Method for detecting manganese and iron elements of lithium manganese iron phosphate

Abstract

The invention discloses a method for detecting manganese and iron elements of lithium manganese and iron phosphate, and belongs to the technical field of batteries, the method for detecting manganese and iron elements of lithium manganese and iron phosphate comprises the following steps: providing slices containing lithium manganese and iron phosphate; obtaining an element distribution diagram of manganese and iron elements of the lithium manganese iron phosphate in the slices; generating a grey-scale map according to the pixel value corresponding to each pixel point in the element distribution map; and converting the grey-scale map into an element distribution intensity curve graph, and obtaining the manganese-iron ratio and the manganese-iron element distribution uniformity of the lithium manganese iron phosphate in the slice through the element distribution intensity curve graph. According to the method, the manganese-iron ratio and the manganese-iron element distribution uniformity of the lithium manganese-iron phosphate can be quantitatively calculated, and an accurate and efficient method is provided for researching the influence of the distribution uniformity of Mn / Fe elements in lithium manganese-iron phosphate material particles on the performance.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method for detecting manganese and iron elements in lithium manganese iron phosphate. Background Art

[0002] Since their commercialization, secondary batteries have undergone continuous iterations and updates in materials and technologies, from their initial widespread use in the 3C digital field to today's electric vehicles and energy storage fields.

[0003] Lithium manganese iron phosphate cathode material combines the advantages of high safety and low cost of lithium iron phosphate materials with the high power of ternary materials. In recent years, the revival of lithium iron phosphate cathodes has driven the application research of lithium iron phosphate materials, which are considered to have the potential to replace lithium iron phosphate cathodes and become the next generation of large-scale cathode materials with broad market prospects. Current research has found that when lithium iron phosphate materials are in extreme cycles or near the end of their life, the originally evenly distributed Mn / Fe elements may become unevenly distributed, and even separate from the original single-phase lithium manganese iron phosphate material into a two-phase material of lithium iron phosphate and lithium manganese phosphate. At this time, the lithium storage mechanism of the material may change from the original single-phase solid solution to two-phase separation, thereby exacerbating the battery attenuation and the test alarm of the BMS system.

[0004] In view of this, this application is filed. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for detecting manganese and iron elements in lithium manganese iron phosphate. The present application can quantitatively calculate the manganese and iron ratio of lithium manganese iron phosphate and the uniformity of manganese and iron element distribution.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for detecting manganese and iron elements in lithium manganese iron phosphate comprises the following steps:

[0008] Provide slices containing lithium iron manganese phosphate;

[0009] Obtaining element distribution maps of manganese and iron elements in lithium manganese iron phosphate in the slice;

[0010] Generate a grayscale image according to the pixel values ​​corresponding to each pixel point in the element distribution image;

[0011] The grayscale image is converted into an element distribution intensity curve diagram, and the manganese-iron ratio and the manganese-iron element distribution uniformity of the lithium manganese iron phosphate in the slice are obtained through the element distribution intensity curve diagram.

[0012] As an embodiment of the present application, the thickness of the slice is 50 to 150 nm.

[0013] As an implementation scheme of the present application, the slices containing lithium manganese iron phosphate are obtained by cutting the positive electrode sheet using a focused ion beam device.

[0014] As an implementation scheme of the present application, the target lithium iron manganese phosphate particles in the positive electrode plate are selected by an electron beam, and the positive electrode plate is cut by an ion beam to obtain the slice containing lithium iron manganese phosphate.

[0015] As an implementation scheme of the present application, an element distribution diagram of manganese and iron elements in lithium manganese iron phosphate in a slice is obtained by an EDS element distribution tester.

[0016] As an implementation scheme of the present application, the pixels of the grayscale image are 8 to 16 bits.

[0017] As an implementation scheme of the present application, the grayscale image is converted into an element distribution intensity curve diagram by setting a threshold value according to the pixel value of the grayscale image, binarizing the pixel value of the grayscale image according to the threshold value, and converting it into an element distribution intensity curve diagram.

[0018] As an implementation scheme of the present application, the pixel points with pixel values ​​greater than the threshold in the grayscale image are regarded as valid pixel points.

[0019] As an implementation scheme of the present application, the pixel points in the grayscale image that are ≤ the threshold value are regarded as invalid pixel points.

[0020] As an implementation scheme of the present application, setting a threshold according to the pixel value of a grayscale image is specifically as follows: traversing the pixel value of each pixel point in the grayscale image, calculating the pixel value difference of each adjacent pixel point, determining two target pixel values ​​based on the maximum difference between adjacent pixel points, and using the smaller pixel value among the target pixel values ​​as the threshold.

[0021] The beneficial effects of the present invention are as follows: the present application obtains the element distribution map of manganese and iron elements of sliced ​​lithium manganese iron phosphate, generates a grayscale image according to the pixel values ​​corresponding to each pixel point of the element distribution map, and then converts the grayscale image into an element distribution intensity curve map. Finally, according to the element distribution intensity curve map, the manganese-iron ratio of the lithium manganese iron phosphate and the uniformity of the manganese-iron element distribution can be quantitatively calculated, thereby providing an accurate and efficient method for studying the influence of the uniformity of the Mn / Fe element distribution inside the lithium manganese iron phosphate material particles on the performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 SEM image of the preparation process of slicing the pole piece containing lithium manganese iron phosphate in Example 1:

[0023] (a) Lithium iron manganese phosphate particles are sliced ​​and adhered to the mechanical needle;

[0024] (b) extracting slices;

[0025] (c) sectioning;

[0026] (d) The slice is transferred to the sample stage;

[0027] (e) connecting the slice to the sample stage;

[0028] (f) The ion beam cuts the connection between the robot and the slice;

[0029] (g) Slicing and grinding;

[0030] (h) Slice polishing and impurity removal;

[0031] (i) Ultrathin sectioning.

[0032] Figure 2 Element distribution diagram of manganese and iron elements in lithium manganese iron phosphate in the slice of Example 1.

[0033] Figure 3 It is the grayscale image of Example 1.

[0034] Figure 4 This is the effective pixel point collection diagram of Example 1.

[0035] Figure 5 This is the element intensity distribution curve of Example 1.

[0036] Figure 6 Element distribution diagram of manganese and iron elements in lithium manganese iron phosphate in the slice of Example 2

[0037] Figure 7 This is the grayscale image of Example 2.

[0038] Figure 8 This is the effective pixel point collection diagram of Example 2.

[0039] Fig. 9 This is the element intensity distribution curve of Example 2. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0041] In the present application, the technical features described in an open manner include closed technical solutions composed of the listed features, and also include open technical solutions containing the listed features.

[0042] In this application, when it comes to numerical ranges, unless otherwise specified, the above numerical ranges are deemed to be continuous and include the minimum and maximum values ​​of the range, as well as each value between such minimum and maximum values. Further, when a range refers to an integer, each integer between the minimum and maximum values ​​of the range is included. In addition, when multiple ranges are provided to describe features or characteristics, the ranges can be merged. In other words, unless otherwise specified, all ranges disclosed herein should be understood to include any and all subranges included therein.

[0043] The reagents or instruments used in this application without indicating the manufacturer are all conventional products that can be obtained through commercial purchase.

[0044] The present application embodiment provides a method for detecting manganese and iron elements in lithium manganese iron phosphate, comprising the following steps:

[0045] Provide slices containing lithium iron manganese phosphate;

[0046] Obtaining element distribution maps of manganese and iron elements in lithium manganese iron phosphate in the slice;

[0047] Generate a grayscale image according to the pixel values ​​corresponding to each pixel point in the element distribution image;

[0048] The grayscale image is converted into an element distribution intensity curve diagram, and the manganese-iron ratio and the manganese-iron element distribution uniformity of the lithium manganese iron phosphate in the slice are obtained through the element distribution intensity curve diagram.

[0049] The present application obtains the element distribution map of manganese and iron elements of sliced ​​lithium manganese iron phosphate, generates a grayscale image according to the pixel values ​​corresponding to each pixel point of the element distribution map, and then converts the grayscale image into an element distribution intensity curve map. Finally, according to the element distribution intensity curve map, the manganese-iron ratio and the uniformity of manganese-iron element distribution of the lithium manganese iron phosphate can be quantitatively calculated, which provides an accurate and efficient method for studying the influence of the uniformity of Mn / Fe element distribution inside lithium manganese iron phosphate material particles on the performance.

[0050] Through the method of the present application, the manganese-iron ratio of lithium iron manganese phosphate and the uniformity of manganese-iron element distribution of the positive electrode plate of the secondary battery before and after cycling can be analyzed. Based on the analysis results, the element changes and element distribution of lithium iron manganese phosphate in the positive electrode plate of the secondary battery can be determined.

[0051] Among them, when analyzing the manganese-iron ratio of lithium manganese iron phosphate and the uniformity of manganese-iron element distribution on the positive electrode of the secondary battery before and after the cycle, it is necessary to ensure that the analysis method is consistent.

[0052] In one embodiment, the slice containing lithium iron manganese phosphate is derived from a secondary battery.

[0053] Wherein, the preparation method of the slice containing lithium manganese iron phosphate is:

[0054] The secondary battery is disassembled to obtain the positive electrode sheet of the secondary battery; the positive electrode sheet is prepared into slices containing lithium manganese iron phosphate.

[0055] In one embodiment, the thickness of the slice is 50 to 150 nm, for example, it can be 50 nm, 60 nm, 80 nm, 100 nm, 120 nm, 150 nm or a range consisting of any two of them. Controlling the thickness of the slice to 50 to 150 nm can eliminate the thickness effect on the strength of manganese and iron elements in lithium manganese iron phosphate, which is convenient for subsequent characterization.

[0056] In one embodiment, the slices containing lithium manganese iron phosphate are obtained by cutting the positive electrode sheet using a focused ion beam device.

[0057] In one embodiment, the target lithium iron manganese phosphate particles in the positive electrode plate are selected by an electron beam, and the positive electrode plate is cut by an ion beam to obtain the slice containing the lithium iron manganese phosphate.

[0058] In one embodiment, an element distribution diagram of manganese and iron elements in the lithium manganese iron phosphate in the slice is obtained by an EDS element distribution tester.

[0059] In one implementation manner, the pixel of the grayscale image is 8 to 16 bits.

[0060] For example, the pixel of the grayscale image is 8 bits. In the 8-bit mode, there are 2 8 = 256 color levels, that is, the pixel value range covers 0 to 255, which is enough to subdivide the conventional color composition. After this step, the pixel points that make up the image can be converted into corresponding 8-bit values ​​(pixel values) for display. Get the corresponding 8-bit value.

[0061] In one embodiment, converting the grayscale image into the element distribution intensity curve graph is specifically as follows: setting a threshold value according to the pixel value of the grayscale image, binarizing the pixel value of the grayscale image according to the threshold value, and converting it into the element distribution intensity curve graph.

[0062] In one implementation manner, the pixel points in the grayscale image whose pixel values ​​are greater than a threshold are regarded as valid pixel points.

[0063] In one implementation manner, pixels in the grayscale image that are less than or equal to a threshold are regarded as invalid pixels.

[0064] Among them, when the 8-bit value is less than or equal to the threshold, it is defined as an invalid signal point, which is converted into a binary value of 0, thereby further converting the 8-bit mode into a binary black and white mode, that is, valid pixels are judged to be black (corresponding value 1) and invalid pixels are judged to be white (corresponding value 0), thereby selecting valid pixels.

[0065] In one of the embodiments, setting a threshold according to the pixel value of the grayscale image is specifically as follows: traversing the pixel value of each pixel in the grayscale image, calculating the pixel value difference of each adjacent pixel, determining two target pixel values ​​based on the maximum difference between adjacent pixels, and using the smaller pixel value among the target pixel values ​​as the threshold.

[0066] The present application is further described below with specific embodiments:

[0067] Example 1

[0068] A method for detecting manganese and iron elements in lithium manganese iron phosphate comprises the following steps:

[0069] (1) Disassemble the secondary battery without cycle aging and at 50% SOC in a room with controlled room temperature and humidity (humidity less than 10%), separate the negative electrode and the separator, take out the corresponding positive electrode and soak it in DMC organic solvent for 20 minutes, dry it and cut it into 3*3cm 2 .

[0070] (2) Transfer the dried positive electrode sample to a focused ion beam device, such as Figure 1 As shown, an electron beam is used to select the target lithium iron manganese phosphate particles, and then an ion beam (current 1A) is used to cut the particles into sheets and thin them. A robot extracts the sample and sticks it to a transmission electron microscope sample holder. A small current (50μA) is used to clean the sample until a slice containing lithium iron manganese phosphate with a thickness of 100nm is obtained.

[0071] (3) Place the slices into a transmission electron microscope using an EDS element distribution tester, and use the normal TEM (transmission electron microscope) mode to find the corresponding position of the lithium iron manganese phosphate particles. After selecting the lithium iron manganese phosphate particles, switch to the STEM (scanning electron microscope) mode. At this time, the scale just includes the single lithium iron manganese phosphate particles to avoid the influence of other particles. The magnification corresponds to a 50nm ruler. After eliminating the interference of spherical aberration and astigmatism, focus on the lithium iron manganese phosphate particles to obtain a high-magnification scale STEM image of the corresponding particles. Open the EDS energy spectrum to scan and collect the Mn / Fe elements of the target lithium iron manganese phosphate particles, and finally obtain the following Figure 2 Elemental distribution of manganese and iron in lithium manganese iron phosphate in the slice shown.

[0072] (4) According to the pixel values ​​corresponding to each pixel point in the element distribution map, use image processing software to convert it into an 8-bit grayscale value mode to generate the following Figure 3Grayscale image shown;

[0073] (5) Traverse the pixel value of each pixel in the grayscale image, calculate the pixel value difference of each adjacent pixel, determine two target pixel values ​​based on the maximum difference of the adjacent pixels, take the smaller pixel value of the target pixel value as the threshold, and set the threshold value to 49 in this embodiment. Binarize the pixel value of the grayscale image according to the threshold value. When the 8-bit value is less than or equal to 49, it is defined as an invalid signal point, which is converted into a binary value of 0, thereby further converting the 8-bit mode into a binary black-and-white mode, that is, judging the valid pixel point as black (corresponding to the value 1) and the invalid pixel point as white (corresponding to the value 0), thereby selecting the valid pixel point;

[0074] (5) Figure 4 As shown, a specific area (dashed box part) in the particle is selected, and the image of the corresponding area is analyzed by collecting effective pixel points using the contour image function of the image processing software. The higher the element distribution intensity, the denser the effective pixel points at the corresponding position are distributed. At this time, the entire horizontal direction is divided into 1000 equal parts at a fixed height of the selected area, so as to obtain the corresponding pixel points of Mn / Fe elements in each horizontal direction. The pixel point intensity is the vertical axis and the horizontal position is the horizontal axis, so as to obtain the element intensity distribution curve in the horizontal direction ( Figure 5 ), after reading the intensity value, the image processing software can use the average value to calculate the Mn / Fe ratio inside the particles to be 1.30, and the manganese and iron elements are evenly distributed.

[0075] Example 2

[0076] A method for detecting manganese and iron elements in lithium manganese iron phosphate comprises the following steps:

[0077] (1) Disassemble the secondary battery that has been cycle-aged and has a capacity retention rate of 80% SOH and 50% SOC in a room with controlled room temperature and humidity (humidity less than 10%), separate the negative electrode and the separator, take out the corresponding positive electrode, soak it in DMC organic solvent for 20 minutes, dry it, and cut it into 3*3cm 2 .

[0078] (2) The dried positive electrode sheet sample was transferred to a focused ion beam device, and the target lithium iron manganese phosphate particles were selected using an electron beam. The particles were then cut into sheets and thinned using an ion beam (current 1A). The sample was extracted by a robot and pasted onto a transmission electron microscope sample holder. The sample was cleaned with a low current (50μA) until a slice containing lithium iron manganese phosphate with a thickness of 100nm was obtained.

[0079] (3) Place the slices into a transmission electron microscope using an EDS element distribution tester, and use the normal TEM (transmission electron microscope) mode to find the corresponding position of the lithium iron manganese phosphate particles. After selecting the lithium iron manganese phosphate particles, switch to the STEM (scanning electron microscope) mode. At this time, the scale just includes the single lithium iron manganese phosphate particles to avoid the influence of other particles. The magnification corresponds to a 50nm ruler. After eliminating the interference of spherical aberration and astigmatism, focus on the lithium iron manganese phosphate particles to obtain a high-magnification scale STEM image of the corresponding particles. Open the EDS energy spectrum to scan and collect the Mn / Fe elements of the target lithium iron manganese phosphate particles, and finally obtain the following Figure 6 Elemental distribution of manganese and iron in lithium manganese iron phosphate in the slice shown.

[0080] (4) According to the pixel values ​​corresponding to each pixel point in the element distribution map, use image processing software to convert it into an 8-bit grayscale value mode to generate the following Figure 7 Grayscale image shown;

[0081] (5) As in Example 1, 49 is selected as the threshold, and the pixel value of the grayscale image is binarized according to the threshold. When the 8-bit value is less than or equal to 49, it is defined as an invalid signal point, which is converted into a binary value of 0, thereby further converting the 8-bit mode into a binary black-and-white mode, that is, valid pixels are determined to be black (corresponding to a value of 1) and invalid pixels are determined to be white (corresponding to a value of 0), thereby selecting valid pixels;

[0082] (5) Figure 8 As shown, a specific area (dashed box part) in the particle is selected, and the image of the corresponding area is analyzed by collecting effective pixel points using the contour image function of the image processing software. The higher the element distribution intensity, the denser the effective pixel points at the corresponding position are distributed. At this time, the entire horizontal direction is divided into 1000 equal parts at a fixed height of the selected area, so as to obtain the corresponding pixel points of Mn / Fe elements in each horizontal direction. The pixel point intensity is the vertical axis and the horizontal position is the horizontal axis, so as to obtain the element intensity distribution curve in the horizontal direction ( Fig. 9 ), after reading the values, the Mn / Fe ratio on the left was calculated using the average value of the two segments to find that it was 1.28, which was very close to Example 1. The Mn / Fe ratio on the right was 1.08, indicating that the proportion of Mn elements decreased significantly, which may be related to the dissolution of Mn during the cycle. In addition, the X-axis can be used to accurately locate the position where the corresponding changes occurred. It can be seen that the element intensity decreased at around the horizontal position 380 (the overall level was divided into 1000 equal parts), that is, there was a significant loss of Mn elements on the right side of the particle horizontal position 380.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A method for detecting manganese and iron elements in lithium manganese iron phosphate, characterized in that: The following steps are involved: Provide slices containing lithium manganese iron phosphate; Obtaining element distribution maps of manganese and iron elements in lithium manganese iron phosphate in the slice; Generate a grayscale image according to the pixel values ​​corresponding to each pixel point in the element distribution image; The grayscale image is converted into an element distribution intensity curve diagram, and the manganese-iron ratio and the manganese-iron element distribution uniformity of the lithium manganese iron phosphate in the slice are obtained through the element distribution intensity curve diagram.

2. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 1, characterized in that: The thickness of the slice is 50-150 nm.

3. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 1, characterized in that: The positive electrode sheet is cut by a focused ion beam device to obtain the slice containing lithium manganese iron phosphate.

4. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 3, characterized in that: The target lithium iron manganese phosphate particles in the positive electrode plate are selected by an electron beam, and the positive electrode plate is cut by an ion beam to obtain the slice containing the lithium iron manganese phosphate.

5. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 1, characterized in that: The element distribution map of manganese and iron elements in lithium manganese iron phosphate in the slice is obtained by EDS element distribution tester.

6. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 1, characterized in that: The pixels of the grayscale image are 8 to 16 bits.

7. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 1, characterized in that: The grayscale image is converted into an element distribution intensity curve graph by setting a threshold value according to the pixel value of the grayscale image, binarizing the pixel value of the grayscale image according to the threshold value, and converting the grayscale image into an element distribution intensity curve graph.

8. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 7, characterized in that: The pixel points in the grayscale image whose pixel values ​​are greater than the threshold are regarded as valid pixel points.

9. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 7, characterized in that: The pixel points whose values ​​are less than or equal to the threshold value in the grayscale image are regarded as invalid pixel points.

10. The method for detecting manganese and iron elements in lithium manganese iron phosphate according to claim 7, characterized in that: The specific steps of setting the threshold according to the pixel value of the grayscale image are as follows: traverse the pixel value of each pixel in the grayscale image, calculate the pixel value difference of each adjacent pixel, determine two target pixel values ​​based on the maximum difference between the adjacent pixels, and use the smaller pixel value among the target pixel values ​​as the threshold.

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