A graphene thermal conductivity detection system and method based on data analysis

Abstract

The present invention belongs to the technical field of graphene thermal conductivity detection, and relates to a graphene thermal conductivity detection system and method based on data analysis. The system follows the principle that a graphene material that can still better exert thermal conductivity under various thermal conductivity influencing factors has better thermal conductivity, and accurately quantifies key parameters such as boundary effect changes, crystal orientation changes, heat flow path changes, and thermal conductivity weakening degree, thereby providing accurate data support for evaluating the comprehensive thermal conductivity of graphene materials. The system comprehensively and deeply evaluates the potential of the comprehensive thermal conductivity of target graphene materials under the influence of structural shape, structural defects, and base material thermal resistance, and not only considers the thermal conductivity performance of graphene materials under different conditions, but also deeply explores the essential characteristics of its thermal conductivity, providing a more direct and scientific basis for evaluating the thermal conductivity of graphene in different application scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of graphene thermal conductivity detection, and relates to a graphene thermal conductivity detection system and method based on data analysis. Background Art

[0002] Graphene, as a new type of material with extremely high thermal conductivity, has a unique lattice structure that allows heat to be quickly transferred within its structure, giving it broad application prospects in the field of heat dissipation materials.

[0003] However, the thermal conductivity of graphene is not constant, and it is affected by many factors, such as the mass fraction of graphene, dispersion state, and the choice of composite material matrix. Therefore, accurate and efficient detection of the thermal conductivity of graphene is of great significance for its promotion in practical applications.

[0004] In the prior art, graphene is usually prepared into a film for thermal conductivity testing. For example, the method for measuring thermal conductivity of graphene film disclosed in Chinese Patent Publication No. CN118425220A determines the linear expansion coefficient of the graphene film by measuring the size of the graphene film. The heating temperature of the graphene film is obtained, and the relationship between the heat flow rate and the thermal expansion coefficient of the graphene film before and after the heating temperature and the change relationship of the thermal expansion area of ​​the graphene film before and after the heating temperature are obtained according to Fourier's law and the heat flow principle. The thermal conductivity of the graphene film is calculated in this way, which helps to accurately measure the thermal conductivity of the graphene film at high temperature, thereby accurately evaluating the thermal conductivity of the graphene film.

[0005] For example, the Chinese patent publication number CN110887864A is a testing method for a graphene thermally conductive film. The method accurately tests the thermal conductivity of the graphene film and can distinguish the performance differences of graphene films of different thicknesses by sampling the samples to be tested, creating a testing environment, placing the samples to be tested, placing a heating block, placing a temperature measuring device, collecting temperature data, acquiring multiple sets of data, and analyzing the temperature data.

[0006] The prior art has derived several testing methods for the thermal conductivity of graphene, but all of them have limitations. Specifically, the prior art mostly analyzes the thermal conductivity of graphene by focusing on the thermal diffusion performance of graphene films in thermal conductivity tests. However, it ignores the limitations of different shapes, different defects, and different base materials on the potential of the thermal conductivity of graphene films, and thus cannot comprehensively and representatively analyze the thermal conductivity of graphene. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a graphene thermal conductivity detection system and method based on data analysis is proposed.

[0008] The technical solution adopted by the present invention to solve its technical problem is: in the first aspect, the present invention provides a graphene thermal conductivity detection system based on data analysis, including: a thin film sample preparation module, a first-level sample thermal conductivity test module, a second-level sample thermal conductivity test module, a third-level sample thermal conductivity test module, a thermal conductivity evaluation module and a cloud database.

[0009] The film sample preparation module is connected to the first-level sample thermal conductivity testing module, the first-level sample thermal conductivity testing module is connected to the second-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module is connected to the tertiary sample thermal conductivity testing module, the first-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module, and the tertiary sample thermal conductivity testing module are all connected to the thermal conductivity evaluation module, and the cloud database is respectively connected to the first-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module, and the tertiary sample thermal conductivity testing module.

[0010] The thin film sample preparation module is used to prepare various thin film sample sets using target graphene materials, including structure shape type thin film sample sets, structure defect type thin film sample sets and substrate material type thin film sample sets.

[0011] The first-level sample thermal conductivity test module is used to mark the samples of each shape in the structural shape film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes during the thermal conductivity test of each first-level sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of the structural shape.

[0012] The secondary sample thermal conductivity test module is used to mark the defective samples in the structural defect film sample set as secondary samples, monitor the changes in the heat flow path of each secondary sample during the thermal conductivity test, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of structural defects.

[0013] The three-level sample thermal conductivity test module is used to mark each base material sample in the base material film sample set as a three-level sample, monitor the change of base material thermal resistance during the thermal conductivity test of each three-level sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of base material thermal resistance.

[0014] The thermal conductivity evaluation module is used to evaluate the thermal conductivity of the target graphene material and provide feedback based on the comprehensive thermal conductivity of the target graphene material under the influence of structural shape, structural defects, and base material thermal resistance.

[0015] A cloud database is used to store the preset ideal thermal conductivity of the target graphene material.

[0016] In a second aspect, the present invention provides a method for detecting thermal conductivity of graphene based on data analysis, comprising: S1. using target graphene material to prepare various film sample sets, including structural shape film sample sets, structural defect film sample sets and substrate material film sample sets.

[0017] S2. Mark the samples of each shape in the structure-shaped thin film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes of each first-level sample during the thermal conductivity test, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape.

[0018] S3. Mark each defective sample in the structural defect film sample set as a secondary sample, monitor the change of the heat flow path of each secondary sample during the thermal conductivity test, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects.

[0019] S4. Mark each base material sample in the base material film sample set as a third-level sample, monitor the change of base material thermal resistance during the thermal conductivity test of each third-level sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the base material thermal resistance.

[0020] S5. Evaluate the thermal conductivity of the target graphene material and provide feedback based on the comprehensive thermal conductivity of the target graphene material affected by its structural shape, structural defects, and base material thermal resistance.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention evaluates the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape by examining the boundary effect coefficient and crystal orientation deviation coefficient of each shape in the structure-shaped thin film sample set during the thermal conductivity test, thereby helping to gain a deeper understanding of the thermal conductivity characteristics of the graphene material under different structural shape conditions, thereby providing more accurate performance parameters for its application.

[0022] (2) The present invention examines the degree of thermal conductivity weakening caused by changes in the heat flow path of each defect in the structural defect type film sample set during the thermal conductivity test, evaluates the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects, and accurately evaluates the degree of weakening of the thermal conductivity of graphene by different structural defects, which helps to deeply understand the impact of defects on the thermal conductivity mechanism of graphene, thereby providing a scientific basis for its performance optimization.

[0023] (3) The present invention examines the degree of thermal conductivity hindrance caused by the thermal resistance of the base material of each base material in the base material film sample set during the thermal conductivity test, evaluates the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the base material thermal resistance, and provides an effective method and tool for evaluating the comprehensive thermal conductivity of the target graphene material.

[0024] (4) The present invention follows the principle that graphene that can still perform well in thermal conductivity under the presence of various thermal conductivity influencing factors has better thermal conductivity. By analyzing the thermal conductivity performance of the target graphene material under the influence of factors such as different shapes, structural defects, and substrate materials, the thermal conductivity nature of graphene is more comprehensively and deeply explored, providing a more direct basis for evaluating the thermal conductivity of graphene in different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0026] Figure 1 It is a schematic diagram of module connection of the present invention.

[0027] Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the first aspect of the present invention provides a graphene thermal conductivity detection system based on data analysis, including: a thin film sample preparation module, a first-level sample thermal conductivity testing module, a second-level sample thermal conductivity testing module, a third-level sample thermal conductivity testing module, a thermal conductivity evaluation module and a cloud database.

[0030] The film sample preparation module is connected to the first-level sample thermal conductivity testing module, the first-level sample thermal conductivity testing module is connected to the second-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module is connected to the tertiary sample thermal conductivity testing module, the first-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module, and the tertiary sample thermal conductivity testing module are all connected to the thermal conductivity evaluation module, and the cloud database is respectively connected to the first-level sample thermal conductivity testing module, the second-level sample thermal conductivity testing module, and the tertiary sample thermal conductivity testing module.

[0031] The film sample preparation module is used to prepare various film sample sets using target graphene materials, including film sample sets of structural shapes, film sample sets of structural defects, and film sample sets of substrate materials.

[0032] Specifically, the application of target graphene material to prepare various types of thin film sample sets includes: using the target graphene material as a raw material to form a preset thickness film through chemical vapor deposition, transferring it to a temporary supporting substrate, laser cutting it into thin film samples of preset shapes with the same preset area, and then separating the substrates one by one to generate a structural shape type thin film sample set.

[0033] Exemplarily, the above-mentioned preset shapes include but are not limited to rectangle, rhombus, hexagon and pentagon, etc.

[0034] A shape is randomly selected from each preset shape as the preset shape, and a preset thickness film is made by chemical vapor deposition using the target graphene material as the raw material, which is then transferred to a temporary supporting substrate and laser cut into film samples with the same preset area and the same preset shape. After each preset structural defect is introduced into the film sample corresponding to the preset shape, the substrates are separated one by one to generate a set of structural defect film samples.

[0035] It should be noted that the above-mentioned method of introducing various preset structural defects into the thin film samples by ion beam etching is that each thin film sample with the same preset area and the same preset shape is placed in the vacuum chamber of the ion beam etching equipment, and the preset ion source is selected according to the ion beam operation specification manual, and the scanning path and scanning time of the ion beam are controlled by the preset ion energy and the preset ion beam current density, so as to introduce various preset structural defects on the graphene thin film sample.

[0036] Exemplarily, each preset structural defect includes but is not limited to a vacancy defect, an edge defect, a lattice defect, and the like.

[0037] Using the target graphene material as raw material, a thin film of preset thickness is generated by chemical vapor deposition, which is then transferred to a temporary supporting substrate. After being laser cut into thin film samples with the same preset area and the same preset shape, the substrates are separated one by one, and various types of substrate materials are introduced to attach to the corresponding thin film samples to generate a set of substrate material thin film samples.

[0038] Illustratively, the above-mentioned various types of substrate materials include but are not limited to glass, silicon, metal, etc.

[0039] The first-level sample thermal conductivity test module is used to mark samples of various shapes in the structural shape film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes during the thermal conductivity test of each first-level sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape.

[0040] Specifically, the evaluation of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape includes: placing each primary sample on a thermal conductivity tester platform, performing a thermal conductivity test on the sample to output the thermal conductivity coefficient of each primary sample. .

[0041] Exemplarily, the process of obtaining the thermal conductivity of each of the first-level samples is as follows: a laser flash method thermal conductivity meter is used to emit high-energy laser pulses to heat the front surface of each first-level sample, causing its temperature to rise rapidly, and a curve of the average temperature value of the back side of each first-level sample changing with time is recorded and plotted, from which the thermal diffusion coefficient of each first-level sample is determined, and the thermal conductivity of each first-level sample is obtained by multiplying the thermal diffusion coefficient according to the preset density and preset specific heat capacity of the target graphene material.

[0042] Taking the geometric center position of each first-level sample as the center of the circle, concentric circle areas are planned one by one with a preset radius to obtain each monitoring area of ​​each first-level sample. The temperature value of each monitoring area at each preset time point during the thermal conductivity test of each first-level sample is collected. Taking the direction of the sample center to the edge of the sample as the reference direction, the temperature gradient of each monitoring area at each preset time point during the thermal conductivity test of each first-level sample with respect to its adjacent external monitoring area is calculated. Each monitoring area of ​​each first-level sample is screened into a central area and an edge area. The average temperature gradients of the central area and the edge area at each preset time point during the thermal conductivity test of each first-level sample are sorted out and recorded as ,in is the number of each primary sample, , is the number of each preset time point, , according to the formula The boundary effect coefficient of each first-level sample during the thermal conductivity test is obtained, where The number of preset time points.

[0043] It should be noted that the specific process of screening each monitoring area of ​​each first-level sample into a central area and an edge area is as follows: sum up the number of monitoring areas of each first-level sample, take its integer divisor with 2 as the number of monitoring areas corresponding to the central area, and record it as the number of control monitoring areas of each first-level sample, so as to screen each monitoring area with the number of previous control monitoring areas closest to the geometric center of each first-level sample as the central area, and screen it and each monitoring area as the edge area.

[0044] Measure the average deviation angle between the internal crystal orientation of each first-level sample and the thermal conductivity direction of the thermal conductivity tester at each preset time point during the thermal conductivity test of each first-level sample , according to the formula The crystal orientation deviation coefficient of each first-level sample during the thermal conductivity test is obtained, where It is the preset rated deviation angle between the crystal orientation and the heat conduction direction.

[0045] It should be noted that the average deviation angle between the internal crystal orientation of each first-level sample at each preset time point in the above-mentioned measurement of the thermal conductivity performance test and the thermal conductivity direction of the thermal conductivity tester is obtained by measuring the internal crystal orientation of each first-level sample at each preset time point in the process of measuring the thermal conductivity performance test by an X-ray diffractometer, and calculating the average of the deviation angles between the internal crystal orientation and the thermal conductivity direction of the thermal conductivity tester.

[0046] Specifically, the evaluation of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape also includes: extracting the preset ideal thermal conductivity of the target graphene material stored in the cloud database , combined with the boundary effect coefficient and crystal orientation deviation coefficient of each first-level sample during the thermal conductivity test, analyze the reference reasonable thermal conductivity of each first-level sample , set the influence weight of each first-level sample corresponding to the preset shape , according to the formula Evaluate the potential of the comprehensive thermal conductivity of the target graphene material affected by its structural shape.

[0047] It should be noted that the basis for setting the influence weights of the preset shapes corresponding to the above-mentioned first-level samples is the influence mechanism of each preset shape on the thermal conductivity of the graphene structure. Specifically, the literature on comparative research on thermal conductivity of graphene structures of different shapes can be found. If the theoretical thermal conductivity of graphene under a certain preset shape structure is the best, then the preset shape is given the largest influence weight. Based on this, the influence weights of the preset shapes corresponding to the first-level samples are set, and the cumulative value of the influence weights of the preset shapes corresponding to the first-level samples is required to be 1.

[0048] It should also be noted that the calculation formula for the reference reasonable thermal conductivity of the above-mentioned first-level samples is:

[0049] in is a natural constant, for .

[0050] The embodiment of the present invention examines the boundary effect coefficient and crystal orientation deviation coefficient of samples of various shapes in a set of structural shape-like thin film samples during the thermal conductivity test, evaluates the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape, and helps to gain a deeper understanding of the thermal conductivity characteristics of the graphene material under different structural shape conditions, thereby providing more accurate performance parameters for its application.

[0051] The secondary sample thermal conductivity test module is used to mark each defective sample in the structural defect film sample set as a secondary sample, monitor the changes in the heat flow path during the thermal conductivity test of each secondary sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects.

[0052] Specifically, the evaluation of the comprehensive thermal conductivity potential of the target graphene material under the influence of structural defects includes: placing each secondary sample on a thermal conductivity tester platform and outputting the thermal conductivity coefficient of each secondary sample.

[0053] According to the preset shape of the secondary sample, the corresponding primary sample with the same preset shape is extracted as the reference sample, and the thermal conductivity coefficient output by the thermal conductivity test of the reference sample is subtracted from the thermal conductivity coefficient output by the thermal conductivity test of each secondary sample to obtain the deviation thermal conductivity coefficient of each secondary sample under the influence of its corresponding structural defect. ,in is the number of each secondary sample, .

[0054] Monitor the thermal imaging images at each preset time point during the thermal conductivity test of each secondary sample, identify the interference form of the structural defect area of ​​each secondary sample on the heat flow path, the interference form is blocking or detour, and evaluate the degree of thermal conductivity weakening of each secondary sample under the influence of structural defects , combined with the preset ideal thermal conductivity of the target graphene material, analyze the reference reasonable deviation thermal conductivity of each secondary sample under the influence of its corresponding structural defects .

[0055] It should be noted that the specific identification process of the interference form of the above-mentioned secondary sample structural defect area with respect to the heat flow path includes: comparing the temperature values ​​of the secondary sample structural defect area and its adjacent areas at the same time point, continuously collecting the absolute deviation temperature values ​​of each unit time point within the reference time length of each secondary sample structural defect area and its adjacent areas, if the absolute deviation temperature value of a unit time point is greater than the preset deviation temperature threshold, then the unit time point is marked as a temperature mutation time point, and the proportion of the temperature mutation time point is counted. If it is greater than the preset value, then the interference form of the secondary sample structural defect area with respect to the heat flow path is identified as blocking, otherwise it is identified as detour.

[0056] It should also be noted that the calculation formula for the reference reasonable deviation thermal conductivity of the above-mentioned secondary samples under the influence of their corresponding structural defects is: .

[0057] Set the influence weight of each secondary sample corresponding to the structural defects , according to the formula

[0058] Evaluate the potential of comprehensive thermal conductivity of target graphene materials affected by structural defects.

[0059] Specifically, the evaluation of the degree of weakening of thermal conductivity of each secondary sample due to structural defects includes: if it is identified that the interference form of the heat flow path in the structural defect area of ​​a secondary sample is blocking, then the ratio of the area of ​​the structural defect area of ​​the secondary sample to the preset area of ​​the sample is used as the degree of weakening of thermal conductivity of the secondary sample due to the structural defects.

[0060] If the interference form of the heat flow path in the structural defect area of ​​a secondary sample is identified as circuitous, the length of the circuitous path of the heat flow in the structural defect area of ​​the secondary sample is collected, and its ratio to the shortest heat flow path length in a single direction of the secondary sample is used as the degree of thermal conductivity weakening of the secondary sample under the influence of structural defects.

[0061] This is used to evaluate the degree of thermal conductivity reduction of each secondary sample due to structural defects.

[0062] The embodiments of the present invention examine the degree of thermal conductivity weakening caused by changes in the heat flow path of each defect-reflecting sample in a structural defect type film sample set during the thermal conductivity test, evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects, and accurately evaluate the degree of weakening of the thermal conductivity of graphene by different structural defects, which helps to deeply understand the impact of defects on the thermal conductivity mechanism of graphene, thereby providing a scientific basis for its performance optimization.

[0063] The three-level sample thermal conductivity test module is used to mark each base material sample in the base material film sample set as a three-level sample, monitor the change of the base material thermal resistance during the thermal conductivity test of each three-level sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the base material thermal resistance.

[0064] Specifically, the evaluation of the comprehensive thermal conductivity potential of the target graphene material under the influence of the base material thermal resistance includes: placing each of the three-level samples on a thermal conductivity tester platform and outputting the thermal conductivity coefficient of each of the three-level samples.

[0065] Consistent with the method for obtaining the deviation thermal conductivity of each secondary sample under the influence of its corresponding structural defects, the deviation thermal conductivity of each tertiary sample under the influence of its corresponding base material thermal resistance is obtained.

[0066] Monitor the temperature gradient in the vertical direction between the sample and its corresponding base material at each preset time point during the thermal conductivity test of each three-level sample , is the number of each third-level sample, , evaluate the degree of thermal conductivity hindrance of each third-level sample under the influence of the thermal resistance of the base material, combine with the preset ideal thermal conductivity of the target graphene material, and analyze the reference reasonable deviation thermal conductivity of each third-level sample under the influence of its corresponding base material thermal resistance.

[0067] It should be noted that the calculation process of the reference reasonable deviation thermal conductivity of the above-mentioned three-level samples under the influence of their corresponding base material thermal resistance is consistent with the calculation process of the reference reasonable deviation thermal conductivity of each secondary sample under the influence of its corresponding structural defects.

[0068] The influence weight of the corresponding base material is set for each third-level sample, which is consistent with the analysis method of the comprehensive thermal conductivity potential of the target graphene material under the influence of structural defects, and the comprehensive thermal conductivity potential of the target graphene material under the influence of the base material thermal resistance is evaluated. .

[0069] It should be noted that the principles for setting the influence weights of the structural defects of the above-mentioned secondary samples and the principles for setting the influence weights of the substrate materials of the tertiary samples are consistent with the principles for setting the influence weights of the preset shapes of the primary samples, and depend on the influence mechanisms of the structural defects and the substrate materials on the thermal conductivity of the graphene structure.

[0070] Specifically, the calculation formula for evaluating the degree of thermal conductivity hindrance of each of the three-level samples under the influence of the base material thermal resistance is: ,in Preset reasonable temperature gradient in the vertical direction between graphene sample and substrate material. is a preset constant.

[0071] The embodiment of the present invention examines the degree of thermal conductivity hindrance caused by the thermal resistance of the base material of each base material in the base material film sample set during the thermal conductivity test, evaluates the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the thermal resistance of the base material, and provides an effective method and tool for evaluating the comprehensive thermal conductivity of the target graphene material.

[0072] The thermal conductivity evaluation module is used to evaluate the thermal conductivity of the target graphene material and provide feedback based on the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural shape, structural defects, and base material thermal resistance.

[0073] The evaluation of the thermal conductivity of the target graphene material includes: accumulating the product of the comprehensive thermal conductivity potential of the target graphene material under the influence of structural shape, structural defects, and base material thermal resistance and the corresponding preset weights, and using the accumulated value as the thermal conductivity evaluation index of the target graphene material.

[0074] The embodiments of the present invention follow the principle that the graphene that can still better exert its thermal conductivity in the presence of various thermal conductivity influencing factors has a more excellent thermal conductivity. By analyzing the thermal conductivity performance of the target graphene material under the influence of factors such as different shapes, structural defects, and substrate materials, the thermal conductivity nature of graphene is more comprehensively and deeply explored, providing a more direct basis for evaluating the thermal conductivity of graphene in different application scenarios.

[0075] The cloud database is used to store the preset ideal thermal conductivity of the target graphene material.

[0076] Reference Figure 2 As shown, the second aspect of the present invention provides a method for detecting thermal conductivity of graphene based on data analysis, including: S1. using target graphene material to prepare various film sample sets, including structural shape type film sample sets, structural defect type film sample sets and substrate material type film sample sets.

[0077] S2. Mark the samples of each shape in the structure-shaped thin film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes of each first-level sample during the thermal conductivity test, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape.

[0078] S3. Mark each defective sample in the structural defect film sample set as a secondary sample, monitor the change of the heat flow path of each secondary sample during the thermal conductivity test, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects.

[0079] S4. Mark each base material sample in the base material film sample set as a third-level sample, monitor the change of base material thermal resistance during the thermal conductivity test of each third-level sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the base material thermal resistance.

[0080] S5. Evaluate the thermal conductivity of the target graphene material and provide feedback based on the comprehensive thermal conductivity of the target graphene material affected by its structural shape, structural defects, and base material thermal resistance.

[0081] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A graphene thermal conductivity detection system based on data analysis, characterized in that: The system includes: The film sample preparation module is used to prepare various film sample sets using the target graphene material, including film sample sets of structural shapes, film sample sets of structural defects, and film sample sets of substrate materials; The first-level sample thermal conductivity test module is used to mark the samples of each shape in the structure-shaped thin film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes during the thermal conductivity test of each first-level sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of the structural shape; The secondary sample thermal conductivity test module is used to mark each defective sample in the structural defect film sample set as a secondary sample, monitor the changes in the heat flow path during the thermal conductivity test of each secondary sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of structural defects; The three-level sample thermal conductivity test module is used to mark each base material sample in the base material film sample set as a three-level sample, monitor the change of base material thermal resistance during the thermal conductivity test of each three-level sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of base material thermal resistance; Thermal conductivity evaluation module, used to evaluate the thermal conductivity of the target graphene material and provide feedback based on the comprehensive thermal conductivity of the target graphene material under the influence of structural shape, structural defects, and base material thermal resistance; A cloud database for storing preset ideal thermal conductivity of target graphene materials; The evaluation of the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape includes: placing each first-level sample on a thermal conductivity tester platform, performing a thermal conductivity test on the first-level sample to output the thermal conductivity coefficient of each first-level sample. ; Taking the geometric center position of each first-level sample as the center of the circle, concentric circle areas are planned one by one with a preset radius to obtain each monitoring area of ​​each first-level sample. The temperature value of each monitoring area at each preset time point during the thermal conductivity test of each first-level sample is collected. Taking the direction of the sample center to the edge of the sample as the reference direction, the temperature gradient of each monitoring area at each preset time point during the thermal conductivity test of each first-level sample with respect to its adjacent external monitoring area is calculated. Each monitoring area of ​​each first-level sample is screened into a central area and an edge area. The average temperature gradients of the central area and the edge area at each preset time point during the thermal conductivity test of each first-level sample are sorted out and recorded as ,in is the number of each primary sample, , is the number of each preset time point, , according to the formula The boundary effect coefficient of each first-level sample during the thermal conductivity test is obtained, where is the number of preset time points; Measure the average deviation angle between the internal crystal orientation of each first-level sample and the thermal conductivity direction of the thermal conductivity tester at each preset time point during the thermal conductivity test of each first-level sample , according to the formula The crystal orientation deviation coefficient of each first-level sample during the thermal conductivity test is obtained, where It is the preset rated deviation angle between the crystal orientation and the heat conduction direction; The method of evaluating the comprehensive thermal conductivity of the target graphene material under the influence of the structural shape also includes: extracting the preset ideal thermal conductivity of the target graphene material stored in the cloud database, combining the boundary effect coefficient and the crystal orientation deviation coefficient in the thermal conductivity test process of each first-level sample, and analyzing the reference reasonable thermal conductivity of each first-level sample. , set the influence weight of each first-level sample corresponding to the preset shape , according to the formula Evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of its structural shape; The method of evaluating the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects includes: placing each secondary sample on a thermal conductivity tester platform and outputting the thermal conductivity coefficient of each secondary sample; According to the preset shape of the secondary sample, the corresponding primary sample with the same preset shape is extracted as the reference sample, and the thermal conductivity coefficient output by the thermal conductivity test of the reference sample is subtracted from the thermal conductivity coefficient output by the thermal conductivity test of each secondary sample to obtain the deviation thermal conductivity coefficient of each secondary sample under the influence of its corresponding structural defect. ,in is the number of each secondary sample, ; Monitor the thermal imaging images at each preset time point during the thermal conductivity test of each secondary sample, identify the interference form of the structural defect area of ​​each secondary sample on the heat flow path, the interference form is blocking or detour, evaluate the degree of thermal conductivity weakening of each secondary sample under the influence of structural defects, and analyze the reference reasonable deviation thermal conductivity of each secondary sample under the influence of its corresponding structural defects in combination with the preset ideal thermal conductivity of the target graphene material ; Set the influence weight of each secondary sample corresponding to the structural defects , according to the formula Evaluate the potential of comprehensive thermal conductivity of target graphene materials affected by structural defects; The method of evaluating the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the thermal resistance of the base material includes: placing each of the three-level samples on a thermal conductivity tester platform, and outputting the thermal conductivity coefficient of each of the three-level samples; The deviation thermal conductivity of each tertiary sample under the influence of its corresponding base material thermal resistance is obtained in the same manner as the method for obtaining the deviation thermal conductivity of each secondary sample under the influence of its corresponding structural defects; Monitor the temperature gradient in the vertical direction between the sample and its corresponding base material at each preset time point during the thermal conductivity test of each three-level sample , is the number of each third-level sample, , evaluate the degree of thermal conductivity obstruction of each third-level sample under the influence of the thermal resistance of the base material, and analyze the reference reasonable deviation thermal conductivity of each third-level sample under the influence of the thermal resistance of its corresponding base material in combination with the preset ideal thermal conductivity of the target graphene material; The influence weight of the corresponding base material is set for each third-level sample, which is consistent with the analysis method of the comprehensive thermal conductivity potential of the target graphene material under the influence of structural defects, and the comprehensive thermal conductivity potential of the target graphene material under the influence of the base material thermal resistance is evaluated. .

2. A graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The method of using the target graphene material to prepare various film sample sets includes: using the target graphene material as a raw material to form a film of preset thickness by chemical vapor deposition, transferring it to a temporary supporting substrate, cutting it into film samples of preset shapes with the same preset area by laser, and then separating them from the substrates one by one to generate a film sample set of structural shapes; A shape is randomly selected from each preset shape as a preset shape, a target graphene material is used as a raw material to form a preset thickness film through chemical vapor deposition, and the film is transferred to a temporary support substrate, and is cut into film samples with the same preset area and the same preset shape by laser, and each preset structural defect is introduced into the film sample corresponding to the preset shape and then separated from the substrate one by one to generate a set of film samples with structural defects; Using the target graphene material as raw material, a thin film of preset thickness is generated by chemical vapor deposition, which is then transferred to a temporary supporting substrate. After being laser cut into thin film samples with the same preset area and the same preset shape, the substrates are separated one by one, and various types of substrate materials are introduced to attach to the corresponding thin film samples to generate a set of substrate material thin film samples.

3. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The evaluation of the degree of thermal conductivity weakening of each secondary sample under the influence of the structural defects includes: if the interference form of the heat flow path of the structural defect area of ​​a secondary sample is identified as blocking, then the ratio of the area of ​​the structural defect area of ​​the secondary sample to the preset area of ​​the sample is used as the degree of thermal conductivity weakening of the secondary sample under the influence of the structural defects; If the interference form of the heat flow path in the structural defect area of ​​a secondary sample is identified as a circuitous path, the length of the circuitous path of the heat flow in the structural defect area of ​​the secondary sample is collected, and the ratio of the length of the circuitous path to the shortest heat flow path length in a single direction of the secondary sample is used as the degree of thermal conductivity weakening of the secondary sample under the influence of the structural defect; This is used to evaluate the degree of thermal conductivity reduction of each secondary sample due to structural defects.

4. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The calculation formula for evaluating the degree of thermal conductivity hindrance of each third-level sample under the influence of the base material thermal resistance is: ,in Preset reasonable temperature gradient in the vertical direction between graphene sample and substrate material. is a preset constant.

5. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The evaluation of the thermal conductivity of the target graphene material includes: accumulating the product of the comprehensive thermal conductivity potential of the target graphene material under the influence of structural shape, structural defects, and base material thermal resistance and the corresponding preset weights, and using the accumulated value as the thermal conductivity evaluation index of the target graphene material.

6. A method for detecting thermal conductivity of graphene based on data analysis, performed by a system as claimed in any one of claims 1 to 5, characterized in that: The method includes: S1. Use the target graphene material to prepare various film sample sets, including structure shape film sample sets, structure defect film sample sets and substrate material film sample sets; S2. Mark the samples of each shape in the structure-shaped thin film sample set as first-level samples, monitor the boundary effect changes and crystal orientation changes of each first-level sample during the thermal conductivity test, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of the structure shape; S3. Mark each defective sample in the structural defect film sample set as a secondary sample, monitor the change of the heat flow path during the thermal conductivity test of each secondary sample, and evaluate the potential of the comprehensive thermal conductivity of the target graphene material under the influence of structural defects; S4. Mark each base material sample in the base material film sample set as a third-level sample, monitor the change of base material thermal resistance during the thermal conductivity test of each third-level sample, and evaluate the comprehensive thermal conductivity potential of the target graphene material under the influence of base material thermal resistance; S5. Evaluate the thermal conductivity of the target graphene material and provide feedback based on the comprehensive thermal conductivity of the target graphene material affected by its structural shape, structural defects, and base material thermal resistance.

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