Method and equipment for measuring performance characterization parameters of bismuth telluride-based thermoelectric material

Abstract

The invention relates to the technical field of thermoelectric material performance testing, and provides a method and equipment for measuring performance characterization parameters of a bismuth telluride-based thermoelectric material.The method comprises the steps that firstly, statistical analysis is conducted on geometrical parameters of a material sample, and the distribution proportion of different geometrical shapes is determined; then selecting test samples from the material database to construct a test sample set, and ensuring that the distribution proportion of different geometric forms in the sample set is consistent with a statistical analysis result; and generating a thermoelectric parameter measurement instruction based on the test sample set, carrying out performance test on the material by using the instruction, and collecting data through the test process to finally obtain the performance parameter data of the bismuth telluride-based thermoelectric material. According to the invention, the accuracy, stability and test efficiency of performance parameter measurement of the bismuth telluride-based thermoelectric material can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermoelectric material performance testing, and more specifically, to a method and device for measuring performance characterization parameters of bismuth telluride-based thermoelectric materials. Background Art

[0002] In today's energy utilization and conversion field, thermoelectric materials, as functional materials that can achieve direct conversion of heat energy into electrical energy, have important application prospects. Among them, bismuth telluride-based thermoelectric materials are widely used in fields such as thermoelectric power generation and refrigeration due to their excellent thermoelectric properties, good stability, and relatively low toxicity. In practical applications, it is crucial to accurately evaluate the performance parameters of bismuth telluride-based thermoelectric materials. These parameters include the Seebeck coefficient, thermal conductivity, and electrical conductivity, which directly determine the material's thermoelectric conversion efficiency and application effect.

[0003] Traditional thermoelectric material performance testing methods typically rely on testing samples of a single geometric form. While this method can provide certain performance parameter information, it cannot fully reflect the performance distribution of the material under different geometric forms. In addition, existing testing methods are often relatively fixed in the choice of test temperature gradients, making it difficult to flexibly adjust them according to the specific needs of the target application scenario, resulting in limited applicability of the test results. At the same time, data collection and analysis during the test process also have certain limitations. For example, the statistics on the stability time of parameters are not accurate enough, making it impossible to effectively evaluate the stability of material performance.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing testing methods cannot fully reflect the performance distribution of bismuth telluride-based thermoelectric materials in different geometric forms, the setting of the test temperature gradient lacks flexibility, and the statistical analysis of parameter stabilization time and performance stability is not accurate enough, which makes it difficult to meet the high-precision requirements for thermoelectric material performance evaluation in practical applications. Summary of the Invention

[0005] The present invention provides a method and device for measuring performance characterization parameters of bismuth telluride-based thermoelectric materials.

[0006] In a first aspect of the present invention, a method for measuring performance characterization parameters of a bismuth telluride-based thermoelectric material is provided, comprising: performing statistical analysis on geometric parameters of bismuth telluride-based thermoelectric material samples to determine the distribution ratio of samples with different geometric shapes; selecting test samples from a material database, and constructing a test sample set using the test samples; wherein the distribution ratio of test samples with different geometric shapes in the test sample set is the distribution ratio; generating a thermoelectric parameter measurement instruction based on the test sample set, performing a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, and obtaining performance parameter data of the bismuth telluride-based thermoelectric material based on data collected during the test process.

[0007] Furthermore, before performing a performance test on the bismuth telluride-based thermoelectric material based on the test sample set, the method also includes: calculating an effective test interval based on a heat flux density range of a target application scenario and a pre-set temperature gradient range, and determining a test temperature gradient based on the material operating temperature range and the effective test interval; configuring test equipment parameters based on the test temperature gradient and the number of test samples in the test sample set; generating a thermoelectric parameter measurement instruction based on the test sample set, and performing a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, including: initializing a multi-channel test system based on the test equipment parameters; using each test channel to synchronously load the test samples in the test sample set and generate a thermoelectric parameter measurement instruction, and sending the thermoelectric parameter measurement instruction to the multi-channel test system.

[0008] Furthermore, generating a thermoelectric parameter measurement instruction based on the test sample set includes: performing geometric calibration processing on the test samples in the test sample set according to a standard test protocol to obtain calibrated test samples; and generating a thermoelectric parameter measurement instruction using the calibrated test samples.

[0009] Furthermore, before generating the thermoelectric parameter measurement instruction based on the test sample set, the method further includes: performing an oxide layer removal process on the test sample according to a surface treatment specification.

[0010] Furthermore, the data collected according to the test process to obtain the performance parameter data of the bismuth telluride-based thermoelectric material includes: recording the gradient application time when the temperature gradient is applied; recording the parameter stabilization time when the stable thermoelectric parameter data is collected; calculating the parameter stabilization time according to the corresponding gradient application time and parameter stabilization time; and statistically analyzing the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material.

[0011] Furthermore, the recording and collecting of the parameter stabilization moment of the stable thermoelectric parameter data includes: recording and collecting the first steady-state moment when the Seebeck coefficient first reaches the steady-state value; and / or recording and collecting the final steady-state moment when the thermal conductivity is completely converged; the determining of the corresponding type of parameter stabilization time according to the corresponding gradient application moment and the parameter stabilization moment includes: determining the Seebeck coefficient stabilization time according to the corresponding gradient application moment and the first steady-state moment; and / or determining the thermal conductivity stabilization time according to the corresponding gradient application moment and the final steady-state moment; the statistical analysis of the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material includes: statistical analysis of the Seebeck coefficient stabilization time of all test samples to obtain corresponding Seebeck coefficient index data; and / or statistical analysis of the thermal conductivity stabilization time of all test samples to obtain index data corresponding to the thermal conductivity stabilization time.

[0012] Furthermore, it also includes: obtaining benchmark thermoelectric parameter data corresponding to each test sample, and counting the standard Seebeck coefficient value contained in the benchmark thermoelectric parameter data; the benchmark thermoelectric parameter data is thermoelectric parameter data obtained by measuring the test sample under a standard test environment; obtaining the actual Seebeck coefficient value measured by the multi-channel test system during the performance test; calculating the parameter deviation based on the standard Seebeck coefficient value and the actual Seebeck coefficient value corresponding to each test sample; performing statistical analysis on the parameter deviation of all test samples to obtain corresponding performance stability index data.

[0013] Furthermore, it also includes: obtaining ideal thermoelectric parameter data corresponding to each test sample, and counting the theoretical thermal conductivity value contained in the ideal thermoelectric parameter data, wherein the ideal thermoelectric parameter data is theoretical thermoelectric parameter data obtained based on material component simulation calculation; obtaining the actual thermal conductivity value measured by the multi-channel test system during the performance test; calculating the thermal conductivity error rate based on the theoretical thermal conductivity value and the actual thermal conductivity value corresponding to each test sample; and counting the thermal conductivity error rate of all test samples to obtain thermal conductivity consistency index data.

[0014] In a second aspect of the present invention, a performance characterization parameter measurement device for a bismuth telluride-based thermoelectric material is provided, comprising: a geometric analysis unit for performing statistical analysis on the geometric parameters of a bismuth telluride-based thermoelectric material sample to determine the distribution ratio of samples with different geometric shapes; a sample set construction unit for selecting test samples from a material database and constructing a test sample set using the test samples; wherein the distribution ratio of test samples with different geometric shapes in the test sample set is the distribution ratio of samples with corresponding geometric shapes; a test execution unit for generating a thermoelectric parameter measurement instruction based on the test sample set, performing a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, and obtaining performance parameter data of the bismuth telluride-based thermoelectric material based on data collected during the test process.

[0015] In a third aspect of the present invention, a material testing device is provided, characterized in that it includes a controller and a memory, the memory is used to store a test program; when the test program is loaded by the controller, the controller executes the performance characterization parameter measurement method of bismuth telluride-based thermoelectric material described in any one of the first aspects.

[0016] According to the above-mentioned embodiment of the present invention, there are at least the following beneficial effects: the method and equipment for measuring the performance characterization parameters of bismuth telluride-based thermoelectric materials of the present invention can realize a comprehensive performance evaluation of samples of different geometric forms. By statistically analyzing the geometric parameters of bismuth telluride-based thermoelectric material samples and constructing a corresponding test sample set, it is possible to ensure that the distribution ratio of the test samples is consistent with the distribution of the actual samples, thereby more accurately reflecting the performance characteristics of the material under different geometric forms. In addition, the effective test interval is calculated according to the heat flux density range of the target application scenario and the pre-set temperature gradient range, and the test temperature gradient is determined accordingly, so that the test conditions can be flexibly adjusted to make the test results more targeted and applicable. At the same time, the precise statistics of the parameter stabilization time and the calculation of the performance stability index data can effectively evaluate the stability and reliability of the material in actual applications.

[0017] The present invention can also achieve efficient and rapid performance testing by synchronously loading test samples and generating thermoelectric parameter measurement instructions through a multi-channel test system. Geometric calibration and oxide layer removal of the test samples can further improve the accuracy and reliability of the test. In addition, by comparing the standard Seebeck coefficient value with the actual measured value to calculate the parameter deviation, and comparing the theoretical thermal conductivity value with the actual measured value to calculate the thermal conductivity error rate, the stability and consistency of the material performance can be more comprehensively evaluated, providing strong technical support for the research and development, optimization and application of bismuth telluride-based thermoelectric materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation.

[0019] in: Figure 1 A schematic flow chart of a method for measuring performance characterization parameters of bismuth telluride-based thermoelectric materials provided in one embodiment of the present invention.

[0020] Figure 2 A schematic diagram of the structure of a performance characterization parameter measurement device for bismuth telluride-based thermoelectric materials provided in one embodiment of the present invention.

[0021] Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0023] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0024] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0025] Reference below Figure 1 , Figure 1 Schematic diagram of a method for measuring the performance characterization parameters of bismuth telluride-based thermoelectric materials according to an embodiment of the present invention. Figure 1 As shown, a method for measuring performance characterization parameters of bismuth telluride-based thermoelectric materials includes: S1, performing statistical analysis on geometric parameters of bismuth telluride-based thermoelectric material samples to determine the distribution ratio of samples with different geometric shapes.

[0026] S2. Select test samples from a material database, and construct a test sample set using the test samples; wherein the distribution ratio of the test samples with different geometric shapes in the test sample set is the distribution ratio.

[0027] S3. Generate a thermoelectric parameter measurement instruction based on the test sample set, perform a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, and obtain performance parameter data of the bismuth telluride-based thermoelectric material by collecting data according to the test process.

[0028] It should be noted that when statistically analyzing the geometric parameters of bismuth telluride-based thermoelectric material samples, geometric parameters refer to physical characteristics such as sample shape, size, and surface-to-volume ratio, which significantly influence the material's thermoelectric performance. For example, sample shape can include block, sheet, or column, while size refers to specific values such as length, width, and thickness. Statistical analysis can determine the distribution ratio of samples with different geometric shapes within the overall sample, that is, the proportion of samples of each shape and size. This distribution ratio provides a basis for the subsequent construction of a test sample set, ensuring that the test sample set truly reflects the diversity of materials in practical applications. The test sample set is selected from a materials database, which stores information on a large number of bismuth telluride-based thermoelectric material samples, including their composition, preparation process, geometric parameters, and known performance data. By selecting representative test samples from the database and constructing a test sample set based on the statistically determined distribution ratio, an accurate sample base is provided for subsequent performance testing. Performance testing is performed by generating thermoelectric parameter measurement instructions based on the test sample set. Thermoelectric parameter measurement instructions are a series of instructions used to control the operation of the test equipment and collect data. These instructions are used to perform performance tests on bismuth telluride-based thermoelectric materials, and the performance parameter data of the materials are obtained based on the data collected during the test. These data include key thermoelectric performance indicators such as the Seebeck coefficient, thermal conductivity, and electrical conductivity, thereby comprehensively evaluating the performance of the materials.

[0029] Specifically, the statistical analysis of geometric parameters involves measuring and analyzing dimensional parameters such as length, width, and thickness, as well as shape characteristics such as block, flake, and columnar, of bismuth telluride-based thermoelectric material samples. The distribution ratio refers to the proportion of samples of each geometric shape within the statistically analyzed samples. For example, block samples account for 30% of the total sample size, flake samples account for 40%, and columnar samples account for 30%. The test sample set is constructed by selecting the corresponding number and geometric shapes of test samples from the materials database based on the statistically determined distribution ratio. The test samples in the materials database contain detailed sample information, such as sample composition (e.g., bismuth telluride purity, doping elements, etc.), preparation process (e.g., sintering temperature and sintering time), and known performance data, such as the range of the initially measured Seebeck coefficient. Thermoelectric parameter measurement instructions are generated based on the characteristics of the test sample set. These instructions set the test equipment parameters based on the test sample geometry and expected test conditions, such as temperature range and current. The collection of performance parameter data is the data related to thermoelectric performance obtained through sensors and data acquisition systems during the test process. After processing, these data can obtain specific parameter values such as Seebeck coefficient, thermal conductivity, and electrical conductivity, thereby comprehensively evaluating the performance of bismuth telluride-based thermoelectric materials.

[0030] Preferably, to further improve the accuracy and reliability of the test, when constructing the test sample set, samples close to the actual usage conditions can be selected based on the application scenarios of bismuth telluride-based thermoelectric materials. For example, in refrigeration applications, samples with high electrical conductivity and low thermal conductivity are preferred; in power generation applications, samples with a high Seebeck coefficient are preferred. When generating thermoelectric parameter measurement instructions, the temperature gradient, current density and other parameters of the test equipment can be set according to the specific geometric parameters of the test sample and the expected performance indicators. For example, for sheet samples, a smaller temperature gradient can be set to simulate the temperature difference conditions in actual applications; for block samples, the current density can be appropriately increased to test its performance under high load conditions. During data processing, a filtering algorithm can be used to remove noise from the collected data to improve data accuracy. For the calculation of the Seebeck coefficient, it can be calculated by measuring the temperature difference at both ends of the sample and the resulting voltage difference, and according to the definition of the Seebeck coefficient, the ratio of the voltage difference to the temperature difference. Thermal conductivity can be calculated by measuring the sample's heat flux, temperature difference, and sample size, and applying Fourier's law to the relationship between heat flux, temperature difference, and sample size. These specific processing steps and calculation methods ensure the accuracy and reliability of test results, providing strong support for the performance evaluation of bismuth telluride-based thermoelectric materials.

[0031] In some embodiments, before performing a performance test on the bismuth telluride-based thermoelectric material based on the test sample set, the method further includes: calculating an effective test interval based on a heat flux density range of a target application scenario and a pre-set temperature gradient range, and determining a test temperature gradient based on the material operating temperature range and the effective test interval; configuring test equipment parameters based on the test temperature gradient and the number of test samples in the test sample set; generating a thermoelectric parameter measurement instruction based on the test sample set, and performing a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, including: initializing a multi-channel test system based on the test equipment parameters; using each test channel to synchronously load the test samples in the test sample set and generate a thermoelectric parameter measurement instruction, and sending the thermoelectric parameter measurement instruction to the multi-channel test system.

[0032] It should be noted that before conducting performance tests on bismuth telluride-based thermoelectric materials, it is necessary to calculate the effective test interval based on the heat flux range of the target application scenario and the pre-set temperature gradient range. The target application scenario here refers to the environmental conditions when the thermoelectric material is actually used, such as the specific working conditions in refrigeration or power generation equipment. Heat flux refers to the amount of heat passing through a unit area per unit time, while the temperature gradient refers to the temperature change per unit length. By calculating the effective test interval, it is possible to determine the temperature range within which the test can most accurately reflect the performance of the material in actual applications. Next, the test temperature gradient is determined based on the operating temperature range of the material and the effective test interval. This step is to ensure that the test conditions match the actual working conditions of the material. Finally, the test equipment parameters are configured based on the test temperature gradient and the number of test samples in the test sample set. This includes setting parameters such as the temperature controller and current source of the test equipment to ensure the accuracy and reliability of the test. A multi-channel test system is initialized based on the test equipment parameters. A multi-channel test system refers to a device that can measure multiple test samples at the same time. Test samples in the test sample set are synchronously loaded through each test channel and thermoelectric parameter measurement instructions are generated, and thermoelectric parameter measurement instructions are sent to the multi-channel test system, thereby realizing performance testing of bismuth telluride-based thermoelectric materials.

[0033] Specifically, the heat flux density range for the target application scenario can be determined based on the design parameters of the actual equipment used. For example, in refrigeration equipment, the heat flux density is between 100-500 W / m², while in power generation equipment, the heat flux density is between 500-1000 W / m². The pre-set temperature gradient range refers to the expected temperature variation applied to the sample during the test, for example, it can be set to 10-50 K / m². The effective test range refers to the temperature range within these two parameters that effectively reflects the material performance, for example, the calculated result is 20-40 K / m². The material's operating temperature range refers to the temperature range within which the bismuth telluride-based thermoelectric material can operate normally, for example, -20°C to +100°C. The test temperature gradient refers to the rate of temperature change applied to the sample during the actual test. It needs to be determined based on the effective test range and the material's operating temperature range, for example, it can be set to 25 K / m². Test equipment parameters include the set temperature of the temperature controller and the current level of the current source. These parameters need to be configured based on the test temperature gradient and the number of test samples. Initializing a multi-channel test system involves setting the parameters of each channel so that it can operate according to preset test conditions. The number of test samples in a test sample set refers to the total number of samples involved in the test, which could be 10 samples, for example. Thermoelectric parameter measurement instructions control the operation of the test system and contain information about test parameters such as temperature and current, guiding the operation of the test system.

[0034] Preferably, when calculating the effective test range, the specific operating conditions of the target application scenario can be considered. For example, for a thermoelectric material used for power generation from waste heat from automobile exhaust, its heat flux range can be calculated based on the temperature and flow rate of the automobile exhaust. Assuming the exhaust temperature is 300°C and the flow rate is 10 m³ / s, thermodynamic calculations can yield a heat flux range of 800-1200 W / m². The effective test range can be determined by modeling and analyzing the relationship between the material's thermoelectric properties and the temperature gradient. For example, a mathematical model based on the material's physical properties can be established, and parameters such as the material's composition and structure can be input. Through simulation calculations, the material's performance change curve under different temperature gradients can be obtained, thereby determining that the effective test range within the heat flux range is 30-60 K / m. When determining the test temperature gradient, the material's operating temperature range and the effective test range can be combined to select a temperature gradient value that covers the material's main operating temperature range and is within the effective test range, such as 40 K / m. When configuring the test equipment parameters, the temperature controller's heating rate and the current source's current can be set based on the test temperature gradient and the number of test samples. For example, for 10 test samples, the temperature controller's heating rate is set to 2°C / min and the current source's current is set to 1 A. When initializing a multi-channel test system, each channel can be calibrated individually to ensure consistent test conditions for each channel. For example, by calibrating the sensor's sensitivity and accuracy, the temperature and current measurements of each channel are accurate. When generating thermoelectric parameter measurement instructions, instructions containing specific test conditions can be generated based on the test equipment parameters and the test sample's geometric parameters. For example, the instructions contain information such as the test temperature, current, and measurement time for each sample, thereby enabling accurate performance testing of bismuth telluride-based thermoelectric materials.

[0035] In some embodiments, generating a thermoelectric parameter measurement instruction based on the test sample set includes: performing geometric calibration processing on the test samples in the test sample set according to a standard test protocol to obtain calibrated test samples; and generating a thermoelectric parameter measurement instruction using the calibrated test samples.

[0036] It should be noted that before conducting performance tests on bismuth telluride-based thermoelectric materials, the test samples need to be geometrically calibrated. This process is based on the standard test protocol, and its purpose is to ensure that the geometric parameters of all test samples are consistent, thereby improving the accuracy and repeatability of the test results. The standard test protocol is a set of standardized operating procedures that specify in detail the processing methods of test samples, the operating steps of the test equipment, and the data collection methods. The geometric calibration process mainly adjusts the size, shape and surface quality of the test samples to eliminate deviations that may occur during the manufacturing process. Through this processing step, it can be ensured that each test sample is evaluated for performance under the same test conditions, thereby providing an accurate sample basis for the subsequent generation of thermoelectric parameter measurement instructions.

[0037] Specifically, the geometric calibration process includes measuring and adjusting the dimensions of the test sample. For example, for sheet test samples, its length, width and thickness need to be accurately measured, and it is ensured that these dimensions meet the requirements of the standard test protocol. If the size of the sample exceeds the allowable error range, it can be corrected by methods such as machining or chemical etching. The standard test protocol usually specifies the dimensional tolerance of the test sample. For example, the tolerance of length and width can be set to ±0.1 mm, and the tolerance of thickness can be set to ±0.05 mm. In addition, the geometric calibration process also involves checking the surface flatness and edge quality of the test sample to ensure that the measurement results are not affected by surface defects during the test process. For example, surface flatness can be detected by optical measuring equipment, and edge quality can be observed by microscope to see if there are burrs or cracks. The calibration of these geometric parameters can ensure that the thermoelectric performance of the test sample during the test process accurately reflects the true properties of the material.

[0038] Preferably, when performing the geometric calibration process, high-precision measuring equipment, such as a laser scanner or a three-coordinate measuring machine, can be used to accurately measure the size of the test sample. For samples whose dimensions exceed the standard range, they can be precisely processed by a CNC machining center to ensure that their dimensions meet the standard requirements. When adjusting the size of the test sample, it is necessary to consider the physical properties of the material, such as the hardness and brittleness of bismuth telluride-based thermoelectric materials, to avoid damage to the material during the processing process. After the geometric calibration process is completed, the calibrated test samples can be marked and numbered so that each sample can be accurately identified in subsequent testing processes. When generating thermoelectric parameter measurement instructions, the parameters of the test equipment, such as the electrode spacing, the position of the heat flux sensor, etc., can be set according to the size and shape of the calibrated test sample to ensure the accuracy of the test results. In addition, the calibrated test sample can also be pre-tested to verify whether its geometric parameters meet the requirements of the standard test protocol, thereby further improving the reliability and consistency of the test.

[0039] In some embodiments, before generating the thermoelectric parameter measurement instruction based on the test sample set, the method further includes: performing an oxide layer removal process on the test sample according to a surface treatment specification.

[0040] It should be noted that before performing performance tests on test samples of bismuth telluride-based thermoelectric materials, the test samples need to be treated to remove the oxide layer. This is because an oxide layer may form on the surface of bismuth telluride-based thermoelectric materials during preparation, storage or transportation, and the presence of the oxide layer will affect the accuracy and reliability of the test. The oxide layer may change the electrical and thermal properties of the material surface, resulting in deviations in the measured thermoelectric parameters. Therefore, removing the oxide layer from the test samples according to the surface treatment specifications is a key step to ensure the accuracy of the test results. Surface treatment specifications refer to a set of standardized operating procedures used to guide how to remove the oxide layer on the surface of the test sample while avoiding damage to the material itself.

[0041] Specifically, oxide layer removal treatment involves removing the oxide layer from the surface of a test sample through physical or chemical methods to restore the original surface properties. Physical methods include mechanical polishing or plasma cleaning, while chemical methods involve the use of specific chemical reagents for surface treatment. For example, mechanical polishing involves gently grinding the surface of a test sample with diamond paste to remove the oxide layer. Plasma cleaning uses high-energy plasma particles to impact the surface, removing the oxide layer without damaging the underlying material. Chemical methods involve using dilute acidic solutions, such as hydrochloric acid or hydrofluoric acid, to dissolve the surface oxide layer. When performing oxide layer removal treatment, the treatment time and conditions must be strictly controlled to avoid overtreatment and damage to the material surface. For example, the mechanical polishing time can be adjusted based on the hardness of the material and the thickness of the oxide layer, typically ranging from several minutes to more than ten minutes. The chemical treatment time is determined by the concentration and reaction rate of the chemical reagent, typically ranging from tens of seconds to several minutes.

[0042] To ensure the efficiency and safety of the oxide layer removal process, automated equipment can be used. For example, automated plasma cleaning equipment can automatically control the cleaning time and power according to preset parameters, thereby achieving uniform treatment of the test sample surface. During the chemical treatment process, a titration device can be used to precisely control the amount of chemical reagents used, and a timer can be used to control the treatment time. After the treatment is completed, the test sample needs to be cleaned and dried to remove any residual chemical reagents.

[0043] Furthermore, to verify the complete removal of the oxide layer, surface analysis techniques such as X-ray photoelectron spectroscopy (XPS) or scanning electron microscopy (SEM) can be used to examine the surface of the treated test sample. These methods ensure that the oxide layer on the surface of the test sample is effectively removed while maintaining the original properties of the material, thus providing a reliable sample basis for subsequent performance testing.

[0044] In some embodiments, the data collected according to the test process to obtain the performance parameter data of the bismuth telluride-based thermoelectric material includes: recording the gradient application moment when the temperature gradient is applied; recording the parameter stabilization moment when the stable thermoelectric parameter data is collected; calculating the parameter stabilization time according to the corresponding gradient application moment and parameter stabilization moment; and statistically analyzing the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material.

[0045] It should be noted that this method pays special attention to the measurement and statistics of parameter stabilization time in the process of performance testing of bismuth telluride-based thermoelectric materials. Parameter stabilization time refers to the time required from the application of the temperature gradient to the thermoelectric parameters, such as the Seebeck coefficient and thermal conductivity, reaching a stable state. This indicator can reflect the speed at which the material reaches a stable working state in practical applications, and is of great significance for evaluating the dynamic performance and response characteristics of the material. By recording the gradient application moment when the temperature gradient is applied and the parameter stabilization moment when the stable thermoelectric parameter data is collected, the parameter stabilization time can be calculated. By performing statistical analysis on the parameter stabilization time of all test samples, the thermoelectric parameter data of bismuth telluride-based thermoelectric materials can be obtained, thereby providing more comprehensive information for the performance evaluation of the material.

[0046] Specifically, the gradient application moment refers to the precise point in time during the test when the temperature gradient begins to be applied to the test sample. The parameter stabilization moment refers to the point in time when thermoelectric parameters, such as the Seebeck coefficient or thermal conductivity, reach a stable state. The Seebeck coefficient, which refers to the electromotive force generated per unit temperature difference, is a key parameter in measuring the performance of thermoelectric materials; thermal conductivity reflects the material's ability to conduct heat. The parameter stabilization duration is calculated by measuring the time difference between these two moments. For example, if the temperature gradient is applied to a test sample at 0 seconds and the Seebeck coefficient reaches a stable state at 120 seconds, the Seebeck coefficient stabilization duration for that sample is 120 seconds. Similarly, the thermal conductivity stabilization duration can be calculated by recording the time point at which the thermal conductivity fully converges. In practice, these time points can be accurately recorded using high-precision temperature control equipment and data acquisition systems, and the data analyzed using software algorithms to determine the parameter stabilization duration for each test sample.

[0047] To improve the accuracy and reliability of parameter stabilization time measurements, multiple monitoring points can be introduced during the test process. For example, when measuring the Seebeck coefficient, a temperature sensor and a voltage sensor can be placed at each end of the test sample to monitor the temperature difference and the resulting voltage in real time. By analyzing the voltage variation over time, the moment when the Seebeck coefficient reaches a steady state can be more accurately determined. For thermal conductivity measurements, a heat flow sensor can be placed on the side of the test sample to monitor the changes in heat flow in real time, thereby determining the time when thermal conductivity reaches a steady state.

[0048] Furthermore, to reduce measurement errors, repeated testing of multiple test samples can be performed, and the resulting parameter stability data can be statistically analyzed, such as calculating the mean and standard deviation, to assess the stability and consistency of material performance. Through these refined operational steps and data processing methods, a more comprehensive understanding of the dynamic properties of bismuth telluride-based thermoelectric materials can be achieved, providing strong support for material optimization and application.

[0049] In some embodiments, the recording and collecting of the parameter stabilization moment of stable thermoelectric parameter data includes: recording and collecting the first steady-state moment when the Seebeck coefficient first reaches the steady-state value; and / or recording and collecting the final steady-state moment when the thermal conductivity is completely converged; the determining of the corresponding type of parameter stabilization time according to the corresponding gradient application moment and the parameter stabilization moment includes: determining the Seebeck coefficient stabilization time according to the corresponding gradient application moment and the first steady-state moment; and / or determining the thermal conductivity stabilization time according to the corresponding gradient application moment and the final steady-state moment; the performing of statistics on the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material includes: performing statistics on the Seebeck coefficient stabilization time of all test samples to obtain corresponding Seebeck coefficient index data; and / or performing statistics on the thermal conductivity stabilization time of all test samples to obtain index data corresponding to the thermal conductivity stabilization time.

[0050] It should be noted that this embodiment further refines the method for measuring the stabilization time of the performance parameters of bismuth telluride-based thermoelectric materials. Specifically, it not only records the first steady-state moment when the Seebeck coefficient first reaches the steady-state value, but also records the final steady-state moment when the thermal conductivity fully converges. This dual-parameter measurement method can more comprehensively reflect the stability characteristics of the material under different thermoelectric performance indicators. By calculating the stabilization time of the Seebeck coefficient and the stabilization time of thermal conductivity respectively, the dynamic response capability of the material in practical applications can be more accurately evaluated. Statistical analysis of these stabilization time data of all test samples can obtain more detailed performance stability index data, thereby providing more valuable information for material optimization and application.

[0051] Specifically, the first steady-state moment, when the Seebeck coefficient first reaches a steady-state value, refers to the point in time during the test when the measured value of the Seebeck coefficient no longer changes over time or when the change is less than a set threshold. The final steady-state moment, when the thermal conductivity fully converges, refers to the point in time when the measured value of the thermal conductivity remains stable for a certain period of time and no longer changes significantly. The Seebeck coefficient measures a material's ability to generate an electromotive force under a temperature gradient, while thermal conductivity reflects its ability to conduct heat. During the test, high-precision temperature control and data acquisition systems enable real-time monitoring of the changes in these two parameters. For example, when the rate of change of the Seebeck coefficient is less than 0.01 μV / K / min, it is considered to have reached a steady-state value, while when the rate of change of the thermal conductivity is less than 0.05 W / (m·K) / min, it is considered to have fully converged. By recording these time points and combining them with the gradient application time, the Seebeck coefficient and thermal conductivity stability times can be calculated, respectively. Statistical analysis of these parameter stability times can include calculating the mean and standard deviation to assess the stability and consistency of material properties.

[0052] Preferably, when measuring the stability time of the Seebeck coefficient and thermal conductivity, a multi-point monitoring approach can be employed to improve data accuracy. For example, multiple temperature sensors and voltage sensors can be placed at each end of the test sample to monitor changes in the Seebeck coefficient; heat flow sensors can be placed on the side of the sample to monitor changes in thermal conductivity. By analyzing the data collected by these sensors, the steady-state moments of the Seebeck coefficient and thermal conductivity can be more accurately determined. Furthermore, to reduce measurement errors, filtering algorithms can be introduced during data processing to remove noise signals. For example, a sliding average filter algorithm can be used to smooth the collected data to improve data stability. When calculating parameter stability time, a reasonable threshold can be set to determine whether the parameter has reached steady state, such as a rate of change of less than 0.01 μV / K / min for the Seebeck coefficient and less than 0.05 W / (m·K) / min for the thermal conductivity. By statistically analyzing the stability time data of multiple test samples, the stability time distribution of the Seebeck coefficient and thermal conductivity can be determined, providing a more comprehensive basis for the performance evaluation of bismuth telluride-based thermoelectric materials.

[0053] In some embodiments, it also includes: obtaining benchmark thermoelectric parameter data corresponding to each test sample, and counting the standard Seebeck coefficient value contained in the benchmark thermoelectric parameter data; the benchmark thermoelectric parameter data is thermoelectric parameter data obtained by measuring the test sample under a standard test environment; obtaining the actual Seebeck coefficient value measured by the multi-channel test system during the performance test; calculating the parameter deviation based on the standard Seebeck coefficient value and the actual Seebeck coefficient value corresponding to each test sample; performing statistical analysis on the parameter deviation of all test samples to obtain corresponding performance stability index data.

[0054] It should be noted that this embodiment evaluates the performance stability of bismuth telluride-based thermoelectric materials by comparing benchmark thermoelectric parameter data with actual measurement data. Benchmark thermoelectric parameter data refers to the thermoelectric parameter data obtained by measuring the test sample under a standard test environment, which reflects the performance of the material under ideal conditions. The standard test environment generally refers to tests conducted under specific conditions such as temperature, humidity and pressure to ensure the repeatability and consistency of the test results. The actual Seebeck coefficient value refers to the Seebeck coefficient value measured by the multi-channel test system during the performance test, which reflects the performance of the material under actual test conditions. By calculating the difference between the standard Seebeck coefficient value and the actual Seebeck coefficient value, the parameter deviation can be obtained to evaluate the stability of the material performance. By performing statistical analysis on the parameter deviation of all test samples, performance stability index data can be obtained, providing a more comprehensive reference for material performance evaluation.

[0055] Specifically, benchmark thermoelectric parameter data includes standard Seebeck coefficient values, which are obtained through precise measurement methods under standard test conditions. The standard test environment typically includes a temperature range, such as 20°C to 30°C, humidity, such as 40% to 60% relative humidity, and pressure, such as 1 atmosphere. Actual Seebeck coefficient values are measured using a multi-channel test system under actual test conditions, which differ from the standard test environment. The Seebeck coefficient measures a material's ability to generate an electromotive force under a temperature gradient and is typically expressed in μV / K. Parameter deviation refers to the degree of difference between the actual Seebeck coefficient and the standard Seebeck coefficient value, which can be expressed as the difference or relative error between the two. For example, if the standard Seebeck coefficient value is 200 μV / K and the actual measured value is 195 μV / K, the parameter deviation can be expressed as a difference of 5 μV / K or a relative error of 2.5%. Statistical analysis of the parameter deviations of all test samples can include calculating statistical metrics such as the mean deviation and standard deviation to assess the stability and consistency of material properties.

[0056] Preferably, more sophisticated data processing methods can be used when calculating parameter deviation. For example, the actual measurement data can be filtered to remove possible noise interference, thereby improving data accuracy. The Seebeck coefficient measurement data can be smoothed using a low-pass filter or a sliding average filter algorithm. When calculating parameter deviation, a reasonable threshold can be set to determine whether the deviation is within an acceptable range. For example, when the deviation is less than a set value, such as 5μV / K or 2%, the material performance can be considered stable.

[0057] Furthermore, to more comprehensively evaluate material performance, parameter deviation can be combined with other performance indicators, such as thermal conductivity and electrical conductivity, for analysis. For example, by constructing a comprehensive performance evaluation model and inputting parameters such as the Seebeck coefficient, thermal conductivity, and electrical conductivity, along with their deviations, the model can output a comprehensive performance stability indicator. Through these refined operational steps and data processing methods, the performance stability of bismuth telluride-based thermoelectric materials can be more accurately assessed, providing strong support for the research and development and application of materials.

[0058] In some embodiments, it also includes: obtaining ideal thermoelectric parameter data corresponding to each test sample, and counting the theoretical thermal conductivity value contained in the ideal thermoelectric parameter data, wherein the ideal thermoelectric parameter data is theoretical thermoelectric parameter data obtained based on material component simulation calculation; obtaining the actual thermal conductivity value measured by the multi-channel test system during the performance test; calculating the thermal conductivity error rate based on the theoretical thermal conductivity value and the actual thermal conductivity value corresponding to each test sample; and counting the thermal conductivity error rate of all test samples to obtain thermal conductivity consistency index data.

[0059] It should be noted that this embodiment further expands the method for evaluating the performance of bismuth telluride-based thermoelectric materials, and calculates the thermal conductivity error rate by comparing the ideal thermoelectric parameter data with the actual measurement data. Ideal thermoelectric parameter data refers to the theoretical thermoelectric parameter data obtained based on the simulation calculation of the material components, wherein the theoretical thermal conductivity value is calculated by a theoretical model according to the physical properties and structural characteristics of the material. The actual thermal conductivity value is measured by a multi-channel testing system during the performance test. By calculating the difference between the theoretical thermal conductivity value and the actual thermal conductivity value, the thermal conductivity error rate can be obtained to evaluate the consistency of the material performance. By performing statistical analysis on the thermal conductivity error rate of all test samples, thermal conductivity consistency index data can be obtained, providing a more comprehensive reference for material performance evaluation.

[0060] Specifically, ideal thermoelectric parameter data is calculated using theoretical models, which are typically based on the material's crystal structure, composition, and physical properties. Theoretical thermal conductivity values are calculated using these models and reflect the material's ability to conduct heat under ideal conditions. Actual thermal conductivity values are obtained through experimental measurements and reflect the material's ability to conduct heat under actual test conditions. Thermal conductivity is a parameter that measures a material's ability to conduct heat, typically expressed in W / (m·K). The thermal conductivity error rate refers to the degree of difference between the actual and theoretical thermal conductivity values and can be expressed by calculating the difference or relative error between the two. For example, if the theoretical thermal conductivity value is 100 W / (m·K) and the actual measured value is 95 W / (m·K), the thermal conductivity error rate can be expressed as a difference of 5 W / (m·K) or a relative error of 5%. Statistical analysis of the thermal conductivity error rates of all tested samples can include calculating statistical indicators such as the average error rate and standard deviation to assess the consistency and reliability of material performance.

[0061] Preferably, when calculating the thermal conductivity error rate, a more sophisticated data processing method can be used. For example, the actual measurement data can be filtered to remove possible noise interference, thereby improving the accuracy of the data. The thermal conductivity measurement data can be smoothed using a low-pass filter or a sliding average filter algorithm. When calculating the thermal conductivity error rate, a reasonable threshold can be set to determine whether the error is within an acceptable range. For example, when the error rate is less than a certain set value, such as 5%, it can be considered that the material performance consistency is good.

[0062] Furthermore, to more comprehensively evaluate material performance, the thermal conductivity error rate can be combined with other performance indicators, such as the Seebeck coefficient and electrical conductivity. For example, by constructing a comprehensive performance evaluation model and inputting parameters such as thermal conductivity, Seebeck coefficient, and electrical conductivity, as well as their error rates, the model can output a comprehensive performance consistency indicator. Through these refined operational steps and data processing methods, the performance consistency of bismuth telluride-based thermoelectric materials can be more accurately evaluated, providing strong support for the research and development and application of materials.

[0063] The above-described embodiments of the present invention have the following beneficial effects: the present invention can improve the accuracy and reliability of bismuth telluride-based thermoelectric material performance testing. By statistically analyzing the sample geometric distribution and constructing a proportionally matched test sample set, the representativeness of the test results can be ensured. The effective test interval can be calculated based on the heat flux density and temperature gradient range of the target application scenario to optimize the test conditions. The use of a multi-channel test system for synchronous sample loading and geometric calibration and surface treatment can improve test efficiency and data consistency. By recording the parameter stability time and calculating the parameter deviation, the stability of the material performance can be evaluated. By comparing the theoretical thermal conductivity with the actual measured value, the reliability of the material preparation process can be verified.

[0064] This method comprehensively characterizes the key performance indicators of bismuth telluride-based thermoelectric materials. By analyzing the steady-state moments of the Seebeck coefficient and thermal conductivity, the dynamic response characteristics of the material can be accurately determined. By calculating the parameter stability time of different samples, batch consistency of the material can be assessed. By calculating the deviation between the standard Seebeck coefficient and the actual value, the measurement accuracy of the test system can be quantified. And by analyzing the thermal conductivity error rate, the degree of match between the material composition and the theoretical design can be verified. These combined methods can provide more reliable performance data support for the research, development, production, and application of bismuth telluride-based thermoelectric materials.

[0065] like Figure 2 As shown, some embodiments of a performance characterization parameter measurement device for bismuth telluride-based thermoelectric materials include: a geometric analysis unit 201, used to perform statistical analysis on the geometric parameters of bismuth telluride-based thermoelectric material samples to determine the distribution ratio of samples with different geometric shapes; a sample set construction unit 202, used to select test samples from a material database and construct a test sample set using the test samples; wherein the distribution ratio of test samples with different geometric shapes in the test sample set is the distribution ratio of samples with corresponding geometric shapes; a test execution unit 203, used to generate a thermoelectric parameter measurement instruction based on the test sample set, perform a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, and obtain performance parameter data of the bismuth telluride-based thermoelectric material based on data collected during the test process.

[0066] It is understood that the modules recorded in the performance characterization parameter measurement device of the bismuth telluride-based thermoelectric material are the same as those in the reference Figure 1 The steps in the performance characterization parameter measurement method for bismuth telluride-based thermoelectric materials described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the performance characterization parameter measurement method for bismuth telluride-based thermoelectric materials are also applicable to the performance characterization parameter measurement device for bismuth telluride-based thermoelectric materials and the modules contained therein, and will not be repeated here.

[0067] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0068] like Figure 3As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0069] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0070] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0071] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for measuring performance characterization parameters of bismuth telluride-based thermoelectric materials, characterized in that: include: Statistical analysis of the geometric parameters of bismuth telluride-based thermoelectric material samples was performed to determine the distribution ratio of samples with different geometric shapes. Test samples were selected from the material database and used to construct a test sample set. The distribution ratio of test samples with different geometric shapes in the test sample set is the distribution ratio; a thermoelectric parameter measurement instruction is generated based on the test sample set, the performance test of the bismuth telluride-based thermoelectric material is performed using the thermoelectric parameter measurement instruction, and performance parameter data of the bismuth telluride-based thermoelectric material is obtained by collecting data according to the test process.

2. The performance characterization parameter measurement method according to claim 1, characterized in that: Before performing a performance test on the bismuth telluride-based thermoelectric material based on the test sample set, the method further includes: calculating an effective test interval based on a heat flux density range of a target application scenario and a pre-set temperature gradient range, and determining a test temperature gradient based on the material operating temperature range and the effective test interval; configuring test equipment parameters based on the test temperature gradient and the number of test samples in the test sample set; generating a thermoelectric parameter measurement instruction based on the test sample set, and performing a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, including: initializing a multi-channel test system based on the test equipment parameters; using each test channel to synchronously load the test samples in the test sample set and generate a thermoelectric parameter measurement instruction, and sending the thermoelectric parameter measurement instruction to the multi-channel test system.

3. The performance characterization parameter measurement method according to claim 1, characterized in that: Generating a thermoelectric parameter measurement instruction based on the test sample set includes: performing geometric calibration processing on the test samples in the test sample set according to a standard test protocol to obtain calibrated test samples; and generating a thermoelectric parameter measurement instruction using the calibrated test samples.

4. The performance characterization parameter measurement method according to claim 1, characterized in that: Before generating the thermoelectric parameter measurement instruction based on the test sample set, the method further includes: performing an oxide layer removal process on the test sample according to a surface treatment specification.

5. The performance characterization parameter measurement method according to any one of claims 1 to 4, characterized in that: The method of collecting data according to the test process to obtain performance parameter data of the bismuth telluride-based thermoelectric material includes: recording the gradient application time when the temperature gradient is applied; recording the parameter stabilization time when the stable thermoelectric parameter data is collected; calculating the parameter stabilization time according to the corresponding gradient application time and parameter stabilization time; and statistically analyzing the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material.

6. The performance characterization parameter measurement method according to claim 5, characterized in that: The recording and collecting of the parameter stabilization moment of stable thermoelectric parameter data includes: recording and collecting the first steady-state moment when the Seebeck coefficient first reaches the steady-state value; and / or recording and collecting the final steady-state moment when the thermal conductivity is completely converged; the determining of the corresponding type of parameter stabilization time according to the corresponding gradient application moment and the parameter stabilization moment includes: determining the Seebeck coefficient stabilization time according to the corresponding gradient application moment and the first steady-state moment; and / or determining the thermal conductivity stabilization time according to the corresponding gradient application moment and the final steady-state moment; the statistical analysis of the parameter stabilization time of all test samples to obtain the thermoelectric parameter data of the bismuth telluride-based thermoelectric material includes: statistical analysis of the Seebeck coefficient stabilization time of all test samples to obtain corresponding Seebeck coefficient index data; and / or statistical analysis of the thermal conductivity stabilization time of all test samples to obtain index data corresponding to the thermal conductivity stabilization time.

7. The performance characterization parameter measurement method according to any one of claims 1 to 4, characterized in that: Also includes: Obtaining benchmark thermoelectric parameter data corresponding to each test sample, and calculating the standard Seebeck coefficient value contained in the benchmark thermoelectric parameter data; The reference thermoelectric parameter data is thermoelectric parameter data obtained by measuring the test sample under a standard test environment; Obtaining an actual Seebeck coefficient value measured by the multi-channel test system during a performance test; Calculate the parameter deviation based on the standard Seebeck coefficient value and the actual Seebeck coefficient value corresponding to each test sample; The parameter deviations of all test samples are statistically analyzed to obtain the corresponding performance stability index data.

8. The performance characterization parameter measurement method according to claim 7, characterized in that: Also includes: Obtaining ideal thermoelectric parameter data corresponding to each test sample and calculating the theoretical thermal conductivity value contained in the ideal thermoelectric parameter data, wherein the ideal thermoelectric parameter data is theoretical thermoelectric parameter data obtained based on material composition simulation calculation; Obtaining an actual thermal conductivity value measured by the multi-channel testing system during a performance test; Calculate the thermal conductivity error rate based on the theoretical thermal conductivity value and the actual thermal conductivity value corresponding to each test sample; The thermal conductivity error rate of all test samples was statistically analyzed to obtain the thermal conductivity consistency index data.

9. A performance characterization parameter measurement device for bismuth telluride-based thermoelectric materials, characterized in that: include: A geometric analysis unit is used to perform statistical analysis on the geometric parameters of bismuth telluride-based thermoelectric material samples and determine the distribution ratio of samples with different geometric shapes; a sample set construction unit, configured to select test samples from a material database and construct a test sample set using the test samples; The distribution ratio of test samples with different geometric shapes in the test sample set is the distribution ratio of samples with corresponding geometric shapes; the test execution unit is used to generate a thermoelectric parameter measurement instruction based on the test sample set, perform a performance test on the bismuth telluride-based thermoelectric material using the thermoelectric parameter measurement instruction, and obtain performance parameter data of the bismuth telluride-based thermoelectric material by collecting data according to the test process.

10. A material testing device, characterized in that: The method comprises a controller and a memory, wherein the memory is used to store a test program; when the test program is loaded by the controller, the controller executes the performance characterization parameter measurement method of the bismuth telluride-based thermoelectric material according to any one of claims 1 to 8.

Images