Porous carbon material preparation device and pore morphology complexity characterization method and system

Abstract

The invention belongs to the related technical field of porous material structure characterization, and provides a porous carbon material preparation device and a pore morphology complexity characterization method and system.The shape fractal dimension is calculated through the area and perimeter of a single pore, and the depth fractal dimension is calculated according to the deviation value of the actual pore shape of the single pore and an ideal circle; and the total fractal dimension of the pore morphology complexity is obtained, and quantitative characterization of the pore structure complexity is realized. The pore morphology complexity quantification method is simple to operate, and the irregularity of the porous carbon pore channel can be incorporated into the performance index of the porous carbon.

Description

Technical Field

[0001] This invention belongs to the technical field of porous material structure characterization, and particularly relates to porous carbon material preparation apparatus, pore morphology complexity characterization method and system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Porous carbon materials, due to their high specific surface area, good electrical conductivity, and chemical stability, are widely used in gas adsorption, energy storage electrodes, catalyst supports, and environmental remediation. Traditional pore structure characterization methods mainly rely on nitrogen adsorption (BET) and pore size distribution (DFT) analysis, which can obtain parameters such as specific surface area (SSA), pore volume (PV), and micro / mesopore ratio. However, these parameters only reflect the number and size distribution of pores and cannot fully describe the complexity and irregularity of pore morphology. Furthermore, each test requires the preparation of actual samples, which is time-consuming, labor-intensive, and involves a lot of repetitive work.

[0004] Recent studies on pore adsorption have shown that the morphological complexity of pores (adsorption surface roughness) has a significant impact on gas adsorption and diffusion behavior. For example, irregular pore walls can cause spatial fluctuations in the adsorption potential energy distribution, leading to the formation of locally high-density adsorption phase regions at pore channel protrusions. However, a method for quantitatively describing the pore morphological complexity of porous carbon is currently lacking, as is a systematic computational framework for analyzing pyrolysis experimental parameters and predicting performance.

[0005] Currently, there is no method to quantitatively characterize the morphological complexity of pores. Furthermore, the preparation and characterization of porous carbon materials have largely relied on experimental trial and error, resulting in a significant amount of repetitive work. Therefore, there is an urgent need for a method that can accurately assess pore morphological complexity and predict the performance indicators of porous carbon materials. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a porous carbon material preparation apparatus, a pore morphology complexity characterization method and system, thereby realizing the quantitative characterization of the pore morphology complexity of porous carbon materials.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for characterizing the pore morphology complexity of porous carbon materials, comprising: Acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with desired pore boundary clarity; The shape fractal dimension is determined based on the boundary perimeter and area of ​​a single pore cross-sectional region, and the depth fractal dimension is determined based on the deviation of the actual pore shape from the ideal circle. The total fractal dimension used to represent the complexity of pore morphology is determined based on the shape fractal dimension and the depth fractal dimension.

[0008] Secondly, the present invention provides a porous carbon material preparation apparatus, employing the aforementioned method for characterizing the pore morphology complexity of porous carbon materials, characterized in that it includes: The sample preparation unit is used to prepare porous carbon material samples according to the input experimental conditions. The pyrolysis activation unit is used to perform pyrolysis experiments on porous carbon material samples prepared by the sample preparation unit. The testing unit is used to determine the characterization data of porous carbon material samples after pyrolysis experiments. The analysis and control unit is used to calculate the pore morphology complexity based on the quantitative characterization method of the porous carbon material pore morphology complexity and the characterization data of the porous carbon material sample, and to generate optimized experimental conditions based on the calculated pore morphology complexity of the porous carbon material sample using a trained neural network model, and to feed back the optimized experimental conditions to the sample preparation unit.

[0009] Thirdly, the present invention provides a system for characterizing the pore morphology complexity of porous carbon materials, comprising: The preprocessing module is configured to: acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with pore boundaries reaching the desired clarity; The first calculation module is configured to: determine the shape fractal dimension based on the boundary perimeter and area of ​​a single pore cross-sectional region, and determine the depth fractal dimension based on the deviation between the actual pore shape and the ideal circle of a single pore. The second calculation module is configured to determine the total fractal dimension used to represent the complexity of the pore morphology based on the shape fractal dimension and the depth fractal dimension.

[0010] Fourthly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0011] Fifthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0012] The above one or more technical solutions have the following beneficial effects: This invention calculates the shape fractal dimension by the area and perimeter of a single pore, calculates the depth fractal dimension based on the deviation of the actual pore shape from the ideal circle, and obtains the total fractal dimension of the pore morphology complexity accordingly, thereby achieving a quantitative characterization of the pore structure complexity.

[0013] The present invention provides a method for quantifying complex pore morphology. This method is simple to operate and can incorporate the irregularity of porous carbon pore channels into the performance indicators of porous carbon.

[0014] Advantages of additional aspects 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

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0016] Figure 1 This is a schematic diagram of the method for quantifying the complexity of pore morphology in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the porous carbon autonomous research system in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the pore cross-section in Embodiment 1 of the present invention; In the diagram, 1 is the sample preparation unit; 2 is the pyrolysis activation unit; 3 is the testing unit; and 4 is the analysis and control unit. Detailed Implementation

[0017] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0019] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0020] Example 1 This embodiment discloses a method for characterizing the pore morphology complexity of porous carbon materials, including, Acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with desired pore boundary clarity; The shape fractal dimension is determined based on the boundary perimeter and area of ​​a single pore cross-sectional region, and the depth fractal dimension is determined based on the deviation of the actual pore shape from the ideal circle. The total fractal dimension, used to represent the complexity of pore morphology, is determined based on the shape fractal dimension and the depth fractal dimension.

[0021] like Figure 1 As shown, scanning electron microscope (SEM) images of porous carbon materials are subjected to grayscale conversion, contrast enhancement, and sharpening operations to obtain high-quality images with clear pore boundaries (e.g., pore boundary pixel values ​​within 5 pixels) and low noise. Subsequently, edge enhancement, binarization, median filtering, and noise reduction operations are performed on the images, ultimately retaining only pore regions with pore shape factors and pore area sizes within a reasonable range. All pores that meet the requirements are saved for subsequent morphology complexity analysis.

[0022] The specific process for grayscale conversion, contrast enhancement, and sharpening is as follows: First, the original SEM image is converted into a grayscale image; then, the adaptive histogram equalization (CLAHE) method is used to enhance the image contrast to highlight the edges of the pores; finally, Laplacian sharpening or nonlocal mean denoising algorithm is used to improve the edge sharpness and suppress background noise.

[0023] Edge enhancement is achieved by convolving the image with a 3×3 convolution kernel of the Laplacian operator. This operation significantly improves the contrast of areas with drastic grayscale changes in the image (such as the boundaries of pores), making the boundaries clearer.

[0024] The binarization process uses the Otsu adaptive thresholding method, which automatically determines the optimal segmentation threshold based on the image grayscale distribution, effectively separating the pore areas from the background areas in the image and avoiding the instability caused by manual threshold setting.

[0025] Median filtering employs a non-linear smoothing method with a 3×3 neighborhood as the window. By replacing the center pixel value with the median value within the neighborhood, it effectively removes salt-and-pepper noise and small isolated pixels, making the edges of the apertures smoother without losing structural details.

[0026] In this embodiment, the aperture shape factor is calculated. :

[0027] Among them, the area of ​​a single hole Circumference of a single hole .

[0028] The noise reduction operation is performed by controlling the aperture shape factor. (For example The value <0.8 eliminates areas with excessively large pore shape factors, which are typically regions where multiple pores are connected together; what is desired are areas with individual pores. Furthermore, by controlling the pore area size (e.g., with an overall image size of 1024×1024, controlling the area of ​​each pore to be between 100-2000), small and oversized pores are removed, as the shape of small pores is usually not visible in the image, thus ensuring that all pores are within the same scale.

[0029] The total fractal dimension representing the pore morphology complexity is calculated for the processed pores. This total fractal dimension includes the shape fractal dimension and the depth fractal dimension; one represents the number of protrusions on the pore cross-section, and the other represents the degree of deviation of these protrusions from the pore center. The two fractal dimensions are then combined into a single total fractal dimension representing the pore morphology complexity.

[0030] In this embodiment, the shape fractal dimension is obtained by calculating the relationship between the perimeter and area of ​​the pore cross-section region.

[0031]

[0032] in, The shape fractal dimension; It is a constant, such as the size of C can be controlled to keep the fractal dimension in the range of 0-1.

[0033] The depth fractal dimension is obtained by calculating the deviation of the actual pore shape from the ideal circle.

[0034]

[0035] in, Let be the distance from the i-th boundary point to the center; Let be the radius of the ideal equivalent circle; It is a weighted index; is the depth fractal dimension, representing the average degree of topographic undulation in the depth direction of the pores; N represents the number of pixels outside the maximizing circle; i represents each pixel; the weighting exponent W can be set to a number greater than 1 (e.g., W=2), in which case the pixels farther away from the maximizing circle have a greater influence on the value of DL.

[0036] Among them, such as Figure 3 As shown, a circle is drawn on the cross-section of the pore as the ideal equivalent circle, with the center referring to the center of the ideal equivalent circle.

[0037] Finally, the two fractal dimensions are combined into the total fractal dimension.

[0038]

[0039] in, Let be the total fractal dimension.

[0040] The mean of the total fractal dimension of all pores that meet the requirements in the SEM under the pyrolysis conditions is the quantified pore morphology complexity.

[0041] The method described in this embodiment is applicable to the structural evaluation of porous carbon materials such as coal-based activated carbon, graphite carbon, and petroleum coke.

[0042] Example 2 This embodiment proposes a porous carbon material preparation system, including: a sample preparation unit, a pyrolysis activation unit, a testing unit, and an analysis and control unit; the analysis and control unit calculates the pore morphology complexity and other pyrolysis data based on the porous carbon material pore morphology complexity characterization method of Embodiment 1, and uses a neural network model to generate optimized experimental conditions.

[0043] The first dataset collected includes elemental analysis of precursors (such as percentage content of carbon, hydrogen, oxygen, nitrogen, and sulfur), industrial analysis data (such as ash content, volatile matter content, and fixed carbon content), pyrolysis condition data (such as pre-acid washing, pre-oxidation, type of activator, activator ratio, heating rate, activation temperature, and activation time), and performance indicators (such as pore morphology complexity, specific surface area, pore volume, micropore ratio, mesopore ratio, and macropore ratio).

[0044] The collected data is used to train a neural network model to receive experimental representation data and generate a set of optimized experimental conditions.

[0045] The experimental characterization data are the performance indicators: Scanning Electron Microscopy (SEM) and Beta-Temperature Surface Area Test (BET). The SEM images were used to generate pore morphology complexity based on Example 1. The BET test data itself includes specific surface area, pore volume, micropore ratio, mesopore ratio, and macropore ratio.

[0046] Microscopic experimental conditions include: percentage content of carbon, hydrogen, oxygen, nitrogen, and sulfur; ash content, volatile matter content, and fixed carbon content; pre-acid washing; pre-oxidation; type and ratio of activator; heating rate; activation temperature; and activation time.

[0047] The automated porous carbon research system includes a sample preparation unit, a pyrolysis activation unit, a testing unit, and an analysis and control unit. The entire process is automated by robotic arms and conveyor belts.

[0048] like Figure 2 As shown, first, a set of experimental conditions are input into the system, and the robotic arm controls the automatic sample preparation in sample preparation unit 1. Then, the sample is transmitted to pyrolysis activation unit 2 for automatic pyrolysis experiment (based on the input experimental data), and then transmitted to test unit 3 for automatic measurement of characterization data such as specific surface area, pore volume, pore size distribution and SEM image.

[0049] The analysis and control unit 4 receives and processes the characterization data, including converting SEM images into pore morphology complexity data and using a trained neural network model to generate a set of optimized experimental conditions through reverse engineering, which are then fed back to the sample preparation unit. This process is repeated to continuously optimize sample performance. If the sample performance does not improve within 3-5 cycles, the loop stops, and the final experimental conditions are output.

[0050] Among them, the neural network model can be one of the following: Generative Adversarial Network (GAN), Physical Information Neural Network (PINN), Variational Autoencoder (VAE), etc.

[0051] Before operating the system, some coal samples, biomass and other precursor materials for preparing porous carbon are collected and elemental and industrial analyses are performed to obtain corresponding data.

[0052] The experimental conditions fed back from the analysis and control unit 4 to the sample preparation unit 1 include elemental analysis and industrial analysis data based on existing fixed data for coal types and biomass.

[0053] The controllable conditions in sample preparation unit 1 are the type of precursor, the pretreatment measures of the precursor (whether pre-acid washing or pre-oxidation is performed), the type of activator (sodium hydroxide, potassium hydroxide, carbon dioxide, water vapor, etc.), and the mixing ratio of activator and precursor.

[0054] The controllable conditions in pyrolysis activation unit 2 are heating rate, pyrolysis temperature, and pyrolysis time.

[0055] Test Unit 3 includes tests for specific surface area (BET) and scanning electron microscopy (SEM).

[0056] The analysis and control unit 4 receives and processes the test characterization data, uses the trained neural network model to reverse design and output optimized experimental conditions, and transmits them to each unit for the next round of the cycle.

[0057] Example 3 The purpose of this embodiment is to provide a system for characterizing the pore morphology complexity of porous carbon materials, including: The preprocessing module is configured to: acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with pore boundaries reaching the desired clarity; The first calculation module is configured to: determine the shape fractal dimension based on the boundary perimeter and area of ​​a single pore cross-sectional region, and determine the depth fractal dimension based on the deviation between the actual pore shape and the ideal circle of a single pore. The second calculation module is configured to determine the total fractal dimension used to represent the complexity of the pore morphology based on the shape fractal dimension and the depth fractal dimension.

[0058] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0059] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0060] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0061] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0062] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software units within the processor. The software units can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0063] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0064] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program units, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program units include routines, programs, libraries, objects, classes, components, data structures, etc., that perform a specific task or implement a specific abstract data type. In various embodiments, the functionality of program units can be combined or divided among program units as needed. The machine-executable instructions for the program units can execute within a local or distributed device. In a distributed device, the program units can reside in both local and remote storage media.

[0065] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0066] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0067] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for characterizing the pore morphology complexity of porous carbon materials, characterized in that, include: Acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with desired pore boundary clarity; The shape fractal dimension is determined based on the boundary perimeter and area of ​​a single pore cross-sectional region, and the depth fractal dimension is determined based on the deviation of the actual pore shape from the ideal circle. The total fractal dimension used to represent the complexity of pore morphology is determined based on the shape fractal dimension and the depth fractal dimension.

2. The method for characterizing the pore morphology complexity of porous carbon materials as described in claim 1, characterized in that, The fractal dimension of the shape is determined based on the boundary perimeter and area of ​​the pore cross-section region, specifically: in, The shape fractal dimension, It is a constant. The area of ​​a single hole; This is the circumference of a single hole.

3. The method for characterizing the pore morphology complexity of porous carbon materials as described in claim 1, characterized in that, The depth fractal dimension is determined based on the deviation of the actual pore shape from the ideal circle, specifically: in, Let be the distance from the i-th boundary point to the center; Let be the radius of the ideal equivalent circle; It is a weighted index; is the depth fractal dimension, representing the average degree of topographic undulation in the pore depth direction.

4. The method for characterizing the pore morphology complexity of porous carbon materials as described in claim 1, characterized in that, The total fractal dimension used to represent the complexity of pore morphology is determined based on the shape fractal dimension and the depth fractal dimension, specifically as follows: in, Let be the total fractal dimension. The depth fractal dimension; Let be the shape fractal dimension.

5. The method for characterizing the pore morphology complexity of porous carbon materials as described in claim 1, characterized in that, The preprocessing of porous carbon material images specifically includes: grayscale conversion, contrast enhancement, sharpening, edge enhancement, binarization, median filtering, and noise reduction.

6. The method for characterizing the pore morphology complexity of porous carbon materials as described in claim 5, characterized in that, The noise reduction operation excludes regions where the aperture shape factor is greater than the set aperture shape factor by setting a control aperture shape factor.

7. A porous carbon material preparation apparatus, employing the method for characterizing the pore morphology complexity of porous carbon materials as described in any one of claims 1-6, characterized in that, include: The sample preparation unit is used to prepare porous carbon material samples according to the input experimental conditions. The pyrolysis activation unit is used to perform pyrolysis experiments on porous carbon material samples prepared by the sample preparation unit. The testing unit is used to determine the characterization data of porous carbon material samples after pyrolysis experiments. The analysis and control unit is used to calculate the pore morphology complexity based on the quantitative characterization method of the porous carbon material pore morphology complexity and the characterization data of the porous carbon material sample, and to generate optimized experimental conditions based on the calculated pore morphology complexity of the porous carbon material sample using a trained neural network model, and to feed back the optimized experimental conditions to the sample preparation unit.

8. A system for characterizing the pore morphology complexity of porous carbon materials, characterized in that, include: The preprocessing module is configured to: acquire images of porous carbon materials and perform preprocessing to obtain images of porous carbon materials with pore boundaries reaching the desired clarity; The first calculation module is configured to: determine the shape fractal dimension based on the boundary perimeter and area of ​​a single pore cross-sectional region, and determine the depth fractal dimension based on the deviation between the actual pore shape and the ideal circle of a single pore. The second calculation module is configured to determine the total fractal dimension used to represent the complexity of the pore morphology based on the shape fractal dimension and the depth fractal dimension.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

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