Method for analyzing and determining aggregation structure in condensed state

Abstract

The invention provides a method for analyzing and determining an aggregation structure in a condensed state, which comprises the following steps: step 1, measuring a same condensed state substance sample by adopting at least two structure determination means with different principles, at least another kind of structure measurement means is used for obtaining nanoscale or mesoscale aggregation structure information so as to obtain multi-source structure measurement data representing different structure hierarchies. According to the method, multi-scale structure measurement data is obtained by adopting at least two structure measurement means with different principles for the same condensed substance sample, and structure information of different scales is subjected to combined treatment in the same analysis process; and the structural characteristics of the aggregation structure on the atomic scale, the nanoscale and the mesoscale can be simultaneously included in an analysis range, so that the characterization integrity of the overall characteristics of the aggregation structure in the condensed state is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of condensed matter structure analysis, and in particular to a method for analyzing and measuring aggregation structure in condensed matter. BACKGROUND

[0002] Condensed matter is widely present in the fields of material science, physics, chemistry and related engineering, and its microstructure characteristics directly affect the mechanical properties, electrical properties, thermal properties and chemical stability of materials. In various condensed matter systems, the aggregation structure is often formed by atomic, molecular or nanoscale structural units through interaction, and has certain spatial distribution characteristics. Such aggregation structure usually presents multi-scale, non-uniform and partially disordered characteristics, and is an important structural basis for determining the performance of condensed matter. Existing structure measurement methods rely on a single or a few measurement methods, and different measurement methods have different focuses in spatial resolution and structure sensitive scale. Therefore, they can only reflect the local characteristics of the aggregation structure in a specific scale range, and it is difficult to comprehensively characterize the overall situation of the aggregation structure in different scales. In addition, the data obtained by different structure measurement methods differ in data form and physical meaning. The existing analysis methods mostly use separate analysis or empirical comparison methods, and lack a unified data processing and fusion analysis mechanism, which makes it difficult to effectively correlate different measurement results, and affects the stability and consistency of the aggregation structure analysis results. Therefore, a method for analyzing and measuring aggregation structure in condensed matter is proposed. SUMMARY

[0003] Therefore, the present application provides a method for analyzing and measuring aggregation structure in condensed matter to solve or alleviate the technical problems in the prior art, and at least provides a beneficial choice.

[0004] The technical solution of the present application is as follows: The method for analyzing and measuring aggregation structure in condensed matter provided by the present application comprises the following steps: Step 1: Multi-scale structure data acquisition, at least two different principle structure measurement methods are used to measure the same condensed matter sample, at least one type of structure measurement method is used to obtain structure information at the lattice scale or atomic scale, and at least another type of structure measurement method is used to obtain aggregation structure information at the nanoscale or mesoscale, thereby obtaining multi-source structure measurement data representing different structure levels.

[0005] The structure measurement method can include at least two combinations of X-ray diffraction measurement method, small-angle X-ray scattering measurement method, electron microscopic imaging measurement method and neutron scattering measurement method, to realize complementary acquisition of structure information at different spatial scales.

[0006] Step 2: Structural Parameter Extraction and Unified Representation. The multi-source structural measurement data are subjected to denoising, scale correction, and feature mapping to extract structural parameters reflecting the characteristics of the aggregated structure. These structural parameters may include one or more of the following: coordination number distribution parameters, aggregate unit size distribution parameters, aggregate morphology and contour parameters, local density distribution parameters, and scattered signal intensity distribution parameters.

[0007] More preferably, the extracted structural parameters are normalized, and structural parameters from different sources are converted into a unified data representation, preferably a numerical vector form, and a structural parameter vector matrix is ​​constructed according to the corresponding structural level for subsequent structural modeling and inversion calculations.

[0008] Step 3: Fusion Analysis and Structural Inversion. The above set of structural parameters is input into the fusion modeling module. Under the joint constraints of the multi-source structural parameters, structural inversion calculation is performed. Specifically, an initial clustered structural model is constructed based on the set of structural parameters, and the initial clustered structural model is corrected through iterative optimization to reduce the deviation between the inverted clustered structural model and the measurement data of each structure.

[0009] During the iterative optimization process, the weight coefficients of the structural parameters in the structural inversion calculation are adjusted according to the fitting error magnitude corresponding to different structural parameters, so that the multi-source structural parameters form a synergistic constraint on the aggregated structural model.

[0010] During the fusion analysis process, the cluster structure model is constrained by pre-constructed cluster structure morphology constraints, which are used to limit the basic morphology or spatial distribution characteristics of the cluster structure, but not to limit the cluster structure model obtained by the final inversion.

[0011] Finally, the obtained aggregated structure model is output in a standard structural file format, along with the corresponding structural parameter statistical results, for subsequent structural comparison analysis or material performance research.

[0012] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: I. This invention obtains multi-scale structural measurement data by using at least two different structural measurement methods on the same condensed matter sample, and performs joint processing of structural information at different scales in the same analysis process. This allows the structural features of aggregated structures at the atomic, nano, and mesoscales to be included in the analysis at the same time, thereby improving the completeness of the characterization of the overall features of aggregated structures in condensed matter and avoiding the loss of structural information caused by relying on a single scale measurement.

[0013] Second, this invention extracts structural parameters from multi-source structural measurement data and performs normalization processing, converting structural parameters from different sources into a unified data representation that can be fused. In the process of fusion modeling, joint constraints are applied to the multi-source structural parameters to perform structural inversion, thereby achieving collaborative analysis of different measurement results under the same modeling framework. This improves the consistency and stability of aggregate structure analysis results and reduces the reliance on empirical judgment in the analysis process.

[0014] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0017] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Example 1 like Figure 1 As shown, this embodiment of the invention provides a method for analyzing and determining aggregate structures in condensed matter, comprising the following steps: Step 1: Multi-scale structural data acquisition In this embodiment, the same condensed matter sample is used as the analysis object; To obtain structural information covering different structural levels, the sample was measured using at least two different structural measurement methods. One method was used to obtain structural information at the lattice or atomic scale, while the other method was used to obtain aggregated structural information at the nanoscale or mesoscale. Specifically, in this embodiment, structural data reflecting local atomic arrangement or short-range ordering characteristics can be obtained by structural measurement methods based on diffraction principles. At the same time, structural data reflecting the size of aggregate units, morphology of aggregates and spatial distribution characteristics can be obtained by structural measurement methods based on scattering principles or microscopic imaging principles. If necessary, neutron scattering measurement methods can be introduced to enhance the structural sensitivity to specific scale ranges or components. By acquiring structural measurement data of different scales and principles on the same sample, a multi-source structural measurement dataset representing different structural levels is formed. This allows the subsequent analysis process to be constrained by both local structural information and overall aggregated structural information, thereby avoiding the problem of incomplete structural information caused by relying on only a single measurement method.

[0020] Step 2: Extraction and Unified Representation of Structural Parameters After obtaining the multi-source structural measurement data from step one, the various types of measurement data are processed separately to extract structural parameters that reflect the characteristics of the aggregated structure. In this specific implementation, background signal subtraction, noise suppression, and scale correction are first performed on diffraction and scattering data to eliminate the impact of differences in measurement conditions on data comparability. For microscopic imaging data, image denoising and contrast correction are performed to ensure the stability of morphology and boundary information extraction. After preprocessing, corresponding structural parameters are extracted according to the physical meaning of different measurement data. Diffraction or correlation function data are used to obtain local structural parameters such as coordination number distribution, scattering data are used to obtain aggregate size distribution and scattering intensity distribution parameters, and microscopic imaging data are used to obtain aggregate morphology contour parameters and spatial distribution related parameters. Furthermore, local density distribution parameters can be extracted to characterize the non-uniformity of aggregates in space. The above structural parameters are normalized to eliminate differences in dimensions and orders of magnitude. The normalized structural parameters are then uniformly converted into numerical vector form. The numerical vectors are then organized according to the spatial scale or structural level corresponding to the structural parameters to construct a structural parameter vector matrix, forming a set of structural parameters for structural modeling and inversion calculation. Through this continuous processing, structural information from different measurement methods is uniformly represented in form.

[0021] Step 3: Fusion Analysis, Structural Inversion, and Result Output In this embodiment, the set of structural parameters formed in step two is input into the fusion modeling module to perform structural inversion calculation; An initial clustering structure model is constructed based on a set of structural parameters to describe the initial distribution relationship of clustering units in space. This initial model serves as the starting state for inversion calculations without limiting the final structural form. Subsequently, the initial aggregated structure model is iteratively optimized under the joint constraints of multi-source structural parameters. In each iteration, the model is corrected according to the fitting deviation between the current structural model and various structural parameters, so that the inverted aggregated structure model gradually approaches the state consistent with the multi-source structural measurement data. During the iteration process, the weight coefficients of different structural parameters in the inversion calculation are dynamically adjusted according to the fitting error magnitude, so that the multi-source structural parameters form a synergistic constraint on the aggregated structure model. At the same time, pre-constructed aggregated structure morphology constraints are applied during the inversion process to limit the reasonable range of the aggregated structure in terms of spatial distribution or basic morphology, but do not limit the specific morphology of the final inverted aggregated structure model, so as to ensure the physical rationality of the structure while maintaining adaptability to different condensed matter systems. After the inversion is completed, the final aggregated structure model is output in a standard structure file format, and the statistical results of the corresponding structural parameters are output simultaneously for subsequent structural comparison analysis, material performance research or further theoretical simulation.

[0022] Through the above implementation methods, the present invention can comprehensively utilize structural measurement data at different spatial scales in the same analysis process to achieve unified analysis and determination of the aggregate structure of condensed matter. It can be clearly traced back to the complementary acquisition of multi-scale structural data in step one, the unified representation of multi-source structural parameters in step two, and the joint constraint inversion of multi-source structural parameters and their synergistic relationship in step three, rather than originating from abstract conclusions or empirical judgments.

[0023] Example 2 Methods for analyzing and determining the aggregation structure of colloidal / nanoparticle dispersion systems Step 1: Multi-scale structural data acquisition In this embodiment, colloidal or nanoparticle dispersion system samples are used as the measurement objects (e.g., condensed state system formed by particles dispersed in solvent). The same sample is selected to carry out nanoscale and mesoscale structure measurements to obtain complementary multi-source structure measurement data. Specifically, this embodiment employs small-angle X-ray scattering (SAXS) to acquire scattering curve data, characterizing the characteristic size, scale distribution, and spatial correlation within the aggregates. Simultaneously, electron microscopy is used to acquire microscopic image data of the samples, characterizing the morphology, aggregation, and spatial distribution characteristics of the aggregates within the observation area. Both scattering measurements and microscopic imaging are performed on the same batch of samples, maintaining consistent sample preparation conditions. This results in a multi-source structural measurement dataset characterizing different structural levels, allowing subsequent analysis to include both the statistical structural information reflected by the scattering signals and the morphological and spatial distribution information reflected by the images.

[0024] Step 2: Extraction and Unified Representation of Structural Parameters After obtaining the multi-source structure measurement data from step one, the scattering curve data and microscopic image data are preprocessed and structural parameters are extracted to construct a unified data input. For scattering curve data, background subtraction and noise suppression are performed, and scale correction is applied before extracting scattering signal intensity distribution parameters. Simultaneously, aggregate size distribution parameters are extracted from the scattering features. For microscopic image data, image denoising and contrast correction are performed. Aggregate morphology contour parameters are extracted based on aggregate boundaries and connected regions, and local density distribution parameters reflecting spatial distribution characteristics are further extracted. After parameter extraction, the above structural parameters are normalized to eliminate dimensional differences, and structural parameters from different sources are uniformly converted into numerical vector form. Subsequently, they are organized into a structural parameter vector matrix according to structural hierarchy, forming a set of structural parameters for structural inversion. By unifying the structural information of the scattering channel and the imaging channel, we can avoid the difficulty of coordinating the analysis of different measurement results under the same framework due to differences in data format.

[0025] Step 3: Fusion Analysis, Structural Inversion, and Result Output Input the set of structural parameters formed in step two into the fusion modeling module to perform structural inversion calculation; During implementation, an initial clustering structure model is constructed based on a set of structural parameters. This initial model expresses the spatial distribution relationship and basic morphological framework of the clustering units, without limiting the final structural form. Subsequently, iterative optimization is carried out under the joint constraints of multi-source structural parameters. The fitting deviation between the current structural model and the scattering intensity distribution parameters, size distribution parameters, morphological contour parameters, and local density distribution parameters is calculated, and the structural model is corrected round by round accordingly, so that the inversion model gradually converges to a state consistent with the multi-source measurement data. During the iteration process, the weight coefficients of different structural parameters are adjusted according to the changes in the fitting error, so that parameters with larger fitting deviations impose stronger constraints on the model in subsequent iterations, thereby forming a collaborative constraint mechanism for multi-source structural information. Simultaneously, pre-constructed aggregate structure morphology constraints are applied to the inversion process to limit the reasonable range of the aggregate structure in terms of spatial distribution or basic morphology, but no rigid restrictions are placed on the specific morphology of the final inverted structure, so as to balance structural rationality and system adaptability. After the inversion is completed, the aggregate structure model is output and structural analysis results are generated. The aggregate structure model is exported in a standard structural file format, and the corresponding structural parameter statistical results are output simultaneously for comparative analysis of aggregate structures under different samples or different working conditions and subsequent material performance studies. In this embodiment, the complementary acquisition of multi-scale structural data in step one enables scattering statistics and microscopic morphology information to participate in constraints simultaneously within the same process; the normalization and vectorization of structural parameters in step two enable data from different sources to have a fusionable input form; the multi-source joint constraints, dynamic weight adjustment, and morphological constraints in step three work together to ensure that the inverted structure is consistent with the multi-source measurement data while avoiding unreasonable spatial distribution results.

[0026] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing and determining aggregated structures in condensed matter, characterized in that, Includes the following steps: Step 1: Measure the same condensed matter sample using at least two different structural determination methods, wherein at least one type of structural determination method is used to obtain structural information at the lattice scale or atomic scale, and at least another type of structural determination method is used to obtain aggregated structural information at the nanoscale or mesoscale, so as to obtain multi-source structural measurement data characterizing different structural levels. Step 2: Denoise, scale, and feature map the multi-source structural measurement data obtained in Step 1 respectively, extract structural parameters that reflect the characteristics of the clustered structure, normalize the structural parameters, convert structural parameters from different sources into a unified data representation, and construct a set of structural parameters for structural modeling. Step 3: Input the set of structural parameters obtained in Step 2 into the fusion modeling module, perform structural inversion calculation under the joint constraints of multi-source structural parameters, and generate an aggregated structural model consistent with the multi-source structural measurement data by adjusting the weight relationship of different structural parameters in the modeling process, and output the corresponding structural analysis results.

2. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step one, the structural measurement method includes a combination of at least two of the following: X-ray diffraction measurement method, small-angle X-ray scattering measurement method, electron microscopy imaging measurement method, or neutron scattering measurement method, for complementary acquisition of structural information at different spatial scales.

3. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step two, the structural parameters include one or more of the following: coordination number distribution parameters, aggregate size distribution parameters, aggregate morphology and profile parameters, local density distribution parameters, and scattered signal intensity distribution parameters.

4. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step two, the unified data representation is in the form of numerical vectors, and a structural parameter vector matrix is ​​constructed according to the corresponding structural hierarchy for subsequent structural inversion calculations.

5. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step three, an initial clustered structure model is constructed based on the set of structural parameters obtained in step two. Subsequently, the initial clustered structure model is modified through iterative optimization to reduce the deviation between the inverted clustered structure model and the measurement data of each structure.

6. The method for analyzing and determining aggregated structures in condensed matter according to claim 5, characterized in that: In the iterative optimization process of step three, the weight coefficients of the structural parameters in the structural inversion calculation are adjusted according to the fitting error magnitude corresponding to different structural parameters, so that the multi-source structural parameters form a synergistic constraint on the aggregated structural model.

7. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step three, the fusion analysis process is constrained by pre-constructed cluster structure morphology constraints. These constraints are used to limit the basic morphology or spatial distribution characteristics of the cluster structure, but do not limit the final inverted cluster structure model.

8. The method for analyzing and determining aggregated structures in condensed matter according to claim 1, characterized in that: In step three, the aggregated structure model is output in a standard structure file format, and the statistical results of the structural parameters corresponding to the aggregated structure model are also output at the same time for subsequent structural comparison analysis or material performance research.

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