumscribed rectangle and the particle area.

[0008] Optionally, after obtaining the processed image, it also includes: using a preset triangulation algorithm to obtain the adjacent spacing values ​​of each dissolved type of loaded nanoparticles in the microscopic image; eliminating the values ​​in the adjacent spacing values ​​that meet the preset abnormal conditions to obtain a neighboring particle network diagram and a Voronoi diagram.

[0009] Optionally, the preprocessing of the microscopic image to obtain a processed image includes: obtaining scale information of the microscopic image; based on the scale information, filtering and denoising the microscopic image using a preset filtering and denoising processing strategy, and performing contrast-limited adaptive histogram equalization, adaptive threshold binarization, morphological opening operation de-debris preprocessing and image segmentation on the filtered and denoised microscopic image to obtain the processed image.

[0010] Optionally, the preset formula for calculating the average number of atoms in the particles is:

[0011]

[0012] Among them, N M，p is the average atomic number of the granular metal M, ρ M is the density of metal M, AM is the relative atomic mass of metal M, N A is Avogadro's constant, f i The diameter is φ i The proportion of particles.

[0013] Optionally, the preset perovskite material average depth calculation formula is:

[0014]

[0015] Where d is the average depth, n is the number of particles, and a p is the pseudo cubic unit cell parameter of the perovskite, L is the length of the perovskite material, W is the width of the perovskite material, x M is the stoichiometric number of the doping element M.

[0016] Optionally, the dispersion degree is the ratio of the number of dissolved supported nanoparticles in the microscopic image to the actual matrix area corresponding to the microscopic image.

[0017] Optionally, after obtaining the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius, it includes: generating a histogram of normal distribution fitting based on the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius.

[0018] The second aspect of the present application provides an automatic quantitative statistical characterization device for loaded nanoparticle information, including: a processing module for obtaining a microscopic image of loaded nanoparticles of the dissolution type, preprocessing the microscopic image to obtain a processed image, and marking the processed image to obtain the position information of the loaded nanoparticles of each dissolution type; an acquisition module for determining the particle diameter of the loaded nanoparticles of each dissolution type based on the position information of the loaded nanoparticles of each dissolution type, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type; a calculation module for calculating the average atomic number of the particles based on the particle diameter, the metal density and the relative atomic mass using a preset average atomic number calculation formula, and calculating the average depth of the perovskite material based on the average atomic number of the particles, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element using a preset average depth calculation formula of the perovskite material.

[0019] Optionally, after obtaining the position information of the loaded nanoparticles of each dissolution class in the processed image, the acquisition module is also used to: calculate the dispersion and particle area of ​​the loaded nanoparticles of each dissolution class based on the position information of the loaded nanoparticles of each dissolution class, and obtain the minimum circumscribed rectangle of the loaded nanoparticles of each dissolution class based on a preset convex hull strategy; calculate the rectangularity, circularity and equivalent circle radius of the loaded nanoparticles of each dissolution class based on the minimum circumscribed rectangle and the particle area.

[0020] Optionally, after obtaining the processed image, the acquisition module is further used to: use a preset triangulation algorithm to obtain the adjacent spacing values ​​of each dissolved type of loaded nanoparticles in the microscopic image; eliminate the values ​​in the adjacent spacing values ​​that meet the preset abnormal conditions to obtain a neighboring particle network diagram and a Voronoi diagram.

[0021] Optionally, the microscopic image is preprocessed to obtain a processed image, and the processing module is further used to: obtain scale information of the microscopic image; based on the scale information, use a preset filtering and denoising processing strategy to filter and denoise the microscopic image, and perform contrast-limited adaptive histogram equalization, adaptive threshold binarization, morphological opening operation de-debris preprocessing and image segmentation on the filtered and denoised microscopic image to obtain the processed image.

[0022] Optionally, the preset formula for calculating the average number of atoms in the particles is:

[0023]

[0024] Among them, N M,p is the average atomic number of the granular metal M, ρ M is the density of metal M, A M is the relative atomic mass of metal M, N A is Avogadro's constant, f i The diameter is φ i The proportion of particles.

[0025] Optionally, the preset calculation formula for the average depth of the perovskite material is:

[0026]

[0027] Where d is the average depth, n is the number of particles, and a p is the pseudo cubic unit cell parameter of the perovskite, L is the length of the perovskite material, W is the width of the perovskite material, x M is the stoichiometric number of the doping element M.

[0028] Optionally, the dispersion degree is the ratio of the number of dissolved supported nanoparticles in the microscopic image to the actual matrix area corresponding to the microscopic image.

[0029] Optionally, after obtaining the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius, the acquisition module is also used to: generate a histogram of normal distribution fitting based on the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius.

[0030] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the automatic quantitative statistical characterization method for loaded nanoparticle information as described in the above embodiment.

[0031] The fourth embodiment of the present application provides a computer program product having a computer program stored thereon, which is executed by a processor to implement the automatic quantitative statistical characterization method for loaded nanoparticle information as described in the above embodiments.

[0032] In the above embodiment, a microscopic image of the loaded nanoparticles of the dissolution type is obtained, the microscopic image is pre-processed to obtain a processed image, and the processed image is marked to obtain the position information of each loaded nanoparticle of the dissolution type, thereby determining the particle diameter of each loaded nanoparticle of the dissolution type, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite, and stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type. The average atomic number of the particles is calculated based on the calculation formula of the particle diameter, metal density, and relative atomic mass, and the average depth of the perovskite material is calculated based on the calculation formula of the average atomic number of the particles, pseudo-cubic unit cell parameters of the perovskite, and stoichiometric number of the doping element. Thus, the problem of a large number of nanoparticles loaded on the matrix material and difficulty in manual analysis and statistics is solved, and the beneficial effect of quickly and accurately converting images into quantitative data for scientific research is achieved.

[0033] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0035] Figure 1 A flowchart of a method for automatic quantitative statistical characterization of supported nanoparticle information provided in accordance with an embodiment of the present application;

[0036] Figure 2 A schematic flow chart of a method for automatic quantitative statistical characterization of supported nanoparticle information according to one embodiment of the present application;

[0037] Figure 3 A schematic diagram of a scanning electron microscope image of a perovskite oxide material sample loaded with dissolved metal nanoparticles according to an embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of a particle microscopic image after contrast-limited adaptive histogram equalization according to one embodiment of the present application;

[0039] Figure 5 This is a schematic diagram of a particle microscopic image after adaptive threshold segmentation and binarization according to one embodiment of the present application;

[0040] Figure 6 A schematic diagram of a microscopic image of particles after morphological opening operation according to one embodiment of the present application;

[0041] Figure 7 This is a schematic diagram of the minimum circumscribed rectangle of a particle according to an embodiment of the present application;

[0042] Figure 8 This is a schematic diagram of the minimum circumscribed circle of a particle according to an embodiment of the present application;

[0043] Figure 9 A schematic diagram of a network diagram of neighboring particles according to an embodiment of the present application;

[0044] Figure 10 A schematic diagram of a Voronoi diagram of a particle according to an embodiment of the present application;

[0045] Figure 11 This is a schematic diagram of statistical analysis of part of the data of an embodiment of the present application;

[0046] Figure 12 Schematic diagram of an automatic quantitative statistical characterization device for supported nanoparticle information according to an embodiment of the present application;

[0047] Figure 13 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0049] The following describes the automatic quantitative statistical characterization method and device for the information of the loaded nanoparticles of the embodiment of the present application with reference to the accompanying drawings. In response to the problem that the number of matrix material loaded nanoparticles is large and difficult to manually analyze and count mentioned in the above background technology, the present application provides an automatic quantitative statistical characterization method for the information of loaded nanoparticles, in which a microscopic image of the loaded nanoparticles of the dissolution type is obtained, the microscopic image is preprocessed to obtain a processed image, the processed image is marked to obtain the position information of each loaded nanoparticle of the dissolution type, thereby determining the particle diameter of each loaded nanoparticle of the dissolution type, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type, and calculating the average atomic number of the particles based on the particle diameter, metal density and relative atomic mass calculation formula, and calculating the average depth of the perovskite material based on the average atomic number of the particles, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element. Thus, the problem that the number of matrix material loaded nanoparticles is large and difficult to manually analyze and count is solved, and the beneficial effect of quickly and accurately converting the image into quantitative data for scientific research is achieved.

[0050] Specifically, Figure 1 This is a flow chart of a method for automatic quantitative statistical characterization of supported nanoparticle information provided in an embodiment of the present application.

[0051] like Figure 1 As shown, the automatic quantitative statistical characterization method of the loaded nanoparticle information includes the following steps:

[0052] In step S101 , a microscopic image of the dissolution-type supported nanoparticles is obtained, and the microscopic image is preprocessed to obtain a processed image, and the processed image is marked to obtain position information of each dissolution-type supported nanoparticle.

[0053] Supported nanoparticles are naturally occurring or artificially manufactured, discrete, nanometer-sized solid materials supported by a matrix. Particle microscopy images are a series of two-dimensional images obtained by observing and recording the particle material using a microscope (e.g., electron microscope, atomic force microscope, etc.).

[0054] It should be noted that both the dissolution-type supported nanoparticles and the non-dissolution-type supported nanoparticles can be characterized by using a microscopic system (such as an electron microscope, an atomic force microscope, etc.) to collect images of the samples.

[0055] Optionally, in some embodiments, preprocessing the microscopic image to obtain a processed image includes: obtaining scale information of the microscopic image; filtering and denoising the microscopic image based on the scale information using a preset filtering and denoising processing strategy, and performing contrast-limited adaptive histogram equalization, adaptive threshold binarization, morphological opening operation de-debris preprocessing and image segmentation on the filtered and denoised microscopic image to obtain a processed image.

[0056] Among them, the preset filtering and denoising processing strategies are available for selection, including Gaussian filtering, bilateral filtering, and adaptive median filtering. The specific filtering and denoising methods are as follows:

[0057] Gaussian filtering: Filters the image using a two-dimensional Gaussian smoothing kernel with a specified standard deviation. The smoothing kernel formula is:

[0058]

[0059] Where q is the pixel position of the center of the kernel, p is the position of other data points in the filter, and σ is the standard deviation parameter that needs to be set;

[0060] Bilateral filtering: Add pixel value weighting terms on the basis of Gaussian filtering to retain the edge information in the image. The filter formula is:

[0061]

[0062] Among them, I p is the pixel value of point p, * is the convolution symbol, W q represents the weighted sum of each pixel value in the filter,

[0063]

[0064] G s is the spatial distance weight, G r is the pixel value weight:

[0065]

[0066] Among them, I q is the pixel value of point q;

[0067] Adaptive median filtering: Compare the pixel values ​​within a certain neighborhood, take the median as the new value of the center pixel of this neighborhood, and determine whether the median point and center point of the current neighborhood are noise points. Dynamically change the window size of the median filter accordingly to take into account both the denoising effect and the detail protection effect.

[0068] It should be noted that different filtering denoising methods are distinguished by the type of noise and different parameter values. For example, Gaussian noise should use Gaussian filtering or bilateral filtering, and then the value of σ should be selected according to the noise amount and particle size. Salt and pepper noise should use adaptive median filtering.

[0069] Among them, contrast-limited adaptive histogram equalization means that in the histogram equalization process, only the histogram distribution within the local area window is used to construct the mapping function, the image is divided into several sub-blocks, the sub-blocks are equalized, and a threshold is set for the histogram of the original image. If a grayscale of the histogram exceeds the threshold, it is cropped, and then the part exceeding the threshold is evenly distributed to each grayscale level, and finally the sub-blocks are interpolated.

[0070] Among them, adaptive threshold binarization refers to a local threshold binarization method based on the integral image. First, all pixel values ​​in the rectangular area from (0,0) to (i,j) in the original image are added together as the value of the integral image position (i,j). In this way, the image is traversed pixel by pixel to obtain a complete integral image. Then, the length and width of the original image are read and the length and width of each pixel neighborhood are calculated according to the following formula.

[0071]

[0072] Where a' is the length or width of each pixel's neighborhood, a is the length or width of the original image, and [x] is the rounding function. For each pixel (i, j) in the original image, the integral graph is used to calculate the mean of the pixel values ​​within its neighborhood. The default sensitivity is t∈(0,1). If the pixel value at position (i, j) is no greater than (1-t) times the mean of its neighborhood pixels, the pixel is set to background; otherwise, it is set to foreground.

[0073] In step S102, the particle diameter of each dissolution type supported nanoparticle is determined based on the position information of each dissolution type supported nanoparticle, and the metal density, relative atomic mass, pseudo cubic unit cell parameter of the perovskite and the stoichiometric number of the doping element of the dissolution type supported nanoparticle are obtained.

[0074] In step S103, based on the particle diameter, metal density and relative atomic mass, the average particle atomic number is calculated using a preset particle average atomic number calculation formula, and based on the average particle atomic number, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric coefficient of the doping element, the average depth of the perovskite material is calculated using a preset perovskite material average depth calculation formula.

[0075] Optionally, in some embodiments, the preset formula for calculating the average number of atoms in the particles is:

[0076]

[0077] Among them, N M，p is the average atomic number of the granular metal M, ρ M is the density of metal M, A M is the relative atomic mass of metal M, N A is Avogadro's constant, f i The diameter is φ i The proportion of particles.

[0078] Optionally, in some embodiments, the preset calculation formula for the average depth of the perovskite material is:

[0079]

[0080] Where d is the average depth, n is the number of particles, and a p is the pseudo cubic unit cell parameter of the perovskite, L is the length of the perovskite material, W is the width of the perovskite material, x M is the stoichiometric number of the doping element M.

[0081] Specifically, the automatic quantitative statistical characterization method of the loaded nanoparticle information is as follows Figure 2 As shown, a microscopic image of the substrate-loaded nanoparticles is first obtained. The microscopic image is substantially clear, and the substrate and the loaded particles can be clearly distinguished.

[0082] The obtained microscopic images of the particles are manually marked to obtain scale information, and then subjected to filtering denoising, contrast-limited adaptive histogram equalization, adaptive threshold binarization, and morphological opening operation to remove debris preprocessing to obtain a preprocessed image; the particles in the preprocessed image are manually segmented and marked in sequence to determine the position information of each particle in the image.

[0083] The particle diameter of each dissolution-type loaded nanoparticle is determined based on the obtained particle information. After inputting the metal density and relative atomic mass, the average atomic number of the particles can be obtained. For the dissolution-type loaded nanoparticles, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element are input to obtain the average depth of the perovskite material corresponding to the total atomic number of the particles.

[0084] It should be noted that the average particle atomic number of both the dissolution-type supported nanoparticles and the non-dissolution-type supported nanoparticles can be calculated using a preset formula for calculating the average particle atomic number.

[0085] Optionally, in some embodiments, after obtaining the position information of the loaded nanoparticles of each dissolution class in the processed image, it also includes: calculating the dispersion and particle area of ​​the loaded nanoparticles of each dissolution class based on the position information of the loaded nanoparticles of each dissolution class, and obtaining the minimum circumscribed rectangle of the loaded nanoparticles of each dissolution class based on a preset convex hull strategy; calculating the rectangularity, circularity and equivalent circle radius of the loaded nanoparticles of each dissolution class based on the minimum circumscribed rectangle and the particle area.

[0086] In some embodiments, the dispersion is the ratio of the number of dissolved loaded nanoparticles in the microscopic image to the actual matrix area corresponding to the microscopic image, the rectangularity is defined as the ratio of the particle area to the area of ​​its minimum circumscribed rectangle, and the circularity is defined as the ratio of the particle area to the area of ​​its minimum circumscribed circle.

[0087] Specifically, based on the position information of each dissolution type of loaded nanoparticles, the dispersion of the particles in the entire field of view is calculated, and then the minimum circumscribed rectangle of the particles and the particle area are obtained to obtain the rectangularity used to describe the irregularity. Subsequently, the circularity of the particles is calculated in the same way, and then based on the particle area, the equivalent circle radius of the particles is calculated using the method of calculating the equivalent circle radius in related technologies.

[0088] Optionally, in some embodiments, after obtaining the processed image, it also includes: using a preset triangulation algorithm to obtain the adjacent spacing values ​​of each dissolved type of loaded nanoparticles in the microscopic image; eliminating the values ​​in the adjacent spacing values ​​that meet the preset abnormal conditions to obtain a neighboring particle network diagram and a Voronoi diagram.

[0089] Specifically, the distance between each pair of adjacent particles is calculated using Delaunay triangulation, and the outliers are removed to generate the corresponding adjacent particle network diagram and Voronoi diagram.

[0090] The Delaunay triangulation is the dual of the Voronoi graph. A triangulation is said to be Delaunay when the circumcircles of all triangles in the triangulation (excluding the boundary) do not contain any vertices in the point set. Triangulation is the process of generating a set of triangles T from a given plane point set P. These two sets satisfy the following conditions: 1) the endpoints of all triangles exactly form the set P; 2) the edges of any two triangles do not intersect; and 3) the union of all triangles forms the convex hull of P.

[0091] Among them, the distance of the spacing is defined as the Euclidean distance, and the calculation formula of the spacing is:

[0092]

[0093] Among them, (x1, y1) and (x2, y2) are two points on the plane.

[0094] Optionally, in some embodiments, after obtaining the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius, it includes: generating a histogram of normal distribution fitting based on the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius.

[0095] It is understood that the extracted dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius data are stored in a spreadsheet, and a histogram with normal distribution fitting is immediately drawn for reference.

[0096] In order to enable those skilled in the art to further understand the automatic quantitative statistical characterization method of supported nanoparticle information in the embodiments of the present application, it is described in detail below with reference to specific embodiments.

[0097] The present invention relates to metal nanoparticles dissolved from perovskite oxide materials. These are supported metal catalysts that play an important role in energy conversion systems such as solid oxide fuel cells. With increasing demands for catalytic activity and resistance to sintering and carbon deposition, the characterization and analysis of dissolved nanoparticles are gaining increasing attention, as their microscopic morphology and distribution have a direct and significant impact on their performance. Only by exploring more characteristic data and identifying their relationship with performance can existing performance be further improved.

[0098] The automatic quantitative statistical characterization method for supported nanoparticle information of the embodiment of the present application is used to count and characterize the metal nanoparticles dissolved from the perovskite oxide material. The specific steps are as follows:

[0099] 1. Image acquisition of nanoparticles on the surface of materials

[0100] Specifically, the perovskite oxide material sample loaded with dissolved metal nanoparticles was placed in a scanning electron microscope for observation, as shown in FIG. Figure 3 As shown, the probe selects the secondary electron probe in the lens barrel, with a magnification of 20-100k times, to collect stable, clear and representative particle microscopic images and save them on the computer.

[0101] 2. Identification and Segmentation of Nanoparticles

[0102] First, the particle microscopic image is manually cropped, the parameter area is cropped and the scale is marked, and then filtering and denoising are performed. The filtering and denoising methods are optional, including Gaussian filtering, bilateral filtering, and adaptive median filtering.

[0103] Specifically, a particle microscopic image with a size of 1024×768 is selected as the input image, and the cropped size is 1024×691, with a scale of 454.32 pixels·μm- 1 The noise type of the image is similar to Gaussian noise, so bilateral filtering is selected. A small piece of background image is selected, and the smoothness of the filter is twice the variance of the pixel values ​​of the selected background area. The smoothness calculated in this embodiment is 895.1573.

[0104] Then contrast-limited adaptive histogram equalization is performed to enhance the contrast between foreground and background. The result is as follows: Figure 4 Then, the image threshold is adaptively determined using a method based on the local mean intensity (first-order statistics) of each pixel neighborhood, with a sensitivity of 0.23. Binarization is performed with this sensitivity, and the result is as follows: Figure 5 As shown in the figure, it is found that the particles and the background are well segmented. Then the binary image is opened using the rhombus as the morphological structure element, where the distance from the rhombus origin to each point of the rhombus is 1. The result is as follows: Figure 6 As shown in the figure, it is found that some impurity pixels are effectively removed. The automatic segmentation effect of the image is good, so no manual segmentation intervention is required. Figure 6 This is the final preprocessed image.

[0105] 3. Quantitative statistical analysis of particle morphology and distribution data

[0106] Based on the obtained particle information, the dispersion of the particles in the entire field of view is calculated, and then the minimum circumscribed rectangle and particle area of ​​the particles are obtained to obtain the rectangularity used to describe the irregularity. Subsequently, the circularity of the particles is calculated in the same way, and the equivalent circle radius of the particles is calculated from the particle area. Then, Delaunay triangulation is used to calculate the distance between each pair of adjacent particles, and the outliers generated are eliminated to generate the corresponding neighboring particle network diagram and Voronoi diagram.

[0107] Finally, after inputting the metal density and relative atomic mass, combined with the previously obtained data, the average atomic number of the particles can be obtained. For dissolution-type loaded nanoparticles, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric coefficients of the doping elements are input to obtain the average depth of the perovskite material corresponding to the total atomic number of the particles.

[0108] Specifically, the image size is 1024×691, the number of particles is 2168, and the density is 632.42 particles / μm. -2 The minimum bounding rectangle of each particle is as follows: Figure 7 As shown, the minimum circumcircle is Figure 8 The neighboring particle network obtained by Delaunay triangulation is shown in Figure 9 As shown, the Voronoi diagram is as follows Figure 10As shown, the distribution of particle area, circularity, rectangularity, equivalent circle radius, and distance between adjacent particles is shown in Figure 11 shown.

[0109] Finally, enter the density of nickel, 8902 kg·m -3 , the relative atomic mass of nickel is 58.69, so the average atomic number of the particle is 4.14378×10 5 Then, by inputting the perovskite pseudo-cubic unit cell parameter of 0.39 (nm), the stoichiometric coefficient of the doping element of 0.1, the perovskite material length (i.e., the image length) of 2254 nm, and the width (i.e., the image width) of 1521 nm, we can obtain the average depth of the perovskite material corresponding to the total number of particles, which is 155.4531 nm.

[0110] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0111] 1. The current statistical characterization technology of supported nanoparticles mainly focuses on the particle size, area and number density of particles, but does not effectively extract other characteristic data, which have more or less influence on the performance of nanoparticles.

[0112] This application achieves the analysis and extraction of characteristic data such as circularity, rectangularity, distance between adjacent particles, and minimum circumscribed circle radius by mathematically processing the data of microscopic photographs of loaded nanoparticles.

[0113] 2. Current statistical characterization techniques for loaded nanoparticles often require conversion and mutual invocation between different tools, resulting in cumbersome operation steps and low efficiency.

[0114] This application realizes an efficient one-stop data acquisition process through the improvement and integration of different functions, which is conducive to the rapid advancement of the research process.

[0115] According to the automatic quantitative statistical characterization method of the loaded nanoparticle information proposed in the embodiment of the present application, a microscopic image of the loaded nanoparticles of the dissolution class is obtained, the microscopic image is pre-processed to obtain a processed image, and the position information of the loaded nanoparticles of each dissolution class is obtained after marking the processed image, thereby determining the particle diameter of the loaded nanoparticles of each dissolution class, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution class, and calculating the average atomic number of the particles based on the particle diameter, metal density and relative atomic mass calculation formula, and calculating the average depth of the perovskite material based on the average atomic number of the particles, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element. Thus, the problems of a large number of nanoparticles loaded in the matrix material and difficulty in manual analysis and statistics are solved. The method can be used for quantitative analysis and local distribution statistical characterization of the microscopic morphology of nanoparticles, and achieves the beneficial effect of quickly and accurately converting images into quantitative data for scientific research.

[0116] Next, the automatic quantitative statistical characterization device for supported nanoparticle information proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0117] Figure 12 Schematic diagram of a block diagram of an automatic quantitative statistical characterization device for information of supported nanoparticles according to an embodiment of the present application.

[0118] like Figure 12 As shown, the automatic quantitative statistical characterization device 10 for supported nanoparticle information includes: a processing module 100 , an acquisition module 200 and a calculation module 300 .

[0119] Among them, the processing module 100 is used to obtain a microscopic image of the loaded nanoparticles of the dissolution type, and pre-process the microscopic image to obtain a processed image, and mark the processed image to obtain the position information of the loaded nanoparticles of each dissolution type; the acquisition module 200 is used to determine the particle diameter of each loaded nanoparticle of the dissolution type based on the position information of the loaded nanoparticles of each dissolution type, and obtain the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type; the calculation module 300 is used to calculate the average particle atomic number based on the particle diameter, metal density and relative atomic mass using a preset particle average atomic number calculation formula, and calculate the average depth of the perovskite material based on the average particle atomic number, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element using a preset perovskite material average depth calculation formula.

[0120] Optionally, in some embodiments, after obtaining the position information of the loaded nanoparticles of each dissolution class in the processed image, the acquisition module 200 is also used to: calculate the dispersion and particle area of ​​the loaded nanoparticles of each dissolution class based on the position information of the loaded nanoparticles of each dissolution class, and obtain the minimum circumscribed rectangle of the loaded nanoparticles of each dissolution class based on a preset convex hull strategy; calculate the rectangularity, circularity and equivalent circle radius of the loaded nanoparticles of each dissolution class based on the minimum circumscribed rectangle and the particle area.

[0121] Optionally, in some embodiments, after obtaining the processed image, the acquisition module 200 is further used to: use a preset triangulation algorithm to obtain the adjacent spacing values ​​of each dissolution type of loaded nanoparticles in the microscopic image; eliminate the values ​​in the adjacent spacing values ​​that meet the preset abnormal conditions to obtain a neighboring particle network diagram and a Voronoi diagram.

[0122] Optionally, in some embodiments, the microscopic image is preprocessed to obtain a processed image, and the processing module 100 is further used to: obtain scale information of the microscopic image; based on the scale information, use a preset filtering and denoising processing strategy to filter and denoise the microscopic image, and perform contrast-limited adaptive histogram equalization, adaptive threshold binarization, morphological opening operation de-debris preprocessing and image segmentation on the filtered and denoised microscopic image to obtain a processed image.

[0123] Optionally, in some embodiments, the preset formula for calculating the average number of atoms in the particles is:

[0124]

[0125] Among them, N M，p is the average atomic number of the granular metal M, ρ M is the density of metal M, A M is the relative atomic mass of metal M, N A is Avogadro's constant, f i The diameter is φ i The proportion of particles.

[0126] Optionally, in some embodiments, the preset calculation formula for the average depth of the perovskite material is:

[0127]

[0128] Where d is the average depth, n is the number of particles, and a p is the pseudo cubic unit cell parameter of the perovskite, L is the length of the perovskite material, W is the width of the perovskite material, x M is the stoichiometric number of the doping element M.

[0129] Alternatively, in some embodiments, the dispersion is the ratio of the number of dissolved supported nanoparticles in the microscopic image to the actual matrix area corresponding to the microscopic image.

[0130] Optionally, in some embodiments, after obtaining the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius, the acquisition module 200 is also used to: generate a histogram of normal distribution fitting based on the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius.

[0131] It should be noted that the above explanations of the embodiment of the method for automatic quantitative statistical characterization of supported nanoparticle information are also applicable to the device for automatic quantitative statistical characterization of supported nanoparticle information of this embodiment, and will not be repeated here.

[0132] According to the automatic quantitative statistical characterization device for the information of loaded nanoparticles proposed in the embodiment of the present application, a microscopic image of loaded nanoparticles of the dissolution type is obtained, the microscopic image is pre-processed to obtain a processed image, and the position information of the loaded nanoparticles of each dissolution type is obtained after marking the processed image, thereby determining the particle diameter of the loaded nanoparticles of each dissolution type, and obtaining the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element of the loaded nanoparticles of the dissolution type, and calculating the average atomic number of the particles based on the particle diameter, metal density and relative atomic mass calculation formula, and calculating the average depth of the perovskite material based on the average atomic number of the particles, pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element. Thus, the problems of a large number of nanoparticles loaded on the matrix material and difficulty in manual analysis and statistics are solved, and the beneficial effect of quickly and accurately converting images into quantitative data for scientific research is achieved.

[0133] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0134] Memory 1301 , processor 1302 , and computer programs stored in the memory 1301 and executable on the processor 1302 .

[0135] When the processor 1302 executes the program, the automatic quantitative statistical characterization method for supported nanoparticle information provided in the above embodiment is implemented.

[0136] Furthermore, the electronic device further includes:

[0137] The communication interface 1303 is used for communication between the memory 1301 and the processor 1302 .

[0138] The memory 1301 is used to store computer programs that can be run on the processor 1302 .

[0139] The memory 1301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0140] If the memory 1301, processor 1302, and communication interface 1303 are implemented independently, the communication interface 1303, memory 1301, and processor 1302 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 13 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0141] Optionally, in a specific implementation, if the memory 1301, the processor 1302 and the communication interface 1303 are integrated on a chip, the memory 1301, the processor 1302 and the communication interface 1303 can communicate with each other through an internal interface.

[0142] The processor 1302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0143] An embodiment of the present application further provides a computer program product having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for automatic quantitative statistical characterization of loaded nanoparticle information.

[0144] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0145] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0146] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, as should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0147] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer program product for use with, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate, or transmit a program for use with, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer program products include the following: an electrical connection having one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer program product may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or, if necessary, processing it in another suitable manner, and then storing it in a computer memory.

[0148] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0149] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer program product, which, when executed, includes one or a combination of the steps of the method embodiment.

[0150] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer program product.

[0151] The computer program product mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for automatic quantitative statistical characterization of supported nanoparticle information, characterized in that: The following steps are involved: Acquiring a microscopic image of the dissolution-type supported nanoparticles, preprocessing the microscopic image to obtain a processed image, and marking the processed image to obtain position information of each dissolution-type supported nanoparticle; Determining the particle diameter of each dissolution type supported nanoparticle based on the position information of each dissolution type supported nanoparticle, and obtaining the metal density, relative atomic mass, pseudo cubic unit cell parameter of perovskite and stoichiometric number of doping element of the dissolution type supported nanoparticle; Based on the particle diameter, the metal density and the relative atomic mass, the average particle atomic number is calculated using a preset average particle atomic number calculation formula; based on the average particle atomic number, the pseudo-cubic unit cell parameters of the perovskite and the stoichiometric number of the doping element, the average depth of the perovskite material is calculated using a preset perovskite material average depth calculation formula.

2. The method according to claim 1, characterized in that After obtaining the position information of each dissolution type supported nanoparticle in the processed image, the method further includes: Calculating the dispersion and particle area of ​​the loaded nanoparticles of each dissolution type based on the position information of the loaded nanoparticles of each dissolution type, and obtaining the minimum circumscribed rectangle of the loaded nanoparticles of each dissolution type based on a preset convex hull strategy; The rectangularity, circularity and equivalent circle radius of the supported nanoparticles of each dissolution type are calculated based on the minimum circumscribed rectangle and the particle area.

3. The method according to claim 2, characterized in that After obtaining the processed image, the method further includes: Using a preset triangulation algorithm, a neighboring spacing value of each dissolved type supported nanoparticle in the microscopic image is obtained; The values ​​that meet the preset abnormal conditions in the adjacent spacing values ​​are eliminated to obtain the adjacent particle network diagram and the Voronoi diagram.

4. The method according to claim 1, wherein The preprocessing of the microscopic image to obtain a processed image comprises: obtaining scale information of the microscopic image; Based on the scale information, a preset filtering and denoising processing strategy is used to filter and denoise the microscopic image, and the microscopic image after filtering and denoising is subjected to contrast-limited adaptive histogram equalization, adaptive threshold binarization, morphological opening operation de-debris preprocessing and image segmentation to obtain the processed image.

5. The method according to claim 1, wherein The preset calculation formula for the average number of atoms in the particles is: Among them, N M，p is the average atomic number of the granular metal M, ρ M is the density of metal M, A M is the relative atomic mass of metal M, N A is Avogadro's constant, f i The diameter is φ i The proportion of particles.

6. The method according to claim 1, characterized in that The preset calculation formula for the average depth of the perovskite material is: Where d is the average depth, n is the number of particles, and a p is the pseudo cubic unit cell parameter of the perovskite, L is the length of the perovskite material, W is the width of the perovskite material, x M is the stoichiometric number of the doping element M.

7. The method according to claim 2, characterized in that The dispersion degree is the ratio of the number of supported nanoparticles of the dissolved type in the microscopic image to the actual substrate area corresponding to the microscopic image.

8. The method according to claim 2, characterized in that After obtaining the dispersion, particle area, circularity, rectangularity, adjacent spacing value, minimum circumscribed circle radius, and equivalent circle radius, including: A histogram of normal distribution fitting is generated based on the dispersion, the particle area, the circularity, the rectangularity, the adjacent spacing value, the minimum circumscribed circle radius, and the equivalent circle radius.

9. An automatic quantitative statistical characterization device for supported nanoparticle information, characterized in that: include: a processing module for obtaining a microscopic image of the dissolution-type supported nanoparticles, preprocessing the microscopic image to obtain a processed image, and marking the processed image to obtain position information of each dissolution-type supported nanoparticle; an acquisition module, configured to determine the particle diameter of each of the supported nanoparticles of the dissolution type based on the position information of the supported nanoparticles of each dissolution type, and to obtain the metal density, relative atomic mass, pseudo-cubic unit cell parameters of the perovskite, and stoichiometric number of the doping element of the supported nanoparticles of the dissolution type; A calculation module is used to calculate the average particle atomic number based on the particle diameter, the metal density and the relative atomic mass using a preset particle average atomic number calculation formula, and to calculate the average depth of the perovskite material based on the average particle atomic number, the pseudo-cubic unit cell parameter of the perovskite and the stoichiometric number of the doping element using a preset perovskite material average depth calculation formula.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for automatic quantitative statistical characterization of loaded nanoparticle information according to any one of claims 1 to 8.

Images