A sample composition analysis method, device and product based on an element distribution map

Abstract

The application discloses an element distribution map-based sample component analysis method and device and a product, and relates to the field of component analysis. First, element distribution map data corresponding to each element in a sample obtained by performing energy spectrum analysis or mass spectrum analysis on the sample and corresponding image data are acquired; non-sample region data in all element distribution map data is filtered according to the image data, and filtered element distribution map data corresponding to each element is obtained; a plurality of data points in the filtered element distribution map data corresponding to each element are subjected to local summation assignment processing, and a local summation result corresponding to each element is obtained; the local summation result corresponding to each element is subjected to division processing according to a molecular formula of sample material, and stoichiometric ratio spatial distribution characteristic data is obtained; and sample component analysis is performed according to the stoichiometric ratio spatial distribution characteristic data, so that the sample component analysis efficiency and accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of component analysis technology, and in particular to a sample component analysis method, device and product based on elemental distribution maps. Background Technology

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

[0003] Instruments commonly used in microbeam analysis techniques include scanning electron microscopes (SEM), transmission electron microscopes (TEM), and scanning transmission electron microscopes (STEM). Using SEM, TEM, or STEM, various analytical techniques can be employed to obtain information about the chemical composition and structure of samples, such as energy dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), and high-angle annular dark field imaging (HAADF). In SEM, TEM, or STEM imaging, the electron beam can perform a surface scan over a region of the sample to generate elemental distribution maps that encompass the spatial distribution of elements.

[0004] Currently, elemental distribution maps obtained by EDS and EELS techniques acquire signals from samples using a point-by-point scanning method. This results in overly dense sampling points, failing to adequately capture localized elemental aggregations or deficiencies. Furthermore, the acquired elemental distribution signals are affected by the thickness of the given sample; when the sample is locally thicker, the acquired signal is relatively stronger, and when the sample is locally thinner, the acquired signal is relatively weaker. In actual measurements, weak elemental distribution signals can be detected not only in the sample area but also in non-sample areas, and these signals are usually unrelated to the element being detected. These shortcomings limit the further application of elemental distribution maps obtained by EDS and EELS for sample composition analysis. Summary of the Invention

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

[0006] To achieve the above objectives, this application provides the following solution.

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

[0008] To obtain the elemental distribution map data and corresponding image data of each element in the sample obtained by energy dispersive spectroscopy or mass spectrometry analysis;

[0009] Based on the data filtering of non-sample area data in all element distribution map data, the filtered element distribution map data corresponding to each element is obtained;

[0010] The local summation and assignment process is performed on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element.

[0011] Based on 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 stoichiometry.

[0012] Sample composition analysis was performed based on the spatial distribution characteristics of stoichiometry.

[0013] Optionally, acquiring the elemental distribution map data and corresponding image data of each element in the sample obtained by energy dispersive spectroscopy or mass spectrometry specifically includes:

[0014] The sample is subjected to energy dispersive spectroscopy (EDS) or mass spectrometry (MS) to obtain elemental distribution data for each element in the sample; the elemental distribution data includes EDS data, EELS data, and SIMS data.

[0015] The sample is imaged using SEM, TEM, or STEM to obtain electron microscope image data; 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 form of an X-row × Y-column matrix.

[0017] Optionally, the step of filtering non-sample region data from all element distribution map data based on image data to obtain filtered element distribution map data corresponding to each element specifically includes:

[0018] Both the image data and the distribution data of each element are plotted as corresponding 2D heat maps; the 2D heat map of the image data consists of sample regions and non-sample regions.

[0019] 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.

[0020] Data points whose signal strength is lower than the filtering threshold in the 2D heat map of the image data are recorded as filtering points;

[0021] Clear the values ​​of the filter points in the distribution map data of each element to zero, and obtain the filtered element distribution map data corresponding to each element.

[0022] Optionally, after clearing the values ​​of the filter points in each element distribution map data to obtain the filtered element distribution map data corresponding to each element, the method further includes:

[0023] Clear the values ​​at the filter points in the image data to zero to obtain the filtered image data;

[0024] The filtered image data is plotted as a corresponding 2D heat map, compared with the 2D heat map of the image data, and the filtering threshold is adjusted according to the comparison result. The process is repeated until the optimal filtered element distribution map data is obtained.

[0025] Optionally, the step of performing local summation and assignment on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element specifically includes:

[0026] Select an integer m that is divisible by the number of rows X of the data points and an integer n that is divisible by the number of columns Y of the data points, respectively;

[0027] For each element, the filtered element distribution map data is used to sum the data points in each m rows × n columns of the filtered element distribution map data into a single data point, thus obtaining the local summation value of that data point; the original X × Y data points are transformed into (X / m) × (Y / n) data points, and the local summation result corresponding to each element is plotted based on the transformed data points.

[0028] Optionally, the step of performing local summation and assignment on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element specifically includes:

[0029] Select an integer m that is divisible by the number of rows X of the data points and an integer n that is divisible by the number of columns Y of the data points, respectively;

[0030] For each element in the filtered element distribution map data, for each data point in the filtered element distribution map data, the sums of the m rows × n columns of data points around the data point are calculated and then averaged. The average value is used as the local summation 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 plotted based on the reassigned data points.

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

[0032] Based on the importance of each element in the molecular formula of the sample material to the material properties, the local summation values ​​of data points located at the same position in the local summation results corresponding to one target element and another target element are directly divided to obtain the spatial distribution characteristic data of the stoichiometry of a single element.

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

[0034] Based on the importance of certain element combinations in the molecular formula of the sample material to the material properties, the local summation values ​​of data points at the same position in the local summation results corresponding to certain target elements among all elements are added together to obtain the first combination element distribution result; the local summation values ​​of data points at the same position in the local summation results corresponding to other target elements among all elements are added together to obtain the second combination element distribution result; the first combination element distribution result and the second combination element distribution result are divided by the data points at the same position to obtain the stoichiometric ratio spatial distribution characteristic data of different element combinations.

[0035] Secondly, this application provides a computer device, including: 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 elemental distribution maps.

[0036] Thirdly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the sample composition analysis method based on elemental distribution maps.

[0037] Based on the specific embodiments provided in this application, the following technical effects are disclosed.

[0038] This application provides a sample composition analysis method, device, and product based on elemental distribution maps. By filtering non-sample region data from the elemental distribution map data, interference from non-sample region signals is removed. The distribution characteristics are amplified by locally summing and assigning values ​​to multiple data points in the filtered elemental distribution map data, effectively capturing local elemental aggregations or missing elements. Furthermore, by dividing the locally summed results corresponding to each element according to the molecular formula of the sample material, the limitations of traditional elemental distribution maps due to sample thickness are overcome, and stoichiometric spatial distribution characteristics that cannot be obtained from traditional elemental distribution maps are identified. Sample composition analysis based on stoichiometric spatial distribution characteristic data can significantly improve the efficiency and accuracy of sample composition analysis. Moreover, this method is universally applicable to various types of elemental distribution map data obtained through different characterization methods, and has broad application prospects. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic flowchart of a sample composition analysis method based on elemental distribution maps according to this application;

[0041] Figure 2 A hotspot diagram of nickel element distribution drawn in a certain embodiment;

[0042] Figure 3 This is a heat map showing the distribution of cobalt in a certain embodiment.

[0043] Figure 4 This is a hotspot map of manganese element distribution drawn in a certain embodiment.

[0044] Figure 5 This is a heat map of oxygen distribution drawn in one embodiment;

[0045] Figure 6 A heat map of the distribution of a high-angle annular dark field image drawn in a certain embodiment;

[0046] Figure 7 This is a heatmap corresponding to the elemental distribution data of nickel after filtering in a certain embodiment;

[0047] Figure 8 This is a heatmap corresponding to the elemental distribution data of cobalt after filtering in a certain embodiment.

[0048] Figure 9This is a heatmap corresponding to the elemental distribution data of manganese after filtering in a certain embodiment.

[0049] Figure 10 This is a heatmap corresponding to the elemental distribution data after oxygen element filtration in a certain embodiment;

[0050] Figure 11 This is a heatmap of HAADF image data after filtering in a certain embodiment;

[0051] Figure 12 A heatmap of nickel summation plotted for a particular embodiment;

[0052] Figure 13 This is a heatmap of cobalt summation plotted in one embodiment.

[0053] Figure 14 A heatmap of manganese summation plotted in a certain embodiment;

[0054] Figure 15 This is a heatmap of oxygen element summation plotted in a certain embodiment;

[0055] Figure 16 This is a 2D heatmap of the distribution results of the first combination of elements in a certain embodiment;

[0056] Figure 17 This is a heatmap of the spatial distribution characteristics of stoichiometric ratios in a certain embodiment. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

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

[0059] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

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

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

[0062] Based on the type of output beam used in the measurement, methods for obtaining elemental distribution maps can be categorized into energy dispersive spectroscopy (EDS) and mass spectrometry (MS). EDS generally refers to the interaction of incident electrons with the sample in SEM, TEM, or STEM, generating a series of signals with elemental characteristics. Various analytical techniques, such as EDS, EELS, and HAADF, are used to obtain the chemical composition and structural information of the sample. Mass spectrometry primarily involves bombarding the sample with a focused primary ion beam, exciting trace amounts of secondary ions on the sample surface. These secondary ions are then separated based on their mass-to-charge ratio, allowing for the measurement of the distribution and abundance of different elements in the sample. Examples include secondary ion mass spectrometry (SIMS). Regardless of whether EDS or MS is used, the input beam (which can include laser beams, X-rays, and other electromagnetic waves, electrons, protons, or ions) can perform a surface scan over 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 EDS or EELS probes in instruments such as transmission electron microscopes and scanning electron microscopes. The acquired raw data includes electron microscope image data and corresponding elemental distribution map data, in txt, xlsx, or csv format, where the data points are distributed in an X-row × Y-column matrix obtained from area scanning. 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, secondary ion mass spectrometry analysis of the sample can be performed to obtain the corresponding surface scan SIMS data as elemental distribution map data.

[0064] Step 2: Based on the data filtering, filter the non-sample area data in all element distribution map data to obtain the filtered element distribution map data corresponding to each element.

[0065] The elemental distribution data is plotted into a chart, and non-sample region signals in the data are filtered out by setting and adjusting the filtering threshold. Specifically, step 2 includes:

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

[0067] Step 2.2: 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.

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

[0069] Step 2.3: Record the data points in the 2D heat map of the image data whose signal strength is lower than the filtering threshold as filtering points.

[0070] In a 2D heatmap of data, the location of data points below the filtering threshold (i.e., filter points) will be recorded, and data points at the same location in all other element distribution maps will also be set to zero.

[0071] Step 2.4: Clear the values ​​of the filter points in the distribution map data of each element to obtain the filtered element distribution map data for each element.

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

[0073] Step 2.5: Clear the values ​​of the filter points in the image data to zero to obtain the filtered image data.

[0074] Draw a chart of the filtered data and compare it with the original data to determine if the filtering effect is ideal. If not, reset the threshold.

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

[0076] The filtered image data and filtered element distribution map data are plotted into a 2D heat map and compared with the 2D heat map plotted from the original image data and element distribution map data. If the non-sample areas are still clearly present, it means that the filtering threshold is set too low and not filtered completely; if the sample area is significantly reduced, it means that the filtering threshold is set too high and filters out part of the sample area. If the above situations occur, the filtering threshold is reset and the process returns to step 2.3. The ideal filtering result is obtained when the non-sample areas are completely filtered and the sample areas are intact. The filtered element distribution map data with the best filtering result is obtained.

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

[0078] This application uses a method of locally summing and assigning values ​​to data points to amplify the distribution characteristics. Step 3 specifically includes two methods.

[0079] Method 1: Select integers m and n that are divisible by the number of rows X and the number of columns Y of the data points, respectively. Specifically, during plotting, statistics are performed on the original image data and the element distribution map data, and the specific values ​​of the number of rows X and the number of columns Y of the data points are fed back. Based on the statistically obtained number of rows X and number of columns Y, integers m and n that are divisible by the number of rows X and the number of columns Y of the data points are selected, respectively, and the data after filtering are locally summed according to the ratio of m×n.

[0080] Then, for each element, the data points in the filtered element distribution map data are summed together to obtain a local summation value. The original X×Y data points are transformed into (X / m)×(Y / n) data points, and the local summation result corresponding to each element is plotted based on the transformed data points.

[0081] Because the data points in the filtered elemental distribution map data are too dense and discrete to fully display the elemental distribution characteristics between different regions of the sample, a local summation operation is performed. Specifically, for each element in the filtered elemental distribution map data obtained in step 2, the data points in every m rows × n columns are summed to obtain a local summation value for that data point. After performing the above local summation operation, the X × Y data points in the filtered elemental distribution map data are transformed into (X / m) × (Y / n) data points. Based on the transformed data points, the local summation result corresponding to each element is plotted, and this local summation result is plotted as a 2D heatmap.

[0082] Method 2: Select an integer m that is divisible by the number of rows X of the data points and an integer n that is divisible by the number of columns Y of the data points. Then, for each element in the filtered element distribution map data, for each data point in the filtered element distribution map data, sum the m rows × n columns of data points around that data point and then calculate the average. Use the average value as the local summation value of that data point. If there are no m rows × n columns of data points around that data point, then retain the original value of that data point. In this way, the original X × Y data points are reassigned. Based on the reassigned data points, the local summation result corresponding to each element is plotted, and this local summation result is plotted as a 2D heatmap.

[0083] The difference between Method 2 and Method 1 is that Method 1 changes the resolution of the data. After filtering, the X×Y data points in the element distribution map are transformed into (X / m)×(Y / n) data points, meaning the resolution decreases from X×Y to (X / m)×(Y / n). In contrast, Method 2 retains the original X×Y resolution. In practical applications, the appropriate method can be chosen based on different resolution requirements.

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

[0085] By superimposing and comparing the distribution characteristics of different elements, the spatial distribution characteristics of stoichiometry can be reflected, and charts can be drawn and data saved accordingly. Step 4 also includes two methods: obtaining spatial distribution characteristic data of stoichiometry for a single element, or obtaining spatial distribution characteristic data of stoichiometry for combinations of different elements.

[0086] Method 1: Based on the importance of each element in the molecular formula of the sample material to the material properties, the local summation values ​​of data points at the same position in the local summation results corresponding to one target element and another target element are directly divided to obtain the spatial distribution characteristic data of the stoichiometry of a single element.

[0087] In this application, the element to be analyzed is referred to as the target element. For example, Li(Ni) 0.8 Co 0.2 In the O2 material sample, elements Ni and O are used as the target elements to be analyzed. The local summation value of the data point at a certain position in the local summation result of Ni is divided and compared with the local summation value of the data point at the same position in the local summation result of O. That is, the point-to-point direct division is used to obtain the spatial distribution characteristic data of the stoichiometry of individual elements Ni / O.

[0088] Method 2: Based on the importance of certain element combinations in the sample material's molecular formula to the material properties, sum the local summation values ​​of data points at the same position in the local summation results corresponding to certain target elements among all elements to obtain the first combination element distribution result. Sum the local summation values ​​of data points at the same position in the local summation results corresponding to other target elements among all elements to obtain the second combination element distribution result. Of course, in practical applications, the first or second combination element distribution result can also directly use the local summation results corresponding to a single element, in which case point-to-point addition is not required. Dividing the first combination element distribution result by the second combination element distribution result for data points at the same position yields the spatial distribution characteristics of the stoichiometric ratios of different element combinations.

[0089] For example, Li(Ni) 0.8 Co 0.2 In the O2 material sample, elements Ni and Co are located at the same site. The points are added one by one. 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 to obtain the first combination of element distribution results. Then, the result of the first combination of elements is obtained by dividing and comparing it with the local summation result of element O (the second combination of element distribution results).

[0090] For example, Li(Ni) 0.8 Co 0.2 In the O2 material sample, the local summation values ​​of data points at the same position in the local summation results of Ni and Co are added together to obtain the first combination elemental distribution result; the local summation values ​​of data points at the same position in the local summation results of Li and O are added together to obtain the second combination elemental distribution result. By dividing the first combination elemental distribution result and the second combination elemental distribution result by the data points at the same position, the spatial distribution characteristic data of the stoichiometric ratio of (Ni+Co) / (Li+O) are obtained.

[0091] For example, Li(Ni) 0.8 Co 0.2 In the O2 material sample, the local summation result of O can be directly used as the first combination element distribution result; the local summation values ​​of data points at the same position in the local summation results of Li, Ni, and Co are added together to obtain the second combination element distribution result. By dividing the first combination element distribution result and the second combination element distribution result by the data points at the same position, the spatial distribution characteristic data of the stoichiometry of O / (Li+Ni+Co) are obtained.

[0092] This application utilizes a method that, based on the importance or contribution of certain elements or element combinations in the molecular formula of a sample material to the analysis of material composition or performance, directly divides or combines and divides the local summation results of the target elements. This method is applicable to materials in various fields, and the combination and division of different elements can be flexibly adjusted according to the material characteristics to reflect the most important features. For example, combining Li, Ni, and Co elements in the examples and then comparing them with O reveals a more realistic characteristic. If Ni and O elements are directly compared, then the contributions of Li and Co elements are ignored, and each element is analyzed individually.

[0093] This application combines the characteristics of elements and selectively performs direct division or combined division on the local summation results of multiple elements, overcoming the interference caused by uneven sample thickness and robustly reflecting the spatial distribution characteristics of element proportions.

[0094] Step 5: Analyze the sample composition based on the spatial distribution characteristics of the stoichiometric ratio.

[0095] The stoichiometric spatial distribution data obtained in step 4 are plotted as a 2D heatmap and saved in CSV format. This data can be used to analyze the initial compositional homogeneity of the sample. Furthermore, by comparing the 2D heatmaps of the stoichiometric spatial distribution data before and after the sample's service (before and after any process), the element migration processes occurring within the sample during use can be analyzed, which can guide sample synthesis and reveal sample failure mechanisms.

[0096] This application proposes a sample composition analysis method based on elemental distribution maps. By deriving spatial distribution characteristics of stoichiometric ratios from elemental distribution map data, this method can eliminate non-sample region signals, overcome interference from sample thickness inhomogeneity, and reveal elemental distribution characteristics unavailable through traditional methods. Furthermore, in addition to obtaining the characteristics of sample composition distribution, it can also detect the distribution of stoichiometric ratios in the sample, which is unavailable through traditional characterization methods. Changes in the spatial distribution characteristics of stoichiometric ratios directly reflect the initial homogeneity of the sample composition and processes such as element migration occurring within the sample during use, playing a crucial role in guiding sample synthesis and revealing sample failure mechanisms.

[0097] This method can be applied to the detection of elemental spatial distribution and the analysis of sample composition and material properties in fields such as energy materials, metallic materials, functional materials, and biomaterials. In the field of energy materials, it will help to accurately optimize material performance, improve the manufacturing process of lithium-ion batteries and novel solar cells by clarifying elemental distribution, enhance energy conversion efficiency, and improve service life, thus driving the energy industry forward. In the field of metallic materials, it will ensure the quality of metallic materials required for aerospace and high-end equipment manufacturing, avoiding component segregation, and will also help improve properties such as strength, toughness, and corrosion resistance, accelerating the development of new alloys. In the field of functional materials, compositional regulation can improve the mechanical, thermal, optical, electrical, and magnetic properties of materials. In the field of biomaterials, it will strictly control the distribution of harmful elements to ensure the biosafety of materials implanted in the human body, optimize the bioactivity of materials based on elemental action mechanisms, promote cell-related functions, and provide key support for the development of biomimetic materials by simulating the elemental distribution of natural materials in living organisms, opening up new paths for the development of biomaterials.

[0098] The method proposed in this application can effectively overcome the influence of uneven sample thickness on conventional elemental distribution maps, eliminate the interference of signals from non-sample areas on the test results, and identify the stoichiometric distribution characteristics that cannot be obtained from traditional elemental distribution maps. It is also universally applicable to the processing of elemental distribution map data obtained by combining different characterization methods, providing new inspiration for the development of related instrument software and hardware.

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

[0100] The following example uses scanning transmission electron microscopy (STEM) to illustrate the application process of the method and product of this application, and its operation steps include S1 to S11.

[0101] S1: First, based on STEM, Li(Ni 0.8 Mn 0.1 Co 0.1 The original elemental distribution data of the O2 material sample were acquired. Then, the software was run to import the EDS data (as elemental distribution data) of nickel (Ni), cobalt (Co), manganese (Mn), and oxygen (O) in txt format obtained from STEM, as well as the HAADF image data, into the software.

[0102] S2: Draw 2D heatmaps of EDS data and HAADF image data for nickel, cobalt, manganese, and oxygen, as shown below. Figures 2 to 6 As shown, these are respectively called hotspot maps of nickel element distribution ( Figure 2Cobalt element distribution hotspot map ( Figure 3 Manganese element distribution hotspot map ( Figure 4 ), Oxygen element distribution hotspot map ( Figure 5 ) and high-angle ring-shaped dark field image heatmap ( Figure 6 The 2D heatmap of the HAADF image data (i.e., the high-angle annular dark field image heatmap) consists of the sample region and the non-sample region.

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

[0104] S4: The software reads the specific values ​​from the 2D heatmap of the HAADF image data at the mouse cursor position. It then reads the values ​​from the sample region and the non-sample region on the 2D heatmap of the HAADF image data, noting that the non-sample region values ​​are lower and the sample region values ​​are higher. A filter threshold, between the values ​​of the non-sample region and the sample region, is then set to filter out signals from the non-sample region.

[0105] S5: Filter the data by setting a filtering threshold based on the above read values. Data points in the HAADF image data that are below the filtering threshold will be set to zero.

[0106] S6: In HAADF image data, points below the set filtering threshold (filter points) will be zeroed and their positions will be recorded. The data points at the positions of these filter points in all other element distribution map data will also be set to zero, thus obtaining the filtered element distribution map data corresponding to each element.

[0107] S7: Draw 2D heatmaps of the filtered data (including filtered image data and filtered element distribution map data), as shown below. Figures 7 to 11 As shown. Among them. Figure 7 This is a heatmap corresponding to the elemental distribution data after filtering nickel. Figure 8 This is a heatmap corresponding to the elemental distribution data after filtering cobalt. Figure 9 This is a heatmap corresponding to the elemental distribution data of manganese after filtering. Figure 10 This is a heatmap corresponding to the elemental distribution data after filtering oxygen. Figure 11 This is the heatmap corresponding to the filtered HAADF image data. If non-sample areas are not completely filtered (filter threshold too low) or the sample area is shrunk (filter threshold too high), the filtering effect is unsatisfactory, and the process returns to S5 to reset the filter threshold. (Comparison) 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 As can be seen, after the filtering process of this application, the non-sample area is completely filtered out, while the sample area remains intact, achieving the ideal filtering result.

[0108] S8: Based on the number of rows X and columns Y obtained from the imported data statistics in S3, select integers m and n that are divisible by the number of rows X and columns Y, respectively. Perform local summation on all filtered data in an m×n ratio. That is, following the first local summation method, sum every m rows × n columns of data points in the filtered element distribution map data obtained in S7 into one data point, obtaining the local summation result for each element. After local summation and merging, the original X×Y data points are transformed into (X / m)×(Y / n) data points. Draw a 2D heatmap of the locally summed data, as shown below. Figures 12 to 15 As shown, these are respectively called the nickel element summation hotspot diagrams ( Figure 12 ), Cobalt element summation heatmap ( Figure 13 ), manganese element summation heatmap ( Figure 14 ) and oxygen element summation heatmap ( Figure 15 ( ). In this case, the values ​​of m and n are the same for different elements such as nickel, cobalt, manganese, and oxygen. From Figures 12 to 15 The results show that the distribution characteristics of different elements are well reflected after local summation and assignment.

[0109] S9: Based on the molecular formula of the sample material in S1, using the second phase division method, the data points at the same position in the local summation results of nickel, cobalt, and manganese elements in S8 are added together to obtain the first combination element distribution result. A 2D heatmap of the first combination element distribution result is then plotted, as shown below. Figure 16 As shown. The local summation result of oxygen in S8 is used as the second combined elemental distribution result, and its 2D heatmap is shown below. Figure 15 As shown. Dividing the distribution result of the second combination of elements by the distribution result of the first combination of elements yields the spatial distribution result of the stoichiometric ratio of O / (Ni+Co+Mn), which is the characteristic data of the spatial distribution of the stoichiometric ratio of the O / (Ni+Co+Mn) element combination. During the calculation, when the denominator is zero, the result is represented by a NumPy function to prevent division by zero errors; NumPy functions are scientific computing libraries in Python based on array objects.

[0110] S10: Draw a heat map of the spatial distribution characteristics of the above stoichiometric ratio data, such as... Figure 17 As shown, this is called a stoichiometric distribution heatmap, which displays the stoichiometric distribution of the sample region in a high-angle annular dark-field image. In the initial high-angle annular dark-field image heatmap ( Figure 6 In the stoichiometric distribution heatmap, we can see a red area with higher values, which is due to the sample being thicker in this region; however, in the stoichiometric distribution heatmap... Figure 17 In the sample, the stoichiometric distribution of all samples was relatively uniform, which proves that the influence of uneven thickness has been successfully overcome, and also means that the sample has a complete and uniform structure and elemental distribution, proving that the synthesis process of the sample is relatively ideal.

[0111] When plotting heatmaps of stoichiometric ratio spatial distribution data, the optimal representation can be achieved by adjusting the values ​​of m and n. If m and n are set too small, the elemental and stoichiometric ratio distribution characteristics will not be clearly displayed; if m and n are set too large, the number of locally summed data points will be significantly reduced, resulting in a substantial decrease in data resolution. Therefore, the values ​​of m and n should be adjusted to both reflect the stoichiometric ratio distribution characteristics and ensure sufficient resolution.

[0112] S11: Save the data from the above-mentioned filtered signal processing, local summation and assignment processing, and stoichiometric ratio distribution results for sample composition analysis. The filtered signal processing results (filtered data) successfully eliminated signals from non-sample areas, effectively avoiding the influence and interference of irrelevant signals on the experimental results. Because the original elemental distribution map data points were too dense and discrete to represent the elemental distribution characteristics between different regions of the sample, local summation and assignment processing was performed to amplify the elemental distribution characteristics and obtain features such as differences in elemental distribution between different regions of the sample. The stoichiometric ratio spatial distribution results, being in proportional form, overcome the interference of sample thickness on the original elemental distribution map data and obtain stoichiometric ratio distribution characteristics in different regions of the sample that are unavailable through traditional characterization methods. This directly reflects whether the sample composition is uniform and what changes have occurred during service, which is of great significance for research on material synthesis and failure mechanisms.

[0113] In one exemplary embodiment, this application also provides a computer device, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, memory, and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned sample composition analysis method based on elemental distribution maps.

[0114] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any reference to memory or other media in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[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 used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0117] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A sample composition analysis method based on elemental distribution maps, characterized in that, include: To obtain the elemental distribution map data and corresponding image data of each element in the sample obtained by energy dispersive spectroscopy or mass spectrometry analysis; Based on the data filtering of non-sample area data in all element distribution map data, the filtered element distribution map data corresponding to each element is obtained; The local summation and assignment process is performed on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element. Based on 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 stoichiometry, specifically including: Based on the importance of certain element combinations in the molecular formula of the sample material to the material properties, the local summation values ​​of data points at the same position in the local summation results corresponding to certain target elements among all elements are added together to obtain the first combination element distribution result; the local summation values ​​of data points at the same position in the local summation results corresponding to other target elements among all elements are added together to obtain the second combination element distribution result; the first combination element distribution result and the second combination element distribution result are divided by the data points at the same position to obtain the stoichiometric ratio spatial distribution characteristic data of different element combinations; Sample composition analysis was performed based on the spatial distribution characteristics of stoichiometry.

2. The sample composition analysis method based on elemental distribution maps according to claim 1, characterized in that, The acquisition of elemental distribution map data and corresponding image data of each element in the sample obtained by energy dispersive spectroscopy or mass spectrometry specifically includes: The sample is subjected to energy dispersive spectroscopy (EDS) or mass spectrometry (MS) to obtain elemental distribution data for each element in the sample; the elemental distribution data includes EDS data, EELS data, and SIMS data. The sample is imaged using SEM, TEM, or STEM to obtain electron microscope image data; 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 the form of an X-row × Y-column matrix.

3. The sample composition analysis method based on elemental distribution maps according to claim 2, characterized in that, The step of filtering non-sample region data from all element distribution map data based on image data to obtain filtered element distribution map data corresponding to each element specifically includes: Both the image data and the distribution data of each element are plotted as corresponding 2D heat maps; the 2D heat map of the image data consists of sample regions and non-sample regions. 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. Data points whose signal strength is lower than the filtering threshold in the 2D heat map of the image data are recorded as filtering points; Clear the values ​​of the filter points in the distribution map data of each element to zero, and obtain the filtered element distribution map data corresponding to each element.

4. The sample composition analysis method based on elemental distribution maps according to claim 3, characterized in that, After clearing the values ​​of the filter points in each element distribution map data to zero, and obtaining the filtered element distribution map data corresponding to each element, the process further includes: Clear the values ​​at the filter points in the image data to zero to obtain the filtered image data; The filtered image data is plotted as a corresponding 2D heat map, compared with the 2D heat map of the image data, and the filtering threshold is adjusted according to the comparison result. The process is repeated until the optimal filtered element distribution map data is obtained.

5. The sample composition analysis method based on elemental distribution maps according to claim 3, characterized in that, The step of performing local summation and assignment on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element specifically includes: Select an integer m that is divisible by the number of rows X of the data points and an integer n that is divisible by the number of columns Y of the data points, respectively; For each element, the filtered element distribution map data is used to sum the data points in each m rows × n columns of the filtered element distribution map data into a single data point, thus obtaining the local summation value of that data point; the original X × Y data points are transformed into (X / m) × (Y / n) data points, and the local summation result corresponding to each element is plotted based on the transformed data points.

6. The sample composition analysis method based on elemental distribution maps according to claim 3, characterized in that, The step of performing local summation and assignment on multiple data points in the filtered element distribution map data corresponding to each element to obtain the local summation result for each element specifically includes: Select an integer m that is divisible by the number of rows X of the data points and an integer n that is divisible by the number of columns Y of the data points, respectively; For each element in the filtered element distribution map data, for each data point in the filtered element distribution map data, the sums of the m rows × n columns of data points around the data point are calculated and then averaged. The average value is used as the local summation 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 plotted based on the reassigned data points.

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

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the sample composition analysis method based on elemental distribution maps as described in claim 1.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the sample composition analysis method based on elemental distribution maps as described in claim 1.

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