Interface characterization method based on synchronous acquisition and decoupling of multi-mode spectral signals

Abstract

The invention discloses an interface characterization method based on multi-mode spectral signal synchronous acquisition and decoupling, and belongs to the technical field of spectral detection. According to the method, a single light source is utilized to excite an interface area containing two or more than two different materials, so that the different materials respectively generate spectral signals with differences, original data containing multiple spectral signals are acquired under the same test condition through a spectrum acquisition device, the data are decoupled based on a signal separation algorithm, and the spectral signals are obtained. And obtaining target spectrums corresponding to different materials. And in combination with spectrum acquisition of a plurality of spatial positions, multi-modal visual analysis of chemical composition, structural stress and morphology distribution of an interface region can be realized. The method is suitable for solid-state batteries, electrochemical catalysis, semiconductor devices and other systems with material interfaces, and has the advantages of wide application range, high signal decoupling precision, high detection efficiency and the like.

Description

Technical Field

[0001] This invention relates to the field of spectral detection technology, and in particular to an interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals. Background Technology

[0002] The microstructure and chemical state of material interfaces have a decisive impact on performance in fields such as energy, electronics, and catalysis. For example, in solid-state batteries, the interfacial reaction between the electrode and the solid electrolyte affects ion transport efficiency and cycle life; in electrochemical catalysis, the interfacial structure between the catalyst and the support directly determines the distribution of active sites and the reaction rate. Real-time, in-situ characterization of these interfaces under operating conditions is of great significance for revealing interfacial reaction mechanisms and optimizing material design.

[0003] Spectroscopic analysis techniques are widely used in materials research due to their non-destructive nature, high sensitivity, and ability to acquire molecular-level information. Raman spectroscopy provides information on chemical composition and crystal structure, while fluorescence signals reflect the electronic structure, defect states, or morphological characteristics of specific materials. However, in multi-material interfaces, the spectral signals of different materials often couple: when one material primarily produces a fluorescence signal while another produces a Raman signal, the fluorescence background severely interferes with Raman signal extraction; when both materials produce Raman signals but their characteristic peak regions overlap, the peak shapes of different materials are difficult to distinguish. Existing techniques typically distinguish signals by testing different regions separately or changing the excitation source, which not only increases testing time and operational complexity but also easily introduces errors caused by environmental changes. Furthermore, some methods rely on dedicated hardware or complex optical path modifications, making them difficult to implement on conventional spectrometer platforms. Therefore, there is an urgent need for a method that can simultaneously acquire and effectively separate multiple spectral signals in a single measurement. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies, such as difficulty in information separation, low testing efficiency, and high equipment modification costs caused by the coupling of spectral signals at multi-material interfaces. It provides an interface characterization method based on synchronous acquisition and decoupling of multi-modal spectral signals, which can acquire and effectively separate the spectral information of different materials in a single measurement, and realize in-situ, multi-modal, and high-precision characterization of the interface evolution process.

[0006] Another objective of this invention is to propose a multimodal spectral signal synchronous acquisition and decoupling system.

[0007] To achieve the above objectives, this invention proposes an interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals, comprising:

[0008] A single light source is applied to the interface region of different materials for excitation, and the interface region is spatially resolved for spectral scanning, so that the different materials generate multiple spectral signals with differences. Obtain raw spectral data containing the aforementioned multiple spectral signals; The original spectral data is decoupled using a signal separation algorithm to obtain target spectral signals corresponding to different materials; Feature parameters are extracted from the target spectral signal, and the evolution process of the interface region is quantitatively characterized based on the changes of the feature parameters with space or time to obtain the characterization results.

[0009] The interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals in this invention embodiment may also have the following additional technical features: In one embodiment of the present invention, in the original spectral signal: the fluorescence signal is characterized by baseline elevation, and the Raman signal is the characteristic peak signal after baseline removal.

[0010] In one embodiment of the present invention, the method is applicable to in-situ characterization of solid-state batteries, electrochemical catalytic systems, semiconductor devices or other systems with material interfaces.

[0011] In one embodiment of the present invention, the signal separation algorithm includes: An asymmetric weighting function is used to assign weights to the Raman characteristic peak region and the baseline region in the original spectrum, and the weighting matrix is ​​initialized. A smoothness constraint is introduced to construct a baseline fitting objective function; The weight matrix is ​​adaptively updated based on the fitting results of the previous iteration, and the Raman peak region is given a lower weight. The iteration is repeated until the baseline fitting converges. The fitted baseline is separated from the original spectrum to obtain the fluorescence signal, while the pure Raman characteristic peak signal after removing the baseline is obtained.

[0012] In one embodiment of the present invention, after acquiring raw spectral data containing the multiple spectral signals, the method further includes: The original spectral data is corrected for the wavenumber axis to eliminate wavenumber shifts caused by instrument drift or environmental disturbances, and to ensure that the spectral peaks are aligned in a uniform wavenumber coordinate system. Noise assessment is performed on the wavenumber-corrected spectral data to identify spectral regions or spatial pixels with a signal-to-noise ratio below a preset threshold, in order to obtain the noise assessment results. Based on the noise assessment results, adaptive smoothing filtering or wavelet denoising methods are used to optimize the low signal-to-noise ratio region, outputting preprocessed spectral data with improved signal-to-noise ratio and preservation of characteristic peak shapes.

[0013] In one embodiment of the present invention, the different spectral signal types generated by the different materials under single light source excitation include: One material produces a fluorescent signal, and the other material produces a Raman signal; or Both materials produce Raman signals, but each has non-overlapping or distinguishable characteristic Raman peak positions, thus forming Raman signals with differences.

[0014] In one embodiment of the present invention, a signal separation algorithm is used to decouple the original spectral data to obtain target spectral signals corresponding to different materials, including: An adaptive iterative reweighted least squares method was used to fit and separate the fluorescence baseline of the original spectrum: the Raman characteristic peak region and the baseline region were distinguished by an asymmetric weight function and the weight matrix was initialized. A smoothness constraint was introduced to construct the baseline fitting objective function. The weight matrix was adaptively updated based on the previous iteration results and a lower weight was assigned to the Raman peak region. The iteration was repeated until convergence to obtain the separated fluorescence signal and the pure Raman characteristic peak signal. Principal component analysis and independent component analysis algorithms are used to analyze the decoupled multimodal signals to obtain the target spectral signals by separating the spectral characteristics of different components at the interface; wherein, the target spectral signals include: the fluorescence signal of the corresponding fluorescent material and the Raman signal of the corresponding Raman active material.

[0015] In one embodiment of the present invention, the characteristic parameters include material composition, material concentration, and local stress, wherein: The composition of the substance was obtained by analyzing the peak positions of the decoupled Raman characteristic peaks; The concentration of the substance was obtained by analyzing the peak intensity of the Raman characteristic peaks after decoupling; The local stress is obtained by analyzing the peak position shift of the Raman characteristic peaks after decoupling; For materials that do not have Raman characteristic peaks but have fluorescence signals, the concentration information of the material can be obtained by analyzing the degree of spectral baseline elevation.

[0016] In one embodiment of the present invention, the material components are obtained by analyzing the peak positions of the decoupled Raman characteristic peaks, including: A standard Raman characteristic peak position database of the interface system to be tested is established in advance, wherein the standard Raman characteristic peak position database contains the characteristic peak position information of each component substance; Identify the peak position values ​​of characteristic peaks in the decoupled Raman spectrum; The identified peak values ​​are compared with a standard database to determine the corresponding material components.

[0017] In one embodiment of the present invention, the substance concentration is obtained by analyzing the peak intensity of the decoupled Raman characteristic peaks, including: A quantitative relationship between the intensity of Raman characteristic peaks and the concentration of each component in the interface system under test is established in advance. Extract the peak intensity values ​​of the corresponding characteristic peaks in the decoupled Raman spectrum; The concentration of the substance is calculated based on the quantitative relationship and the extracted peak intensity values.

[0018] In one embodiment of the present invention, the local stress is obtained by analyzing the peak position shift of the decoupled Raman characteristic peaks, including: Select Raman characteristic peaks that are sensitive to stress changes in the tested material system as stress probe peaks; Determine the standard peak position of the stress probe peak under stress-free conditions; Measure the actual peak position of the stress probe peak in the decoupled Raman spectrum; Calculate the offset between the actual peak position and the standard peak position; Based on the pre-established quantitative relationship between peak position offset and stress magnitude, the local stress value is determined.

[0019] To achieve the above objectives, another aspect of the present invention proposes a multimodal spectral signal synchronous acquisition and decoupling system, comprising: The laser source module is configured to emit a single-wavelength laser and excite the interface region. An optical path coupling module is optically connected to the laser source module and is configured to introduce the single-wavelength laser into the interface region and simultaneously collect multiple mixed spectral signals generated in the interface region. A spectral detection module is optically connected to the optical path coupling module and is configured to acquire the multiple mixed spectral signals and convert them into digital signals for output. The data processing module is connected to the spectral detection module and has a built-in preset signal separation algorithm. It is configured to receive the digital signal, use the signal separation algorithm to perform multimodal signal decoupling processing on the digital signal, and output the target spectral signals corresponding to different materials and the analysis results including material composition, concentration, local stress and morphology distribution.

[0020] The interface characterization method and system based on synchronous acquisition and decoupling of multimodal spectral signals in this invention utilizes a single light source to excite an interface region containing two or more different materials, causing each material to generate distinct spectral signals. Raw data containing multiple spectral signals is acquired under the same test conditions using a spectral acquisition device. The data is then decoupled based on a signal separation algorithm to obtain target spectra corresponding to different materials. By combining spectral acquisition at multiple spatial locations, multimodal visualization analysis of the chemical composition, structural stress, and morphological distribution of the interface region can be achieved, enabling high-precision, in-situ, multimodal characterization of the interface evolution process of different materials.

[0021] Compared with the prior art, the present invention has the following beneficial effects: Single measurement, multi-modal acquisition: Synchronous acquisition of spectral signals of different materials is achieved through a single light source, avoiding the problems of environmental changes and low testing efficiency caused by multiple measurements; No major hardware modifications are required: it can be implemented on conventional Raman spectroscopy platforms or equipment with spectral acquisition capabilities, making it highly adaptable; High signal decoupling accuracy: Based on multiple algorithms, different types of spectral signals (such as fluorescence and Raman, different Raman peak groups) are separated, which significantly improves the identifiability of weak signals; Wide range of applications: It is not only applicable to "fluorescence + Raman" interfaces such as lithium metal-solid electrolyte, but also to interface systems where Raman signals of different materials overlap; Supports interface evolution visualization: By combining surface scanning or multi-point acquisition, two-dimensional or three-dimensional evolution mapping of interface chemistry, stress and morphology can be obtained, providing high-resolution dynamic information for the study of interface mechanisms in multiple fields.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals according to an embodiment of the present invention; Figure 2 A flowchart for decoupling mixed spectral signals; Figure 3 This is a diagram illustrating the decoupling process of mixed spectral signals in one embodiment. Figure 4 A schematic diagram of a multimodal spectral signal synchronous acquisition and decoupling system; Figure 5These are the fluorescence signal of lithium metal and the Raman signal of solid electrolyte in an embodiment of the present invention; Figure 6 The diagram shows the interface between lithium metal and solid electrolyte and the concentration distribution of decomposition product Li2S in an embodiment of the present invention with a current excitation of 0.2 mA. Figure 7 The diagram shows the interface between lithium metal and solid electrolyte and the concentration distribution of the decomposition product Li2S in an embodiment of the present invention with a current excitation of 0.3 mA.

[0024] Figure 8 This is an optical micrograph of the interface between metallic lithium and liquid electrolyte in an embodiment of the present invention; Figure 9 This is a Raman spectrum of a certain point in the scanning area in an embodiment of the present invention; Figure 10 This is a fluorescence intensity distribution diagram of lithium metal in an embodiment of the present invention; Figure 11 This is a Raman peak intensity distribution diagram of the electrolyte in an embodiment of the present invention. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0027] The following description, with reference to the accompanying drawings, describes an interface characterization method and system based on synchronous acquisition and decoupling of multimodal spectral signals according to embodiments of the present invention.

[0028] Figure 1 This is a flowchart of an interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes, but is not limited to, the following steps: S1, apply a single light source to the interface region of different materials to excite it, and perform spatially resolved spectral scanning on the interface region to make the different materials generate multiple spectral signals with differences. S2, acquire the raw spectral data containing the multiple spectral signals; S3, The original spectral data is decoupled using a signal separation algorithm to obtain the target spectral signals corresponding to different materials; S4. Extract feature parameters from the target spectral signal, and quantitatively characterize the evolution process of the interface region based on the changes of the feature parameters with space or time to obtain the characterization result.

[0029] Specifically, a single light source is used to excite an interface region containing two or more different materials, and the interface region is spectrally scanned to generate different spectral signals from the different materials, thereby obtaining multimodal spectral data at different spatial locations. Raw spectral data containing the multiple spectral signals is acquired using a spectral acquisition device. The raw spectral data is preprocessed, including wavenumber correction and signal-to-noise ratio optimization. The raw spectral data is decoupled based on a signal separation algorithm to obtain target spectra corresponding to different materials. Physical or chemical characteristic parameters are extracted based on the target spectra and used to characterize the evolution process of the interface region.

[0030] The interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals in this embodiment of the invention may further include the following specific steps: (1) Single-source excitation: Using a single laser source to excite the interface region containing two or more different materials, and to perform spectral scanning on the interface region, so that each material generates a different spectral signal, thereby obtaining multimodal spectral data at different spatial locations; (2) Synchronous acquisition: Raw spectral data containing the multiple spectral signals are acquired through a spectral acquisition device under the same optical path and test conditions; (3) Data preprocessing: Wavenumber correction, background subtraction and signal-to-noise ratio optimization are performed on the raw spectral data; (4) Signal decoupling: Based on the adaptive iterative reweighted least squares method, principal component analysis and independent component analysis algorithm, the original spectral data is decoupled in multiple modes to obtain target spectral signals corresponding to different materials; (5) Spatial mapping analysis: By collecting and decoupling the spectra at multiple spatial locations in the interface region, two-dimensional or three-dimensional distribution maps of various spectral parameters are generated, thereby realizing the visualization and evolution analysis of the chemical composition, structural stress and morphological distribution of the interface.

[0031] In step (1), the interfaces of different materials include solid-solid interfaces between lithium metal and solid electrolyte, solid-liquid interfaces between lithium metal and liquid electrolyte, interfaces between different semiconductor materials, and interfaces between materials with different spectral signals.

[0032] Using the mapping function of a Raman spectrometer to scan a selected target area, it is understood that the Raman spectrometer will collect the Raman spectrum of each scanning point at a set scanning step size. By setting appropriate parameters, the intensity of the Raman signal can be guaranteed, ensuring that the signal is not distorted while reducing the integration time.

[0033] Raman spectroscopy is a fingerprint spectrum used to analyze the molecular structure of substances. In some embodiments, when a laser is applied to a mixture of lithium metal and a solid electrolyte, not only is the molecular structure information of the electrolyte reflected, but also a strong fluorescence signal is emitted due to the metallic properties of lithium. The fluorescence signal is generally manifested as an increase in the baseline of the Raman spectrum. Based on this, Raman scanning of the interface region between lithium metal and the solid electrolyte can simultaneously obtain the molecular structure information of the solid electrolyte and its reaction products, as well as the distribution information of lithium metal.

[0034] In steps (2)-(3), the original spectral data containing the spectral signal is acquired by the spectral acquisition device, and the spectral signal is preprocessed, including wavenumber correction and signal-to-noise ratio optimization. The original spectral data contains Raman characteristic peak signals or fluorescence signals of each substance. The preprocessing of the spectral signal removes peaks, corrects wavenumbers, and optimizes the signal-to-noise ratio of the original spectrum in order to facilitate subsequent signal decoupling.

[0035] In step (4), the signal separation algorithm achieves the fitting and separation of the fluorescence baseline through an adaptive iterative reweighted least squares method, specifically including: An asymmetric weighting function is used to distinguish the Raman characteristic peak region from the baseline region in the original spectrum, and the weight matrix is ​​initialized. A smoothing parameter (such as a Whittaker smoother) is introduced as a constraint to construct the objective function for baseline fitting. An iterative algorithm is used to adaptively update the weight matrix based on the previous fitting result and assign lower weights to the Raman peak region until the baseline fitting converges. The fitted baseline and the baseline-removed Raman spectrum are separated from the original spectrum to obtain the pure Raman characteristic peak signal and the fluorescence signal.

[0036] The specific process is as follows: Figure 2 As shown, the algorithm can extract fluorescence signals, separate accurate Raman signals, significantly improve the signal-to-noise ratio of Raman feature signals, and obtain non-Raman-active metallic lithium fluorescence signals, thereby achieving high-precision analysis and quantitative characterization of multi-physics field characteristics such as battery interface chemical composition, stress distribution, and morphological evolution.

[0037] Among them, the fluorescence signal is mainly reflected in the baseline rise of the spectrum, while the Raman signal is mainly reflected in the peak position and peak intensity of Raman characteristic peaks, controlling the smoothness. λ =1×10 4 ~1×10 6 Weighting factorp =0.01~0.1, with a maximum iteration count of 2000. Peak fitting was performed on the baseline-corrected Raman spectra using a pseudo-Voigt function fitting algorithm. The pseudo-Voigt function has the following form:

[0038] in G ( x ) is a Gaussian function. L ( x () is the Lorentz function. Control parameters. η High-fit Raman spectra were obtained in the range of 0.3 to 0.7. Figure 3 This demonstrates the signal decoupling process proposed in this embodiment. Figure 3 In the image, (a) represents the preprocessed mixed spectral signal. Figure 3 In the diagram, (b) represents the Raman signal after baseline removal, where the solid line represents the result of fitting using the pseudo-Voigt function. Figure 3 In the diagram, (c) represents the extracted baseline, indicating the magnitude of the fluorescence signal.

[0039] In step (5), the decoupling results of spectral data from multiple spatial locations in the scanned area are organized to generate various two-dimensional or three-dimensional distribution maps, thereby realizing the visual evolution analysis of the interface chemical composition, structural stress, and morphological distribution. Among them, the chemical composition is determined by the peak position of the Raman characteristic peak, the structural stress is determined by the peak position shift of the Raman characteristic peak in the material that is sensitive to stress, and the morphological distribution is determined by the different signals of the two materials at the interface.

[0040] In some embodiments, the area enclosed by the extracted baseline integral and the horizontal axis is used as a measure of fluorescence intensity, semi-quantitatively establishing a mapping relationship with substances exhibiting fluorescent signals. The fluorescence intensity reflects the content of the fluorescent substance at that location. The baseline-corrected Raman signal represents the structural information of the corresponding site, which is related to the solid electrolyte and its decomposition products in the test system. Based on this, a relevant database is established, such as the presence of PS4 in Li6PS5Cl. 3- The electrolyte main peak is at 425 cm⁻¹ -1 The decomposition product Li₂S Raman peak is at 370 cm⁻¹. -1 P2S6 2- Raman Peak 380cm -1 The spatial distribution and concentration of the corresponding substances are obtained based on the Raman peak information obtained from the detection.

[0041] In some embodiments, the spatial resolution of the Raman spectrometer is ≤0.2 μm, and the scanning step size is ≤0.1 μm. It is understood that the spatial resolution and scanning step size can be varied according to actual testing requirements.

[0042] In some embodiments, based on the light transmittance of the solid electrolyte material, depth scanning can be performed, with a single-layer scanning thickness ≤ 1 μm, to obtain the three-dimensional ionic component distribution.

[0043] like Figure 4 As shown, the multimodal spectral synchronous acquisition and decoupling system of the present invention includes a laser source module 100, an optical path coupling module 200, a spectral detection module 300, and a data processing module 400. Among them, The laser source module 100 is configured to emit a single-wavelength laser and excite the interface region. The optical path coupling module 200 is optically connected to the laser source module 100 and is configured to introduce the single-wavelength laser into the interface region and simultaneously collect multiple mixed spectral signals generated in the interface region. The spectral detection module 300 is optically connected to the optical path coupling module 200 and is configured to acquire the multiple mixed spectral signals and convert them into digital signals for output. The data processing module 400 is connected to the spectral detection module 300. It has a built-in preset signal separation algorithm and is configured to receive the digital signal, use the signal separation algorithm to perform multimodal signal decoupling processing on the digital signal, and output the target spectral signal corresponding to different materials and the analysis results including material composition, concentration, local stress and morphology distribution.

[0044] Understandably, the laser source module 100 is used to emit a single-wavelength laser to excite the interface region, the optical path coupling module 200 is used to guide the laser into the region under test and collect multiple spectral signals generated by the region, the spectral detection module 300 is used to acquire the spectral data and convert it into digital signals, and the data processing module 400 has a built-in signal separation algorithm to realize multimodal decoupling and parameter analysis of the spectral signals.

[0045] In some embodiments, the laser source module 100 includes common Raman spectral laser sources such as 532nm, 633nm, and 785nm, and the optical path coupling module 200 provides a laser optical path, including a displacement platform to realize scanning function and the function of collecting scattered light signals to obtain spectral information.

[0046] Specifically, the assembled battery sample is placed on the displacement platform of the optical path coupling module 200, the laser source 100 is turned on to introduce the laser into the optical path of the optical path coupling module 200, the platform is controlled by the computer to realize laser scanning of the target area of ​​the sample, and the Raman spectrum information of each point in the target area is obtained through the spectral detection module 300.

[0047] In some embodiments, the data processing module 400 includes a signal decoupling procedure to decouple the spectral information acquired by the spectral detection module 300. Specifically, baseline extraction and fitting are performed on all acquired Raman spectral data. The baseline is extracted using an adaptive iterative reweighted least squares method, and the smoothness is controlled. λ =1×10 4 ~1×10 6 Weighting factor p =0.01~0.1, with a maximum iteration count of 2000. The area enclosed by the integral-extracted baseline and the horizontal axis is taken as the fluorescence intensity. Peak fitting is performed on the Raman spectrum after baseline correction. The fitting uses the Voigt function, which, combined with the tested system, fits all possible peaks and outputs parameters such as peak position, peak intensity, and full width at half maximum (FWHM).

[0048] In some embodiments, the data processing module 400 includes an analysis and imaging program. Specifically, the fluorescence intensity distribution (corresponding to the distribution of fluorescent substances), the intensity distribution of each peak position (corresponding to the content of each substance), and the peak position shift distribution of the solid electrolyte (corresponding to the stress and ion concentration distribution) are plotted separately, and the plots are interpolated according to the actual situation. Based on the test system, the Raman peaks of each possible substance are fitted, and the concentration distribution of each substance can be known from the Raman peak intensity distribution. Based on this, side reactions, decomposition, and other phenomena in the interface evolution process can be analyzed. The shift magnitude of the stress-sensitive Raman peaks characterizes the local stress magnitude and ion concentration distribution in the system. By combining the spatial distribution images obtained from scanning at each time point with the externally applied electrochemical excitation, the correlation between the electrochemical environment of interface evolution and the changes in interface morphology can be obtained. By analyzing the obtained patterns, the interface evolution mechanism can be elucidated.

[0049] In some embodiments, the tested sample is a symmetric lithium / / solid electrolyte / / lithium battery. By applying a constant current excitation, lithium is deposited in one direction, and the deposition variation with current can be studied. It is understood that the solid electrolyte can be a sulfide solid electrolyte (LPS, LPSC, LGPS), an oxide solid electrolyte (LZTO, LLZTO), or a solid electrolyte with significant Raman activity.

[0050] In some embodiments, the tested sample is a full-cell NCM / / solid-state electrolyte / / lithium battery. By applying certain charge and discharge conditions, metallic lithium is deposited and stripped from the negative electrode, allowing the study of the failure mechanism of the lithium negative electrode in the full cell. It is understood that the mechanism of action of various modified intermediate layers can also be studied using this invention.

[0051] The following section will further elaborate on the interface characterization method and system based on synchronous acquisition and decoupling of multimodal spectral signals through two specific application scenarios.

[0052] Application Scenario 1: Interface between lithium metal anode and sulfide solid electrolyte Utilize Figure 4 The system shown was used to characterize the interface between the lithium metal anode and the sulfide solid electrolyte. First, a Li / / Li6PS5Cl / / Li symmetric cell was prepared, with the structure shown below. Figure 4 The test sample is shown in the image. A relatively flat cross-section was cut from the sample using a scalpel, and then the sample was fixed onto a specially customized electrochemical testing mold, ensuring the flat cut surface faced the direction of the incident laser for easy Raman spectroscopy scanning. The electrochemical workstation applied a constant current excitation (0.2 mA) to the test sample. The interface for lithium metal deposition was determined based on the current direction, and a specific region of this interface was selected as the scanning area. The Raman spectroscopy scanning step size was set to 0.1 μm, and the scanning area size was approximately 20 × 30 μm. Raman tests were performed at the initial state and at specific deposition capacity moments. In this embodiment, a Raman scan was performed every 0.1 mAh of lithium metal deposited. The test sample was placed in an argon atmosphere throughout the process. Experiments showed that the signal from lithium metal was primarily a fluorescence signal, i.e., an overall baseline rise, such as... Figure 5 As shown in (a); the electrolyte signal is a Raman characteristic peak signal (without obvious baseline rise), as shown in (a). Figure 5 As shown in (b), the interface between lithium metal and the solid electrolyte can be separated and decoupled based on this. Furthermore, the same decoupling process is performed on the spectral data of each point to obtain the fluorescence and Raman signals of all points obtained from the area scan. Then, the acquired fluorescence and Raman signals are analyzed and plotted using the data analysis module, as shown below. Figure 6 The image shown is the result of the drawing. Figure 6 In the diagram (a), the interface evolution between lithium metal and the solid electrolyte is shown. Figure 6 (b) in the figure shows the evolution of the concentration distribution of Li2S.

[0053] To investigate the effect of current magnitude on lithium metal deposition, another sample was prepared, and the above procedure was repeated. The difference was that the current excitation from the electrochemical workstation was 0.3 mA. The results obtained are as follows: Figure 7 As shown, comparing the lithium metal interface evolution of samples at 0.2 mA and 0.3 mA reveals that increasing the current leads to uneven lithium metal deposition. The concentration distribution of the reaction product Li₂S indicates that its formation is even more uneven at high currents. The formation of Li₂S leads to uneven stress and increased impedance at the interface. This uneven stress and increased impedance, in turn, result in uneven current density, causing uneven lithium metal deposition. Figure 7As shown in (b), after Li2S is generated and embedded in the solid electrolyte, lithium metal soon grows along the Li2S sites. This illustrates the stress inhomogeneity and increased impedance caused by the uneven decomposition of the solid electrolyte at the interface. The increased impedance leads to preferential deposition of more lithium metal on both sides of the Li2S generation area. However, due to the inhomogeneous medium generated after decomposition, after a period of deposition, lithium metal is squeezed into the generated Li2S area, resulting in a more uneven lithium metal deposition phenomenon. This example demonstrates that Li2S generated by the decomposition at the interface between lithium metal and the sulfide solid electrolyte is a significant cause of uneven lithium metal deposition.

[0054] Application Scenario 2: Interface between lithium metal anode and liquid electrolyte Utilize Figure 4 The system shown was used to characterize the interface between the lithium metal anode and the liquid electrolyte. A Li / / LiPF6 (in EC:EMC = 3:7 Vol%) / / Cu half-cell was prepared. A current flowing from Li towards Cu was applied, causing Li to deposit on the Cu surface. The region on the Cu surface where lithium metal was deposited was selected for scanning. The Raman spectroscopy scan step size was set to 0.5 μm, and the scan area size was approximately 40 × 50 μm. Figure 8 Optical micrographs of the selected Li / / electrolyte battery interface region are shown. Due to limited optical contrast, the dendritic structure is difficult to clearly identify. The corresponding Raman spectrum of this region (…) Figure 9 This simultaneously contains characteristic signals of metallic lithium (broad fluorescence background), molecular fluorescence of organic liquid electrolytes, and Raman characteristic peaks of the solvent. To extract spatial distribution information, in... Figure 8 The area shown was subjected to high-resolution Raman scanning. Figure 10 By integrating the baseline signal extracted from each pixel, a fluorescence intensity distribution map was generated, clearly revealing the lithium-rich protrusion structure corresponding to dendrite growth. This map, with laser spot size and scanning step size as resolution limits, reveals the spatial morphological characteristics of the lithium metal structure. Figure 11 The intensity distribution of the Raman signal in the electrolyte was further demonstrated, reflecting the spatial distribution of the liquid environment. These results indicate that the spectral decomposition method can effectively separate lithium fluorescence from the Raman characteristics of the electrolyte, achieving simultaneous visualization of lithium morphology and electrolyte structure in liquid battery systems.

[0055] This invention is not limited to the specific embodiments described above. The interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals mentioned in this invention can be widely applied to basic fields and other related fields, and can be implemented using various other specific embodiments. For example, the design concept of characterizing the interface evolution of lithium metal and sulfide solid electrolytes based on this interface evolution analysis method can be applied to other solid electrolytes or interfaces between metals and Raman-active materials. Therefore, any design that adopts the design concept of this invention with some simple changes or modifications falls within the scope of protection of this invention.

[0056] In summary, the embodiments of this invention elucidate the interface evolution law of lithium metal and sulfide solid electrolytes, providing mechanistic guidance for suppressing lithium dendrite growth in sulfide solid electrolytes. By using adaptive iterative reweighted least squares and pseudo-Voigt function decoupling to obtain fluorescence and Raman signals, the growth mechanism of lithium metal in sulfide solid electrolytes was analyzed. The non-uniformity of Li2S formation is a significant cause of lithium metal non-uniformity. When improving the interface between lithium metal and sulfide solid electrolytes, preventing electrolyte decomposition or artificially depositing a uniform Li2S interface layer can be considered to prevent lithium dendrite growth. Furthermore, the application of these embodiments in liquid battery interfaces effectively separates lithium fluorescence from electrolyte Raman characteristics, verifying the effectiveness of fluorescence and Raman signal separation.

[0057] The interface characterization method and system based on synchronous acquisition and decoupling of multimodal spectral signals in this invention synchronously acquires fluorescence and Raman mixed spectral signals of the interface region using a Raman spectrometer, and utilizes the adaptive iterative reweighted least squares method (…). λ =1×10 4 ~1×10 6 ) and dynamic pseudo-Voigt function ( η The system separates signals (0.3~0.7 g / L) and qualitatively characterizes the formation and enrichment regions of metallic lithium based on the spatial distribution of fluorescence intensity. It also analyzes the spatial distribution and relative concentration of interfacial products (such as Li₂O and Li₂S) using Raman peaks. Simultaneously, it uses peak shifts in the solid electrolyte to characterize its stress and lithium-ion concentration distribution. The system integrates a Raman spectrometer and an electrochemical workstation to achieve simultaneous dynamic monitoring of interfacial evolution morphology and chemical composition. This invention is applicable to systems with material interfaces, such as solid-state batteries, electrochemical catalysis, and semiconductor devices, and has advantages such as wide applicability, high signal decoupling accuracy, and high detection efficiency.

[0058] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0059] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An interface characterization method based on synchronous acquisition and decoupling of multimodal spectral signals, characterized in that, Includes the following steps: A single light source is applied to the interface region of different materials for excitation, and the interface region is spatially resolved for spectral scanning, so that the different materials generate multiple spectral signals with differences. Obtain raw spectral data containing the aforementioned multiple spectral signals; The original spectral data is decoupled using a signal separation algorithm to obtain target spectral signals corresponding to different materials; Feature parameters are extracted from the target spectral signal, and the evolution process of the interface region is quantitatively characterized based on the changes of the feature parameters with space or time to obtain the characterization results.

2. The method according to claim 1, characterized in that, The signal separation algorithm includes: An asymmetric weighting function is used to assign weights to the Raman characteristic peak region and the baseline region in the original spectrum, and the weighting matrix is ​​initialized. A smoothness constraint is introduced to construct a baseline fitting objective function; The weight matrix is ​​adaptively updated based on the fitting results of the previous iteration, and the Raman peak region is given a lower weight. The iteration is repeated until the baseline fitting converges. The fitted baseline is separated from the original spectrum to obtain the fluorescence signal, while the pure Raman characteristic peak signal after removing the baseline is obtained.

3. The method according to claim 1, characterized in that, After acquiring the raw spectral data containing the multiple spectral signals, the method further includes: The original spectral data is corrected for the wavenumber axis to eliminate wavenumber shifts caused by instrument drift or environmental disturbances, and to ensure that the spectral peaks are aligned in a uniform wavenumber coordinate system. Noise assessment is performed on the wavenumber-corrected spectral data to identify spectral regions or spatial pixels with a signal-to-noise ratio below a preset threshold, in order to obtain the noise assessment results. Based on the noise assessment results, adaptive smoothing filtering or wavelet denoising methods are used to optimize the low signal-to-noise ratio region, outputting preprocessed spectral data with improved signal-to-noise ratio and preservation of characteristic peak shapes.

4. The method according to claim 1, characterized in that, The different spectral signal types generated by the different materials under a single light source excitation include: One material produces a fluorescent signal, and the other material produces a Raman signal; or Both materials produce Raman signals, but each has non-overlapping or distinguishable characteristic Raman peak positions, thus forming Raman signals with differences.

5. The method according to claim 2, characterized in that, The original spectral data is decoupled using a signal separation algorithm to obtain target spectral signals corresponding to different materials, including: An adaptive iterative reweighted least squares method was used to fit and separate the fluorescence baseline of the original spectrum: the Raman characteristic peak region and the baseline region were distinguished by an asymmetric weight function and the weight matrix was initialized. A smoothness constraint was introduced to construct the baseline fitting objective function. The weight matrix was adaptively updated based on the previous iteration results and a lower weight was assigned to the Raman peak region. The iteration was repeated until convergence to obtain the separated fluorescence signal and the pure Raman characteristic peak signal. Principal component analysis and independent component analysis algorithms are used to analyze the decoupled multimodal signals to obtain the target spectral signals by separating the spectral characteristics of different components at the interface; wherein, the target spectral signals include: the fluorescence signal of the corresponding fluorescent material and the Raman signal of the corresponding Raman active material.

6. The method according to claim 1, characterized in that, The characteristic parameters include material composition, material concentration, and local stress, wherein: The composition of the substance was obtained by analyzing the peak positions of the decoupled Raman characteristic peaks; The concentration of the substance was obtained by analyzing the peak intensity of the Raman characteristic peaks after decoupling; The local stress is obtained by analyzing the peak position shift of the Raman characteristic peaks after decoupling; For materials that do not have Raman characteristic peaks but have fluorescence signals, the concentration information of the material can be obtained by analyzing the degree of spectral baseline elevation.

7. The method according to claim 6, characterized in that, The composition of the substance was obtained by analyzing the peak positions of the decoupled Raman characteristic peaks, including: A standard Raman characteristic peak position database of the interface system to be tested is established in advance, wherein the standard Raman characteristic peak position database contains the characteristic peak position information of each component substance; Identify the peak position values ​​of characteristic peaks in the decoupled Raman spectrum; The identified peak values ​​are compared with a standard database to determine the corresponding material components.

8. The method according to claim 6, characterized in that, The concentration of the substance is obtained by analyzing the peak intensity of the decoupled Raman characteristic peaks, including: A quantitative relationship between the intensity of Raman characteristic peaks and the concentration of each component in the interface system under test is established in advance. Extract the peak intensity values ​​of the corresponding characteristic peaks in the decoupled Raman spectrum; The concentration of the substance is calculated based on the quantitative relationship and the extracted peak intensity values.

9. The method according to claim 6, characterized in that, The local stress is obtained by analyzing the peak position shift of the decoupled Raman characteristic peaks, including: Select Raman characteristic peaks that are sensitive to stress changes in the tested material system as stress probe peaks; Determine the standard peak position of the stress probe peak under stress-free conditions; Measure the actual peak position of the stress probe peak in the decoupled Raman spectrum; Calculate the offset between the actual peak position and the standard peak position; Based on the pre-established quantitative relationship between peak position offset and stress magnitude, the local stress value is determined.

10. A system for synchronous acquisition and decoupling of multimodal spectral signals for implementing the method of any one of claims 1–9, characterized in that, include: The laser source module is configured to emit a single-wavelength laser and excite the interface region. An optical path coupling module is optically connected to the laser source module and is configured to introduce the single-wavelength laser into the interface region and simultaneously collect multiple mixed spectral signals generated in the interface region. A spectral detection module is optically connected to the optical path coupling module and is configured to acquire the multiple mixed spectral signals and convert them into digital signals for output. The data processing module is connected to the spectral detection module and has a built-in preset signal separation algorithm. It is configured to receive the digital signal, use the signal separation algorithm to perform multimodal signal decoupling processing on the digital signal, and output the target spectral signals corresponding to different materials and the analysis results including material composition, concentration, local stress and morphology distribution.

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