Sample component analysis method and device based on element distribution diagram and product

Abstract

The invention discloses a sample component analysis method and device based on an element distribution diagram and a product, and relates to the field of component analysis. The method comprises the following steps: firstly, acquiring element distribution diagram data and corresponding image data corresponding to each element in a sample obtained by performing energy spectrum analysis or mass spectrum analysis on the sample; filtering non-sample area data in all element distribution diagram data according to the image data to obtain filtered element distribution diagram data corresponding to each element; performing local summation assignment processing on a plurality of data points in the filtered element distribution diagram data corresponding to each element to obtain a local summation result corresponding to each element; performing division processing on the local summation result corresponding to each element according to the molecular formula of the sample material to obtain stoichiometric ratio spatial distribution characteristic data; sample component analysis is carried out according to the stoichiometric ratio spatial distribution characteristic data, and the sample component analysis efficiency and accuracy can be improved.

Description

Technical Field

[0001] This application relates to the technical field of component analysis, and particularly to a method, device, and product for analyzing the components of a sample based on an elemental distribution map. Background Art

[0002] Microbeam analysis is a technique for analyzing small samples or regions. It involves using high-energy beams (such as electrons, ions, or photons) to obtain information about the chemical composition, structure, and physical properties of a sample. Most microbeam analysis techniques focus the microbeam on the object to be analyzed and then measure the output beam generated by the interaction of the input beam with the atoms and molecules that make up the sample. The input beam can include light (including laser beams), X-rays, and other electromagnetic waves, electrons, protons, or ions. The measured output beam also includes light, X-rays, electrons, and ions.

[0003] Instruments commonly used in microbeam analysis techniques mainly include a Scanning Electron Microscope (SEM), a Transmission Electron Microscope (TEM), and a Scanning Transmission Electron Microscopy (STEM), etc. Using SEM, TEM, or STEM, the chemical composition and structural information of a sample can be obtained through a variety of different analysis techniques, such as Energy Dispersive X-Ray Spectroscopy (EDS), Electron Energy Loss Spectroscopy (EELS), and High Angle Annular Dark Field (HAADF). In SEM, TEM, or STEM imaging, the electron beam can perform a surface scan on a region of the sample to generate elemental distribution map data covering the spatial distribution of elements.

[0004] Currently, the elemental distribution maps obtained by EDS and EELS techniques collect signals from the sample in a point-by-point scanning manner. The collection points are too dense to fully capture phenomena such as local elemental aggregation or elemental deficiency. Moreover, the collected elemental distribution signals are affected by the thickness of the given sample. When a local part of the sample is relatively thick, the collected signal is relatively strong, and when a local part of the sample is relatively thin, the collected signal is relatively weak. In actual measurements, in addition to signals being collected in the sample area, weak elemental distribution signals can also be detected in the non-sample area, and these signals are usually irrelevant to the elements being detected. The above disadvantages limit the further application of the elemental distribution maps obtained by EDS and EELS in sample component analysis. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and product for sample composition analysis based on element distribution maps, so as to improve the efficiency and accuracy of sample composition analysis.

[0006] To achieve the above object, the following solutions are provided in this application.

[0007] In the first aspect, this application provides a method for sample composition analysis based on element distribution maps, including:

[0008] Obtaining element distribution map data corresponding to each element in the sample and corresponding image data obtained by energy spectrum analysis or mass spectrum analysis of the sample;

[0009] Filtering non-sample area data in all element distribution map data according to the image data to obtain filtered element distribution map data corresponding to each element;

[0010] Performing local summation assignment processing on multiple data points in the filtered element distribution map data corresponding to each element to obtain local summation results corresponding to each element;

[0011] Performing division processing on the local summation results corresponding to each element according to the molecular formula of the sample material to obtain stoichiometric ratio spatial distribution characteristic data;

[0012] Performing sample composition analysis based on the stoichiometric ratio spatial distribution characteristic data.

[0013] Optionally, the obtaining of the element distribution map data corresponding to each element in the sample and the corresponding image data obtained by energy spectrum analysis or mass spectrum analysis of the sample specifically includes:

[0014] Performing energy spectrum analysis or mass spectrum analysis on the sample to obtain element distribution map data corresponding to each element in the sample; the element distribution map data includes EDS data, EELS data, and SIMS data;

[0015] Performing SEM, TEM, or STEM imaging on the sample to obtain image data of electron microscope imaging; the image data includes HAADF image data and SEM image data;

[0016] The distribution of data points in the image data and the element distribution map data is in the matrix form of X rows × Y columns.

[0017] Optionally, the filtering of non-sample area data in all element distribution map data according to the image data to obtain filtered element distribution map data corresponding to each element specifically includes:

[0018] Plotting the image data and the element distribution map data of each element as corresponding 2D heat maps; the 2D heat map of the image data consists of a sample area and a non-sample area;

[0019] Read the signal intensities of the sample area and the non-sample area in the 2D hot spot map of the image data, and set the filtering threshold based on this;

[0020] Mark the data points with signal intensities lower than the filtering threshold in the 2D hot spot map of the image data as filtered points;

[0021] Set the values at the positions of the filtered points in each elemental distribution map data to zero to obtain the filtered elemental distribution map data corresponding to each element.

[0022] Optionally, after setting the values at the positions of the filtered points in each elemental distribution map data to zero to obtain the filtered elemental distribution map data corresponding to each element, it further includes:

[0023] Set the values at the positions of the filtered points in the image data to zero to obtain the filtered image data;

[0024] Plot the filtered image data as a corresponding 2D hot spot map, compare it with the 2D hot spot map of the image data, adjust the filtering threshold according to the comparison result, and return to the step of marking the data points with signal intensities lower than the filtering threshold in the 2D hot spot map of the image data as filtered points until the filtered elemental distribution map data with the optimal filtering result is obtained.

[0025] Optionally, the process of performing local summation assignment processing on multiple data points in the filtered elemental distribution map data corresponding to each element to obtain the local summation results corresponding to each element specifically includes:

[0026] Respectively select an integer m divisible by the number of rows X of the data points and an integer n divisible by the number of columns Y of the data points;

[0027] For the filtered elemental distribution map data corresponding to each element, add every m rows × n columns of data points in the filtered elemental distribution map data as one data point to obtain the local summation value of this data point; the original X × Y data points are converted into (X / m) × (Y / n) data points, and the local summation results corresponding to each element are plotted according to the converted data points.

[0028] Optionally, the process of performing local summation assignment processing on multiple data points in the filtered elemental distribution map data corresponding to each element to obtain the local summation results corresponding to each element specifically includes:

[0029] Respectively select an integer m divisible by the number of rows X of the data points and an integer n divisible by the number of columns Y of the data points;

[0030] For the filtered element distribution map data corresponding to each element, for each data point in the filtered element distribution map data, add up the data points in an m-row × n-column area around the data point and then calculate the average. Take the obtained average value as the local summation value of the data point; if there are no m-row × n-column data points around the data point, retain the original value of the data point; the original X × Y data points are re-assigned, and the local summation results corresponding to each element are plotted based on the re-assigned data points.

[0031] Optionally, dividing the local summation results corresponding to each element according to the molecular formula of the sample material to obtain stoichiometric ratio spatial distribution characteristic data specifically includes:

[0032] According to the importance of each element in the molecular formula of the sample material to the material properties, directly divide the local summation values of the data points at the same position in the local summation results corresponding to a certain target element and another target element to obtain the stoichiometric ratio spatial distribution characteristic data of a single element.

[0033] Optionally, dividing the local summation results corresponding to each element according to the molecular formula of the sample material to obtain stoichiometric ratio spatial distribution characteristic data specifically includes:

[0034] According to the importance of certain element combinations in the molecular formula of the sample material to the material properties, add up the local summation values of the data points at the same position in the local summation results corresponding to certain target elements among all elements to obtain the first combined element distribution result; add up the local summation values of the data points at the same position in the local summation results corresponding to other target elements among all elements to obtain the second combined element distribution result; divide the first combined element distribution result and the second combined element distribution result of the data points at the same position to obtain the stoichiometric ratio spatial distribution characteristic data of different element combinations.

[0035] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the sample composition analysis method based on the element distribution map.

[0036] In a third aspect, the present application provides a computer program product, including a computer program, which implements the sample composition analysis method based on the element distribution map when executed by a processor.

[0037] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application.

[0038] A method, device, and product for analyzing sample composition based on an elemental distribution map provided by this application filter the data of non-sample regions in the elemental distribution map data, removing the interference of signals from non-sample regions; by locally summing and assigning values to multiple data points in the filtered elemental distribution map data to amplify the distribution characteristics, it can fully capture phenomena such as local elemental aggregation or elemental deficiency; further dividing the local summation results corresponding to each element according to the molecular formula of the sample material successfully overcomes the drawback that traditional elemental distribution maps are affected by the sample thickness and identifies the spatial distribution characteristics of stoichiometry that cannot be obtained from traditional elemental distribution maps; analyzing the sample composition based on the spatial distribution characteristic data of stoichiometry can significantly improve the efficiency and accuracy of sample composition analysis. And the method of this application is generally applicable to various types of elemental distribution map data obtained by different characterization means, having a wide application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following-described drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 It is a schematic flowchart of a method for analyzing sample composition based on an elemental distribution map of this application;

[0041] Figure 2 It is a hot spot map of nickel element distribution drawn in a certain embodiment;

[0042] Figure 3 It is a hot spot map of cobalt element distribution drawn in a certain embodiment;

[0043] Figure 4 It is a hot spot map of manganese element distribution drawn in a certain embodiment;

[0044] Figure 5 It is a hot spot map of oxygen element distribution drawn in a certain embodiment;

[0045] Figure 6 It is a hot spot map of high-angle annular dark field image distribution drawn in a certain embodiment;

[0046] Figure 7 It is a hot spot map corresponding to the filtered elemental distribution map data of nickel element in a certain embodiment;

[0047] Figure 8 It is a hot spot map corresponding to the filtered elemental distribution map data of cobalt element in a certain embodiment;

[0048] Figure 9The hot map corresponding to the element distribution map data after manganese element filtration in a certain embodiment;

[0049] Figure 10 The hot map corresponding to the element distribution map data after oxygen element filtration in a certain embodiment;

[0050] Figure 11 The hot map corresponding to the HAADF image data after filtration in a certain embodiment;

[0051] Figure 12 The summed hot map of nickel elements drawn in a certain embodiment;

[0052] Figure 13 The summed hot map of cobalt elements drawn in a certain embodiment;

[0053] Figure 14 The summed hot map of manganese elements drawn in a certain embodiment;

[0054] Figure 15 The summed hot map of oxygen elements drawn in a certain embodiment;

[0055] Figure 16 The 2D hot map of the distribution result of the first combined elements drawn in a certain embodiment;

[0056] Figure 17 The hot map of the stoichiometric ratio spatial distribution characteristic data drawn in a certain embodiment. Detailed implementation manners

[0057] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0058] The purpose of the present application is to propose a method, device and product for sample composition analysis based on element distribution maps to improve the efficiency and accuracy of sample composition analysis.

[0059] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0060] In an exemplary embodiment, as Figure 1 shown, a method for sample composition analysis based on element distribution maps is provided, including the following steps 1 to 5.

[0061] Step 1: Obtain the elemental distribution map data corresponding to each element in the sample and the corresponding image data obtained by energy spectrum analysis or mass spectrum analysis of the sample.

[0062] According to the type of the measured output beam, the means for obtaining the elemental distribution map can be divided into energy spectrum analysis and mass spectrum analysis. Among them, energy spectrum analysis generally refers to that in SEM, TEM or STEM, the incident electrons interact with the sample to generate a series of signals with elemental characteristics, and various different analysis techniques are used to obtain the chemical composition and structural information of the sample, such as EDS, EELS and HAADF. Mass spectrum analysis mainly bombards the sample with a focused primary ion beam to excite a small amount of secondary ions on the sample surface, and then separates them according to the mass-to-charge ratio of the secondary ions, so as to realize the measurement of the distribution and content of different elements in the sample, such as Secondary Ion Mass Spectrum (SIMS), etc. Whether energy spectrum analysis or mass spectrum analysis is adopted, the input beam (which can include laser beam, X-ray and other electromagnetic waves, electrons, protons or ions) can perform area scanning on a region of the sample to generate elemental distribution map data covering the spatial distribution of elements.

[0063] Specifically, by performing SEM, TEM or STEM imaging on the sample, the spatial distribution signals of each element in the sample material can be captured by the EDS or EELS probe in instruments such as transmission electron microscopes and scanning electron microscopes. The obtained original data includes electron microscope image data and the corresponding elemental distribution map data, which are in the formats of txt, xlsx or csv, and the distribution of data points is in the matrix form of X rows × Y columns obtained by area scanning. Among them, the image data can be SEM image data or HAADF image data. The elemental distribution map data can be EDS data or EELS data. In addition, by performing secondary ion mass spectrometry analysis on the sample, the corresponding area scanning SIMS data can be obtained as the elemental distribution map data.

[0064] Step 2: Filter the non-sample area data in all the elemental distribution map data according to the image data to obtain the filtered elemental distribution map data corresponding to each element.

[0065] Draw the elemental distribution map data into a chart, and filter the non-sample area signals in the data by setting and adjusting the filtering threshold. Specifically, Step 2 includes:

[0066] Step 2.1: Draw both the image data and the elemental distribution map data of each element into corresponding 2D heat maps; the 2D heat map of the image data consists of a sample area and a non-sample area (i.e., the area outside the sample).

[0067] Step 2.2: Read the signal intensities of the sample area and the non-sample area in the 2D heat map of the image data, and set the filtering threshold based on this.

[0068] The 2D hotspot map of the electron microscope image data drawn above consists of a sample area and a non-sample area. Numerical points corresponding to the sample area and the non-sample area are selected for comparison. Since the numerical values of the corresponding data points in the non-sample area are generally low, a threshold value between the two areas is set as the filtering threshold. Data points below the filtering threshold will be set to zero to filter out the signals / data in the non-sample area.

[0069] Step 2.3: Mark the data points with signal intensity below the filtering threshold in the 2D hotspot map of the image data as filtered points.

[0070] In the 2D hotspot map of the image data, the positions of the data points below the filtering threshold (i.e., the filtered points) will be recorded, and the data points at the same positions in the data of all other element distribution maps will also be set to zero.

[0071] Step 2.4: Clear the numerical values at the positions of the filtered points in the data of each element distribution map to obtain the filtered element distribution map data corresponding to each element.

[0072] After setting the filtering threshold, each data point in the image data is screened one by one, and the positions of the data points below the filtering threshold will be recorded. After the screening process is completed, the data points corresponding to the positions of the filtered points in the image data and the data of each element distribution map are set to zero to achieve the filtering purpose.

[0073] Step 2.5: Clear the numerical value at the position of the filtered point in the image data to obtain the filtered image data.

[0074] Draw a chart of the filtered image data and judge whether the filtering effect is ideal by comparing it with the original data. If it is not ideal, reset the threshold.

[0075] Step 2.6: Draw the filtered image data as a corresponding 2D hotspot map, compare it with the 2D hotspot map of the image data, adjust the filtering threshold according to the comparison result, and return to the above step 2.3 until the filtered element distribution map data with the optimal filtering result is obtained.

[0076] Draw the filtered image data and the filtered element distribution map data after the filtering process as 2D hotspot maps, and compare them with the 2D hotspot maps drawn from the original image data and the element distribution map data. If the non-sample area is still obvious, it means that the filtering threshold is set too small and the filtering is not complete; if the sample area is significantly reduced, it means that the filtering threshold is set too large and a part of the sample area is filtered. In such cases, reset the filtering threshold and return to step 2.3, and adjust until the non-sample area is completely filtered and the sample area is intact, which is the ideal filtering result, and the filtered element distribution map data with the optimal filtering result is obtained.

[0077] Step 3: Perform local summation assignment processing on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation results corresponding to each element.

[0078] This application uses the method of local summation assignment of data points to amplify the distribution characteristics. The specific steps of Step 3 include two methods.

[0079] Method 1: Select an integer m divisible by the number of rows X of the data points and an integer n divisible by the number of columns Y of the data points respectively. Specifically, during plotting, the original image data and the element distribution map data are statistically analyzed, and the specific values of the number of rows X and the number of columns Y of the data points are fed back. According to the statistically obtained number of rows X and the number of columns Y, select an integer m divisible by the number of rows X of the data points and an integer n divisible by the number of columns Y of the data points respectively, and perform local summation on all the data after filtering the signals according to the ratio of m×n.

[0080] Then, for the filtered element distribution map data corresponding to each element, add every m rows × n columns of data points in the filtered element distribution map data as one data point respectively to obtain the local summation value of this data point; the original X×Y data points are converted into (X / m)×(Y / n) data points, and the local summation results corresponding to each element are plotted according to the converted data points.

[0081] Since the data points in the filtered element distribution map data are too dense and discrete to fully display the element distribution characteristics between different regions in the sample, local summation operation is performed. Specifically, for the filtered element distribution map data corresponding to each element obtained in Step 2, add every m rows × n columns of data points in it as one data point respectively to obtain the local summation value of this data point. After performing the above local summation operation, the X×Y data points in the filtered element distribution map data are converted into (X / m)×(Y / n) data points, and the local summation results corresponding to each element are plotted according to the converted data points, and the local summation results are plotted as a 2D heat map.

[0082] Method 2: Select an integer m divisible by the number of rows X of the data points and an integer n divisible by the number of columns Y of the data points respectively. Then, for the filtered element distribution map data corresponding to each element, for each data point in its filtered element distribution map data, add the m rows × n columns of data points around this data point and then take the average, and take the obtained average value as the local summation value of this data point. If there are not m rows × n columns of data points around this data point, keep the original value of this data point. In this way, the original X×Y data points are re-assigned values, and the local summation results corresponding to each element are plotted according to the re-assigned data points, and the local summation results are plotted as a 2D heat map.

[0083] The difference between Method 2 and Method 1 is that Method 1 will change the resolution of the data. The X×Y data points in the filtered element distribution map data will be converted into (X / m)×(Y / n) data points, that is, the resolution is reduced from X×Y to (X / m)×(Y / n); while in Method 2, the resolution of the data remains unchanged, and the resolution of X×Y is still retained. In practical applications, it can be selected according to different requirements for resolution.

[0084] Step 4: Divide the partial summation results corresponding to each element according to the molecular formula of the sample material to obtain the stoichiometric ratio spatial distribution characteristic data.

[0085] Overlaying and comparing the distribution characteristics of different elements can reflect the stoichiometric ratio spatial distribution characteristics, and charts can be drawn and data can be saved based on this. Step 4 also includes two methods, and the stoichiometric ratio spatial distribution characteristic data of a single element or the stoichiometric ratio spatial distribution characteristic data of different element combinations can be obtained.

[0086] Method 1: According to the importance of each element in the molecular formula of the sample material to the material performance, directly divide the local summation values of the data points at the same position in the local summation results corresponding to a certain target element and another target element to obtain the stoichiometric ratio spatial distribution characteristic data of a single element.

[0087] In this application, the element to be analyzed is called the target element. For example, Li(Ni 0.8 Co 0.2 )O 2 In the material sample, the elements Ni and O are used as the target elements to be analyzed. Divide and compare the local summation value of the data point at a certain position in the local summation result of Ni with the local summation value of the data point at the same position in the local summation result of O, that is, directly divide point by point, and the stoichiometric ratio spatial distribution characteristic data of a single element of Ni / O can be obtained.

[0088] Method 2: According to the importance of certain element combinations in the molecular formula of the sample material to the material performance, add the local summation values of the data points at the same position in the local summation results corresponding to certain target elements among all elements to obtain the first combined element distribution result. Add the local summation values of the data points at the same position in the local summation results corresponding to other target elements among all elements to obtain the second combined element distribution result. Of course, in practical applications, the first combined element distribution result or the second combined element distribution result can also directly adopt the local summation result corresponding to a single element, and in this case, no point-by-point addition processing is required. Divide the first combined element distribution result and the second combined element distribution result of the data points at the same position to obtain the stoichiometric ratio spatial distribution characteristic data of different element combinations.

[0089] For example, in a sample of Li(Ni 0.8 Co 0.2 )O 2 material, elements Ni and Co are located at the same site. Point-to-point addition is performed, that is, the local summation values of the data points at the same position in the local summation results of Ni and Co are added correspondingly to obtain the first combined element distribution result; then, it is divided and compared with the local summation result of element O (the second combined element distribution result) to obtain the stoichiometric ratio spatial distribution characteristic data of (Ni+Co) / O.

[0090] Another example is Li(Ni 0.8 Co 0.2 )O 2 In the material sample, the local summation values of the data points at the same position in the local summation results of Ni and Co can be added correspondingly to obtain the first combined element distribution result; the local summation values of the data points at the same position in the local summation results of Li and O are added correspondingly to obtain the second combined element distribution result. The first combined element distribution result of the data points at the same position is divided and compared with the second combined element distribution result to obtain the stoichiometric ratio spatial distribution characteristic data of (Ni+Co) / (Li+O).

[0091] Another example is Li(Ni 0.8 Co 0.2 )O 2 In the material sample, the local summation result of O can be directly used as the first combined element distribution result; the local summation values of the data points at the same position in the local summation results of Li, Ni, and Co are added correspondingly to obtain the second combined element distribution result. The first combined element distribution result of the data points at the same position is divided and compared with the second combined element distribution result to obtain the stoichiometric ratio spatial distribution characteristic data of O / (Li+Ni+Co).

[0092] In this application, according to the importance or contribution degree of certain elements or element combinations in the molecular formula of the sample material to the material composition analysis or performance analysis, the method of directly dividing or combining and dividing the local summation results of the target elements can be applied to materials in a variety of different fields, and different elements can be flexibly combined and divided according to the material characteristics to reflect the most important characteristics. For example, combining the Li, Ni, and Co elements in the embodiment and then comparing with O shows more real characteristics. If the Ni element is directly compared with the O element, then the contributions of the Li and Co elements are ignored at this time, and individual elements are analyzed separately.

[0093] Combined with the characteristics of elements, the present application selectively performs direct division or division after combination on the partial summation results of multiple elements, overcomes the interference caused by uneven sample thickness, and can robustly reflect the spatial distribution characteristics of element ratios.

[0094] Step 5: Perform sample composition analysis based on the spatial distribution characteristic data of stoichiometric ratios.

[0095] Plot the spatial distribution characteristic data of stoichiometric ratios obtained in Step 4 into a 2D hotspot map, and save the data in csv format. Based on this, the initial composition uniformity of the sample can be analyzed. Further, by comparing the 2D hotspot maps of the spatial distribution characteristic data of stoichiometric ratios before and after the sample is in service (before and after any process), the element migration process occurring inside the sample during use can be analyzed, which can guide sample synthesis and reveal the sample failure mechanism.

[0096] A method for sample composition analysis based on an element distribution map proposed in the present application calculates the spatial distribution characteristic data of stoichiometric ratios from the element distribution map data, and can eliminate the signals in non-sample regions, overcome the interference caused by uneven sample thickness, and reflect the element distribution characteristics that cannot be obtained by traditional means for the element distribution maps obtained by means such as EDS, EELS, and SIMS. Further, in addition to obtaining the characteristics of the sample composition distribution, the distribution of stoichiometric ratios in the sample that cannot be obtained by traditional characterization means can also be detected. The change in the spatial distribution characteristic data of stoichiometric ratios can directly reflect the initial composition uniformity of the sample and the element migration and other processes occurring inside the sample during use, which plays an important role in guiding sample synthesis and revealing the sample failure mechanism, etc.

[0097] The method of the present application can be applied to fields such as energy materials, metal materials, functional materials, or biological materials for element spatial distribution detection and sample composition and material property analysis. In the field of energy materials, it will help to accurately optimize material properties, improve the preparation processes of lithium-ion batteries, new solar cells, etc. by clarifying the element distribution, enhance the energy conversion efficiency, and at the same time improve the service life, promoting the development of the energy industry. In the field of metal materials, on the one hand, it ensures the quality of metal materials required for aerospace, high-end equipment manufacturing, etc., avoiding composition segregation, and on the other hand, it helps to improve properties such as strength, toughness, and corrosion resistance, accelerating the research and development of new alloys. In the field of functional materials, the material properties such as force, heat, light, electricity, and magnetism can be improved through composition regulation. In the aspect of biological materials, it strictly controls the distribution of harmful elements to ensure the biological safety of materials implanted into the human body, and can also optimize the biological activity of materials according to the element action mechanism, promote the realization of cell-related functions, and more importantly, provide key support for the development of biomimetic materials, simulate the element distribution of natural materials in living organisms, and open up new paths for the development of biological materials.

[0098] The method of the present application can effectively overcome the influence caused by the uneven thickness of the sample in the conventional elemental distribution map, eliminate the interference of the signal in the non-sample area on the test result, and identify the stoichiometric ratio distribution characteristics that cannot be obtained from the traditional elemental distribution map. It is generally applicable to the processing of elemental distribution map data obtained by combining different characterization means, providing new inspiration for the software and hardware development of related instruments.

[0099] Therefore, in an exemplary embodiment, the present application further provides a computer program product (which can be the software corresponding to the method of the present application), including a computer program, which when executed by a processor implements the sample composition analysis method based on the elemental distribution map.

[0100] Taking the application in a scanning transmission electron microscope (STEM) as an example below, the application process of the method and product of the present application is described, and its operation steps include S1 to S11.

[0101] S1: First, based on STEM, the original elemental distribution map data of the Li(Ni 0.8 Mn 0.1 Co 0.1 )O 2 material sample is collected. Then the software is run, and the EDS data (as elemental distribution map data) of nickel (Ni), cobalt (Co), manganese (Mn), and oxygen (O) elements in txt format obtained by STEM and the HAADF image data are imported into the software.

[0102] S2: Draw 2D hotspot maps of the EDS data of nickel, cobalt, manganese, and oxygen elements and the HAADF image data, as shown in Figures 2 to 6 respectively, which are respectively called the nickel element distribution hotspot map ( Figure 2 ), the cobalt element distribution hotspot map ( Figure 3 ), the manganese element distribution hotspot map ( Figure 4 ), the oxygen element distribution hotspot map ( Figure 5 ) and the high-angle annular dark-field image hotspot map ( Figure 6 ). Among them, the 2D hotspot map of the HAADF image data (i.e., the high-angle annular dark-field image hotspot map) consists of a sample area and a non-sample area.

[0103] S3: The software statistically calculates the specific values of the number of rows X and the number of columns Y of the imported EDS data and HAADF image data.

[0104] S4: The software reads out the specific values on the 2D hotspot map of the HAADF image data at the position of the mouse cursor. The values of the sample area and the non-sample area on the 2D hotspot map of the HAADF image data are read respectively. Among them, the value of the non-sample area is lower and the value of the sample area is higher. Thus, a filtering threshold is set between the values of the non-sample area and the sample area to filter the signal in the non-sample area.

[0105] S5: Set a filtering threshold according to the above read value for filtering. The numerical values of the data points in the HAADF image data that are lower than this filtering threshold will be set to zero.

[0106] S6: In the HAADF image data, the points (filtering points) that are lower than the set filtering threshold will be set to zero and the positions of these filtering points will be recorded. The data points at the positions of these filtering points in the data of the distribution maps of all other elements will also be set to zero, obtaining the filtered distribution map data of each element.

[0107] S7: Plot 2D heat maps of the filtered data (including the filtered image data and the filtered distribution map data of each element), as shown respectively in Figures 7 to 11 Shown below. Among them Figure 7 is the heat map corresponding to the filtered distribution map data of nickel element. Figure 8 is the heat map corresponding to the filtered distribution map data of cobalt element. Figure 9 is the heat map corresponding to the filtered distribution map data of manganese element. Figure 10 is the heat map corresponding to the filtered distribution map data of oxygen element. Figure 11 is the heat map corresponding to the filtered HAADF image data. If the non-sample area is not completely filtered (the filtering threshold is too small) or the sample area is reduced (the filtering threshold is too large), the filtering effect is not ideal, and return to S5 to reset the filtering threshold. By comparing Figure 2 and Figure 7 , Figure 3 and Figure 8 , Figure 4 and Figure 9 , Figure 5 and Figure 10 as well as Figure 6 and Figure 11 it can be seen that after the filtering process of this application, the non-sample area is completely filtered and the sample area is intact, achieving an ideal filtering result.

[0108] S8: According to the number of rows X and the number of columns Y counted from the imported data in S3, respectively select an integer m that can be divided evenly by the number of rows X of the data points and an integer n that can be divided evenly by the number of columns Y of the data points, and perform a local summation assignment process on all the data after filtering signals according to the ratio of m×n. That is, according to the first local summation assignment method, add every m rows × n columns of data points in the filtered distribution map data of each element obtained in S7 to form a data point, obtaining the local summation result of each element; after the local summation and combination, the original X×Y data points are converted into (X / m)×(Y / n) data points, and plot 2D heat maps of the data after local summation, as shown respectively in Figures 12 to 15 Shown below, and are respectively called the summation heat map of nickel element ( Figure 12)、Sum hot map of cobalt element( Figure 13 )、Sum hot map of manganese element( Figure 14 ) and sum hot map of oxygen element( Figure 15 ). Among them, the m and n selected for different elements such as nickel, cobalt, manganese, and oxygen are the same. From Figures 12 to 15 The shown results can be seen that after the local summation assignment process, the distribution characteristics of different elements are well reflected.

[0109] S9: According to the molecular formula of the sample material in S1, using the second division processing method, add the data points at the same position in the local summation results of nickel, cobalt, and manganese elements in S8 to obtain the first combined element distribution result, and draw a 2D hot map of the first combined element distribution result, as Figure 16 shown. Take the local summation result of oxygen in S8 as the second combined element distribution result, and its 2D hot map is as Figure 15 shown. Divide the second combined element distribution result by the first combined element distribution result to obtain the spatial distribution result of the O / (Ni+Co+Mn) stoichiometric ratio, that is, obtain the characteristic data of the spatial distribution of the O / (Ni+Co+Mn) element combination. When the denominator is zero during the calculation process, this result will be specially represented by the numpy function to prevent division by zero errors; the numpy function is a scientific computing library based on array objects in Python.

[0110] S10: Draw a hot map of the above-mentioned stoichiometric ratio spatial distribution characteristic data, as Figure 17 shown, which is called the stoichiometric ratio distribution hot map. This stoichiometric ratio distribution hot map shows the stoichiometric ratio distribution of the sample area in the high-angle annular dark field image. In the initial high-angle annular dark field image hot map( Figure 6 ), it can be seen that there are red areas with higher values, which is due to the thicker thickness of the sample in this area; but in the stoichiometric ratio distribution hot map( Figure 17 ), the stoichiometric ratio distributions of all samples are relatively uniform. On the one hand, it proves that the influence caused by uneven thickness has been successfully overcome. On the other hand, it means that the sample has a complete and uniform structure and element distribution, proving that the synthesis process of this sample is relatively ideal.

[0111] When drawing the hot map of the stoichiometric ratio spatial distribution characteristic data, the best presentation effect can be obtained by adjusting the values of m and n. When the set values of m and n are too small, the element distribution and stoichiometric ratio distribution characteristics cannot be clearly reflected; when the set values of m and n are too large, the number of local summation data points will be greatly reduced, resulting in a significant reduction in the resolution of the obtained data. Therefore, the values of m and n should be adjusted to be able to reflect the stoichiometric ratio distribution characteristics and ensure the clarity of the resolution as much as possible.

[0112] S11: Save the data of the above filtering signal processing result, local summation assignment processing result, and stoichiometric ratio distribution result, and perform sample composition analysis based on this. Among them, the filtering signal processing result (filtered data) successfully eliminates the signals from non-sample areas, effectively avoiding the influence and interference of irrelevant signals on the experimental results. Since the data points of the original elemental distribution map collected are too dense and discrete to present the elemental distribution characteristics between different regions in the sample, local summation assignment processing is performed to magnify the elemental distribution characteristics and obtain characteristics such as the elemental distribution differences between different regions in the sample. The stoichiometric ratio spatial distribution result, being in the form of a ratio, overcomes the interference of the original elemental distribution map data affected by the sample thickness and obtains the stoichiometric ratio distribution characteristic information of different regions in the sample that cannot be obtained by traditional characterization means. It can directly reflect characteristics such as whether the sample composition is uniform and what changes occur during the service process, and is of great significance for research on material synthesis, failure mechanisms, etc.

[0113] In an exemplary embodiment, the present application further provides a computer device, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface, and a communication interface. Among them, the processor, memory, and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the sample composition analysis method based on the elemental distribution map.

[0114] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiment methods as described above. Among them, any reference to a memory or other medium provided in the various embodiments of the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0115] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of the relevant data need to comply with the relevant regulations.

[0116] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0117] In this article, specific examples are used to elaborate on the principles and implementation methods of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A sample composition analysis method based on element distribution diagram, characterized in that: include: Obtaining element distribution map data and corresponding image data corresponding to each element in the sample obtained by performing energy spectrum analysis or mass spectrum analysis on the sample; Filtering non-sample area data in all element distribution map data according to the image data to obtain filtered element distribution map data corresponding to each element; Performing local summation and assignment processing on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation results corresponding to each element; According to the molecular formula of the sample material, the local summation results corresponding to each element are divided to obtain the spatial distribution characteristic data of the stoichiometric ratio; The sample composition analysis is carried out based on the spatial distribution characteristic data of the stoichiometric ratio.

2. The sample composition analysis method based on element distribution diagram according to claim 1, characterized in that: The obtaining of element distribution map data and corresponding image data corresponding to each element in the sample obtained by performing energy spectrum analysis or mass spectrum analysis on the sample specifically includes: Performing energy spectrum analysis or mass spectrum analysis on the sample to obtain element distribution map data corresponding to each element in the sample; the element distribution map data includes EDS data, EELS data and SIMS data; Performing SEM, TEM or STEM imaging on the sample to obtain image data of electron microscope imaging; the image data includes HAADF image data and SEM image data; The distribution of data points in the image data and the element distribution map data is in a matrix form of X rows×Y columns.

3. The sample composition analysis method based on element distribution diagram according to claim 2, characterized in that: The filtering of non-sample area data in all element distribution map data according to the image data to obtain filtered element distribution map data corresponding to each element specifically includes: The image data and each element distribution map data are plotted as corresponding 2D heat maps; wherein the 2D heat map of the image data is composed of sample areas and non-sample areas; Read the signal intensity of the sample area and non-sample area in the 2D heat map of the image data, and set the filtering threshold based on this; The data points in the 2D heat map of the image data with signal strength lower than the filtering threshold are recorded as filtering points; The values ​​of the filter point positions in each element distribution map data are cleared to zero to obtain the filtered element distribution map data corresponding to each element.

4. The sample composition analysis method based on element distribution diagram according to claim 3, characterized in that: After clearing the values ​​of the filter point positions in the element distribution map data to zero to obtain the filtered element distribution map data corresponding to each element, the method further includes: Clear the values ​​of the filter point positions in the image data to zero to obtain filtered image data; The filtered image data is plotted as a corresponding 2D heat map, and compared with the 2D heat map of the image data, the filtering threshold is adjusted according to the comparison result, and the step of recording the data points in the 2D heat map of the image data whose signal strength is lower than the filtering threshold as filtering points is returned to, until the filtered element distribution map data with the best filtering result is obtained.

5. The sample composition analysis method based on element distribution diagram according to claim 3, characterized in that: The method of performing local summation and assignment processing on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation results corresponding to each element specifically includes: Select an integer m that is divisible by the number of rows X of data points and an integer n that is divisible by the number of columns Y of data points respectively; For the filtered element distribution map data corresponding to each element, each m rows × n columns of data points in the filtered element distribution map data are added together as one data point to obtain the local sum value of the data point; the original X × Y data points are converted into (X / m) × (Y / n) data points, and the local summation result corresponding to each element is plotted based on the converted data points.

6. The sample composition analysis method based on element distribution diagram according to claim 3, characterized in that: The method of performing local summation and assignment processing on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation results corresponding to each element specifically includes: Select an integer m that is divisible by the number of rows X of data points and an integer n that is divisible by the number of columns Y of data points respectively; For the filtered element distribution map data corresponding to each element, for each data point in the filtered element distribution map data, the m rows × n columns of data points around the data point are added and then averaged, and the obtained average value is used as the local sum value of the data point; if there are no m rows × n columns of data points around the data point, the original value of the data point is retained; the original X × Y data points are reassigned, and the local summation result corresponding to each element is drawn according to the reassigned data points.

7. The sample composition analysis method based on element distribution diagram according to claim 5 or 6, characterized in that: The method of dividing the local summation results corresponding to each element according to the molecular formula of the sample material to obtain the stoichiometric ratio spatial distribution characteristic data specifically includes: According to the importance of each element in the molecular formula of the sample material on the material properties, the local summation values ​​of the data points at the same position in the local summation results corresponding to a certain target element and another target element are directly divided to obtain the stoichiometric ratio spatial distribution characteristic data of a single element.

8. The sample composition analysis method based on element distribution diagram according to claim 5 or 6, characterized in that: The method of dividing the local summation results corresponding to each element according to the molecular formula of the sample material to obtain the stoichiometric ratio spatial distribution characteristic data specifically includes: According to the importance of the influence of certain element combinations in the molecular formula of the sample material on the material properties, the local summation values ​​of the data points located at the same position in the local summation results corresponding to certain target elements among all the elements are added together to obtain the first combination element distribution result; the local summation values ​​of the data points located at the same position in the local summation results corresponding to other target elements among all the elements are added together to obtain the second combination element distribution result; the first combination element distribution result of the data points located at the same position is divided by the second combination element distribution result to obtain the stoichiometric ratio spatial distribution characteristic data of different element combinations.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sample composition analysis method based on the element distribution map as described in claim 1.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the sample composition analysis method based on the element distribution map described in claim 1 is implemented.

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