Electrochemical corrosion and durability detection method of PET (Polyethylene Terephthalate)-based adhesive tape in electrolyte

Abstract

The invention relates to a method for detecting electrochemical corrosion and durability of a PET (Polyethylene Terephthalate)-based adhesive tape in an electrolyte in the field of intelligent manufacturing, which comprises the following steps: acquiring initial surface topography data of a material in an electrochemical environment, and determining microstructure characteristics according to the initial surface topography data; acquiring electric field distribution data in the electrochemical environment, and determining ion migration characteristics according to the electric field distribution data; determining the initial corrosion state of the surface of the material according to the ion migration characteristics and an electrochemical impedance spectroscopy measurement value; obtaining corrosion morphology evolution data according to the surface corrosion initial state; determining corrosion-dissolution associated parameters according to the corrosion morphology evolution data and the concentration data of the dissolved matter in the electrolyte; according to the corrosion-dissolution associated parameters, analyzing the dynamic relationship between the microscopic change of the surface of the material and the concentration of the dissolved substance to obtain the tolerance degradation trend of the material; and predicting a durability evaluation result of the material in a long-term electrochemical environment according to the tolerance degradation trend of the material.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, and more particularly to PET tape, specifically to a method for testing the electrochemical corrosion and durability of PET-based tape in an electrolyte. Background Technology

[0002] Materials performance testing is crucial for ensuring the safety and stability of high-performance devices such as batteries, especially the testing of material tolerance in electrolyte environments, which directly affects battery life and operational safety. With the rapid development of new energy technologies, the durability of internal battery materials has become a focus of industry attention.

[0003] However, existing detection methods have significant limitations when simulating real battery operating environments. Many methods focus only on static immersion or single environmental factors, making it difficult to comprehensively reflect the performance degradation of materials under complex electrochemical environments. In particular, existing technologies often ignore the influence of dynamic electric fields on material corrosion and lack correlation analysis between microscopic changes on the material surface and dissolved substances in the electrolyte, leading to discrepancies between detection results and actual application scenarios, and making it difficult to accurately predict the performance of materials in long-term use.

[0004] In this field, the core challenge lies in effectively simulating the complex electrochemical environment inside batteries and its dynamic impact on materials. First, the presence of alternating electric fields during battery operation accelerates ion migration, significantly affecting the corrosion rate of materials. However, existing methods struggle to accurately control and simulate the effects of this dynamic electric field. For example, a PET-based adhesive tape may undergo irreversible surface corrosion in the electrolyte due to rapid ion migration induced by the electric field, a process difficult to capture with conventional testing. Second, the correlation between the surface corrosion morphology of materials and the concentration of dissolved substances in the electrolyte has not been effectively quantified, leading to an inability to accurately assess the degree of material degradation. For instance, tiny corrosion pits may appear on the tape surface, but the relationship between the depth distribution of these pits and the concentration of dissolved substances has not been clearly established, affecting the scientific validity of tolerance assessments. These two technical factors are interrelated: the ion migration accelerated by the electric field directly leads to intensified surface corrosion, while changes in corrosion morphology further influence the dynamic evolution of dissolved substance concentration. Therefore, how to accurately quantify the correlation between the corrosion morphology of the material surface and the concentration of dissolved substances under the action of an alternating electric field while simulating the complex electrochemical environment of a battery has become a key issue in improving the scientificity and accuracy of electrolyte tolerance testing for PET-based adhesive tapes. Summary of the Invention

[0005] This invention provides a method for electrochemical corrosion and durability testing of PET-based adhesive tape in an electrolyte, mainly comprising:

[0006] The process involves: acquiring initial surface morphology data of the material in an electrochemical environment; determining microstructural features based on the initial surface morphology data; acquiring electric field distribution data in the electrochemical environment; determining ion migration characteristics based on the electric field distribution data; determining the initial corrosion state of the material surface based on the ion migration characteristics and electrochemical impedance spectroscopy measurements; acquiring corrosion morphology evolution data based on the initial surface corrosion state; determining corrosion-dissolution correlation parameters based on the corrosion morphology evolution data and the concentration of dissolved substances in the electrolyte; analyzing the dynamic relationship between microscopic changes on the material surface and the concentration of dissolved substances based on the corrosion-dissolution correlation parameters to obtain the material's tolerance degradation trend; and predicting the durability assessment results of the material in a long-term electrochemical environment based on the material's tolerance degradation trend. Furthermore, the step of acquiring initial surface morphology data of the material in an electrochemical environment and determining microstructural features based on the initial surface morphology data includes: acquiring initial surface morphology data generated by imaging the material surface with a high-resolution scanning electron microscope; extracting microstructural feature values ​​based on the initial surface morphology data; if the feature values ​​indicate the presence of a porous structure on the surface, then using an image segmentation algorithm to separate the porous regions; calculating the area ratio and distribution density of the porous regions; determining the electrolyte permeation risk level based on the distribution density of the porous regions; and using a support vector machine model to perform a correlation analysis between the permeation risk level and the feature values. Furthermore, the step of acquiring electric field distribution data in the electrochemical environment and determining ion migration characteristics based on the electric field distribution data includes: acquiring the geometric model and material property parameters of the battery structure; constructing a finite element calculation mesh based on the material property parameters and boundary conditions; applying an alternating electric field with specific frequency and amplitude parameters as an excitation source; solving Maxwell's equations using the finite element method to obtain electric field distribution data; calculating ion migration rates based on the electric field distribution data and ion species characteristics; and integrating the ion migration rate data and distribution map to determine the spatial distribution pattern of the electric field-induced characteristics. Furthermore, the step of determining the initial state of material surface corrosion based on the ion migration characteristics and electrochemical impedance spectroscopy measurements includes: acquiring electrochemical impedance spectroscopy measurements; calculating charge transfer resistance based on the measurements; determining the initial state of surface corrosion if the charge transfer resistance is less than a preset threshold; separating ion migration characteristics from the impedance spectrum; calculating dynamic parameters based on the ion migration characteristics; and using linear regression analysis to analyze the relationship between the dynamic parameters and the initial corrosion state.Furthermore, the step of obtaining corrosion morphology evolution data based on the initial state of surface corrosion includes: acquiring an initial state morphology map of the surface obtained by atomic force microscopy; identifying corrosion pit regions based on the morphology map, and extracting the depth and area values ​​of each corrosion pit; processing the depth and area values ​​using a clustering algorithm to obtain a depth set and an area set; generating a depth distribution map based on the depth set, and generating an area variation map based on the area set; if the depth distribution map exhibits multi-peak characteristics, it is determined that there are multiple dominant mechanisms in the corrosion process; and determining the evolution stage of surface corrosion based on the evolution data and distribution map. Furthermore, determining the corrosion-dissolution correlation parameters based on the corrosion morphology evolution data and the electrolyte dissolution concentration data includes: acquiring corrosion pit depth distribution data and electrolyte dissolution mass spectrum concentration data; establishing the correlation between data sources based on the pit depth distribution morphology and dissolution concentration values; processing the pit depth and dissolution concentration data using principal component analysis; if the variance contribution rate of the first principal component in the principal component analysis results exceeds a preset threshold, then the principal component is determined as a key correlation factor; calculating the comprehensive correlation index between pit depth distribution and dissolution concentration based on the key correlation factor; and using a support vector machine algorithm, with the comprehensive correlation index as input, to determine the evolution trend of the corrosion state. Furthermore, if the leaching concentration distribution data exceeds a preset threshold, a correlation model between the corrosion pit depth and the leaching concentration is established using regression analysis to determine the corrosion-leaching correlation parameters. This includes: acquiring leaching concentration data at monitoring points; if the leaching concentration data exceeds a preset threshold, extracting the corrosion pit depth value at the corresponding monitoring point; using the leaching concentration data exceeding the threshold as the independent variable and the corrosion pit depth value as the dependent variable to form a data point set; fitting the data point set using regression to obtain the correlation model; calculating the residual value based on the correlation model and evaluating the fitting effect; determining whether the residual value meets the significance requirement, and if so, determining the parameter set from the correlation model. Furthermore, the step of analyzing the dynamic relationship between microscopic changes on the material surface and the concentration of dissolved substances based on the corrosion-dissolution correlation parameters to obtain the material's tolerance degradation trend includes: acquiring a time-series sequence of surface microscopic images of the material in a corrosive environment and corresponding dissolved substance concentration data at each time point; extracting the morphological change amount at each time point from the surface microscopic image sequence using image processing technology; calculating the corrosion-dissolution correlation parameters at each time point based on the morphological change amount and the corresponding dissolved substance concentration value; performing cluster analysis on the corrosion-dissolution correlation parameters at all time points using the K-means clustering algorithm; judging that the material's tolerance has degraded if the clustering results show that the parameter values ​​migrate to higher value categories over time; and generating a material tolerance degradation trend line by fitting the cluster migration path of the parameter values.Furthermore, the step of predicting the durability assessment results of the material in a long-term electrochemical environment based on the material's tolerance degradation trend includes: acquiring a historical degradation data sequence of the material's tolerance in an electrochemical environment; processing the degradation data sequence using a time series analysis method to obtain a long-term degradation trend of the material's performance; establishing a prediction model of performance degradation and time variables based on the long-term degradation trend; inputting electrochemical environment parameters into the prediction model, and the model outputting predicted material performance values ​​at future time points; if the predicted material performance value is lower than a preset failure threshold, then determining that the material has failed at the corresponding time point; and combining the failure judgments at all time points to obtain the durability assessment results of the material in a long-term electrochemical environment.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses a method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte, addressing the problem of the dynamic correlation between material surface corrosion and dissolved substance concentration in battery operating environments. Initial and corrosion morphology data are acquired using high-resolution scanning electron microscopy and atomic force microscopy. Finite element analysis is used to calculate the electric field-induced ion migration characteristics, and electrochemical impedance spectroscopy is employed to measure surface charge transfer resistance, quantifying the initial corrosion state. Furthermore, mass spectrometry analysis is used to detect the dissolved substance concentration in the electrolyte, constructing a regression model between corrosion pit depth and dissolved substance concentration to reveal corrosion-dissolved substance correlation parameters. Based on this, this invention utilizes machine learning clustering algorithms to analyze the dynamic relationship between microscopic changes on the material surface and dissolved substance concentration, combined with time series analysis to predict the durability performance of PET-based adhesive tape in long-term electrochemical environments. The final technical effect is the accurate assessment and detection of the corrosion behavior and durability of PET-based adhesive tape, providing a scientific basis for optimizing battery material design. Attached Figure Description

[0009] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0010] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the relevant invention and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0011] like Figure 1 This embodiment of the method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte may specifically include:

[0012] S101. Obtain the initial surface morphology data of PET-based tape in electrolyte, and use a high-resolution scanning electron microscope to image the surface microstructure to obtain the initial morphology features.

[0013] Initial surface morphology data of PET-based adhesive tape in electrolyte was obtained using a high-resolution scanning electron microscope (SEM). Microstructural features were determined based on this initial data, and feature values, including average surface roughness Ra, peak density Spd, and average pore diameter, were extracted. If the feature values ​​indicated the presence of a porous structure, a threshold-based Otsu image segmentation algorithm was used to separate the porous regions. The area ratio and distribution density (number of pores per unit area) of the porous regions were calculated. The electrolyte permeation risk level was determined based on the pore region distribution density: less than 5 pores per square micrometer was considered low risk, 5 to 20 pores per square micrometer was medium risk, and more than 20 pores per square micrometer was high risk. A support vector machine (SVM) model was used to perform correlation analysis between the permeation risk level and the feature values. Ra, Spd, average pore diameter, and area ratio were used as input features, and the risk level was used as the output label. A radial basis function (RBF) kernel was used for training and prediction.

[0014] First, PET-based adhesive tape samples were precisely cut to standard 10 mm × 10 mm dimensions using an automated sample preparation system. An approximately 10 nm thick platinum conductive layer was then uniformly deposited onto the sample surface using an ion sputtering system to eliminate charge accumulation effects during subsequent scanning electron microscopy (SEM) imaging. Next, the samples were immersed in a simulated electrolyte environment. The electrolyte consisted of 1 mol / L LiPF6 dissolved in a 1:1 volume ratio of ethylene carbonate and diethyl carbonate. The immersion temperature was maintained at a constant 25°C for 24 hours to simulate actual battery operating conditions. After immersion, the samples were dried using supercritical carbon dioxide drying technology. The critical pressure was set at 7.38 MPa, and the critical temperature at 31°C to minimize the collapse and deformation of the surface microstructure due to capillary forces. Next, high-resolution imaging of the processed sample was performed using a field emission scanning electron microscope (FET). The imaging process was conducted under a vacuum level better than 1 × 10⁻³ Pa, with an electron beam acceleration voltage of 5 kV and a working distance of 8 mm. A secondary electron detector was used to acquire signals and obtain depth information of the surface morphology. The obtained raw image data was analyzed using image processing algorithms. First, a median filtering algorithm (3 × 3 pixels window) was used to remove random noise. Then, the Canny edge detection algorithm (Gaussian filter standard deviation σ = 1.0, high / low threshold ratio set to 1:2) was used to automatically identify and quantify surface features, such as calculating the average diameter and distribution density of pores, as well as the surface roughness parameter Sa. Analysis revealed that micropores with an average diameter of approximately 200 nm appeared on the surface of the tape after immersion, with a distribution density of 15 pores per square micrometer. The surface arithmetic mean height Sa increased from the initial 25 nm to 52 nm, indicating that electrolyte immersion caused significant surface swelling and microstructural changes. These quantitative data provide crucial information for evaluating the material's resistance to electrolyte corrosion.

[0015] S102. By applying an alternating electric field to simulate the battery operating environment, the electric field distribution and ion migration rate are calculated using the finite element analysis method to determine the characteristics of electric field-induced ion migration.

[0016] Obtain the geometric model and material property parameters of the battery structure. Construct a finite element method (FEM) mesh based on the material property parameters and boundary conditions. Apply an alternating electric field with a specific frequency and amplitude as the excitation source. Solve Maxwell's equations using the finite element method to obtain electric field distribution data. Calculate ion migration rates based on the electric field distribution data and ion species characteristics. Integrate the migration rate data and distribution map to determine the spatial distribution pattern of induced features.

[0017] To simulate the battery operating environment, a two-dimensional axisymmetric geometric model was first constructed, comprising a positive electrode, a negative electrode, and an electrolyte. The electrolyte layer thickness was set to 50 micrometers, and the active material thicknesses for both the positive and negative electrodes were 100 micrometers. Boundary conditions were set: the negative electrode potential was 0V, and an alternating sinusoidal voltage signal with an amplitude of 5V and a frequency of 1kHz was applied to the positive electrode to simulate dynamic operating conditions. Subsequently, the electric field distribution was calculated using the finite element method. Specifically, the "AC / DC module" in COMSOL Multiphysics software was used, selecting the "Current" physics interface to mesh the geometric model. The maximum element size was set to 5 micrometers to ensure computational accuracy. After solving, an electric field intensity distribution cloud map was obtained. For example, the effective value of the electric field intensity calculated at the center point of the electrolyte region was approximately 1 × 10⁻⁶. 5 V / m. Based on this electric field distribution, the ion migration rate was further calculated through a coupled "dilute mass transport" physical field interface, where the lithium ion transference number was set to 0.4 and the diffusion coefficient was 1×10⁻¹. 0 m² / s. By solving the Nernst-Planck equation, the characteristics of ion migration can be quantitatively analyzed. For example, within one complete cycle of an alternating electric field, the peak average migration rate of lithium ions in the electrolyte can reach 2 × 10⁻⁻⁻⁻⁶. 6 The migration rate was measured in m / s, and Fourier transform analysis of the migration rate spectrum revealed a significant peak at the 1kHz fundamental frequency, accompanied by a 3kHz harmonic component generated by nonlinear effects, with an amplitude of approximately 5% of the fundamental frequency. This series of calculations and analyses revealed the periodic driving and relaxation characteristics of ion migration under alternating electric fields, providing key parameter basis for optimizing battery dynamic performance.

[0018] S103. Extract dynamic parameters for accelerated migration from ion migration characteristics, and use electrochemical impedance spectroscopy to measure the charge transfer resistance of the material surface to obtain the initial state of surface corrosion.

[0019] Electrochemical impedance spectroscopy (EIS) measurements are obtained, and the charge transfer resistance Rct is calculated based on the measurements. If Rct is less than a preset threshold of 50 ohms, the initial state of surface corrosion is determined. Ion migration characteristics are separated from the impedance spectrum. Specifically, the ion diffusion impedance Wd is extracted by fitting the arc-shaped features in the low-frequency region of the Nyquist plot. Dynamic parameters such as the diffusion coefficient D are then calculated based on Wd, and the relationship between D and the corrosion state is analyzed using linear regression.

[0020] In the ion migration characteristic analysis, the metal substrate under the coating was first measured using electrochemical impedance spectroscopy (EIS). The frequency scan range was set from 100 kHz to 10 mHz, and a sinusoidal perturbation potential with an amplitude of 10 mV was applied. The acquired raw impedance data were fitted using an equivalent circuit model, such as the R(QR)(QR) model, where R_s represents the solution resistance, the first RQ parallel unit represents the coating pore resistance R_po and coating capacitance CPE_po, and the second RQ parallel unit represents the charge transfer resistance R_ct and double-layer capacitance CPE_dl. Using a nonlinear least squares fitting algorithm, such as the Levenberg-Marquardt algorithm, iterative optimization was performed until the chi-square value χ² was less than 1 × 10^-4, accurately resolving the key dynamic parameter reflecting the initial state of surface corrosion—the charge transfer resistance R_ct.

[0021] For example, the initial value of R_ct extracted from the fitting results is 8.5 × 10^5 Ω·cm². This high value indicates that interfacial charge transfer is difficult, the corrosion reaction is strongly suppressed, and the material is in a passivated state. To further extract dynamic parameters that accelerate migration, the reciprocal of R_ct is defined as an approximate index of the corrosion reaction rate constant k_corr, i.e., k_corr ∝ 1 / R_ct. By monitoring the change sequence of R_ct with time or environmental stress (such as humidity increasing to 95% RH), its logarithmic decay rate is calculated using a time series analysis algorithm.

[0022] For example, during the 72 hours of the accelerated experiment, R_ct decreased from 8.5×10^5 Ω·cm² to 2.1×10^4 Ω·cm². By fitting the exponential decay function R_ct(t) = R_ct0 × exp(-βt), the decay coefficient β can be calculated to be 0.056 h^-1. This β value is a dynamic parameter characterizing the degradation of protective performance caused by ion migration. Its increase directly reflects the increase of surface corrosion active sites and the acceleration of the initial corrosion process.

[0023] S104. For the initial state of surface corrosion, atomic force microscopy is used to scan the morphology of corrosion pits, quantify the depth distribution and area change of corrosion pits, and obtain corrosion morphology evolution data.

[0024] Obtain the initial surface morphology image from atomic force microscopy (AFM). Identify corrosion pit regions based on the morphology image and extract the depth and area values ​​for each pit. Use a clustering algorithm to process the depth and area values, obtaining depth and area sets. Generate a depth distribution map from the depth sets and an area variation map from the area sets. If the depth distribution map exhibits multi-peak characteristics, it indicates the presence of multiple dominant mechanisms in the corrosion process. Determine the evolution stage of surface corrosion based on the evolution number and distribution map.

[0025] For the initial state of surface corrosion, the sample surface was first scanned using an atomic force microscope in tapping mode, with a scanning range of 50 μm × 50 μm and a scanning resolution of 512 × 512 pixels to obtain high-resolution morphological images. Subsequently, the acquired three-dimensional morphological data was processed using the instrument's accompanying nanoscale analysis software. A plane fitting algorithm was used to eliminate background noise caused by the overall tilt of the sample, and median filtering was used to remove random noise generated during the scanning process. Next, corrosion pit regions were identified by setting a threshold height; for example, areas with a depression greater than 5 nm relative to the average surface height were identified as corrosion pits. Based on this, the depth and projected area of ​​each individual corrosion pit were quantitatively analyzed using the volumetric analysis module in the software.

[0026] For example, a histogram of corrosion pit depth distribution can be obtained through calculation. The data shows that the depth is mainly distributed between 5 nanometers and 50 nanometers, with an average depth of 20 nanometers. The percentage of the total corrosion pit coverage area to the scanned area is also calculated, initially at 0.8%. To analyze morphological evolution, this initial data is used as a baseline and compared with scan data from different corrosion time points. Image registration algorithms are used to align the scanned images from different time points, and then pixel-level difference calculations are employed to calculate the incremental changes in the depth and area of ​​corrosion pits in the same region.

[0027] For example, after 24 hours of corrosion, analysis revealed that the average depth increased to 35 nanometers and the area ratio increased to 2.1%. The uniformity of corrosion development was assessed by calculating the kurtosis variation of the depth distribution. All quantitative data were imported into statistical analysis software, and linear regression models were used to fit the curves of depth and area changes over time, thereby extracting key kinetic parameters such as corrosion rate, completing the entire information processing chain from morphology acquisition to quantitative analysis.

[0028] S105. Extract pit depth distribution characteristics from corrosion morphology evolution data, and use mass spectrometry to detect the concentration of dissolved substances in the electrolyte to obtain dissolved substance concentration distribution data.

[0029] Data on the distribution of corrosion pit depth and the concentration of dissolved substances in the electrolyte were acquired. Based on the pit depth distribution and dissolved substance concentration values, a correlation was established between the data sources. Principal component analysis (PCA) was used to process the two types of data: pit depth and dissolved substance concentration. If the variance contribution rate of the first principal component in the PCA results exceeded a preset threshold, that principal component was identified as a key correlation factor. A comprehensive correlation index between pit depth distribution and dissolved substance concentration was calculated based on the key correlation factor. A support vector machine (SVM) algorithm was then used, with the comprehensive correlation index as input, to determine the evolution trend of the corrosion state.

[0030] In processing the corrosion morphology evolution data, the surface of the aluminum alloy sample was first scanned using a high-resolution three-dimensional confocal microscope to obtain a three-dimensional morphology data matrix containing 1024×1024 pixels, with each pixel corresponding to an actual size of 0.5 micrometers. Using image processing algorithms, such as threshold-based region segmentation, all corrosion pits with a depth greater than 5 micrometers were identified and extracted. By calculating the height difference between the lowest point of each pit and the original surface, a set of pit depth data was obtained; for example, 500 effective corrosion pits were identified, with depth values ​​ranging from 5.2 micrometers to 87.6 micrometers. Subsequently, a kernel density estimation algorithm was used to analyze this depth dataset. A Gaussian kernel function with a bandwidth of 2 micrometers was set to generate a probability density distribution curve of the pit depth, thereby quantitatively characterizing the feature of depths concentrated in the 15-30 micrometer range. Next, in the electrolyte leachate concentration detection stage, electrolyte samples were collected periodically after the corrosion experiment and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). For target elements such as aluminum, copper, and magnesium, the instrument response was calibrated using an internal standard method (e.g., adding scandium as an internal standard element) to obtain time-series data of each element's concentration. For example, at the 24th hour of the experiment, the aluminum ion concentration was measured to be 15.6 mg / L, and the copper ion concentration was 0.8 mg / L. Finally, the leachate concentration data at different time points were correlated with the corresponding pit depth distribution characteristics. For example, Pearson correlation coefficient calculations revealed that when the median pit depth distribution exceeded 25 micrometers, the growth rate of aluminum ion concentration in the electrolyte significantly increased, with a correlation coefficient reaching 0.92. This reveals a quantitative coupling relationship between the depth development of corrosion pits and the metal dissolution rate, providing key input parameters for the corrosion kinetics model.

[0031] S106. If the data on the distribution of dissolved substance concentration exceeds the preset threshold, a correlation model between the depth of the corrosion pit and the concentration of dissolved substance is established by regression analysis to determine the corrosion-dissolution correlation parameters.

[0032] Acquire the leaching concentration data at the monitoring points. If the leaching concentration exceeds a preset threshold, extract the corrosion pit depth value for the corresponding monitoring point. Use the leaching concentration data exceeding the threshold as the independent variable and the corrosion pit depth value as the dependent variable to form a data point set. Fit the data point set using regression to obtain the correlation modulus. Calculate the residual value based on the correlation modulus and evaluate the fitting effect. Determine whether the residual value meets the significance requirement; if so, determine the parameter set from the correlation modulus.

[0033] During monitoring, when the system detected a fluoride ion concentration of 15.6 mg / L in a certain area, exceeding the preset threshold of 10 mg / L, it automatically triggered a correlation analysis process. The system first retrieved a dataset of corrosion pit depths collected concurrently in that area, for example, depths of 12.3 μm, 18.7 μm, and 25.1 μm, corresponding to fluoride ion concentrations of 15.6 mg / L, 17.2 mg / L, and 19.8 mg / L, respectively. Subsequently, a univariate linear regression analysis was performed using the least squares method, with the leachate concentration as the independent variable X and the corrosion pit depth as the dependent variable Y, fitting the model Y = aX + b. By calculating the covariance and variance of the data points, the regression parameters a were found to be 2.15 and b to be -20.34, thus establishing the correlation model as "corrosion pit depth = 2.15 × fluoride ion concentration - 20.34". The correlation coefficient R² of this model was calculated to be 0.92, indicating a strong positive correlation between concentration and depth. The slope a=2.15 and intercept b=-20.34 in this model are identified as key corrosion-dissolution correlation parameters and will be stored in the material corrosion knowledge base. This will be used to predict the development trend of corrosion pit depth in real time for similar materials under similar environments, based solely on the monitoring data of dissolution concentration, thereby achieving quantitative assessment and early warning of corrosion status.

[0034] S107. Based on the corrosion-dissolution correlation parameters, a machine learning clustering algorithm is used to analyze the dynamic relationship between microscopic changes on the material surface and the concentration of dissolved substances, thereby obtaining the material's tolerance degradation trend.

[0035] A time-series sequence of surface microscopic images of the material in a corrosive environment and corresponding leaching concentration data at each time point were acquired. Image processing techniques were used to extract the morphological changes at each time point from the surface microscopic image sequence. Corrosion-leaching correlation parameters were calculated for each time point based on the morphological changes and corresponding leaching concentration values. A trend line for material tolerance degradation was generated by fitting the cluster migration path of the parameter values. Specifically, the average value of the cluster centers within each time period was used as a quantitative indicator of the migration path, recording the trend of parameter values ​​changing from low-value categories to high-value categories; then, a linear regression method was used to fit the relationship between these center values ​​and time to generate a degradation trend line, which was used to predict the rate of decline in material tolerance and the future degree of degradation. The trend line for material tolerance degradation was generated by fitting the cluster migration path of the parameter values.

[0036] First, potentiodynamic polarization tests were performed on 316L stainless steel in 0.5 mol / L NaCl solution using an electrochemical workstation to obtain parameters such as corrosion current density and potential. Simultaneously, inductively coupled plasma mass spectrometry (ICP-MS) was used to monitor the dissolution concentrations of iron, chromium, and nickel ions in the solution online; for example, the corrosion current density was measured to be 1.2 × 10⁻⁻⁻⁻⁶ at 0.5 V. 6A / cm² corresponds to an iron ion dissolution concentration of 8.7 μg / L and a chromium ion concentration of 1.3 μg / L. Based on this, a 15-dimensional corrosion-dissolution correlation parameter dataset was constructed, including corrosion rate, passivation film breakdown potential, and dissolution rates of various metal ions. Subsequently, the dataset was processed using a K-means clustering algorithm based on Euclidean distance, with the cluster number K=3. After 150 iterations, the dataset converged, classifying the material surface state into three categories: "stable passivation," "localized corrosion initiation," and "active dissolution." Analysis revealed that data points belonging to the "localized corrosion initiation" cluster were characterized by corrosion current densities between 5.0 × 10⁻⁻⁻⁶. 7 Up to 2.0×10⁻ 6 The ratio of chromium to ferrochrome dissolution concentration decreased from the normal 0.15 to below 0.08, indicating selective dissolution of chromium in the passivation film and the beginning of material degradation. Furthermore, for each cluster, a predictive model was established using a random forest regression algorithm, with corrosion-dissolution parameters as input and material mass loss rate as output. The model contained 100 decision trees with a maximum depth of 10. After training, the model achieved a coefficient of determination R² of 0.91 on the test set. By inputting continuously monitored new parameters, such as when the chromium-ferrochrome dissolution ratio remained below 0.05 and the nickel ion dissolution rate surged to 0.4 μg / (L·h), the model predicted that the mass loss rate would increase at a rate of 0.15% per cycle, thus quantifying the dynamic trend of material tolerance evolving from slow degradation to accelerated failure.

[0037] S108. By analyzing the material's resistance to degradation, time series analysis is used to predict the durability performance of PET-based tape in a long-term electrochemical environment, and the durability assessment results are obtained.

[0038] A historical degradation data sequence of PET-based adhesive tape under electrochemical conditions was obtained. Time series analysis was used to process this degradation data sequence to obtain the long-term degradation trend of the material properties. Based on the long-term degradation trend, a predictive model of performance degradation versus time variables was established. Electrochemical environmental parameters were input into the prediction model, and the model output predicted material properties at future time points. If the predicted material properties are lower than a preset failure threshold, the tape is judged to have failed at the corresponding time point. The durability assessment results of PET-based adhesive tape under long-term electrochemical conditions were obtained by combining the failure judgments at all time points.

[0039] First, key performance data, such as adhesive strength, of the PET-based adhesive tape under simulated electrochemical conditions (e.g., 85°C, 85% relative humidity, and a 1.5V bias voltage) were obtained through accelerated aging experiments. Sampling and testing were conducted every 240 hours, resulting in a data sequence at 10 time points: an initial value of 15.2 MPa, followed by values ​​of 14.8, 14.3, 13.7, 13.0, 12.2, 11.3, 10.5, 9.8, and 9.2 MPa. Next, time series analysis was used to model and analyze the adhesive strength data. An autoregressive integral moving average (ARIMA) model was used for fitting, and the sequence was stabilized through differencing. After first-order differencing, the root mean square error of the sequence was reduced to within 0.15. Then, the fitted ARIMA(1,1,1) model was used for prediction, with autoregressive coefficient of 0.85 and moving average coefficient of -0.30. Extrapolation predictions were made for the adhesive strength at five future time points (i.e., cycles 11 to 15, each cycle 240 hours), yielding predicted values ​​of 8.7, 8.3, 7.9, 7.6, and 7.3 MPa, respectively, with 95% confidence intervals calculated. Furthermore, a comparative analysis was conducted based on the industry standard adhesive strength failure threshold (e.g., 7.0 MPa) and the predicted results. The prediction curve showed that the median of the predicted adhesive strength would reach the failure threshold around cycle 14, while the lower limit of the confidence interval would enter the failure range even earlier. Therefore, combining the model predictions with the threshold comparison, the estimated durability of the PET-based tape in the electrochemical environment was assessed to be approximately 3360 hours (i.e., 14 cycles), and it was pointed out that its performance degradation trend follows an exponential decay law, posing a risk to long-term reliability.

[0040] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for electrochemical corrosion and durability testing of PET-based adhesive tape in an electrolyte, characterized in that, include: S101, Obtain initial surface morphology data of the material in an electrochemical environment, and determine the microstructure characteristics based on the initial surface morphology data; S102, Obtain electric field distribution data in the electrochemical environment, and determine ion migration characteristics based on the electric field distribution data; S103, Determine the initial state of corrosion on the material surface based on the ion migration characteristics and electrochemical impedance spectroscopy measurements; S104, Obtain corrosion morphology evolution data based on the initial state of surface corrosion; S105, Determine corrosion-dissolution correlation parameters based on the corrosion morphology evolution data and the concentration data of dissolved substances in the electrolyte; S106, If the distribution data of dissolved substance concentration exceeds a preset threshold, establish a correlation model between corrosion pit depth and dissolved substance concentration using regression analysis to determine the corrosion-dissolution correlation parameters; S107, Based on the corrosion-dissolution correlation parameters, analyze the dynamic relationship between the microscopic changes on the material surface and the concentration of dissolved substances to obtain the material's tolerance degradation trend; S108, predict the durability assessment results of the material in a long-term electrochemical environment based on the material's tolerance degradation trend.

2. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S101 further includes: acquiring initial surface morphology data generated by high-resolution scanning electron microscopy imaging of the material surface; extracting microstructural feature values ​​based on the initial surface morphology data; if the feature values ​​indicate the presence of a porous structure on the surface, separating the porous regions using an image segmentation algorithm; calculating the area ratio and distribution density of the porous regions; determining the electrolyte permeation risk level based on the distribution density of the porous regions; and performing a correlation analysis between the permeation risk level and the feature values ​​using a support vector machine model.

3. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S102 further includes: obtaining the geometric model and material property parameters of the battery structure; constructing a finite element calculation mesh based on the material property parameters and boundary conditions; applying an alternating electric field with specific frequency and amplitude parameters as an excitation source; solving Maxwell's equations using the finite element method to obtain electric field distribution data; calculating ion migration rates based on the electric field distribution data and ion species characteristics; and integrating the ion migration rate data and distribution map to determine the spatial distribution pattern of the electric field-induced characteristics.

4. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S103 further includes: obtaining electrochemical impedance spectroscopy measurements; calculating charge transfer resistance based on the measurements; determining the initial state of surface corrosion if the charge transfer resistance is less than a preset threshold; separating ion migration characteristics from the impedance spectrum; calculating dynamic parameters based on the ion migration characteristics; and using linear regression analysis to analyze the relationship between the dynamic parameters and the initial state of corrosion.

5. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S104 further includes: acquiring an initial surface morphology image obtained by atomic force microscopy scanning; identifying corrosion pit regions based on the morphology image, and extracting the depth and area values ​​of each corrosion pit; processing the depth and area values ​​using a clustering algorithm to obtain a depth set and an area set; generating a depth distribution map based on the depth set, and generating an area variation map based on the area set; if the depth distribution map exhibits multi-peak characteristics, it is determined that there are multiple dominant mechanisms in the corrosion process; and determining the evolution stage of surface corrosion based on the evolution data and distribution map.

6. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S105 further includes: acquiring corrosion pit depth distribution data and electrolyte leaching mass spectrum concentration data; establishing a correlation between data sources based on the pit depth distribution morphology and leaching concentration values; processing the pit depth and leaching concentration data using principal component analysis; if the variance contribution rate of the first principal component in the principal component analysis results exceeds a preset threshold, then determining that principal component as a correlation factor; calculating a comprehensive correlation index between pit depth distribution and leaching concentration based on the correlation factor; and using a support vector machine algorithm, with the comprehensive correlation index as input, determining the evolution trend of the corrosion state.

7. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S106 further includes: acquiring the leaching concentration data of the monitoring points; if the leaching concentration data exceeds a preset threshold, extracting the corrosion pit depth value of the corresponding monitoring point; using the leaching concentration data exceeding the threshold as the independent variable and the corrosion pit depth value as the dependent variable to form a data point set; fitting the data point set using a regression method to obtain an association model; calculating the residual value based on the association model and evaluating the fitting effect; determining whether the residual value meets the significance requirement, and if so, determining the parameter set from the association model.

8. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S107 further includes: acquiring a time-series sequence of surface microscopic images of the material in a corrosive environment and corresponding time point data of leaching concentration; extracting the morphological change at each time point from the surface microscopic image sequence using image processing; calculating the corrosion-leaching correlation parameters at each time point based on the morphological change and the corresponding leaching concentration; performing cluster analysis on the corrosion-leaching correlation parameters at all time points using a K-means clustering algorithm; determining that the material's tolerance has degraded if the clustering results show that the parameter values ​​migrate to higher value categories over time; and generating a material tolerance degradation trend line by fitting the cluster migration path of the parameter values.

9. The method for electrochemical corrosion and durability testing of PET-based adhesive tape in electrolyte according to claim 1, characterized in that, Step S108 further includes: acquiring a historical degradation data sequence of the material's tolerance in an electrochemical environment; processing the degradation data sequence using time series analysis to obtain a long-term degradation trend of the material's performance; establishing a prediction model for performance degradation versus time variables based on the long-term degradation trend; inputting electrochemical environment parameters into the prediction model, and the model outputs predicted material performance values ​​for future time points; if the predicted material performance value is lower than a preset failure threshold, then determining that the material has failed at the corresponding time point; and combining the failure determinations at all time points to obtain a durability assessment result for the material in a long-term electrochemical environment.

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