Micrometer multi-film scanning electron microscope measurement and identification method for soft film of battery current collector

Abstract

The invention discloses a method for measuring and identifying micron multi-film layers of a soft film layer of a battery current collector by using a scanning electron microscope, and belongs to the technical field of detection and analysis of battery current collectors. The thickness of each film layer in the battery current collector is accurately measured by taking a cross-section image based on a scanning electron microscope and combining a high magnification function; based on an electron energy spectrum analysis technology, micron multi-film-layer energy spectrum data of a soft film layer of a battery current collector is collected and processed, component composition of each film layer in the battery current collector is identified, and a component distribution diagram is drawn. According to the invention, the problem of poor measurement and identification effect of the multiple film layers of the battery current collector caused by deviation between a test result and an actual situation due to the fact that stress or heat influence is easily introduced when a soft material and a complex film layer are processed in an existing measurement technology is solved. According to the invention, the deviation between the test result and the actual situation can be effectively prevented, and the multi-film measurement identification effect of the battery current collector can be improved.

Description

Technical Field

[0001] This invention relates to the field of battery current collector detection and analysis technology, specifically a scanning electron microscope method for measuring and identifying micron-sized multilayer soft film layers of battery current collectors. Background Technology

[0002] Battery current collectors typically consist of a multilayer film structure, and the thickness and performance of each layer have a significant impact on the overall battery performance. Accurately measuring the thickness and composition of each film layer is a key technical challenge during the manufacturing process.

[0003] Current measurement techniques are prone to introducing stress or heat effects when dealing with soft materials and complex films, leading to deviations between test results and actual conditions, resulting in poor measurement and identification of multi-film layers in battery current collectors. Summary of the Invention

[0004] The purpose of this invention is to provide a scanning electron microscope method for measuring and identifying micron-sized multilayer soft films in battery current collectors. Through proper sample preparation, this method avoids errors caused by sample softness, stress, or heat in traditional methods, and can efficiently and accurately measure film thickness and identify components, thus solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A scanning electron microscope (SEM) method for measuring and identifying micron-sized multilayer flexible films in battery current collectors includes:

[0007] Micron-sized multilayer samples of soft film layers for battery current collectors were prepared. Cross-sectional images were captured using a scanning electron microscope, and the thickness of each film layer in the battery current collector was accurately measured using high-magnification function.

[0008] Based on electronic energy dispersive spectroscopy (EDS) analysis, we collected and processed the energy dispersive spectral data of the micron-multilayer soft film layer of the battery current collector to determine the energy dispersive spectral characteristics of the micron-multilayer soft film layer of the battery current collector.

[0009] The energy spectrum characteristics of the micron-sized multilayer soft film layer of the battery current collector were analyzed to identify the composition of each film layer in the battery current collector and to draw the composition distribution map.

[0010] Preferably, based on scanning electron microscopy to capture cross-sectional images and combined with high-magnification function to accurately measure the thickness of each film layer in the battery current collector, the following operations are performed:

[0011] The battery current collector sample to be measured is placed under a scanning electron microscope. The surface of the battery current collector sample is scanned by focusing a high-energy electron beam to excite the battery current collector sample to generate an electronic signal. According to the high magnification function of the scanning electron microscope, the focal length of the scanning electron microscope is adjusted to make the cross-sectional image clearly visible, and a high-resolution micron-level multi-layer cross-sectional image of the soft film layer of the battery current collector is generated.

[0012] The cross-sectional images of the micron-sized soft film layer of the battery current collector were analyzed, and the thickness of the micron-sized soft film layer of the battery current collector was determined by measuring along the vertical direction of the film layer with a scale.

[0013] Preferably, the composition of each film layer in the battery current collector is identified, and the following operations are performed:

[0014] Based on the requirements for scanning electron microscopy measurement and identification of micron- and multi-layer soft films in battery current collectors, a scanning electron microscopy measurement and identification model for micron- and multi-layer soft films in battery current collectors is constructed.

[0015] The battery current collector soft film layer micron-multilayer scanning electron microscope measurement and recognition model was deployed in a real battery current collector soft film layer micron-multilayer scanning electron microscope measurement and recognition environment;

[0016] The energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are input into the scanning electron microscope measurement and identification model of the micron-multilayer soft film layer of the battery current collector;

[0017] Based on the scanning electron microscopy (SEM) measurement and identification model of the micron-multilayer soft film layer of the battery current collector, the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are analyzed, and the composition of the micron-multilayer soft film layer of the battery current collector is identified, thereby determining the SEM measurement and identification results of the micron-multilayer soft film layer of the battery current collector.

[0018] Preferably, to prepare a micron-sized multilayer sample of a flexible film layer for the battery current collector, the following operations are performed:

[0019] The battery current collector soft film layer is embedded into a block solid using resin cold mounting technology, thereby fixing the micron-multilayer sample of the battery current collector soft film layer. During the resin mounting process, the battery current collector soft film layer micron-multilayer sample is completely encapsulated, with only the cross-section to be tested exposed.

[0020] Argon ion polishing technology was used to process the cross section of the micron-sized multilayer soft film sample of the battery current collector. The cross section was smoothed by ion beam bombardment, which reduced stress introduction and avoided damage to the original morphology. The ion beam intensity was controlled and the angle was adjusted multiple times to gradually optimize the cross section balance.

[0021] Liquid nitrogen is introduced for cooling during the polishing process to keep the micron-sized multilayer sample of the battery current collector soft film in a low-temperature environment, reducing structural deformation or compositional changes of the battery current collector soft film caused by processing heat.

[0022] Preferably, the energy spectrum data of the soft film layer of the battery current collector (micron-level multilayer film) are collected, and the following operations are performed:

[0023] Based on electron energy dispersive spectroscopy (EDS) analysis, the battery current collector sample to be tested is placed under a scanning electron microscope and scanned using the scanning electron microscope.

[0024] In this process, the scanning electron microscope irradiates the current collector sample of the battery under test with a scanning electron beam, and the X-ray energy spectrum reflected by the current collector sample is received by the energy spectrometer, thereby collecting the energy spectrum data of the micron-multilayer soft film layer of the current collector.

[0025] Preferably, the energy spectrum data of the micron-level multilayer flexible film layer of the battery current collector is processed by performing the following operations:

[0026] The energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector is cleaned to remove noise from the energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector, thereby reducing the interference of noise on the scanning electron microscope measurement and identification of the micron- and multi-layer soft film layer of the battery current collector.

[0027] The energy spectrum data of the flexible film layer of the battery current collector is normalized to remove the dimensional differences in the energy spectrum data of the flexible film layer of the battery current collector, thus forming standardized energy spectrum data of the flexible film layer of the battery current collector.

[0028] Feature extraction was performed on the energy spectrum data of the micron-multilayer soft film layer of the battery current collector. Features related to the scanning electron microscopy measurement and identification of the micron-multilayer soft film layer of the battery current collector were extracted from the energy spectrum data, and the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector were determined, including the position and intensity characteristics of the X-ray peaks.

[0029] Preferably, a scanning electron microscope (SEM) measurement and identification model for the micron-level multilayer soft film layer of the battery current collector is constructed, and the following operations are performed:

[0030] Historical data of micron-level multiple layers of flexible film layer for battery current collector were collected, and the collected historical data of micron-level multiple layers of flexible film layer for battery current collector were divided to determine the training set and the test set.

[0031] Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the scanning electron microscope measurement and recognition behavior of the micron-multilayer soft film layer of the battery current collector from the training set, and to identify the composition of the micron-multilayer soft film layer of the battery current collector, thereby determining the scanning electron microscope measurement and recognition model of the micron-multilayer soft film layer of the battery current collector.

[0032] The test set is input into the micron-multilayer scanning electron microscope (SEM) measurement and recognition model of the soft film layer of the battery current collector. The SEM measurement and recognition model of the soft film layer of the battery current collector is tested based on the test set to evaluate the performance of the SEM measurement and recognition model of the soft film layer of the battery current collector and determine the model test evaluation results.

[0033] Based on the model test and evaluation results, the scanning electron microscope (SEM) measurement and identification model for the micron-multilayer soft film layer of the battery current collector was adjusted and optimized to determine the optimal SEM measurement and identification model for the micron-multilayer soft film layer of the battery current collector, which is used to identify the composition of the micron-multilayer soft film layer of the battery current collector.

[0034] Preferably, a component distribution diagram is drawn, and the following operations are performed:

[0035] Based on the scanning electron microscope measurement and identification results of the micron-multilayer soft film layer of the battery current collector, the components contained in the micron-multilayer soft film layer of the battery current collector are determined. Combining the components contained in the micron-multilayer soft film layer of the battery current collector, a component distribution map is generated, in which different colors or grayscale represent the components of the micron-multilayer soft film layer of the battery current collector.

[0036] Preferred options also include:

[0037] A high-sensitivity acoustic array with a set spacing of regular hexagonal grids is used to perform acoustic diaphragm puncture detection on micron-sized multilayer samples of soft membrane layers of battery current collectors to obtain acoustic puncture detection data.

[0038] A membrane damage prediction model was constructed, and acoustic puncture detection data was used to train the membrane damage prediction model. The trained membrane damage prediction model that meets the accuracy of membrane damage identification was obtained through model training.

[0039] The trained membrane damage prediction model was used to identify membrane damage based on acoustic puncture detection data of micron-level multilayer soft membranes in battery current collectors, and the damage identification results were obtained.

[0040] Based on the damage identification results, a battery thermal runaway risk analysis is performed. The risk level of the battery is classified based on the risk analysis results. Based on the set matching rules, the thermal runaway prevention schemes in the prevention measure scheme library are matched according to the risk level. The thermal runaway prevention schemes of the matching results are recommended as the battery thermal runaway risk mitigation schemes.

[0041] Preferably, an acoustic diaphragm puncture test is performed by performing the following operations:

[0042] Acoustic signals are acquired and detected, and acoustic spatiotemporal characteristics are analyzed to obtain the spatial domain characteristics, time domain characteristics, and frequency domain characteristics of the acoustic signals.

[0043] Based on the spatial domain, time domain, and frequency domain characteristics of acoustic signals, a membrane damage feature vector of acoustic signals is constructed.

[0044] The feature vector of membrane damage is normalized to obtain acoustic puncture detection data after feature value standardization.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention addresses the characteristics of flexible materials in battery current collectors by employing a resin cold-mounting technique to embed the flexible film layer of the battery current collector into a block-shaped solid. During the resin mounting process, the micron-sized multilayer sample of the flexible film layer is completely encapsulated, with only the test section exposed for subsequent processing. Argon ion polishing technology is used to process the test section of the micron-sized multilayer sample of the battery current collector. Ion beam bombardment smooths the section, reducing stress introduction and avoiding damage to the original morphology. To ensure polishing effectiveness, the ion beam intensity is controlled, and the angle is adjusted multiple times to gradually optimize the cross-sectional balance. Liquid nitrogen is introduced during polishing for cooling, maintaining the micron-sized multilayer sample of the battery current collector in a low-temperature environment. This reduces structural deformation or compositional changes in the flexible film layer caused by processing heat, effectively preventing deviations between test results and actual conditions.

[0047] 2. This invention uses scanning electron microscopy to capture cross-sectional images and combines them with high magnification to accurately measure the thickness of each film layer in the battery current collector. Based on electron energy dispersive spectroscopy (EDS) analysis, it collects and processes the EDS data of the micron-level multiple layers of the soft film layer in the battery current collector, determines the EDS characteristics of the micron-level multiple layers of the soft film layer in the battery current collector, and analyzes the EDS characteristics of the micron-level multiple layers of the soft film layer in the battery current collector to identify the composition of each film layer in the battery current collector and draw a composition distribution map, which can improve the measurement and identification effect of the multi-layer film layer in the battery current collector. Attached Figure Description

[0048] Figure 1 This is a flowchart of the scanning electron microscope measurement and identification method for micron-level multilayer soft film layers of battery current collectors according to the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] To address the issue that existing measurement techniques are prone to introducing stress or heat when handling soft materials and complex films, leading to discrepancies between test results and actual conditions, and resulting in poor measurement and identification of multi-film layers in battery current collectors, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0051] A scanning electron microscope (SEM) method for measuring and identifying micron-sized multilayer flexible films in battery current collectors includes:

[0052] Prepare micron-sized multilayer samples of soft film for battery current collectors.

[0053] In this embodiment, a micron-sized multilayer sample of a flexible film layer for battery current collector is prepared by performing the following operations:

[0054] To address the characteristics of flexible materials in battery current collectors, a resin cold mounting technique is employed to mount the flexible film layer of the battery current collector into a block-shaped solid, thereby fixing the micron-sized multilayer sample of the flexible film layer of the battery current collector. During the resin mounting process, the micron-sized multilayer sample of the flexible film layer of the battery current collector is completely encapsulated, with only the cross-section to be tested exposed for subsequent processing.

[0055] It should be noted that, in order to ensure the stable bonding between the resin and the micron-multilayer flexible film sample of the battery current collector, a cold-mounting resin material with low shrinkage rate is preferred. During the mounting process, it is ensured that the micron-multilayer flexible film sample of the battery current collector is completely covered to avoid inclusions or cracks during processing.

[0056] Argon ion polishing technology was used to process the cross section of the micron-sized multilayer soft film sample of the battery current collector. The cross section was smoothed by ion beam bombardment to reduce stress introduction and avoid damage to the original morphology. To ensure the polishing effect, the ion beam intensity was controlled and the angle was adjusted multiple times to gradually optimize the cross section balance.

[0057] It should be noted that low-energy ion beams can be used to reduce surface roughness during initial polishing, while angle adjustment techniques should be used during fine polishing to ensure cross-sectional uniformity.

[0058] Liquid nitrogen is introduced for cooling during the polishing process to keep the micron-sized multilayer sample of the battery current collector soft film in a low-temperature environment, reducing structural deformation or compositional changes of the battery current collector soft film caused by processing heat.

[0059] It should be noted that the liquid nitrogen cooling device must operate synchronously with the polishing instrument to ensure uniform temperature and prevent condensation, thereby significantly reducing the impact of thermal effects on the film structure during processing.

[0060] Therefore, through proper sample preparation, errors caused by sample softness, stress, or heat in traditional methods can be avoided.

[0061] The thickness of each film layer in the battery current collector is accurately measured by taking cross-sectional images using a scanning electron microscope and combining it with high magnification.

[0062] In this embodiment, the thickness of each film layer in the battery current collector is accurately measured by taking cross-sectional images using a scanning electron microscope and combining it with high magnification. The following operations are performed:

[0063] The battery current collector sample to be measured is placed under a scanning electron microscope. The surface of the battery current collector sample is scanned by focusing a high-energy electron beam to excite the battery current collector sample to generate an electronic signal. According to the high magnification function of the scanning electron microscope, the focal length of the scanning electron microscope is adjusted to make the cross-sectional image clearly visible, and a high-resolution micron-level multi-layer cross-sectional image of the soft film layer of the battery current collector is generated.

[0064] The cross-sectional images of the micron-sized soft film layer of the battery current collector were analyzed, and the thickness of the micron-sized soft film layer of the battery current collector was determined by measuring along the vertical direction of the film layer with a scale.

[0065] It should be noted that by using a scanning electron microscope to capture cross-sectional images and combining them with high magnification, the thickness of each film layer in the battery current collector can be accurately measured.

[0066] Based on electronic energy dispersive spectroscopy (EDS) analysis, the EDS data of the micron-multilayer soft film layer of the battery current collector were collected and processed to determine the EDS characteristics of the micron-multilayer soft film layer of the battery current collector.

[0067] In this embodiment, energy spectrum data of the soft film layer of the battery current collector at multiple micrometers are collected, and the following operations are performed:

[0068] Based on electron energy dispersive spectroscopy (EDS) analysis, the battery current collector sample to be tested is placed under a scanning electron microscope and scanned using the scanning electron microscope.

[0069] In this process, the scanning electron microscope irradiates the current collector sample of the battery under test with a scanning electron beam, and the X-ray energy spectrum reflected by the current collector sample is received by the energy spectrometer, thereby collecting the energy spectrum data of the micron-multilayer soft film layer of the current collector.

[0070] In this embodiment, the energy spectrum data of the micron-multilayer soft film layer of the battery current collector is processed by performing the following operations:

[0071] The energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector is cleaned to remove noise from the energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector, thereby reducing the interference of noise on the scanning electron microscope measurement and identification of the micron- and multi-layer soft film layer of the battery current collector.

[0072] The energy spectrum data of the flexible film layer of the battery current collector is normalized to remove the dimensional differences in the energy spectrum data of the flexible film layer of the battery current collector, thus forming standardized energy spectrum data of the flexible film layer of the battery current collector.

[0073] Feature extraction was performed on the energy spectrum data of the micron-multilayer soft film layer of the battery current collector. Features related to the scanning electron microscopy measurement and identification of the micron-multilayer soft film layer of the battery current collector were extracted from the energy spectrum data, and the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector were determined, including the position and intensity characteristics of the X-ray peaks.

[0074] The energy spectrum characteristics of the micron-sized multilayer soft film layer of the battery current collector were analyzed to identify the composition of each film layer in the battery current collector and to draw the composition distribution map.

[0075] In this embodiment, the composition of each film layer in the battery current collector is identified, and the following operations are performed:

[0076] Based on the requirements for scanning electron microscopy measurement and identification of micron- and multi-layer soft films in battery current collectors, a scanning electron microscopy measurement and identification model for micron- and multi-layer soft films in battery current collectors is constructed.

[0077] In constructing the scanning electron microscope measurement and identification model for the micron-multilayer soft film layer of the battery current collector, historical data of the micron-multilayer soft film layer of the battery current collector are collected, and the collected historical data of the micron-multilayer soft film layer of the battery current collector are divided to determine the training set and the test set.

[0078] Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the scanning electron microscope measurement and recognition behavior of the micron-multilayer soft film layer of the battery current collector from the training set, and to identify the composition of the micron-multilayer soft film layer of the battery current collector, thereby determining the scanning electron microscope measurement and recognition model of the micron-multilayer soft film layer of the battery current collector.

[0079] The test set is input into the micron-multilayer scanning electron microscope (SEM) measurement and recognition model of the soft film layer of the battery current collector. The SEM measurement and recognition model of the soft film layer of the battery current collector is tested based on the test set to evaluate the performance of the SEM measurement and recognition model of the soft film layer of the battery current collector and determine the model test evaluation results.

[0080] Based on the model testing and evaluation results, the scanning electron microscope (SEM) measurement and identification model for the micron-multilayer soft film layer of the battery current collector was adjusted and optimized to determine the optimal model for identifying the composition of the micron-multilayer soft film layer of the battery current collector.

[0081] The optimal scanning electron microscope (SEM) measurement and identification model for flexible film layers in battery current collectors was deployed in a real-world SEM measurement and identification environment for flexible film layers in battery current collectors.

[0082] The energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are input into the scanning electron microscope measurement and identification model of the micron-multilayer soft film layer of the battery current collector;

[0083] Based on the scanning electron microscopy (SEM) measurement and identification model of the micron-multilayer soft film layer of the battery current collector, the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are analyzed, and the composition of the micron-multilayer soft film layer of the battery current collector is identified, thereby determining the SEM measurement and identification results of the micron-multilayer soft film layer of the battery current collector.

[0084] It should be noted that, based on electron energy dispersive spectroscopy (EDS) analysis technology, the EDS data of the micron-multilayer soft film layer of the battery current collector is collected and processed to determine the EDS characteristics of the micron-multilayer soft film layer of the battery current collector. By analyzing the EDS characteristics of the micron-multilayer soft film layer of the battery current collector, the composition of each film layer in the battery current collector can be identified.

[0085] In this embodiment, a component distribution diagram is drawn by performing the following operations:

[0086] Based on the scanning electron microscope measurement and identification results of the micron-multilayer soft film layer of the battery current collector, the components contained in the micron-multilayer soft film layer of the battery current collector are determined. Combining the components contained in the micron-multilayer soft film layer of the battery current collector, a component distribution map is generated, in which different colors or grayscale represent the components of the micron-multilayer soft film layer of the battery current collector.

[0087] In addition to the foregoing embodiments, it also includes:

[0088] A high-sensitivity acoustic array with a set spacing of regular hexagonal grids is used to perform acoustic diaphragm puncture detection on micron-sized multilayer samples of soft membrane layers of battery current collectors to obtain acoustic puncture detection data.

[0089] A membrane damage prediction model was constructed, and acoustic puncture detection data was used to train the membrane damage prediction model. The trained membrane damage prediction model that meets the accuracy of membrane damage identification was obtained through model training.

[0090] The trained membrane damage prediction model was used to identify membrane damage based on acoustic puncture detection data of micron-level multilayer soft membranes in battery current collectors, and the damage identification results were obtained.

[0091] Based on the damage identification results, a battery thermal runaway risk analysis is performed, and the thermal runaway risk value for each damage point is calculated using the following formula:

[0092]

[0093] Among them, R i D represents the thermal runaway risk value at the i-th damage point; p The probability of damage can be pre-defined, or the acoustic puncture detection data can be input into a pre-trained hybrid model of random forest and temporal convolutional network for processing, and the output is the probability of damage; β represents the temperature sensitivity coefficient; T i The temperature at the i-th damage point can be measured using an infrared measuring instrument; dI / dt represents the rate of change of current during battery use; I0 represents the rated current of the battery.

[0094] Based on the risk analysis results, batteries are classified into risk levels. For example, a thermal runaway risk value of less than 0.3 is Level 1, 0.3 to 0.6 is Level 2, 0.6 to 0.9 is Level 3, and above 0.9 is Level 4. The thermal runaway risk value of the damage point with the highest damage level can be used as the basis for classification. Based on the set matching rules, the risk level is matched with the thermal runaway prevention schemes in the prevention measure scheme library, and the matched thermal runaway prevention schemes are recommended as the battery thermal runaway risk mitigation schemes.

[0095] In this embodiment, cross-scale parameter fusion enables a more comprehensive risk assessment; this solution upgrades battery safety monitoring from "passive response" to "active prevention and control," providing an inherently safe solution for high-energy-density battery systems; in addition, the use of thermal runaway risk values ​​enables quantitative analysis, improving the objectivity and reliability of the results; the use of a high-sensitivity acoustic array with a regular hexagonal grid arrangement achieves omnidirectional acoustic coverage, improving positioning accuracy; and array beamforming technology is used to suppress environmental noise, improving anti-interference performance.

[0096] Based on the aforementioned embodiments, an acoustic diaphragm puncture test is performed, and the following operations are performed:

[0097] Acoustic signals are acquired and detected, and acoustic spatiotemporal characteristics are analyzed to obtain the spatial domain characteristics, time domain characteristics, and frequency domain characteristics of the acoustic signals.

[0098] Based on the spatial domain, time domain, and frequency domain characteristics of acoustic signals, a membrane damage feature vector of acoustic signals is constructed.

[0099] The feature vector of membrane damage is normalized to obtain acoustic puncture detection data after feature value standardization.

[0100] In this embodiment, spatial domain features, time domain features, and frequency domain features of acoustic signals are extracted to characterize the spatiotemporal features of acoustic signals, thereby realizing the digitization of acoustic signals and the adaptability of this solution to the scenario. By constructing the membrane damage feature vector of acoustic signals, a foundation is provided for implementing digital analysis. Through normalization processing, the adaptation problem of different detection objects can be solved, while ensuring the smooth progress of subsequent analysis and ensuring the accuracy of damage analysis.

[0101] In summary, by preparing samples properly, errors caused by sample softness, stress, or heat in traditional methods can be avoided. This method can efficiently and accurately measure film thickness and identify components. It is suitable for the analysis of various soft, multi-layered film structures, especially in fields such as battery current collectors. It can be widely used in scenarios such as battery performance optimization, material research and development, and quality control.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A scanning electron microscope method for measuring and identifying micron-sized multilayer flexible films in battery current collectors, characterized in that, include: Micron-sized multilayer samples of soft film layers for battery current collectors were prepared. Cross-sectional images were captured using a scanning electron microscope, and the thickness of each film layer in the battery current collector was accurately measured using high-magnification function. Based on electronic energy dispersive spectroscopy (EDS) analysis, we collected and processed the energy dispersive spectral data of the micron-multilayer soft film layer of the battery current collector to determine the energy dispersive spectral characteristics of the micron-multilayer soft film layer of the battery current collector. The energy spectrum characteristics of the micron-sized multilayer soft film layer of the battery current collector were analyzed to identify the composition of each film layer in the battery current collector and to draw the composition distribution map.

2. The method for measuring and identifying micron-level multilayer flexible film layers of battery current collectors using scanning electron microscopy as described in claim 1, characterized in that, Based on cross-sectional images captured by scanning electron microscopy and combined with high-magnification function, the thickness of each film layer in the battery current collector is accurately measured, and the following operations are performed: The battery current collector sample to be measured is placed under a scanning electron microscope. The surface of the battery current collector sample is scanned by focusing a high-energy electron beam to excite the battery current collector sample to generate an electronic signal. According to the high magnification function of the scanning electron microscope, the focal length of the scanning electron microscope is adjusted to make the cross-sectional image clearly visible, and a high-resolution micron-level multi-layer cross-sectional image of the soft film layer of the battery current collector is generated. The cross-sectional images of the micron-sized soft film layer of the battery current collector were analyzed, and the thickness of the micron-sized soft film layer of the battery current collector was determined by measuring along the vertical direction of the film layer with a scale.

3. The method for measuring and identifying micron-level multilayer flexible film layers in battery current collectors as described in claim 1, characterized in that, Identify the composition of each film layer in the battery current collector and perform the following operations: Based on the requirements for scanning electron microscopy measurement and identification of micron- and multi-layer soft films in battery current collectors, a scanning electron microscopy measurement and identification model for micron- and multi-layer soft films in battery current collectors is constructed. The battery current collector soft film layer micron-multilayer scanning electron microscope measurement and recognition model was deployed in a real battery current collector soft film layer micron-multilayer scanning electron microscope measurement and recognition environment; The energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are input into the scanning electron microscope measurement and identification model of the micron-multilayer soft film layer of the battery current collector; Based on the scanning electron microscopy (SEM) measurement and identification model of the micron-multilayer soft film layer of the battery current collector, the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector are analyzed, and the composition of the micron-multilayer soft film layer of the battery current collector is identified, thereby determining the SEM measurement and identification results of the micron-multilayer soft film layer of the battery current collector.

4. The scanning electron microscope measurement and identification method for micron-level multilayer flexible film layers of battery current collectors as described in claim 1, characterized in that, To prepare micron-sized multilayer samples of flexible film layers for battery current collectors, the following operations were performed: The battery current collector soft film layer is embedded into a block solid using resin cold mounting technology, thereby fixing the micron-multilayer sample of the battery current collector soft film layer. During the resin mounting process, the battery current collector soft film layer micron-multilayer sample is completely encapsulated, with only the cross-section to be tested exposed. Argon ion polishing technology was used to process the cross section of the micron-sized multilayer soft film sample of the battery current collector. The cross section was smoothed by ion beam bombardment, which reduced stress introduction and avoided damage to the original morphology. The ion beam intensity was controlled and the angle was adjusted multiple times to gradually optimize the cross section balance. Liquid nitrogen is introduced for cooling during the polishing process to keep the micron-sized multilayer sample of the battery current collector soft film in a low-temperature environment, reducing structural deformation or compositional changes of the battery current collector soft film caused by processing heat.

5. The scanning electron microscope measurement and identification method for micron-level multilayer flexible film layers of battery current collectors as described in claim 1, characterized in that, Collect energy spectrum data of the micron-level multilayer soft film layer of the battery current collector, and perform the following operations: Based on electron energy dispersive spectroscopy (EDS) analysis, the battery current collector sample to be tested is placed under a scanning electron microscope and scanned using the scanning electron microscope. In this process, the scanning electron microscope irradiates the current collector sample of the battery under test with a scanning electron beam, and the X-ray energy spectrum reflected by the current collector sample is received by the energy spectrometer, thereby collecting the energy spectrum data of the micron-multilayer soft film layer of the current collector.

6. The method for measuring and identifying micron-level multilayer flexible film layers of battery current collectors using scanning electron microscopy as described in claim 5, characterized in that... The energy spectrum data of the micron-level multilayer flexible film layer of the battery current collector are processed by performing the following operations: The energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector is cleaned to remove noise from the energy dispersive spectral data of the micron- and multi-layer soft film layer of the battery current collector, thereby reducing the interference of noise on the scanning electron microscope measurement and identification of the micron- and multi-layer soft film layer of the battery current collector. The energy spectrum data of the flexible film layer of the battery current collector is normalized to remove the dimensional differences in the energy spectrum data of the flexible film layer of the battery current collector, thus forming standardized energy spectrum data of the flexible film layer of the battery current collector. Feature extraction was performed on the energy spectrum data of the micron-multilayer soft film layer of the battery current collector. Features related to the scanning electron microscopy measurement and identification of the micron-multilayer soft film layer of the battery current collector were extracted from the energy spectrum data, and the energy spectrum characteristics of the micron-multilayer soft film layer of the battery current collector were determined, including the position and intensity characteristics of the X-ray peaks.

7. The scanning electron microscope measurement and identification method for micron-level multilayer flexible film layers of battery current collectors as described in claim 3, characterized in that, To construct a scanning electron microscope (SEM) measurement and identification model for micron-level multilayer flexible films used in battery current collectors, the following operations were performed: Historical data of micron-level multiple layers of flexible film layer for battery current collector were collected, and the collected historical data of micron-level multiple layers of flexible film layer for battery current collector were divided to determine the training set and the test set. Based on deep learning technology, a training set is used to train the deep learning model, enabling the deep learning model to autonomously learn the scanning electron microscope measurement and recognition behavior of the micron-multilayer soft film layer of the battery current collector from the training set, and to identify the composition of the micron-multilayer soft film layer of the battery current collector, thereby determining the scanning electron microscope measurement and recognition model of the micron-multilayer soft film layer of the battery current collector. The test set is input into the micron-multilayer scanning electron microscope (SEM) measurement and recognition model of the soft film layer of the battery current collector. The SEM measurement and recognition model of the soft film layer of the battery current collector is tested based on the test set to evaluate the performance of the SEM measurement and recognition model of the soft film layer of the battery current collector and determine the model test evaluation results. Based on the model test and evaluation results, the scanning electron microscope (SEM) measurement and identification model for the micron-multilayer soft film layer of the battery current collector was adjusted and optimized to determine the optimal SEM measurement and identification model for the micron-multilayer soft film layer of the battery current collector, which is used to identify the composition of the micron-multilayer soft film layer of the battery current collector.

8. The method for measuring and identifying micron-level multilayer flexible film layers in battery current collectors using scanning electron microscopy as described in claim 3, characterized in that, To draw a component distribution diagram, perform the following operations: Based on the scanning electron microscope measurement and identification results of the micron-multilayer soft film layer of the battery current collector, the components contained in the micron-multilayer soft film layer of the battery current collector are determined. Combining the components contained in the micron-multilayer soft film layer of the battery current collector, a component distribution map is generated, in which different colors or grayscale represent the components of the micron-multilayer soft film layer of the battery current collector.

9. The method for measuring and identifying micron-level multilayer flexible film layers of battery current collectors using scanning electron microscopy as described in claim 1, characterized in that, Also includes: A high-sensitivity acoustic array with a set spacing of regular hexagonal grids is used to perform acoustic diaphragm puncture detection on micron-sized multilayer samples of soft membrane layers of battery current collectors to obtain acoustic puncture detection data. A membrane damage prediction model was constructed, and acoustic puncture detection data was used to train the membrane damage prediction model. The trained membrane damage prediction model that meets the accuracy of membrane damage identification was obtained through model training. The trained membrane damage prediction model was used to identify membrane damage based on acoustic puncture detection data of micron-level multilayer soft membranes in battery current collectors, and the damage identification results were obtained. Based on the damage identification results, a battery thermal runaway risk analysis is performed. The risk level of the battery is classified based on the risk analysis results. Based on the set matching rules, the thermal runaway prevention schemes in the prevention measure scheme library are matched according to the risk level. The thermal runaway prevention schemes of the matching results are recommended as the battery thermal runaway risk mitigation schemes.

10. The scanning electron microscope measurement and identification method for micron-level multilayer flexible film layers of battery current collectors as described in claim 9, characterized in that, To perform an acoustic diaphragm puncture test, perform the following operations: Acoustic signals are acquired and detected, and acoustic spatiotemporal characteristics are analyzed to obtain the spatial domain characteristics, time domain characteristics, and frequency domain characteristics of the acoustic signals. Based on the spatial domain, time domain, and frequency domain characteristics of acoustic signals, a membrane damage feature vector of acoustic signals is constructed. The feature vector of membrane damage is normalized to obtain acoustic puncture detection data after feature value standardization.

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