Degradation life test method, algorithm and application of new energy thermal management composite materials

Abstract

The objective of the present invention is to provide a method, algorithm and application for degradation life testing of new energy thermal management composite materials. [Solution] This invention discloses a composite material degradation life test method, algorithm, and application. A specimen is placed in four temperature environments, subjected to heat and humidity, thermal shock, and thermal cycles for a specified time; the physicochemical and electrical properties of the specimen are measured using a laminated composite specimen; the parameters of the micro-vaporization expansion vibration equation are fitted; the constants in the parameter dynamics correlation equation (2) are fitted; new values ​​of the parameters at any temperature environment are calculated using the constant equation (2); and the new values ​​of the parameters are substituted into equation (1) to evaluate or predict the physicochemical and electrical properties of the specimen at any time. The maximum degradation temperature can reach 298°C, which not only breaks the limitations of GB / T 20028, ASTM G 166, ASTM G 169, ISO 2578, and UL 746B standards, but also achieves a linear correlation coefficient R 2 is higher than the two "9" accuracy levels, and the predictions are more accurate.

Description

[Technical Field]

[0001] This invention relates to the field of test methods and algorithms for predicting the long-term degradation life of polymer matrix composite materials, and proposes a method for evaluating and predicting the long-term reliability, safety, and environmental adaptability of polymer matrix composite materials under actual use conditions. Specific application examples include the evaluation and prediction of the actual use life of insulating and thermally conductive materials used in the thermal management of new energy power battery packs and 5G / 6G devices, as well as the interfaces between chips and heat sinks, and heat sources and cooling sinks. [Background technology]

[0002] With the rise of the clean energy automobile industry and 5G / 6G technology, the world is at the same starting line. The same can be said for the thermal management interface insulating and thermal conductive material technology that is essential for these industrial power battery packs. Although there are many types of interface insulating and thermal conductive materials both at home and abroad, the entire industrial chain of design, manufacturing, and application of interface insulating and thermal conductive materials has not yet established a standard method for evaluating or predicting their actual service life.

[0003] Currently, the reliability assessment methods tentatively used by academia, clean energy vehicle manufacturers, 5G / 6G finished product manufacturers, and research institutes and manufacturers of interface insulating and thermally conductive materials all come from the IEC 60068-2 series of standards, and the corresponding standard (IDT) for this series is the Chinese translation of GB / T 2423. While the IEC 60068-2 standard can be used as an "emergency response plan" to pass steady-state tests under three degradation conditions: damp heat, thermal shock, and thermal cycling, it does not address the difficult problem of evaluating or predicting the long-term practical service life of interface insulating and thermally conductive materials under actual usage conditions.

[0004] Without standard methods for the scientific evaluation of interface thermal materials in terms of reliability, safety, and environmental compatibility, there will inevitably be long-term hazards worldwide.

[0005] In fact, the IEC 60068-2 standard only applies to evaluating the short-term deterioration performance of small electrical and electronic components in terms of humidity and heat resistance, and is not suitable for evaluating or predicting the long-term reliability, safety, and environmental adaptability of insulating and thermally conductive materials for thermal management interfaces in new energy power battery packs and 5G / 6G equipment. This is because the target lifespan of electrical and electronic components in IEC 60068-2 electrical products is generally 8 to 10 years, while the design lifespan of non-durable consumer goods such as mobile phones and computers is generally less than 8 years. Therefore, it is acceptable and sufficient to determine the deterioration trend over 8 to 10 years according to the IEC 60068-2 series of standards.

[0006] However, interface insulating and thermally conductive materials are polymer matrix composites, and their degradation behavior is fundamentally different from that of electrical and electronic components. The design requirements and the target practical service life are also changed. For example, new energy power battery packs are Previously, the CMP structure involved assembling cells into battery modules using mechanical fixtures, and then integrating the modules into battery packs using mechanical fixtures, i.e., the so-called Cell to Model to Pack structure; Currently and in the future, CTP structure, thermally conductive structural adhesives are used to directly bond cells to the complete battery pack in one step, i.e., cell-to-pack structure. This method reduces the number of mechanical parts by about 40%, increases volume utilization by (15-20)%, extends the range per unit volume by 15-17%, improves manufacturing efficiency by nearly 50%, and significantly reduces manufacturing costs; More importantly, the reliability, safety, and environmental adaptability of interface insulating and thermally conductive materials required for power battery packs of clean energy vehicles must be reflected in six specific targets. 1) Functional mission: High strength, high toughness, high thermal conductivity, and high insulation, for example, can replace metal fixtures between cells and water-cooling plates and heating tapes to form CTP power battery packs by direct bonding and sealing; 2) Actual service life: The actual service life in a 45℃ environment is more than 50 years, including 25 years for road operation and 25 years for energy storage operation; 3) Operating temperature: -45℃~60℃ cycle, can be used normally for more than 50 years; 4) Impact in disasters: Under the combined impact of a 12m free fall and a 45° inclined run, the cell will not explode even if the positive and negative electrodes are short-circuited; 5) Flame retardant: Fires will self-extinguish when away from flames, and flame retardant performance exceeds the strictest V0 standard of UL94; 6) Withstand voltage: Even if the minimum thickness of the adhesive layer is as thin as 0.28 mm, a voltage of 2500 V will not cause dielectric breakdown.

[0007] Obviously, if we continue to use the IEC 60068-2 series of standards, it will be impossible to predict the physicochemical and electrical properties of insulating and thermally conductive interface materials 50 years from now in a very short time frame.

[0008] Furthermore, the academic community has officially recognized framework standards and examples for predicting the half-life of polymer matrix composites through high-temperature accelerated aging, such as GB / T 20028, ASTM G166, ASTM G169, ISO 2578, and UL 746B. However, for the application of specific molded products in processes, there are many installation structural factors related to the process environment, and the degradation factors are far more complex than the simple material testing conditions in a laboratory. Therefore, these framework standards cannot directly provide a method for predicting the actual service life of molded materials under specific application conditions. [1] In this research project, the shortcomings of currently accepted standards for predicting the long-term degradation life of polymer matrix composites were identified. When predictions are made using the Arrhenius equation for the activation energy of a single substance, the linear correlation coefficient R is high for predicting the degradation tendency of highly pure, single-component materials and single crystalline phase regions. 2 However, when predicting deterioration trends in multi-component composite materials and complex installation structures, the linear correlation coefficient R 2 is low and the prediction deviation exceeds the tolerance boundary.

[0009] Therefore, to date, no reports have been published domestically or internationally regarding the long-term deterioration trends and test methods and algorithms for reliability evaluation of interface insulating and thermally conductive materials that exhibit a certain degree of permanent compression set in the actual use environment of a "double-sided metal plate sandwich structure." Furthermore, both domestically and internationally, we are all at the same starting point, and there are no direct test methods, algorithms, or conclusions that can be cited.

[0010] Therefore, the test methods and algorithms for the long-term degradation life of polymer matrix composite materials, especially interface insulating and thermally conductive materials, are related to the long-term safety of new energy power battery packs and are a global technical challenge and difficult problem that needs to be solved in advance.

[0011] Therefore, it is necessary to invent a degradation life test method and algorithm for polymer matrix composites, especially for interface insulating and thermally conductive materials. [Prior art documents] [Non-patent literature]

[0012] [Non-Patent Document 1] Xiao Yanghua, Transition and Volatilization of tert-Butylferrocene and Its Effect on Burning Rate, Papers from the China Society of Aeronautics and Astronautics (North Sea Fleet Command) Conference, September 1984; Propulsion Technology 1985, 6(2):49-60. Summary of the Invention [Problem to be solved by the invention]

[0013] The objective of this invention is to provide a degradation life test method, algorithm and application for polymer matrix composite materials, especially insulating and thermally conductive interfacial materials, to solve the technical problem of evaluating or predicting the 50-year degradation trends of the physicochemical and electrical properties of power battery packs for clean energy vehicles. [Means for solving the problem]

[0014] In order to achieve the first object, the degradation life test method and algorithm of the present invention for a new energy thermal management composite material includes the steps of preparing a test object as a standard specimen for degradation life test, which may be one or any two of an open-type specimen, a closed-type specimen, and a jig-compressed specimen; placing the standard specimen in at least four designated constant temperature environments, and further subjecting the standard specimen to at least one of moist heat, thermal shock, and thermal cycles for a designated time or cumulative number of cycles in each temperature environment; testing the physicochemical and electrical properties of the test object using the standard specimen or laminated composite test piece; fitting the 15 parameters in the micro-vaporization expansion vibration equation (1) using the measured values ​​of the physicochemical and electrical properties; and further calculating the 15 parameters. Dynamic correlation equation (2), substituting the fitted constants into the kinetic correlation equation (2) to calculate new values ​​of the 15 parameters in an arbitrarily specified constant temperature environment, and substituting the new values ​​of the 15 parameters one by one into equation (1) to evaluate or predict the physicochemical and electrical properties of the test object at an arbitrarily specified time under at least one condition of moist heat, cold-heat shock, or cold-heat cycle for an arbitrarily specified time or cumulative number of cycles.

[0015] To achieve the second objective, the application of the degradation life test method and algorithm for new energy thermal management composite materials of the present invention includes using the degradation life test method and algorithm to evaluate or predict the physicochemical properties and electrical properties of a test object in a specified constant temperature environment for a specified time or cumulative number of cycles, or to evaluate or predict the half-life of any of the physicochemical properties and electrical properties of a test object in a specified constant temperature environment, or to evaluate or predict the rated temperature of any of the physicochemical properties and electrical properties of a test object after a specified actual use time of 20,000 hours; the physicochemical properties and electrical properties include at least one of color, density, thermal conductivity, oil separation rate, compression set rate, specific heat, hardness, tensile strength, elongation at break, butt tensile bond strength, lap shear bond strength, glass transition temperature, coefficient of linear expansion, dielectric breakdown strength, DC or AC leakage resistance, volume resistivity, dielectric constant, loss factor, oxygen index, flame retardancy, vacuum volatile matter, water absorption, mildew resistance, smoke density, smoke index, and gas toxicity index.

[0016] Furthermore, the composite material may be a polymer matrix composite material in a solid, fluid, or melt state, or a mixture of any two of the solid, fluid, or melt states; or a rubber, plastic, fiber, or thermosetting material, or a composite thereof; or an elastomer, adhesive, sealant, or foam material, or a composite thereof.

[0017] Furthermore, the test object includes a specimen of the composite material prepared in a shape that complies with corresponding physical, chemical, and electrical property test standards.

[0018] Furthermore, the open specimen includes the test object not being coated, wrapped, sandwiched or sealed using a material, packaging material or container different from the chemical composition of the test object, and exposing the test object to a deteriorating environment.

[0019] Furthermore, the closed specimen may be one in which a part or all of the surface area of ​​the test object is isolated from the deterioration environment by using a material, packaging material, or container that is different from the chemical composition of the test object, and by using any of coating, wrapping, sandwiching, or sealing methods.

[0020] Furthermore, the compression specimen using the jig has a test object sandwiched between at least two rigid plates, and the distance between the two rigid plates is adjusted to a specified thickness, compression ratio, or pressure using a fixture; the shape of the edge contour of the rigid plate includes an arched line, a straight line, a broken line, or a shape defined by connecting any two head and tail ends of an arched line, a straight line, or a broken line; the dimensions of the rigid plate correspond to the size of the test object required for physical and chemical property and electrical property tests, and if the rigid plate is prone to warping deformation under compressive stress, one side of the rigid plate may be provided with a TIFF0007749194000001.tif14166-shaped reinforcement material or any combination of two of them is provided to counter warpage deformation.

[0021] Furthermore, the combined specimen may include a specimen in which part of the surface area of ​​the test object is in an open specimen state and the other part is in a closed specimen state, or a specimen compressed by a jig is made into a closed specimen state.

[0022] Furthermore, the specified constant temperature includes setting the constant temperature required for the test at a temperature of 400°C or less in at least one oven, drying room, or storage room within the allowable temperature measurement error range; or setting the constant temperature as the average temperature of the ratio of the area under the temperature curve to the corresponding time, with the temperature curve as the vertical axis and time as the horizontal axis.

[0023] Furthermore, the moist heat includes controlling the relative humidity to a range of 5 to 100% by controlling the moisture content of an air atmosphere, an oxidizing atmosphere, a reducing atmosphere, an inert gas atmosphere, or a mixed medium in an oven, a drying chamber, or a storage chamber in the specified constant temperature environment.

[0024] Furthermore, the thermal shock includes a specified time in a specified high-temperature environment, followed by transferring the test object to a low-temperature environment at a specified rate of temperature decrease, followed by a specified time; or a specified time in a specified low-temperature environment, followed by transferring the test object to a high-temperature environment at a specified rate of temperature increase, followed by a specified time.

[0025] Furthermore, the cooling and heating cycle involves alternately transferring the test object between a specified high constant temperature environment and a specified low constant temperature environment according to a specified temperature drop rate and temperature rise rate until a specified time has elapsed or a specified number of cumulative cycles has been reached; the alternate transfer has the temperature curve on the vertical axis and time on the horizontal axis, and the contour shape of the temperature curve includes any of a straight line, a broken line, and an arc line, or a loop consisting of two connected head and tail ends, or a wavy shape with ups and downs.

[0026] Furthermore, the specified time or cumulative number of cycles may involve placing the standard specimen in a temperature-controlled oven, drying chamber, or storage room, and then removing it from the oven, drying chamber, or storage room after a specified time or cumulative number of cycles in accordance with a given test procedure, and placing it in another specified constant temperature environment.

[0027] Furthermore, the laminated composite test specimen includes at least one layer of material or part having a known performance index and known dimensions attached to the upper and lower rigid plates of the compression specimen by the jig during a constant temperature process or when testing the physicochemical and electrical properties, so that the measuring equipment can accurately measure the physicochemical and electrical properties.

[0028] Furthermore, the actual measured values ​​include data on physicochemical and electrical properties measured using measuring instruments or equipment that meet the standard requirements for physicochemical and electrical properties, and in accordance with the operations and conditions specified in the standards.

[0029] Furthermore, the micro-vaporization expansion vibration includes mathematically modeling the superimposed mechanism of micro-vaporization, expansion, transition, volatilization and chemical reaction of low molecular weight substances caused by the test object based on the observation of the vibration phenomenon of the physicochemical properties and electrical properties of the test object, and mathematically deriving a general formula (1) for the deterioration vibration tendency of the physicochemical properties and electrical properties of the test object.

[0030] Furthermore, the mathematical model also includes a failure mode and effect analysis of degradation, a simplified treatment of physical degradation, and a simplified treatment of chemical degradation.

[0031] Here, in the Aging-DFEAM, as shown in FIG. 10, the test object 4.2 is sandwiched between upper and lower rigid plates 4.1 and 4.3, both made of metal, and fastened in a "sandwich" shape with metal screws 8. The compression ratio of the test object 4.2 is adjusted to a specified value within the range of 0-40%, for example, three compression ratios of 10%, 20%, and 30% are used as the jig-compressed specimens. The average thickness of the air layer sandwiched between the upper and lower rigid plates 4.1 and 4.3 and the test object 4.2 is reduced to less than half the average particle size of the powder filler in the test object 4.2. Generally, the average particle size d of the thermally conductive powder or filler is 50 In particular, in the case of powders that have undergone particle size adjustment, the range is 1.5 to 15 μm and the thickness of the test object 4.2 is in the range of 0.25 to 5 mm.

[0032] Here, the simplified physical deterioration process includes the following: 1) As shown in Figure 10, at room temperature, low molecular weight substances do not vaporize or expand, but transition and volatilize in the form of ionized molecules. a) Except for the metal upper rigid plate 4.1 and the metal lower rigid plate 4.3 of the "sandwich," low-molecular-weight substances, including air, sulfides, nitrogen oxides, ozone, and moisture, undergo "breathing" due to seasonal temperature cycles. They are primarily transported to and from the inside and outside of the test object 4.2 through the smallest "gap" between the two interface resistances of the upper metal rigid plate 4.1, the test object 4.2, and the lower metal rigid plate 4.3. This is considered an inert substance transport process, and because the time is short, its impact on the chemical degradation rate of the test object 4.2 is negligible. Outdoor seasonal temperature cycles can reach -45 to 65°C. However, due to the presence of installation pressure, the cyclical fluctuations in the thickness of the interfacial gas film are negligible, so the impact on interfacial thermal resistance and other physicochemical and electrical properties is also negligible. b) The amount of air, sulfides, nitrogen oxides, ozone and low molecular weight substances including moisture remaining at the interface between the metallic upper rigid plate 4.1, the test object 4.2 and the lower rigid plate 4.3 is less than 1 / 100,000 of the weight of the test object 4.2, and the effect on the chemical degradation rate of the test object 4.2 is negligible; c) The spontaneous volatilization of other low-molecular-weight substances contained within test object 4.2 generates a concentration gradient, which acts as a driving force for diffusion. The ionized molecules of the low-molecular-weight substances diffuse without undergoing a phase change, first from the interior of test object 4.2 along the thickness direction of test object 4.2 into the interfacial "gap" between the metal upper rigid plate 4.1, test object 4.2, and lower rigid plate 4.3. The low-molecular-weight transition / diffusion direction 11 then continues along this "gap" to the interface between test object 4.2 and the outside air near bolt 8 in the volatilization direction 9 of the low-molecular-weight substances, and the volatilization direction 9 of the low-molecular-weight substances further moves away from test object 4.2, resulting in a predominantly unidirectional transition. While ignoring the effect on the chemical degradation rate of test object 4.2, a positive effect is observed on the improvement of the intrinsic thermal conductivity of test object 4.2. The physical effect on the interfacial thermal resistance is ignored, resulting in a short-term and slight increase in the apparent thermal conductivity; and other physical, chemical, and electrical properties are positively and negatively affected. 2) As shown in Figure 10, at high temperatures, low molecular weight substances undergo micro-vaporization and expansion, transition, and volatilization. d) First, only when the actual use temperature is higher than the boiling point of the low molecular weight substance, when micro-vaporization occurs inside the test object 4.2, the gas phase low molecular weight substance will further condense into gas clusters of micro elements, which will produce a micro-expansion effect and significantly reduce the inherent thermal conductivity, hardness, density and compression set rate of the test object 4.2; e) Next, the low-molecular-weight substance inside the test object 4.2 moves in a gaseous state. Under the driving force of the gas expansion pressure gradient, it first migrates along the thickness direction of the test object 4.2 to the interface "gap" between the upper metal rigid plate 4.1, the test object 4.2, and the lower rigid plate 4.3, and then migrates unidirectionally to the outside of the test object 4.2 through the interface "gap." As the volatilization direction 9 of the low-molecular-weight substance moves further away from the test object 4.2, the thickness and area of ​​the interface gas film between the upper metal rigid plate 4.1, the test object 4.2, and the lower rigid plate 4.3 increase significantly and irregularly, the fluctuation amplitude of the interface thermal resistance increases, and the apparent thermal conductivity forms a peak-and-valley oscillation state, forming a time-varying chaotic system; f) Finally, as the low molecular weight substances continue to evaporate, the content of low molecular weight substances inside test object 4.2 becomes lower and lower. The expansion energy of micro-vaporization gradually decreases, and the micro-expansion effect gradually disappears. The intrinsic thermal conductivity of test object 4.2 gradually increases and returns to its initial value. The interfacial thermal resistance also decreases to near its initial state before micro-vaporization, forming an upper peak of apparent thermal conductivity. However, as the chemical degradation time progresses, the increase in apparent thermal conductivity after competition becomes smaller and decreases significantly. 3) At any temperature, the release of mechanical compressive internal stress is shown in Figures 7 and 8. The non-detachable combined specimen 4, which includes a metallic upper rigid plate 4.1, a test object 4.2, and a lower rigid plate 4.3, is advantageous in accelerating the release rate of internal mechanical stress or increasing the intensity of internal mechanical stress due to the active or passive thermodynamic movement of the materials of the non-detachable combined specimen 4, whether it is micro-vaporization, transition, and volatilization caused by the microscopic movement of low-molecular-weight substances, or macroscopic stress caused by thermal expansion, cold contraction, or mechanical compression.

[0033] Here, the simplified treatment of chemical degradation includes: As shown in Figure 10, external air, sulfides, nitrogen oxides, ozone, and low-molecular-weight substances including water are not only transported into the interior of the test object 4.2 through the interfacial "gaps" but also migrate inward from the edge surface straight lines of the test object 4.2. The impact on the chemical degradation rate of the test object 4.2 is mainly controlled by the diffusion rate of the sulfides, nitrogen oxides, ozone, oxygen, and water-active low-molecular-weight substances inside the test object 4.2. The diffusion rate conforms to Fick's Law of Diffusion, but is also inversely proportional to the thickness of the test object 4.2 and the square of the diameter. When the width or diameter of the test object 4.2 is large enough, for example, the diameter of the laboratory specimen is more than 30mm, the low molecular weight substances inside the test object 4.2 will migrate outward, and the sulfides, nitrogen oxides, ozone, oxygen, and moisture on the edge surface of the test object 4.2 will migrate inward. The degradation effect on the inside of the test object 4.2 will be treated as a secondary factor. The chemical degradation effect on the test object 4.2 due to the exchange of external substances will also be ignored. The chemical degradation process will be mainly simplified by thermal degradation (competition between decomposition and cross-linking); Therefore, the main factors affecting the rate of chemical degradation of test object 4.2 are: The molecular chain structure of the polymer matrix and the chemical stability of the additive system, the degradation effect of which is sensitive to duration and temperature; In the degradation of mechanical compressive stress, the degradation effect of this factor is sensitive to the stress duration, compression rate and temperature. When the compression rate is constant, it is only sensitive to the stress duration and temperature, but in the case of elastic materials, the decay rate of mechanical stress is very fast; The catalytic degradation of chemical elements and their compounds in contact with the surfaces of the metallic upper rigid plate 4.1 and lower rigid plate 4.3, the degradation effect of which is sensitive to the type of chemical elements and their compounds, the duration of contact and the temperature; This is stress deterioration caused by sudden changes in the ambient temperature gradient, thermal expansion, and cold contraction. The deterioration effect of this factor is sensitive to the rate of change of the temperature gradient, but is not sensitive to highly elastic materials such as rubber.

[0034] Furthermore, the micro-vaporization expansion vibration equation (1) is expressed by the following equation:

[0035]

number

[0036] Furthermore, the parameters include the following: TIFF0007749194000003.tif20166 contains a total of 15 parameters, of which 14 are independent parameters and the remaining △P1 is also a linearly related parameter, meaning that the parameters do not change with time but change with temperature; for brevity, the symbol "Q" is used to represent any of the 15 parameters.

[0037] Furthermore, the constants include three constants under each parameter "Q" name in the micro-vaporization expansion oscillation equation (1) that do not change with time or temperature, but only change with the chemical composition of the test object; for simplicity, the three letters "A, B, C" represent the three constants under each parameter name; when evaluating or predicting any of the physicochemical properties or electrical properties, each parameter and its corresponding constant in the kinetic correlation equation (2) are substituted one by one;

[0038]

number

[0039] Furthermore, for fitting the parameters, the measured values ​​(P) of the physicochemical and electrical properties are used as verification samples; they are increased or decreased in as small a step as possible using an electronic calculation program or the Parallax method, and input into Equation (1). The "Q" values ​​of 15 different parameters are then iteratively processed to obtain the values ​​(P t ) calculated value; calculated value (P t When the standard deviation of the difference between the predicted value (P) and the actual measured value (P) converges to a minimum value, the "Q" of the corresponding 15 parameters is determined as the optimal value; due to the frequency doubling effect in mathematics, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0040] Furthermore, in fitting the constants, different "C" values ​​are temporarily input, and the iteration process is repeated in formula (2). A graph is drawn with the logarithm of each of the 15 optimal parameter "Q" values ​​on the vertical axis and 1 / (T+C) on the horizontal axis. If the graph is close to a straight line connecting the points, then "A, B, C" are the best-fit values; or, using the least squares method, electronic calculation program, or parallax method, the values ​​are increased or decreased in as small a step as possible, and different "C" values ​​are input, and the iteration process is repeated in formula (2). The R output from the calculation program system is 2 If the value is ≧0.990, it is considered to be a straight line; the obtained 15 parameters correspond to "A, B, C" respectively as the optimal constants; where the minimum boundary value of "C" is -273.

[0041] Furthermore, the material of the rigid plate is one of the following: ore to be filtered, stainless steel, carbon steel, copper alloy, aluminum alloy, ceramic, polytetrafluoroethylene, polyimide, and polyphenylene sulfide; or the two rigid plates are each selected from two different materials and used in combination. [Effects of the Invention]

[0042] The advantageous technical effects of the degradation life test method, algorithm and application of the new energy thermal management composite material of the present invention include: (1) The maximum degradation test temperature reaches 400°C, reducing the laboratory degradation test time by 90% from over 1,000 hours; (2) It is suitable for predicting the degradation life of materials with three phases, i.e., solid, liquid, and gas, and breaks the limitation of GB / T 20028, ASTM G 166, ASTM G169, ISO 2578, and UL 746B, which requires that the extended prediction temperature range be less than 0.8 times the difference between the highest and lowest test temperatures; (3) Suitable for evaluating or predicting the long-term practical service life of all polymer matrix composites; (4) Linear correlation coefficient R 2 is two "9" accuracy levels higher than GB / T 20028, ASTM G 166, ASTM G 169, ISO 2578, and UL 746B, making predictions more accurate. [Brief explanation of the drawings]

[0043] [Figure 1] FIG. 1 is a schematic diagram of the mathematical conversion of intrinsic thermal conductivity and intrinsic thermal conductivity for a given thickness of the present invention. [Figure 2] 1 is a photograph of the upper rigid plate, test object, and lower rigid plate of a compression specimen using a circular jig of the present invention. [Figure 3] 1 is a schematic diagram showing the case where the thermal conductivity of an elastic test object of the present invention having the same diameter is measured. [Figure 4] 1 is a schematic diagram showing the measurement of the equivalent thermal conductivity of elastic test objects of different diameters according to the present invention. FIG. [Figure 5] 1 is a schematic diagram showing the case where the upper and lower elastic bodies of the present invention having the same diameter are combined with a rigidity test object to measure the apparent thermal conductivity. [Figure 6] 1 is a schematic diagram showing the case where the upper and lower elastic bodies of different diameters according to the present invention are combined with a rigidity test object and the apparent thermal conductivity is measured. [Figure 7]1 is a schematic diagram showing the case where the apparent thermal conductivity is measured by combining the upper and lower elastic bodies of the present invention with the same diameters with a compressed specimen in a non-detachable jig. [Figure 8] 1 is a schematic diagram showing the case where the apparent thermal conductivity is measured by combining the upper and lower elastic bodies of different diameters of the present invention with a compressed specimen in a non-detachable jig. [Figure 9] 9A shows a compression specimen made by the circular jig of the present invention, in which "4.1A-4.2-4.3A" is a non-detachable butt-bonded integral type, and in FIG. 9B "4.1-4.2-4.3" is a detachable type. [Figure 10] This is a schematic diagram of the physical model of cavitation, migration, diffusion, and volatilization of small molecule substances and microelements of the present invention. [Figure 11] FIG. 10 is a top view of an example upper square clamping plate of the present invention. [Figure 12] FIG. 10 is a bottom view of an example of a lower rectangular clamping plate of the present invention. [Figure 13] FIG. 1 is a schematic diagram showing a pair of overlapping adhesive strips for measuring shear bond strength according to the international standard, and a pair of non-removable adherends. [Figure 14] 1 is a schematic diagram of a case where a pair of overlapping adhesive pieces of a compression specimen is compressed by a square jig of the present invention. [Figure 15] FIG. 1 is a front view of an upper electrode head for measuring the dielectric breakdown strength of the international standard. [Figure 16] FIG. 1 is a front view of a lower electrode head for measuring the dielectric breakdown strength of the international standard. [Figure 17] FIG. 10 is a front view of a compression specimen using the square jig of the present invention, in which an electrode head and a test object for a dielectric breakdown strength test are compressed. [Figure 18] 1A and 1B are front and partial axial cross-sectional views of a compression specimen using a square jig according to the present invention, showing an electrode head and a test object for a volume resistivity test being compressed. [Figure 19] 1 is a graph showing a comparison of the measured and predicted intrinsic thermal conductivity of P20 at 298° C. and three compression ratios according to Example 1. [Figure 20]1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P40 at 298° C. and three compression ratios according to Example 1. [Figure 21] 1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P20 at 272° C. and three compression ratios according to Example 1. [Figure 22] 1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P40 at 272° C. and three compression ratios according to Example 1. [Figure 23] 1 is a graph showing a comparison of the measured and predicted intrinsic thermal conductivity of P20 at 245° C. and three compression ratios according to Example 1. [Figure 24] 1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P40 at 245° C. and three compression ratios according to Example 1. [Figure 25] 1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P20 at 218° C. and three compression ratios according to Example 1. [Figure 26] 1 is a graph showing a comparison of measured and predicted values ​​of the intrinsic thermal conductivity of P40 at 218° C. and three compression ratios according to Example 1. [Figure 27] 1 is a graph showing the long-term degradation trend of the intrinsic thermal conductivity of P20 and P40 at 195° C. using equation (1.1) according to Application Example 1. [Figure 28] 1 is a graph showing the long-term degradation trend of the intrinsic thermal conductivity of P20 and P40 at 160° C. using equation (1.1) according to Application Example 1. [Figure 29] 1 is a graph showing the long-term degradation trend of the intrinsic thermal conductivity of P20 and P40 at 125° C. using equation (1.1) according to Application Example 1. [Figure 30] 1 is a graph showing the long-term deterioration trend of the intrinsic thermal conductivity of P20 and P40 at 95° C. using equation (1.1) according to Application Example 1. [Figure 31] 1 is a graph showing the long-term deterioration trend of the intrinsic thermal conductivity of P20 and P40 at 75° C. using equation (1.1) according to Application Example 1. [Figure 32]1 is a graph showing the long-term deterioration trend of the intrinsic thermal conductivity of P20 and P40 at 50° C. using equation (1.1) according to Application Example 1. [Figure 33] 1 is a graph showing the long-term deterioration trends of the intrinsic thermal conductivity of P20 and P40 at 37° C. using equation (1.1) according to Application Example 1. [Figure 34] FIG. 10 is a schematic diagram of a method for evaluating the rated temperature of P40 using equation (1.1) according to Application Example 8. [Figure 35] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 245° C. [Figure 36] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 218° C. [Figure 37] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 195° C. [Figure 38] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 150° C. [Figure 39] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 97° C. [Figure 40] 10 is a graph showing a comparison between the measured and predicted values ​​of the compression set rate of S20 in Example 2 when the compression rate is 30% at 85° C. [Figure 41] 10 is a graph showing the long-term deterioration tendency of the compression set rate of S20 at three actual use temperatures using formula (1.2) according to Application Example 2. [Figure 42] FIG. 42 is a partial enlarged view of the time axis of FIG. 41. [Figure 43] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compression ratio is 30% at 245° C. according to Example 3. [Figure 44] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compression ratio is 30% at 218° C. according to Example 3. [Figure 45] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compression ratio is 30% at 195° C. according to Example 3. [Figure 46] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compression ratio is 30% at 150° C. according to Example 3. [Figure 47] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compressibility is 30% at 97° C. according to Example 3. [Figure 48] 10 is a graph showing a comparison between the measured values ​​and the predicted values ​​of the hardness of S20 when the compression ratio is 30% at 85° C. according to Example 3. [Figure 49] 10 is a graph showing the long-term deterioration tendency of the hardness of S20 at three actual use temperatures using formula (1.3) according to Application Example 3. [Figure 50] FIG. 50 is a partial enlarged view of the time axis of FIG. 49. [Figure 51] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 in Example 4 when the compression ratio is 30% at 245° C. [Figure 52] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 in Example 4 when the compression ratio is 30% at 218° C. [Figure 53] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 in Example 4 when the compression ratio is 30% at 195° C. [Figure 54] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 in Example 4 when the compression ratio is 30% at 150° C. [Figure 55] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 when the compression ratio is 30% at 97° C. according to Example 4. [Figure 56] 10 is a graph showing a comparison between the measured and predicted values ​​of the tensile strength of S20 in Example 4 when the compression ratio is 30% at 85° C. [Figure 57] 10 is a graph showing the long-term deterioration tendency of the tensile strength of S20 at three actual use temperatures using formula (1.4) according to Application Example 4. [Figure 58]FIG. 58 is a partial enlarged view of the time axis of FIG. 57. [Figure 59] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 in Example 5 when the compression ratio is 30% at 245° C. [Figure 60] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 in Example 5 when the compression ratio is 30% at 218° C. [Figure 61] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 in Example 5 when the compression ratio is 30% at 195° C. [Figure 62] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 in Example 5 when the compression ratio is 30% at 150° C. [Figure 63] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 when the compression ratio is 30% at 97° C. according to Example 5. [Figure 64] 10 is a graph showing a comparison between the measured and predicted values ​​of shear adhesive strength of S20 in Example 5 when the compression ratio is 30% at 85° C. [Figure 65] 10 is a graph showing the long-term deterioration tendency of the shear adhesive strength of S20 at three actual use temperatures using formula (1.5) according to Application Example 5. [Figure 66] FIG. 66 is a partial enlarged view of the time axis of FIG. 65. [Figure 67] 10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the compression ratio is 30% at 245° C. [Figure 68] 10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the temperature is 218° C. and the compression ratio is 30%. [Figure 69] 10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the compression ratio is 30% at 195° C. [Figure 70] 10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the compression ratio is 30% at 150° C. [Figure 71]10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the temperature is 97° C. and the compression ratio is 30%. [Figure 72] 10 is a graph showing a comparison between the measured and predicted values ​​of the dielectric breakdown strength of S20 in Example 6 when the temperature is 85° C. and the compression ratio is 30%. [Figure 73] 10 is a graph showing the long-term deterioration tendency of the dielectric breakdown strength of S20 at three actual use temperatures using formula (1.6) according to Application Example 6. [Figure 74] FIG. 74 is a partial enlarged view of the time axis of FIG. 73. [Figure 75] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compressibility is 30% at 245° C. according to Example 7. [Figure 76] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compressibility is 30% at 218° C. according to Example 7. [Figure 77] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compressibility is 30% at 195° C. according to Example 7. [Figure 78] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compression ratio is 30% at 150° C. according to Example 7. [Figure 79] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compressibility is 30% at 97° C. according to Example 7. [Figure 80] 10 is a graph showing a comparison between the measured and predicted values ​​of the natural logarithm of the volume resistivity of S20 when the compressibility is 30% at 85° C. according to Example 7. [Figure 81] 10 is a graph showing the long-term deterioration tendency of the natural logarithm of the volume resistivity of S20 at three actual use temperatures using equation (1.7) according to Application Example 5. [Figure 82] FIG. 82 is a partial enlarged view of the time axis of FIG. 81. DETAILED DESCRIPTION OF THE INVENTION

[0044] In FIGS. 1 to 82, components with the same functions and structures are denoted by the same reference numerals, and for the sake of simplicity, the reference numerals of components located in symmetrical positions or in the same series of positions are omitted.

[0045] The following describes the degradation life test method, algorithm and technical content of the application of the new energy thermal management composite material of the present invention, the structural features of the test specimen, and the objectives and effects achieved, with reference to eight examples and the accompanying drawings.

[0046] The eight application examples of the present invention list applications of the present invention to evaluate or predict eight physicochemical properties and electrical properties, namely, thermal conductivity, compression set rate, hardness, tensile strength, shear adhesive strength, dielectric breakdown strength, volume resistivity, and rated temperature, and are not intended to limit the content and applications of the present invention.

[0047] In the eight application examples of the present invention, the wet heat, thermal shock, and thermal cycle conditions of the short-term accelerated aging test were all completed in an air atmosphere, which is not intended to limit the test atmosphere.

[0048] (1. Example 1: Evaluation or Prediction of Practical Use Life of Thermal Conductivity) This Example 1 discloses one of the degradation life test methods, algorithms, and applications of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the thermal conductivity of the interface test object during long-term actual use under actual operating conditions. The test includes jig-compressed specimens with three compression rates of 10%, 20%, and 30%. The jig-compressed specimens in this group were subjected to 12 specified periods of time under specified hygrothermal conditions in four constant temperature environments selected within a temperature range of (218-298)°C. The thermal conductivity of the test object in the jig-compressed specimen was tested using the laminated composite specimens shown in Figures 5 to 8 in accordance with the test procedure specified in ASTM D5470. The actual measured thermal conductivity values ​​were used to calculate the corresponding 15 parameters in the micro-evaporation expansion oscillation equation (1.1). The fitted parameter values ​​were then used to fit three corresponding constants "A, B, and C" to the kinetic correlation equation (2.1); the fitted three constant values ​​were substituted into the kinetic correlation equation (2.1) to calculate new values ​​for each parameter at actual operating temperatures of 195°C, 160°C, 125°C, 95°C, 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.1) to evaluate or predict the long-term change trend of the thermal conductivity of the test object over time after the specified actual operating time under humid heat conditions of 195°C, 160°C, 125°C, 95°C, 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 1.1 to 1.7 below.

[0049] 1.1 Preparation of compression specimens using a jig As shown in Figures 2 and 10.

[0050] 1.1.1 Thermally conductive interface material for new energy power battery pack, test object 4.2, there are two products: 1) Thermally conductive silicone rubber sheet: nominal thermal conductivity 2W / (mK), thickness 2.5mm, abbreviated as P20; 2) Thermally conductive silicone rubber sheet: Nominal thermal conductivity 4W / (mK), thickness 1.5mm, abbreviated as P40.

[0051] 1.1.2 Preparation of compression specimens in a jig includes the following: a) Cut the thermal conductive rubber sheet P20 into a disk shape using a round die with an inner diameter of 30 mm, and sandwich the P20 disk as the test object 4.2 between the upper rigid plate 4.1 and the lower rigid plate 4.3 (actually, two 304 stainless steel plates). Three metal screws 8 are used to fasten the upper rigid plate 4.1 and the lower rigid plate 4.3. The compression ratio of the initial thickness of three groups of test objects 4.2 is controlled to 10%, 20% and 30%, and the total number of test objects 4.2 is more than 432, that is, 3 thermal conductivity parallel sub-size specimens x 3 groups of compression ratios x 4 degradation temperatures x 12 degradation points, which are used to test the apparent thermal conductivity of the laminated composite test specimen sandwiched between the test objects 4.2 during the degradation process; b) Cut the thermal conductive rubber sheet P20 into a disk shape using a round die with an inner diameter of 30 mm, and sandwich the P20 disk as the test object 4.2 between the upper rigid plate 4.1 and the lower rigid plate 4.3 (actually, two copper-nickel plated plates). Three metal screws 8 are used to fasten the upper rigid plate 4.1 and the lower rigid plate 4.3, and the compression ratio of the initial thickness of three groups of test objects 4.2 is controlled to 10%, 20% and 30%. The total number of test objects 4.2 is 432 or more, that is, 3 thermal conductivity parallel sub-size specimens x 3 groups of compression ratios x 4 degradation temperatures x 12 degradation points, which are used to test the apparent thermal conductivity of the laminated composite test specimen sandwiching the test object 4.2 during the degradation process; c) drilling an annular groove on one surface of the upper rigid plate 4.1, drilling at least six countersunk holes evenly spaced around the circumference in the annular groove, three of the countersunk holes being tightly fitted with three metal screws 8 during the degradation process, and the other three countersunk holes being tightly fitted with three engineering plastic screws 8 during the thermal conductivity test after degradation, and then removing the three metal screws 8 and sinking the nuts 10 of the screws 8 into the annular groove; d) drilling, on the plane of the lower rigid plate 4.3, threaded holes evenly spaced around the circumference, into which six screws 8 are fitted; e) Since the rigid plate is small, it does not warp or deform under the conditions of external force in the test of this example, and therefore there is no need to provide a reinforcing material for this rigid plate.

[0052] 1.2 Degradation test procedure 1.2.1 Deteriorating equipment In this Example 1, two standard degradation test boxes conforming to the ISO 188 standard were used.

[0053] In this Example 1, DRL-III and DRL-V thermal conductivity meters conforming to ASTM D5470 were used.

[0054] 1.2.2 Degradation Process In this Example 1, four temperature groups were used, and each group was further divided into 12 degradation time groups. The jig-compressed specimens of each group were placed in four standard degradation test boxes that conformed to the fixed temperatures specified in Table 1. The jig-compressed specimens of each compression rate subgroup of each time group included three parallel sub-size specimens. The jig-compressed specimens were previously marked as temperature groups, time groups, and compression rate subgroups, and operation transition records were created.

[0055] When the temperature of each middle group of jig-compressed specimens reaches the temperature and time specified in Table 1, the middle group of jig-compressed specimens is removed from the large group of high-temperature aging test boxes and left at room temperature for 16 to 96 hours. Then, under standard test conditions, the middle group of jig-compressed specimens are left at room temperature for 30 minutes or more. The apparent thermal conductivity and total thermal resistance of the 5-layer coaxial laminate composite specimens after aging are tested according to the method shown in Figure 8. The apparent thermal conductivity (λ) of the 5-layer coaxial laminate composite specimens is calculated according to Equation (7) using commercially available software or an electronic calculation table. a ) to the equivalent thermal conductivity (λ e4.2 ) and further convert it into the thermal conductivity of the corresponding thickness (λ n4.2 ) and calculate the thermal conductivity (λ) of the corresponding thickness using the least squares method according to Figure 1. n4.2 ) is expanded to the intrinsic thermal conductivity (λ).

[0056] 1.2.3 Degradation temperature and time The degradation temperature and degradation time in this Example 1 were as shown in Table 1.

[0057] [Table 1]

[0058] [Table 2]

[0059] 1.3 Parameter Deterioration Consideration Target In this Example 1, the selection of intrinsic thermal conductivity as the target of deterioration consideration test, evaluation and prediction is not intended to limit the application. Under the given conditions of the test in each test laboratory, any parameter from the apparent thermal conductivity, equivalent thermal conductivity, inherent thermal conductivity, or intrinsic thermal conductivity was arbitrarily selected.

[0060] 1.4 Thermal Conductivity Testing and Conversion 1.4.1 Terms and Definitions To clarify the technical concept, the present invention provides the following terms and definitions.

[0061] 1) Apparent thermal conductivity (λ a ) Total equivalent thermal conductivity of laminated composite specimen measured with a DRL type thermal conductivity meter.

[0062] Intuitively, as shown in Figures 5 and 6, a three-layer laminated composite test specimen is constructed using an upper auxiliary elastic sheet 3 with known thermal conductivity, a rigid test object 4.2, and a lower auxiliary elastic sheet 5 with known thermal conductivity; and as shown in Figures 7 and 8, the upper rigid plate 4.1, the test object 4.2, and the lower rigid plate 4.3 are first clamped and compressed into an irremovable combined specimen 4, and then a five-layer laminated composite test specimen is constructed using the upper auxiliary elastic sheet 3 with known thermal conductivity, the irremovable combined specimen 4, and the lower auxiliary elastic sheet 5 with known thermal conductivity. The total equivalent thermal conductivity of the three-layer laminated composite test specimen and the five-layer laminated composite test specimen, i.e., the apparent thermal conductivity (λ a ) is measured.

[0063] 2) Equivalent thermal conductivity (λ e ) 4.2 Thermal conductivity of a single layer test object measured with a DRL type thermal conductivity meter at a given temperature, pressure, thickness and diameter of the given specimen.

[0064] 3) The thermal conductivity of the material itself (λ n ) 4.2 Thermal conductivity of single layer test object at different thicknesses at given temperature and pressure.

[0065] 4) Specific thermal conductivity (λ) 4.2 Thermal conductivity of a single-layer test object at a given temperature and pressure that does not vary with the size and shape of the specimen.

[0066] Intuitively, as shown in FIG. 1, in this Example 1, the equivalent thermal conductivity (λ) of the same test object 4.2 of various thicknesses at the same pressure was measured using a DRL type thermal conductivity meter. e ) and calculate its own thermal conductivity (λ n ), and then its own thermal conductivity (λ n ) is the vertical axis, and the corresponding thickness (δ 4.2 ) on the horizontal axis, and draw a graph to measure thicknesses greater than 0.75 mm (δ 4.2 ) corresponding to the thermal conductivity of the material itself (λ n ) as a sample, and perform a linear fit to find the thickness (δ 4.2 ) to the intersection of the vertical axis, which is taken as the intrinsic thermal conductivity (λ).

[0067] 5) No phase change There is no microelement cavitation or microelement gas film added during the degradation process at the interface between the interior of test object 4.2 and the laminated composite test specimen, or the effects of microelement cavitation and microelement gas film are negligible.

[0068] 6) The vibration state 4.2 The interior of the test object and the interface of the laminated composite test specimen will have no cavitation of dispersed microelements added during a certain period of time, no gas film of dispersed microelements added, or the non-negligible effects of cavitation of microelements and gas film of microelements will remain, which will change over time.

[0069] 7) Micro-vaporization The cavitation of the added dispersed microelements and the gas film of the added dispersed microelements continued to exist inside the test object 4.2 and at the interface of the laminated composite specimen, or the non-negligible effects of the cavitation of the microelements and the gas film of the microelements remained.

[0070] 8) Contact thermal resistance When separate materials are in contact with each other, the additional resistance to heat transfer that occurs at the contact interface.

[0071] 9) Rated temperature The maximum temperature that can be endured when the physical, chemical and electrical properties decrease by half or increase by two times after 20,000 hours of continuous use in a constant temperature environment.

[0072] 10) Standard test conditions The ambient temperature of the test room is (25±2)°C and the relative humidity is (55±15)%.

[0073] 11) Phase state of matter A physical state in which changes in temperature or pressure of a macroscale material can result in solid-crystal transformations, solid-liquid eutectics, solid-liquid-gas eutectics, and solid-liquid-gas coexistence (microvaporization) on the microscale or mesoscale.

[0074] 1.4.2 Thermal conductivity test method In this Example 1, thermal conductivity testing was performed using laminated composite specimens, including: a) For single-layer specimens, as shown in Figures 3 and 4, the equivalent thermal conductivity (λ) of an elastic specimen or a precisely manufactured rigid test object is e ) and b) In the three-layer specimen, the upper auxiliary elastic sheet 3 and the lower auxiliary elastic sheet 5 are used as shown in Figures 5 and 6. The equivalent thermal conductivity (λ e ) and calibrate c) For the five-layer specimen, as shown in Figures 7 and 8, the apparent thermal conductivity (λ) of the non-removable combined specimen 4 was measured using the upper auxiliary elastic sheet 3 and the lower auxiliary elastic sheet 5. a ) is measured.

[0075] 1.4.3 Thermal conductivity conversion In this Example 1, we propose a conversion relationship for thermal conductivity between different definitions, including:

[0076] 1) Equivalent thermal conductivity (λ) of single-layer specimene ) by its own thermal conductivity (λ n ) is corrected. As shown in Figures 3 and 4, when the material and thickness of the test object 4.2 are the same, but the diameters of the high-temperature probe 2 and the low-temperature probe 6 of the DRL-type thermal conductivity meter are different, the measured equivalent thermal conductivity (λ e ) and its own thermal conductivity (λ n ) exceeds the allowable random error range, and the equivalent thermal conductivity (λ e ) into the thermal conductivity (λ n ) needs to be corrected.

[0077]

number

[0078] 2) Apparent thermal conductivity (λ) of the three-layer specimen a ) to the equivalent thermal conductivity (λ e ) As shown in FIGS. 5 and 6, the thicknesses of the upper auxiliary elastic sheet 3 and the lower auxiliary elastic sheet 5 are known as δ3 and δ5, respectively, and the corresponding diameters ψ3 and ψ5 are known. The corresponding thermal conductivity λ n3 and λ n5 is known, and the thickness δ of the test object 4.2 4.2 and diameter ψ 4.2 If is known, the apparent thermal conductivity λ of the three-layer coaxial laminate composite specimen is a After measuring, the thermal conductivity λ of the test object 4.2 itself is calculated according to equations (4) and (5). n4.2 In heat transfer, there is the following:

[0079]

number

[0080] By rearranging and transforming Equation (4), the thermal conductivity (λ e4.2 ) is derived.

[0081]

number

[0082] 3) Apparent thermal conductivity (λ) of the five-layer specimen a ) to the equivalent thermal conductivity (λ e ) As shown in FIGS. 7 and 8, the thicknesses of the upper auxiliary elastic sheet 3 and the lower auxiliary elastic sheet 5 are known as δ3 and δ5, respectively, and the corresponding diameters ψ3 and ψ5 are known. The corresponding thermal conductivities λ n3 and λ n5 is known, and the thickness of the upper rigid plate 4.1 and the lower rigid plate 4.3 is δ 4.1 and δ 4.3 and known, and the corresponding diameter ψ 4.1 and ψ 4.3 is known, the corresponding thermal conductivity λ n4.1 and λ n4.3 is known, and the thickness δ of the test object 4.2 4.2 and diameter ψ 4.2 If is known, the apparent thermal conductivity λ of a five-layer coaxial laminate composite specimen a After measuring, the thermal conductivity λ of the test object 4.2 itself is calculated according to equations (6) and (7). n4.2 Based on heat transfer theory, the thermal conductivity of the nylon bolt 22 is only about 0.25 W / (mK), and the product of the thermal conductivity and the heat transfer area is less than 4 / 1000 of the test object 4.2, so the heat flow accounts for less than 1 / 1000 of the total heat flow, and therefore the heat flow of the nylon bolt 22 can be completely ignored mathematically.

[0083]

number

[0084] By rearranging and converting the terms in Equation (6), the thermal conductivity (λ e4.2 ) is derived.

[0085]

number

[0086] 1.5 Test Results When expressing the test results of thermal conductivity, the apparent thermal conductivity (λ a ), equivalent thermal conductivity (λ e ), its own thermal conductivity (λ n ), or the intrinsic thermal conductivity (λ), which are equivalent, but in this Example 1, in order to unify the concept, the apparent thermal conductivity (λ) during the test process is used. a ), equivalent thermal conductivity (λ e ), its own thermal conductivity (λ n ) was converted to a specific thermal conductivity (λ) for uniformity. This is only one form of application and is not intended to limit application.

[0087] 1.5.1 Initial Thickness The initial thicknesses of the upper auxiliary elastic sheet 3, the upper rigid plate 4.1, the test object 4.2, the lower rigid plate 4.3, and the lower auxiliary elastic sheet 5 in FIGS.

[0088] 1.5.2 Initial thermal conductivity The initial intrinsic thermal conductivities of the upper auxiliary elastic sheet 3, the upper rigid plate 4.1, the test object 4.2, the lower rigid plate 4.3, and the lower auxiliary elastic sheet 5 in FIGS.

[0089] [Table 3]

[0090] [Table 4]

[0091] 1.5.3 Thermal conductivity after degradation In Example 1, the specimen was aged at a constant temperature of 298°C, and the specific thermal conductivity changed with aging time under three compression rates of 10%, 20%, and 30%. The measured values ​​are shown in Table 4. In Example 1, the specimen was aged at a constant temperature of 272°C, and the specific thermal conductivity changed with aging time under three compression rates of 10%, 20%, and 30%. The measured values ​​are shown in Table 5. In Example 1, the specimen was aged at a constant temperature of 245°C, and the specific thermal conductivity changed with aging time under three compression rates of 10%, 20%, and 30%. The measured values ​​are shown in Table 6. In this Example 1, the material was degraded at a constant temperature of 218°C, and the specific thermal conductivity changed with the degradation time under three compression rates of 10%, 20%, and 30% of the jig. The measured values ​​are shown in Table 7.

[0092] 1.6 Establishment of the thermal conductivity equation (1.1) 1.6.1 T-test In this Example 1, the purpose of conducting the T-test is to examine the degree of difference in P20 or P40 on the thermal conductivity degradation effect under the three conditions of compression rates of 10%, 20%, and 30%.

[0093] The T-test results before degradation are shown in Table 8.

[0094] The T-test results after degradation are shown in Table 9.

[0095] As can be seen from Table 8, under the three compression rates of the jig, the T value of P20 before degradation was greater than the difference boundary value, indicating that there was a significant difference in the effects of the three compression rates on thermal conductivity, and the measured values ​​belonged to different specimen groups. Under the three compression rates of the jig, the T value of P40 before degradation was one value less than the difference boundary value, i.e., at compression rates of 10% and 30%, respectively, there was almost no significant difference, which means that the data of the two groups can be said to correspond to the same specimen, and that P40 has better compressive strength than P20.

[0096] As can be seen from Table 9, under the three compression rates of the jig, the T value of P20 before degradation was slightly larger than the boundary value of the difference, and the T value of P40 before degradation was two levels smaller than the boundary value of the difference, i.e., compressed at compression rates of 10%, 20%, and 20% and 30%, respectively. This indicates that after degradation, the difference between P20 and P40 at different compression rates is negligible. The main difference is due to the effects of high temperature and time degradation. All sample data for the three compression rates can be merged into a sample with the same compression rate.

[0097] Therefore, when applying and predicting the long-term deterioration trend in a pseudo-integrated manner, the data of the three compression rates of 10%, 20%, and 30% can be combined into one set of average values ​​for observation and processing.

[0098] Therefore, the T-test results for the degradation at the other three temperatures are consistent with the above conclusion. To reduce the number of pages, the T-test results before and after degradation at the other three temperatures are omitted in this Example 1.

[0099] [Table 5]

[0100] [Table 6]

[0101] [Table 7]

[0102] [Table 8]

[0103] [Table 9]

[0104] [Table 10]

[0105] [Table 11]

[0106] [Table 12]

[0107] 1.6.2 Parameter fitting of equation (1.1) One application of the algorithm of the present invention is to replace the symbols (P) for physicochemical and electrical properties in equation (1) with the symbol (λ) for the specific thermal conductivity and convert it to equation (1.1).

[0108]

number

[0109] TIFF0007749194000024.tif114166

[0110] In the formula (1.1) of this Example 1, it is difficult to obtain the 15 parameters including the thermal conductivity through linear fitting. However, the average values ​​of the measured values ​​under the three compression ratios in Tables 4 to 7 are used as samples, and starting from the provisional assignment of "Q = 0", the iterative process is repeated using the Parallax method with the smallest possible step value, and input into the formula (1.1). After each parameter has been iterated 50 times or more, the calculated value (λ t The standard deviation of the difference between the predicted value (λ) and the measured value (λ) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for thermal conductivity at various temperatures are obtained. The results of the iterative optimization of P20 and P40 are shown in Tables 10 and 11. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters obtained, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0111] [Table 13]

[0112] [Table 14]

[0113] [Table 15]

[0114] [Table 16]

[0115] 1.6.3 Constant fitting in equation (2.1) In Example 1, the thermal conductivity equation (1.1) includes 15 parameters in Tables 10 and 11. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in Equation (2). Referring to Tables 10 and 11, when fitting, the constants of the parameters corresponding to the thermal conductivity equation (1.1) in Equation (2) need to be replaced with the corresponding symbols to convert it to Equation (2.1).

[0116]

number

[0117] Replace "Q" in equation (2.1) with one of the 15 parameters in Tables 10 and 11, plot the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and start with a provisional assignment of "C=0." Repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method, input different "C" values, and the calculation program system will automatically calculate R. 2If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the obtained 15 parameters one by one are shown in Table 10 and Table 11, respectively.

[0118] [Table 17]

[0119] [Table 18]

[0120] [Table 19]

[0121] [Table 20]

[0122] 1.7 Prediction of changes in thermal conductivity of P20 and P40 In this Example 1, by substituting the three corresponding constants "A, B, C" in Tables 10 and 11 and any actual use temperature below 298°C into the "general formula" or rearrangement formula, which is formula (2.1) in Tables 10 and 11, and substituting each to obtain new values ​​of the 15 corresponding parameters "Q", and then substituting any actual use time and the new "Q" values ​​of the corresponding 15 parameters into formula (1.1), the change tendency of the thermal conductivity of P20 and P40 at any temperature and any time can be evaluated in advance.

[0123] [Table 21]

[0124] [Table 22]

[0125] Table 23

[0126] Table 24

[0127] Table 25

[0128] Table 26

[0129] Table 27

[0130] Table 28

[0131] Table 29

[0132]

Table 30

[0133] Table 31

[0134] Table 32

[0135] Table 33

[0136] [Table 34]

[0137] [Table 35]

[0138] [Table 36]

[0139] [Table 37]

[0140] [Table 38]

[0141] [Table 39]

[0142] [Table 40]

[0143] [Table 41]

[0144] 1) The changes in thermal conductivity with degradation time at the three predicted compression ratios and actual use temperatures of 298°C, 272°C, 245°C, and 218°C are shown in Tables 4 to 7. Graphs were drawn with thermal conductivity on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 4 to 7 are shown in Figures 19 to 26.

[0145] 2) The changes in thermal conductivity with actual use time at the three predicted compression ratios and actual use temperatures of 195°C, 160°C, 125°C, 95°C, 75°C, 50°C, and 37°C are shown in Tables 12 to 18. Graphs were drawn with thermal conductivity on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 12 to 18 are shown in Figures 27 to 33.

[0146] As long as the constraints under actual operating conditions are the same as those under test conditions simulating accelerated aging in the laboratory, with only the temperature being different, Equations (1) and (2) can be expressed as R 2 If it is ≧0.999, it can be used to predict the deterioration tendency of thermal conductivity under actual operating conditions.

[0147] 3) The square error of the deviation between the predicted thermal conductivity result and the actual measured value of the DRL type thermal conductivity meter is within the range of ±1.96 times the standard deviation of the DRL type thermal conductivity meter itself.

[0148] It is worth noting that the recognized high temperature accelerated aging framework standards for predicting material degradation life, GB / T 20028, ASTM G166, ASTM G169, ISO 2578, and UL 746B, have a temperature range limit of "extended predictions of less than 0.8 times the difference between the highest and lowest test temperatures."

[0149] The Arrhenius equation used in these framework standards belongs to a single chemical composition, a single activation energy, and a single crystalline phase region. Therefore, when making predictions, it is necessary to limit the expansion / prediction temperature range so that the test temperature range and the predicted temperature range can be expected to belong to the same activation energy and the same crystalline phase region as much as possible, and to ensure that the prediction results do not exceed the allowable error range.

[0150] However, the algorithm of the present invention, Equation (1), is a mathematical model that is established and tested based on the activation energies of various chemical reactions and mechanisms across phase regions, and has a linear correlation coefficient R 2The accuracy reaches four "9" levels, and when extending and predicting the degradation temperature range, it can break through the range of 0.8 times the test temperature range, which is another ultimate important role of this invention.

[0151] (2. Example 2: Evaluation or Prediction of Actual Use Life of Compression Set Rate) This Example 2 discloses another embodiment of the degradation life test method, algorithm and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the compression set rate of the interface test object during long-term actual use under actual operating conditions, including a jig-compressed specimen using a 30% compression rate. The jig-compressed specimens in this group were subjected to three degradation conditions, namely, damp heat, thermal shock, and thermal cycle, for a specified time or cumulative number of cycles in each specified constant temperature environment. The jig-compressed specimen shown in Figure 9 was used to measure the compression set rate of the test object 4.2 according to the test procedure specified in GB / T 7759.1, GB / T 7759.2 or ASTM D395. The actual measured compression set rate was used to calculate the corresponding 15 parameters in the micro-evaporation expansion oscillation equation (1.2). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" contained in the kinetic correlation equation (2.2); the fitted three constant values ​​were substituted into the kinetic correlation equation (2.2) to calculate new values ​​for each parameter at actual use temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.2) to evaluate or predict the long-term change trend of the compression set rate over time after the specified actual use time or cumulative number of cycles had passed under the conditions of humid heat, thermal shock, and thermal cycling at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 2.1 to 2.5 below.

[0152] 2.1 Preparation of compression specimens using a jig As shown in Figure 9, for the compression specimen in the compression set jig, a uniformly mixed toothpaste-like test object 4.2 is injected between an aluminum alloy upper butt bonded plate 4.1A and a lower butt bonded plate 4.3A, and then placed on a support mold. The upper butt bonded plate 4.1A, test object 4.2, and lower butt bonded plate 4.3A are allowed to harden into a parallel, coaxial, "sandwich" integrated structure. Metal screws 8, upper rigid plate 4.1, and lower rigid plate 4.3 are then used to fasten the upper butt bonded plate 4.1A and lower butt bonded plate 4.3A together, adjusting the thickness of the test object 4.2 to 70% of its initial thickness, i.e., a compression rate of 30%.

[0153] Here, the test object 4.2 is selected as a two-component thermally conductive organic silicone rubber with a nominal thermal conductivity of 2 W / (mK) and an initial thickness of 6.4 mm after casting, and is abbreviated as S20.

[0154] 2.2 Three Deterioration Conditions In this Example 2, the three degradation conditions include damp heat, thermal shock and thermal cycle, and due to the many details, three sections are used to further disclose the following.

[0155] 2.2.1 Humid heat conditions Four large groups of jig-compressed specimens were used, each of which was divided into at least seven subgroups. The number of parallel sub-size specimens in each subgroup was determined in accordance with the corresponding physicochemical and electrical property testing technical standards (the number of parallel sub-size specimens in Example 2 was three). Each group was placed in one of four standard degradation test boxes conforming to the constant temperatures specified in Table 19. The jig-compressed specimens were marked in advance as temperature groups and time subgroups, and a record of the operation transitions was created. After each small group of jig-compressed specimens reaches the temperature and time specified in Table 19, the small group of jig-compressed specimens is removed from the large group of high-temperature degradation test boxes, and left at room temperature for 16 to 96 hours, and then left at room temperature for 30 minutes or more under standard test conditions. The physicochemical and electrical property indicators of the test object 4.2 after degradation are tested according to the test procedures specified in the corresponding physicochemical and electrical property testing technical standards (the compression set rate of this Example 2 is tested according to the test procedures specified in GB / T 7759.1, GB / T 7759.2, or ASTM D395).

[0156] In this case, the temperature of the aging test chamber is 195°C and the temperature of the aging test chamber is 150°C, and the dry air atmosphere is used, and the theoretical relative humidity is ≦15% and ≦30%, respectively; In this case, the relative humidity in the aging test chamber at 97°C is determined based on the vapor-liquid equilibrium relative humidity of one of the saturated salt solutions or glycerin aqueous solution listed in Table 20. A white enamel bowl measuring 420 mm long, 320 mm wide, and 35 mm deep is filled with a saturated potassium sulfate solution conforming to GB / T 16496, a saturated potassium chloride solution conforming to GB / T 7118, or a 15±5% glycerin solution conforming to GB / T 13206. The enamel bowl is placed at the bottom of the aging test chamber. During the isothermal process, water must be added periodically to ensure that water and undissolved solids are always present in the enamel bowl, or that the liquid level of the glycerin aqueous solution remains between the pre-specified maximum and minimum levels. At temperatures between 96°C and 98°C, under sealed conditions with the aging test chamber door and fresh air ventilation system closed, the relative humidity controlled by this example can be measured with an accuracy of (Rh±1)% as listed in Table 20. According to the specifications of the sample removal time in Table 19, the opening and closing time of the box door is short, and the calculation time is a few seconds, ignoring the influence of humidity; Here, in the deterioration test box at 85°C, the relative humidity is achieved by automatically controlling the evaporation of water using a sensor system built into the deterioration test box.

[0157] [Table 42]

[0158] [Table 43]

[0159] [Table 44]

[0160] [Table 45]

[0161] [Table 46]

[0162] [Table 47]

[0163] [Table 48]

[0164] 2.2.2 Thermal shock conditions Four large groups of jig-compressed specimens were used, each of which was divided into at least seven subgroups. The number of parallel sub-size specimens in each subgroup was determined in accordance with the corresponding physicochemical and electrical property testing technical standards (the number of parallel sub-size specimens in Example 2 was three). Each group was placed in one of four standard degradation test boxes conforming to the constant temperatures specified in Table 21. The jig-compressed specimens were marked in advance as temperature groups and time subgroups, and a record of the operation transitions was created. When each group of jig-compressed specimens reaches the temperature and time specified in Table 21, the corresponding group of jig-compressed specimens is removed from the corresponding group of high-temperature aging test box and placed in a freezer with a predetermined temperature specified in Table 22 within 10 seconds. When each group of jig-compressed specimens reaches the constant temperature holding time specified in Table 22, the corresponding group of jig-compressed specimens is removed from the low-temperature freezer and left to stand at room temperature for 16 to 96 hours, and then left to stand for more than 30 minutes under standard test conditions. The physicochemical and electrical property indicators of the test object 4.2 after aging are tested according to the test procedures specified in the corresponding physical and chemical properties and electrical property testing technical standards (the compression set rate of this Example 2 is tested according to the test procedures specified in GB / T 7759.1, GB / T 7759.2 or ASTM D395).

[0165] Here, when evaluating or predicting using formula (1.2) and formula (2.2), the low-temperature constant temperature holding time in Table 22 was not entered, and only the high-temperature constant temperature holding time in Table 21 was entered.

[0166] 2.2.3 Thermal cycle conditions Four large groups of jig-compressed specimens were used, each of which was divided into at least seven subgroups. The number of parallel sub-size specimens in each subgroup was determined in accordance with the corresponding physicochemical and electrical property testing technical standards (the number of parallel sub-size specimens in Example 2 was three). Each group was placed in four standard degradation test boxes conforming to the constant temperatures specified in Table 21. The jig-compressed specimens were previously marked as temperature groups and time subgroups, and operation transition records were created; they were operated according to the thermal cycle. 1) Place the four large groups of jig-compressed specimens into four standard aging test boxes, respectively, and heat them to the temperature specified in Table 23. After the constant temperature holding time reaches 1 hour, turn off the heating system of the aging test box, turn on the outdoor air ventilation system of the aging test box, control the ventilation volume, and lower the temperature to 50°C~room temperature at a rate of (5~10)°C / min. Then, transfer all the jig-compressed specimens in the four standard aging test boxes into a freezer with an initial temperature of 0°C~room temperature; 2) Next, the temperature is lowered to (-40±1)℃ at a rate of (5~10)℃ / min. After the temperature is maintained for 1 hour, the freezer's refrigeration system is turned off, the freezer cover is opened, and the mesh freezer cover is replaced with a mesh cover with a mesh size of (60~80). The temperature is then raised from 0℃ to room temperature at a rate of (5~10)℃ / min. All the compressed specimens are transferred to four thermal degradation test boxes whose initial temperature is room temperature to 50℃. 3) Then, continue to heat the specimens at a rate of (5~10)℃ / min to the temperature specified in Table 23. After the temperature holding time reaches 1 hour, turn off the heating system of the deterioration test box, turn on the external ventilation system of the deterioration test box, control the ventilation volume, and lower the temperature to 50℃~room temperature at a rate of (5~10)℃ / min. Then, transfer all the compressed specimens in the four standard deterioration test boxes to a freezer with an initial temperature of 0℃~room temperature; 4) Repeat step 2) and step 3); 5) After the cumulative number of cycles of the small group of jig-compressed specimens at constant temperature in the high-temperature degradation test box and freezer reaches the number specified in Table 23 and Table 24, respectively, the jig-compressed specimens are removed and left to stand at room temperature for 16 to 96 hours, and then left to stand for 30 minutes or more under standard test conditions. The physicochemical and electrical property indicators of the test object 4.2 after degradation are tested according to the test procedures specified in the corresponding physicochemical and electrical property testing technical standards (the compression set rate of Example 2 is tested according to the test procedures specified in GB / T 7759.1, GB / T 7759.2 or ASTM D395).

[0167] Here, when evaluating or predicting using formula (1.2) and formula (2.2), the low-temperature time in Table 24 was not entered, and only the high-temperature constant temperature holding cumulative time in Table 23 was entered.

[0168] 2.3 Test Results 2.3.1 Results of moist heat degradation Tables 25-3 to 25-6 show the compression set rates after humid heat degradation in four temperature environments of 195°C, 150°C, 97°C, and 85°C, respectively.

[0169] 2.3.2 Thermal shock degradation results The compression set rates after thermal shock degradation in four temperature environments of 245°C, 195°C, 150°C, and 97°C are shown in Tables 25-1 and 25-3 to 25-5, respectively.

[0170] 2.3.3 Results of thermal cycle degradation Tables 25-2 to 25-5 show the compression set rates after thermal cycle degradation in four temperature environments of 218°C, 195°C, 150°C, and 97°C, respectively.

[0171] 2.4 Establishment of the compression set rate equation (1.2) Figures 35 to 40 show the deterioration trends of the compression set rate corresponding to Tables 25-1 to 25-6 after undergoing three deterioration conditions of wet heat, thermal shock, and thermal cycles in six temperature environments of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0172] Table 49

[0173]

Table 50

[0174] Table 51

[0175] Table 52

[0176] Table 53

[0177] Table 54

[0178] Table 55

[0179] Table 56

[0180] Table 57

[0181] Table 58

[0182] Table 59

[0183] [Table 60]

[0184] 2.4.1 T-test A t-test showed that the compression set rate exhibited similar degradation trends under the three degradation conditions of heat and humidity, thermal shock, and thermal cycling. This indicates that the difference in the effects of thermal shock and thermal cycling on the degradation compression set rate of S20 is negligible. This is because the elastic modulus of S20 is very small, so the stress shock caused by a temperature rise and fall rate of (5-10)°C / min does not have a significant negative degradation effect on S20; the main determinants of property degradation are the cumulative time or number of cumulative cycles at high temperature and the mechanical compression rate.

[0185] Therefore, when processing S20 degradation data from long-term use under actual operating conditions, the three data sample groups obtained under the three degradation conditions of damp heat, thermal shock, and thermal cycling should be merged into a larger data sample before data processing.

[0186] 2.4.2 Parameter fitting of equation (1.2) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the common symbols (P) of the physicochemical properties and electrical properties in the equation (1) can be replaced with specific symbols (C A ) and convert to equation (1.2).

[0187]

number

[0188] In the formula (1.2) of this example, some of the 15 parameters included in the compression set rate are negligible, and are therefore assigned as "0." Although it is difficult to obtain all parameters through linear fitting, the average values ​​of the measured values ​​in Tables 25-1 to 25-6 are used as samples, starting with an assignment of "Q = 0." The assignment is gradually increased in relatively small steps, and the iterative process is repeated using a computer program or the parallax method, and input into formula (1.2). After each parameter has been iterated 50 times or more, the calculated value (C At ) and the measured value (C A ) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 26. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0189] 2.4.3 Constant fitting in equation (2.2) In this Example 2, the compression set rate equation (1.2) includes the 15 parameters in Table 26. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When applying, the constants of the parameters corresponding to the compression set rate equation (1.2) in equation (2) need to be replaced with the corresponding symbols to convert it to equation (2.2).

[0190] [Table 61]

[0191] [Table 62]

[0192]

number

[0193] Replace "Q" in equation (2.2) with the 15 parameters in Table 26, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of the obtained compression set rate one by one are shown in Table 26.

[0194] 2.5 Prediction of changes in compression set rate of S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.2) and (2.2) apply, and R 2 If ≧0.999, the prediction of the deterioration tendency of the compression set rate under actual operating conditions is accurate.

[0195] In this example, the three corresponding constants "A, B, C" in Table 26 and any actual use temperature below 245°C are substituted into the "general formula" or transposition formula, which is formula (2.2) in Table 26, to obtain new values ​​for the 15 corresponding parameters "Q", and then any actual use time and the new "Q" values ​​of the 15 parameters are substituted into formula (1.2), thereby predicting the long-term change trend of the compression set rate of S20 at any temperature and any time.

[0196] 1) Tables 25-1 to 25-6 show the changes in compression set over time at actual use temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.2). Graphs were drawn with compression set on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 25-1 to 25-6 are shown in Figures 35 to 40.

[0197] 2) The change in compression set rate over time at actual use temperatures of 75°C, 50°C, and 37°C under a 30% compression rate predicted by equation (1.2) is shown in Table 27. A graph was drawn with compression set rate on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Table 27 are shown in Figures 41 and 42.

[0198] 3) The standard deviation between the predicted results and the measured values ​​of the compression set rate is

[0199] [Table 63]

[0200] [Table 64]

[0201] (3. Example 3: Evaluation or Prediction of Actual Hardness Life) This Example 3 discloses another embodiment of the degradation life test method, algorithm and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the hardness of the interface test object during long-term actual use under actual operating conditions, and includes a jig-compressed specimen using a 30% compression ratio. The jig-compressed specimens in this group are selected at six constant temperatures within the temperature range of (85~245)°C, and are subjected to three degradation conditions, namely, damp heat, thermal shock, and thermal cycle, for a specified time or cumulative number of cycles in each specified constant temperature environment. The jig-compressed specimen in Figure 9 is used to measure the hardness of the test object 4.2 according to the test procedure specified in GB / T 2411, GB / T 6031 or ASTM D2240. The actual hardness measurement value is used to calculate the corresponding 15 parameters in the micro-evaporation expansion vibration equation (1.3). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" included in the kinetic correlation equation (2.3); the fitted three constant values ​​were substituted into the kinetic correlation equation (2.3) to calculate new values ​​for each parameter at actual use temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.3) to evaluate or predict the long-term change trend of hardness over time after a specified actual use time or cumulative number of cycles had passed under the conditions of moist heat, cold shock, and cold-hot cycling at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 3.1 to 3.5 below.

[0202] 3.1 Preparation of compression specimens using a jig As shown in Figure 9, for the hardness jig compression specimen, a uniformly mixed toothpaste-like test object 4.2 is injected between an aluminum alloy upper butt bonded plate 4.1A and a lower butt bonded plate 4.3A, and then placed on a support mold. The upper butt bonded plate 4.1A, test object 4.2, and lower butt bonded plate 4.3A are then hardened into a parallel, coaxial, "sandwich" integrated structure. Metal screws 8, upper rigid plate 4.1, and lower rigid plate 4.3 are then used to tighten the upper butt bonded plate 4.1A and lower butt bonded plate 4.3A, adjusting the thickness of the test object 4.2 to 70% of its initial thickness, i.e., a compression rate of 30%.

[0203] After the degradation process was completed, the adhesive interface between the test object 4.2 and the upper butt adhesive plate 4.1A was smoothly cut and peeled off with a sharp thin blade before proceeding to the hardness test procedure; or the test object 4.2 and the upper butt adhesive plate 4.1A were not cut or peeled off, and the hardness was tested by only contacting the edge of the test object 4.2 between the upper butt adhesive plate 4.1A and the lower butt adhesive plate 4.3A with the hardness tester probe. The results measured by the two test methods were equivalent.

[0204] Here, the test object 4.2 is selected as a two-component thermally conductive organic silicone rubber with a nominal thermal conductivity of 2 W / (mK) and an initial thickness of 12.7 mm after casting, and is abbreviated as S20.

[0205] 3.2 Three Deterioration Conditions In this Example 3, the three degradation conditions include wet heat, thermal shock, and thermal cycle, which are exactly the same as the three degradation conditions disclosed in Example 2.

[0206] 3.3 Test Results 3.3.1 Results of moist heat degradation The hardness after wet heat exposure in four temperature environments of 195°C, 150°C, 97°C, and 85°C is shown in Tables 28-3 to 28-6, respectively.

[0207] 3.3.2 Thermal shock degradation results The hardness after thermal shock in four temperature environments of 245°C, 195°C, 150°C, and 97°C is shown in Tables 28-1, 28-3 to 28-5, respectively.

[0208] 3.3.3 Results of thermal cycle degradation The hardness after thermal cycling in four temperature environments of 218°C, 195°C, 150°C, and 97°C is shown in Tables 28-2 to 28-5, respectively.

[0209] [Table 65]

[0210] [Table 66]

[0211] [Table 67]

[0212] [Table 68]

[0213] [Table 69]

[0214] [Table 70]

[0215] [Table 71]

[0216] [Table 72]

[0217] [Table 73]

[0218] [Table 74]

[0219] [Table 75]

[0220] [Table 76]

[0221] 3.4 Establishment of the hardness equation (1.3) Figures 43 to 48 show the hardness degradation trends corresponding to Tables 28-1 to 28-6 after undergoing three degradation conditions: wet heat, thermal shock, and thermal cycles in six temperature environments: 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0222] 3.4.1 T-test A t-test showed that hardness showed similar degradation trends under the three degradation conditions of moist heat, thermal shock, and thermal cycling. This indicates that the difference in the effects of thermal shock and thermal cycling on the hardness of S20 is negligible. This is because the elastic modulus of S20 is very small, so the stress shock caused by a temperature rise and fall rate of (5-10)°C / min does not have a significant negative degradation effect on S20; the main determinants of property degradation are the cumulative time or number of cumulative cycles at high temperature and the mechanical compression modulus.

[0223] Therefore, when processing S20 degradation data from long-term use under actual operating conditions, the three data sample groups obtained under the three degradation conditions of damp heat, thermal shock, and thermal cycling should be merged into a larger data sample before data processing.

[0224] 3.4.2 Parameter fitting of equation (1.3) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the common symbols (P) of the physicochemical and electrical properties in equation (1) are replaced with the specific hardness symbol (H), and the equation is converted to equation (1.3).

[0225]

number

[0226] In the formula (1.3) of this embodiment, some of the 15 parameters included in the hardness are negligible, so they are assigned as "0." Although it is difficult to obtain all parameters through linear fitting, the average values ​​of the measured values ​​in Tables 28-1 to 28-6 are used as samples, starting with the assignment of "Q = 0." The assignment is gradually increased in relatively small steps, and the iterative process is repeated using an electronic calculation program or the parallax method. After each parameter has been iterated for 50 times or more, the calculated value (H t The standard deviation of the difference between the predicted value (H) and the actual measured value (H) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for the compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 29. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters obtained, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0227] 3.4.3 Constant fitting in equation (2.3) In this Example 3, hardness equation (1.3) includes the 15 parameters in Table 29. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When fitting, the constants of the parameters corresponding to hardness equation (1.3) in equation (2) must be replaced with the corresponding symbols to convert it to equation (2.3).

[0228]

number

[0229] Replace "Q" in equation (2.3) with the 15 parameters in Table 29, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of hardness obtained one by one are shown in Table 29.

[0230] 3.5 Prediction of hardness change in S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.3) and (2.3) apply, and R 2 If ≧0.999, the prediction of hardness degradation tendency under actual operating conditions is accurate.

[0231] In this embodiment, the three corresponding constants "A, B, C" in Table 29 and any actual use temperature below 245°C are substituted into the "general formula" or rearrangement formula, which is formula (2.3) in Table 29, to obtain new values ​​for the 15 corresponding parameters "Q", and then any actual use time and the new "Q" values ​​of the 15 parameters are substituted into formula (1.3), thereby making it possible to predict the long-term change trend of the hardness of S20 at any temperature and any time.

[0232] 1) Tables 28-1 to 28-6 show the changes in hardness over time at actual use temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.3). Graphs were drawn with hardness on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 28-1 to 28-6 are shown in Figures 43 to 48.

[0233] 2) The hardness change trend with actual use time at actual use temperatures of 75°C, 50°C, and 37°C under a 30% compression ratio predicted by equation (1.3) is shown in Table 30. A graph was drawn with hardness on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Table 30 are shown in Figures 49 and 50.

[0234] 3) The standard deviation between the predicted results and the measured hardness values ​​is within ±1.96 sigma.

[0235] [Table 77]

[0236] [Table 78]

[0237] [Table 79]

[0238] [Table 80]

[0239] (4. Example 4: Evaluation or Prediction of Actual Use Life of Tensile Strength) Example 4 discloses another embodiment of the degradation life test method, algorithm, and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the tensile strength of the interface test object during long-term actual use under actual operating conditions. The test object is a jig-compressed specimen using a 30% compression ratio. The jig-compressed specimens in this group are subjected to six constant temperatures within the temperature range of (85~245)°C, and are subjected to three degradation conditions, namely, damp heat, thermal shock, and thermal cycles, for a specified time or cumulative number of cycles in each specified constant temperature environment. The jig-compressed specimens shown in Figures 11 and 12 are used to measure the tensile strength of the test object 4.2 according to the test procedures specified in GB / T 1040.3, GB / T 528, or ASTM D412. The actual measured tensile strength is used to calculate the corresponding 15 parameters in the micro-evaporation expansion vibration equation (1.4). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" included in the kinetic correlation equation (2.4); the fitted constant values ​​were substituted into the kinetic correlation equation (2.4) to calculate new values ​​for each parameter at actual service temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.4) to evaluate or predict the long-term change trend of tensile strength over time after a specified time period or cumulative number of cycles had elapsed under the conditions of moist heat, thermal shock, and thermal cycling at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 4.1 to 4.5 below.

[0240] 4.1 Preparation of compression specimens using a jig To prepare the tensile strength specimen, first, place a layer of isolation film (e.g., PI film) at the bottom of a 1mm deep rigid circular mold, which does not exchange materials or undergo chemical reactions with the test object 4.2. Then, pour the uniformly mixed test object 4.2, such as toothpaste, into the rigid mold, stir it evenly, and then place another layer of isolation film on top of the test object 4.2. Then, flatten it under a press, harden it, and cut it into a blank specimen measuring 155 x 155 x 1mm. This is then measured according to GB / T standards. Five dumbbell specimens were cut onto a 155x155x1mm blank specimen using a Type 2 or Type 1B dumbbell cutting knife conforming to JIS 1040.3. However, the dumbbell specimens could not be separated from the 155x155x1mm blank specimen. A pair of rectangular compression jigs and isolating films, as shown in Figures 11 and 12, were used to clamp the 155x155x1mm blank specimens cut onto the dumbbell specimens. To facilitate demolding, isolating films were placed between the upper rectangular clamping plate 13.10 and the blank specimen, and between the blank specimen and the lower rectangular clamping plate 13.20. Metal screws 8 were used to clamp the upper rectangular clamping plate 13.10 and the lower rectangular clamping plate 13.20 together, compressing the thickness of the blank specimen to 70% of its initial value (30%). This formed a tensile strength compression specimen.

[0241] Here, the test object 4.2 is selected as a two-component thermally conductive organic silicone rubber with a nominal thermal conductivity of 2 W / (mK) and an initial thickness of 1.0 mm after casting, and is abbreviated as S20.

[0242] 4.2 Three Deterioration Conditions In this example, the three degradation conditions include wet heat, thermal shock and thermal cycle, which are exactly the same as those disclosed in Example 2.

[0243] 4.3 Test Results 4.3.1 Results of moist heat degradation The tensile strength after wet heat exposure in four temperature environments of 195°C, 150°C, 97°C, and 85°C is shown in Tables 31-3 to 31-6, respectively.

[0244] 4.3.2 Thermal shock degradation results The tensile strength after thermal shock in four temperature environments of 245°C, 195°C, 150°C, and 97°C is shown in Tables 31-1, 31-3 to 31-5, respectively.

[0245] 4.3.3 Results of thermal cycle degradation The tensile strength after thermal cycling in four temperature environments of 218°C, 195°C, 150°C, and 97°C is shown in Tables 31-2 to 31-5, respectively.

[0246] 4.4 Establishment of the tensile strength equation (1.4) Figures 51 to 56 show the deterioration trends in tensile strength corresponding to Tables 31-1 to 31-6 after undergoing three deterioration conditions: wet heat, thermal shock, and thermal cycles in six temperature environments: 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0247] [Table 81]

[0248] [Table 82]

[0249] [Table 83]

[0250] [Table 84]

[0251] [Table 85]

[0252] [Table 86]

[0253] [Table 87]

[0254] [Table 88]

[0255] [Table 89]

[0256] [Table 90]

[0257] [Table 91]

[0258] [Table 92]

[0259] 4.4.1 T-test The T-test showed that the tensile strength under the three degradation conditions of heat and humidity, thermal shock, and thermal cycle showed similar degradation change trends, which could be evaluated or predicted by combining larger data samples.

[0260] 4.4.2 Parameter fitting of equation (1.4) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the common symbols (P) of the physicochemical and electrical properties in equation (1) are replaced with the specific symbol (σ) of the tensile strength, and the equation is converted to equation (1.4).

[0261]

number

[0262] In the formula (1.4) of this example, some of the 15 parameters included in the tensile strength are negligible, and are therefore assigned as "0." Although it is difficult to obtain all of the parameters through linear fitting, the average values ​​of the measured values ​​in Tables 31-1 to 31-6 are used as samples, starting with the assignment of "Q = 0." The assignment is gradually increased in relatively small steps, and the iterative process is repeated using a computer program or the Parallax method. After each parameter has been iterated for 50 times or more, the calculated value (σ t The standard deviation of the difference between the predicted value (σ) and the actual measured value (σ) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for the compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 32. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters obtained, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0263] 4.4.3 Constant fitting in equation (2.4) In this Example 4, the tensile strength equation (1.4) includes the 15 parameters in Table 32. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When fitting, the constants of the parameters corresponding to the tensile strength equation (1.4) in equation (2) must be replaced with the corresponding symbols to convert it to equation (2.4).

[0264]

number

[0265] Replace "Q" in equation (2.4) with the 15 parameters in Table 32, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of the obtained tensile strength one by one are shown in Table 32.

[0266] 4.5 Prediction of tensile strength change in S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.4) and (2.4) apply, and R 2 If ≧0.999, the prediction of the deterioration tendency of tensile strength under actual operating conditions is accurate.

[0267] In this example, the three corresponding constants "A, B, C" in Table 32 and any actual use temperature below 245°C are substituted into the "general formula" or rearrangement formula, which is formula (2.4) in Table 32, to obtain new values ​​for the 15 corresponding parameters "Q." Then, any actual use time and the new "Q" values ​​for the 15 parameters are substituted into formula (1.4), whereby the long-term change trend of the tensile strength of S20 at any temperature and any time can be predicted.

[0268] 1) Tables 31-1 to 31-6 show the changes in tensile strength over time at actual use temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.4). Graphs were drawn with tensile strength on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 31-1 to 31-6 are shown in Figures 51 to 56.

[0269] 2) The change in tensile strength over time at 30% compression predicted by Equation (1.4) and at temperatures of 75°C, 50°C, and 37°C is shown in Table 33. A graph was drawn with tensile strength on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Table 33 are shown in Figures 57 and 58.

[0270] 3) The standard deviation between the predicted results and the measured values ​​of tensile strength is within ±1.96 sigma.

[0271] [Table 93]

[0272] [Table 94]

[0273] [Table 95]

[0274] [Table 96]

[0275] (5. Example 5: Evaluation or Prediction of Actual Service Life of Shear Adhesion Strength) Example 5 discloses another embodiment of the degradation life test method, algorithm, and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the shear bond strength of interface test objects during long-term actual use under actual operating conditions. The test objects include jig-compressed specimens using a 30% compression ratio; the jig-compressed specimens in this group are subjected to six constant temperatures within the temperature range of (85~245)°C, and are subjected to three degradation conditions of wet heat, thermal shock, and thermal cycles in each specified constant temperature environment for a specified time or cumulative number of cycles; the jig-compressed specimens combined with square clamping plates shown in Figures 11 and 12 are used to measure the shear bond strength of test object 4.2 according to the test procedures specified in GB / T 7124, ISO 4587, or ASTM D1002; the actual measured shear bond strength is used to calculate the corresponding 15 parameters in the micro-evaporation expansion vibration equation (1.5). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" included in the kinetic correlation equation (2.5); the fitted three constant values ​​were substituted into the kinetic correlation equation (2.5) to calculate new values ​​for each parameter at actual use temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.5) to evaluate or predict the long-term change trend of shear bond strength over time after a specified time period or cumulative number of cycles had elapsed under moist heat, thermal shock, and thermal cycling conditions at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 5.1 to 5.5 below.

[0276] 5.1 Preparation of compression specimens using a jig As shown in Figure 13, in accordance with the test procedures and requirements specified in GB / T 7124, ISO 4587, or ASTM D1002, an overlapping adhesive specimen was prepared, including an upper overlapping adhesive specimen 14.10, a test object 4.2, and a lower overlapping adhesive specimen 14.20. The test object 4.2 was bonded to the upper overlapping adhesive specimen 14.10 and the lower overlapping adhesive specimen 14.20, cured, and then overlapped.

[0277] As shown in Figure 14, the compression specimen for the shear bond strength test jig includes a screw 8, a nut 10, an upper square clamping plate 13.10, a lower square clamping plate 13.20, an upper overlap adhesive strip 14.10, a lower overlap adhesive strip 14.20, an upper positioning sheet A 15.1, a lower positioning sheet A 15.2, an upper positioning sheet B 15.3, and a lower positioning sheet B 15.4. The test object 4.2 is formed by bonding and curing the upper overlap adhesive strip 14.10 and the lower overlap adhesive strip 14.20 to form the overlap bonded specimen. The screw 8 and the nut 10 are attached to the upper square clamping plate 13.10, the lower square clamping plate 13.20, the upper overlap adhesive strip 14.10, the entire overlap bonded specimen, the upper positioning sheet A 15.1, the lower positioning sheet A 15.2, and the upper positioning sheet B The upper positioning sheet A 15.1, the lower positioning sheet A 15.2, the upper positioning sheet B 15.3, and the lower positioning sheet B 15.4 are fastened together to compress the thickness of the test object 4.2 to 70% of its initial value, which corresponds to a compression rate of 30%; here, the upper positioning sheet A 15.1, the lower positioning sheet A 15.2, the upper positioning sheet B 15.3, and the lower positioning sheet B 15.4 are used to balance the compression moment and regulate the compressed thickness of the test object 4.2.

[0278] Here, the upper square clamping plate 13.10 is the same as that shown in FIG. 11, and the lower square clamping plate 13.20 is the same as that shown in FIG.

[0279] Here, the test object 4.2 is selected as a two-component thermally conductive organic silicone rubber with a nominal thermal conductivity of 2 W / (mK) and an initial thickness of 0.40 mm after slip casting, and is abbreviated as S20.

[0280] 5.2 Three Deterioration Conditions In this example, the three degradation conditions include wet heat, thermal shock and thermal cycle, which are exactly the same as those disclosed in Example 2.

[0281] 5.3 Test Results 5.3.1 Results of moist heat degradation The shear adhesive strength after wet heat exposure in four temperature environments of 195°C, 150°C, 97°C, and 85°C is shown in Tables 34-3 to 34-6, respectively.

[0282] 5.3.2 Thermal shock degradation results The shear adhesive strength after thermal shock in four temperature environments of 245°C, 195°C, 150°C, and 97°C is shown in Table 34-1, Tables 34-3 to 34-5, respectively.

[0283] 5.3.3 Results of thermal cycle degradation The shear adhesive strength after thermal cycling in four temperature environments of 218°C, 195°C, 150°C, and 97°C is shown in Tables 34-2 to 34-5, respectively.

[0284] 5.4 Establishment of shear bond strength equation (1.5) Figures 59 to 64 show the deterioration trends in shear adhesive strength corresponding to Tables 34-1 to 34-6 after undergoing three deterioration conditions: moist heat, thermal shock, and thermal cycles, in six temperature environments: 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0285] [Table 97]

[0286] [Table 98]

[0287] [Table 99]

[0288] [Table 100]

[0289] [Table 101]

[0290] [Table 102]

[0291] [Table 103]

[0292] [Table 104]

[0293] [Table 105]

[0294] [Table 106]

[0295] [Table 107]

[0296] [Table 108]

[0297] 5.4.1 T-test The t-test shows that the shear bond strength under the three degradation conditions of heat and humidity, thermal shock, and thermal cycling shows a similar degradation trend. This indicates that the difference in the effects of the two factors of thermal shock and thermal cycling on the shear bond strength of S20 degradation is negligible.

[0298] Therefore, when processing S20 degradation data from long-term use under actual operating conditions, the three data sample groups obtained under the three degradation conditions of damp heat, thermal shock, and thermal cycling should be merged into a larger data sample before data processing.

[0299] 5.4.2 Parameter fitting of equation (1.5) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the common symbols (P) of the physicochemical properties and electrical properties in equation (1) are replaced with the symbol (S) of the specific shear adhesive strength, and then converted into equation (1.5).

[0300]

number

[0301] In the formula (1.5) of this example, some of the 15 parameters included in the shear bond strength are negligible, and are therefore assigned as "0." Although it is difficult to obtain all of the parameters through linear fitting, the average values ​​of the measured values ​​in Tables 34-1 to 34-6 are used as samples, starting with the assignment of "Q = 0," and gradually increasing the assignment in relatively small steps. The iterative process is repeated using a computer program or the Parallax method, and input into formula (1.5). After each parameter has been iterated for 50 times or more, the calculated value (S t The standard deviation of the difference between the predicted value (S) and the actual measured value (S) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for the compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 35. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0302] 5.4.3 Constant fitting in equation (2.5) In this Example 5, the shear bond strength equation (1.5) includes the 15 parameters in Table 35. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When fitting, the constants of the parameters corresponding to the shear bond strength equation (1.5) in equation (2) must be replaced with the corresponding symbols to convert it to equation (2.5).

[0303]

number

[0304] Replace "Q" in equation (2.5) with the 15 parameters in Table 35, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of the obtained shear bond strength one by one are shown in Table 35.

[0305] 5.5 Prediction of shear bond strength change for S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.5) and (2.5) apply, and R 2 If ≥ 0.999, the prediction of the deterioration tendency of shear bond strength under actual operating conditions is accurate.

[0306] [Table 109]

[0307] [Table 110]

[0308] [Table 111]

[0309] [Table 112]

[0310] In this Example 5, the three corresponding constants "A, B, C" in Table 35 and any actual use temperature below 245°C are substituted into the "general formula" or rearrangement formula, which is formula (2.5) in Table 35, to obtain new values ​​for the 15 corresponding parameters "Q", and then any actual use time and the new "Q" values ​​for the 15 parameters are substituted into formula (1.5), thereby predicting the long-term change trend of the shear bond strength of S20 at any temperature and any time.

[0311] 1) Tables 34-1 to 34-6 show the change in shear bond strength over time at actual use temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.5). Graphs were drawn with shear bond strength on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Tables 34-1 to 34-6 are shown in Figures 59 to 64.

[0312] 2) The change in shear bond strength with actual use time at actual use temperatures of 75°C, 50°C, and 37°C under a 30% compression ratio predicted by equation (1.5) is shown in Table 36. A graph was drawn with shear bond strength on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Table 36 are shown in Figures 65 and 66.

[0313] 3) The standard deviation between the predicted results and the measured values ​​of shear bond strength is within ±1.96 sigma.

[0314] (6. Example 6: Evaluation or Prediction of Actual Use Life of Dielectric Breakdown Strength) Example 6 discloses another embodiment of the degradation life test method, algorithm and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the dielectric breakdown strength of the interface test object during long-term actual use under actual operating conditions. The test object is a jig-compressed specimen using a 30% compression ratio. The jig-compressed specimens in this group are subjected to six constant temperatures within the temperature range of (85~245)°C, and are subjected to three degradation conditions, namely, damp heat, thermal shock, and thermal cycles, for a specified time or cumulative number of cycles in each specified constant temperature environment. The jig-compressed specimens combined with square clamping plates shown in Figures 11 and 12 are used to measure the dielectric breakdown strength of the test object 4.2 according to the test procedures specified in GB / T 1408.1, GB / T 1695, IEC 60243-1 or ASTM D149. The actual measured dielectric breakdown strength is used to calculate the corresponding 15 parameters in the micro-vaporization expansion oscillation equation (1.6). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" contained in the kinetic correlation equation (2.6); the fitted constant values ​​were substituted into the kinetic correlation equation (2.6) to calculate new values ​​for each parameter at actual operating temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.6) to evaluate or predict the long-term change trend of the dielectric breakdown strength over time after a specified operating time or cumulative number of cycles had elapsed under the conditions of humid heat, thermal shock, and thermal cycling at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 6.1 to 6.5 below.

[0315] 6.1 Preparation of compression specimens using a jig As shown in Figures 15 and 16, an upper electrode 16.10 and a lower electrode 16.20 were fabricated in accordance with the electrode requirements defined in GB / T 1408.1, GB / T 1695, IEC 60243-1, or ASTM D 149. The upper electrode 16.10 comprises an upper electrode head 16.11 and an upper electrode plate 16.12, and the lower electrode 16.20 comprises a lower electrode head 16.21 and a lower electrode plate 16.22. The electrode heads and electrode plates are connected by screwing or welding to form the entire electrode.

[0316] As shown in Figure 17, the compression test specimen using the dielectric breakdown strength jig comprises a test object 4.2, a screw 8, a nut 10, an upper square clamping plate 13.10, a lower square clamping plate 13.20, an upper electrode head 16.11, an upper electrode plate 16.12, a lower electrode head 16.21, and a lower electrode plate 16.22; the electrode heads and electrode plates are screwed or welded together to form the entire electrode; the test object 4.2 is sandwiched between a pair of electrodes; the pair of electrodes is fastened with a pair of square clamping plates and an insulating screw 8, compressing the thickness of the test object 4.2 to 70% of its initial value, which is a compression rate of 30%, to form a dielectric breakdown strength degradation specimen.

[0317] Here, the upper square clamping plate 13.10 is the same as that shown in FIG. 11, and the lower square clamping plate 13.20 is the same as that shown in FIG.

[0318] Here, the test object 4.2 is a cured two-component thermally conductive organic silicone rubber in the form of a sheet, which has a nominal thermal conductivity of 2 W / (mK) and an initial thickness of (0.7-1.2) mm after casting, and is abbreviated as S20.

[0319] 6.2 Three Deterioration Conditions In this example, the three degradation conditions include wet heat, thermal shock and thermal cycle, which are exactly the same as those disclosed in Example 2.

[0320] 6.3 Test Results 6.3.1 Results of moist heat degradation The dielectric breakdown strength after wet heat exposure in four temperature environments of 195°C, 150°C, 97°C, and 85°C is shown in Tables 37-3 to 37-6, respectively.

[0321] 6.3.2 Thermal shock degradation results The dielectric breakdown strength after thermal shock in four temperature environments of 245°C, 195°C, 150°C, and 97°C is shown in Tables 37-1, 37-3 to 37-5, respectively.

[0322] 6.3.3 Results of thermal cycle degradation The breakdown strength after thermal cycling in four temperature environments of 218°C, 195°C, 150°C, and 97°C is shown in Tables 37-2 to 37-5, respectively.

[0323] [Table 113]

[0324] [Table 114]

[0325] [Table 115]

[0326] [Table 116]

[0327] [Table 117]

[0328] [Table 118]

[0329] [Table 119]

[0330] [Table 120]

[0331] [Table 121]

[0332] [Table 122]

[0333] [Table 123]

[0334] [Table 124]

[0335] 6.4 Establishment of the dielectric breakdown strength equation (1.6) Figures 67 to 72 show the deterioration trends in dielectric breakdown strength corresponding to Tables 37-1 to 37-6 after undergoing three deterioration conditions: moist heat, thermal shock, and thermal cycles in six temperature environments: 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0336] 6.4.1 T-test The t-test shows that the breakdown strength under the three degradation conditions of heat and humidity, thermal shock, and thermal cycles shows a similar degradation change trend, which indicates that the difference in the influence of the two factors of thermal shock and thermal cycles on the breakdown strength of S20 degradation is negligible.

[0337] Therefore, when processing S20 degradation data from long-term use under actual operating conditions, the three data sample groups obtained under the three degradation conditions of damp heat, thermal shock, and thermal cycling should be merged into a larger data sample before data processing.

[0338] 6.4.2 Parameter fitting of equation (1.6) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the general symbols (P) of the physicochemical and electrical properties in equation (1) are replaced with the specific symbols (E) of the dielectric breakdown strength, and the equation is converted to equation (1.6).

[0339]

number

[0340] In the formula (1.6) of this example, some of the 15 parameters included in the dielectric breakdown strength are negligible, and are therefore assigned as "0." Although it is difficult to obtain all of the parameters through linear fitting, the average values ​​of the measured values ​​in Tables 37-1 to 37-6 are used as samples, starting with the assignment of "Q = 0," and gradually increasing the assignment in relatively small steps. The iterative process is repeated using a computer program or the parallax method, and input into formula (1.6). After each parameter has been iterated for 50 times or more, the calculated value (E t The standard deviation of the difference between the predicted value (E) and the actual measured value (E) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for the compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 38. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters obtained, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0341] 6.4.3 Constant fitting in equation (2.6) In this Example 5, the dielectric breakdown strength equation (1.6) includes the 15 parameters in Table 38. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When applying, the constants of the parameters corresponding to the dielectric breakdown strength equation (1.6) in equation (2) must be replaced with the corresponding symbols to convert it to equation (2.6).

[0342]

number

[0343] [Table 125]

[0344] [Table 126]

[0345] Replace "Q" in equation (2.6) with the 15 parameters in Table 38, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of the obtained dielectric breakdown strength one by one are shown in Table 38.

[0346] 6.5 Prediction of changes in dielectric breakdown strength of S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.6) and (2.6) apply, and R 2 If ≧0.999, the prediction of the degradation trend of the dielectric breakdown strength under actual operating conditions is accurate.

[0347] In this embodiment, the three corresponding constants "A, B, C" in Table 38 and any actual use temperature below 245°C are substituted into the "general formula" or rearrangement formula, which is formula (2.6) in Table 38, to find new values ​​for the 15 corresponding parameters "Q", and then any actual use time and the new "Q" values ​​of the 15 parameters are substituted into formula (1.6), thereby making it possible to predict the long-term change trend of the dielectric breakdown strength of S20 at any temperature and any time.

[0348] 1) Tables 37-1 to 37-6 show the change in dielectric breakdown strength over time at actual operating temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.6). Graphs are drawn with dielectric breakdown strength on the vertical axis and actual operating time on the horizontal axis, and the trend curves corresponding to Tables 37-1 to 37-6 are shown in Figures 67 to 72.

[0349] 2) The change in dielectric breakdown strength over time at 30% compression, as predicted by equation (1.6), at actual operating temperatures of 75°C, 50°C, and 37°C is shown in Table 39. A graph was drawn with dielectric breakdown strength on the vertical axis and actual operating time on the horizontal axis, and the trend curves corresponding to Table 39 are shown in Figures 73 and 74.

[0350] 3) The standard deviation between the predicted results and the measured values ​​of dielectric breakdown strength is within ±1.96 sigma.

[0351] [Table 127]

[0352] [Table 128]

[0353] (7. Example 7: Evaluation or Prediction of Actual Use Life of Volume Resistivity) Example 7 discloses another embodiment of the degradation life test method, algorithm and application of the novel energy thermal management composite material of the present invention. The short-term accelerated degradation test method is used to evaluate or predict the long-term change trend of the volume resistivity of the interface test object during long-term actual use under actual operating conditions. The test object is a jig-compressed specimen with a compression ratio of 30%. The jig-compressed specimens in this group are subjected to three degradation conditions, namely, moist heat, thermal shock and thermal cycle, in six constant temperatures selected within the temperature range of (85-245)°C for a specified time or cumulative number of cycles. The jig-compressed specimens in this group are subjected to three degradation conditions, namely, moist heat, thermal shock and thermal cycle, in each specified constant temperature environment. The jig-compressed specimens in the combination of square clamping plates shown in Figures 11 and 12 are used to measure the volume resistivity of the test object 4.2 according to the test procedures specified in GB / T 1410, IEC 60093-1 or ASTM D257. The actual measured volume resistivity is used to calculate the corresponding 15 parameters in the micro-evaporation expansion oscillation equation (1.7). The fitted parameter values ​​were then used to fit the three constant values ​​"A, B, and C" included in the kinetic correlation equation (2.7); the fitted three constant values ​​were substituted into the kinetic correlation equation (2.7) to calculate new values ​​for each parameter at actual operating temperatures of 75°C, 50°C, and 37°C; and the new values ​​of the group of parameters were substituted into equation (1.7) to evaluate or predict the long-term change trend of volume resistivity over time after a specified operating time or cumulative number of cycles under humid heat, thermal shock, and thermal cycling conditions at 75°C, 50°C, and 37°C. Details of the implementation steps are further disclosed in sections 7.1 to 7.5 below.

[0354] 7.1 Preparation of compression specimens using a jig As shown in Figure 18, the compression specimen for the volume resistivity jig includes the test object 4.2, the bolt 8, the upper square clamping plate 13.10, the lower square clamping plate 13.20, the insulating sheet 17 (e.g., PTFE insulating sheet) that can withstand temperatures of 245°C or higher, the protected electrode 18, the protected electrode 19, the unprotected electrode 20, and the set screw (electrode head). The annular gap between the protected electrode 18 and the protected electrode 19 is filled with an insulating ring 23 that can withstand temperatures of 245°C or higher (for example, joined with a PTFE insulating ring or insulating sealant) to form an insulator; the protected electrode 19 and the upper square clamping plate 13.10 are separated by an insulating sheet 17 that can withstand temperatures of 245°C or higher, so that the protected electrode 18 and the protected electrode 19 are separated and insulated; a pair of set screws (electrode heads) 21 are fixed or welded vertically to the centers of the upper square clamping plate 13.10 and the lower square clamping plate 13.20, respectively, and are electrically connected to each other, forming a pair of independent electrodes. The pair of electrode heads sandwich the test object 4.2 between them; the upper and lower rectangular clamping plates 13.10 and 13.20 are fastened with metal screws 8 to compress the thickness of the test object 4.2 to 70% of its initial value, which is a compression rate of 30%, to form a volume resistivity jig compression specimen; after the degradation process of the volume resistivity jig compression specimen is completed, the metal screws 8 are replaced with insulating bolts 8 to ensure that no relative displacement occurs between the entire electrode head and the test object 4.2 before, during, and after the degradation process and during the volume resistivity test.

[0355] Here, the protected electrode 18, the protected electrode 19, and the unprotected electrode 20 are manufactured according to the dimensions specified in GB / T 1410, IEC 60093-1, or ASTM D257; Here, the upper square clamping plate 13.10 is the same as that shown in FIG. 11, and the lower square clamping plate 13.20 is the same as that shown in FIG. 12; Here, the test object 4.2 is a cured two-component thermally conductive organic silicone rubber in the form of a sheet with a nominal thermal conductivity of 2 W / (mK) and an initial thickness of (0.7-1.0) mm obtained by slip casting, and is abbreviated as S20.

[0356] 7.2 Three Deterioration Conditions In this example, the three aging conditions under six temperatures include damp heat, thermal shock, and thermal cycling, and the specific details of the three aging conditions are exactly the same as those disclosed in Example 2.

[0357] 7.3 Test Results 7.3.1 Results of moist heat degradation The volume resistivities after wet heating in four temperature environments of 195°C, 150°C, 97°C, and 85°C are shown in Tables 40-3 to 40-6, respectively.

[0358] 7.3.2 Thermal shock degradation results The volume resistivities after thermal shock in four temperature environments of 245°C, 195°C, 150°C, and 97°C are shown in Tables 40-1, 40-3 to 40-5, respectively.

[0359] 7.3.3 Results of thermal cycle degradation The volume resistivities after thermal cycling in four temperature environments of 218°C, 195°C, 150°C, and 97°C are shown in Tables 40-2 to 40-5, respectively.

[0360] 7.4 Establishment of the volume resistivity equation (1.7) Figures 75 to 80 show the deterioration trends of volume resistivity corresponding to Tables 40-1 to 40-6 after undergoing three deterioration conditions of moist heat, thermal shock, and thermal cycles in six temperature environments of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C.

[0361] [Table 129]

[0362] [Table 130]

[0363] [Table 131]

[0364] Table 132

[0365] Table 133

[0366] Table 134

[0367] Table 135

[0368] Table 136

[0369] Table 137

[0370] Table 138

[0371] Table 139

[0372] Table 140

[0373] 7.4.1 T determination The t-test shows that the volume resistivity under the three degradation conditions of heat and humidity, thermal shock, and thermal cycling shows a similar degradation change trend, which indicates that the difference in the influence of the two factors of thermal shock and thermal cycling on the volume resistivity of S20 degradation is negligible.

[0374] Therefore, when processing S20 degradation data from long-term use under actual operating conditions, the three data sample groups obtained under the three degradation conditions of damp heat, thermal shock, and thermal cycling should be merged into a larger data sample before data processing.

[0375] 7.4.2 Parameter fitting of equation (1.7) In another application of the calculation method of the micro-vaporization expansion vibration equation (1) of the present invention, the common symbols (P) of the physicochemical properties and electrical properties in the equation (1) can be replaced with the symbol of the natural logarithm of the specific volume resistivity (ρ v ) and convert to equation (1.7).

[0376]

number

[0377] [Table 141]

[0378] [Table 142]

[0379] In the formula (1.7) of this embodiment, some of the 15 parameters included in the natural logarithm of the volume resistivity are negligible and are therefore assigned as "0." Although it is difficult to obtain all of the parameters through linear fitting, the average values ​​of the measured values ​​in Tables 40-1 to 40-6 are used as samples, starting with the assignment of "Q = 0." The assignment is gradually increased in relatively small steps, and the iterative process is repeated using an electronic calculation program or the parallax method. After each parameter has been iterated for 50 times or more, the calculated value (ρ vt ) and the measured value (ρ v ) converges to a minimum, and the optimal values ​​of the 15 parameters "Q" for compression set rate at various temperatures are obtained. The results of the iterative optimization are shown in Table 41. Due to the mathematical frequency doubling effect, if there are multiple optimal values ​​for the fitted values ​​of the 15 parameters, only the relatively small group of 15 "Q" values ​​closest to "1x" are selected as the optimal parameters.

[0380] 7.4.3 Constant fitting in equation (2.7) In this Example 5, the natural logarithm equation (1.7) of volume resistivity includes the 15 parameters in Table 41. In the mechanism, each parameter does not change with time, but only changes with temperature. Each parameter that changes with temperature also includes a constant represented by the corresponding three codes "A, B, C" in equation (2). When fitting, the constants of the parameters corresponding to the natural logarithm equation (1.7) of volume resistivity in equation (2) must be replaced with the corresponding symbols to convert it to equation (2.7).

[0381]

number

[0382] Replace "Q" in equation (2.7) with the 15 parameters in Table 41, plot a graph with the logarithm of each of the 15 parameters on the vertical axis and 1 / (T+C) on the horizontal axis, and repeat the iterative process using the least squares method, an electronic calculation program, or the parallax method. Enter different "C" values, and the calculation program system will automatically calculate R 2 If the output is ≧0.990, it is considered to be a straight line, and the optimal constants “A, B, C” corresponding to the 15 parameters of the obtained dielectric breakdown strength one by one are shown in Table 41.

[0383] 7.5 Prediction of the natural logarithm change in volume resistivity of S20 As long as the constraints under actual operating conditions are the same as those under laboratory accelerated aging, with only the temperature being different, Equations (1.7) and (2.7) apply, and R 2 If ≧0.999, the prediction of the degradation trend of the natural logarithm of volume resistivity under actual operating conditions is accurate.

[0384] In this example, the three corresponding constants "A, B, C" in Table 41 and any actual use temperature below 245°C are substituted into the "general formula" or rearrangement formula, which is formula (2.7) in Table 41, to find new values ​​for the 15 corresponding parameters "Q", and then any actual use time and the new "Q" values ​​for the 15 parameters are substituted into formula (1.7), thereby predicting the long-term change trend of the natural logarithm of the volume resistivity of S20 at any temperature and any time.

[0385] 1) Tables 40-1 to 40-6 show the change over time in the natural logarithm of volume resistivity at actual operating temperatures of 245°C, 218°C, 195°C, 150°C, 97°C, and 85°C under a 30% compression ratio predicted by equation (1.7). Graphs were drawn with the natural logarithm of volume resistivity on the vertical axis and actual operating time on the horizontal axis, and the trend curves corresponding to Tables 40-1 to 40-6 are shown in Figures 75 to 80.

[0386] 2) The trend of change in the natural logarithm of volume resistivity with actual use time at actual use temperatures of 75°C, 50°C, and 37°C under a 30% compression ratio predicted by equation (1.7) is shown in Table 42. A graph was drawn with the natural logarithm of volume resistivity on the vertical axis and actual use time on the horizontal axis, and the trend curves corresponding to Table 42 are shown in Figures 81 and 82.

[0387] 3) The standard deviation between the predicted results and the measured values ​​of the natural logarithm of volume resistivity is within ±1.96 sigma.

[0388] [Table 143]

[0389] [Table 144]

[0390] 8. Example 8: Evaluation or Prediction of Half-Life and Rated Temperature Another embodiment of the degradation life test method, algorithm, and application of the new energy thermal management composite material of the present invention includes using a short-term accelerated degradation test method to evaluate or predict the rating indexes corresponding to the physicochemical properties and electrical properties of an interface test object during long-term actual use under actual operating conditions, and using the micro-vaporization expansion vibration equation (1) and the dynamic correlation equation (2) to confirm the half-life of either the physicochemical property or the electrical property in a specified actual use temperature environment, or the rating temperature of either the physicochemical property or the electrical property after a specified actual use time of 20,000 hours.

[0391] In this Example 8, a method for determining the half-life and rated temperature of the test object 4.2 named P40 thermal conductivity is disclosed, and the micro-vaporization expansion oscillation equation (1.1), the temperature correlation equation (2.1), and the three constant values ​​"A, B, C" of the 15 groups in Table 11 are determined; and the three constant values ​​"A, B, C" of the 15 groups and different actual use temperatures (T i ) into the kinetic correlation equation (2.1) and calculate the actual operating temperature (T i) 15 parameters in the environment Calculate the new values ​​of the 15 parameters in the group; and substitute the new values ​​into the micro-vaporization expansion oscillation equation (1.1) one by one. Then, use a software program or an electronic calculation table to calculate the actual operating temperature (T i The time required for the thermal conductivity to decrease by half in an environment (thermal conductivity half-life (τ i )) is repeatedly input, for example, if the initial thermal conductivity of the P40 test object is 4.18W / (mK) and the time is input as t, when the value output from the software program or electronic calculation table is 2.09W / (mK), this time t value is calculated based on the actual operating temperature (T i ) thermal conductivity half-life (τ i )

[0392] As shown in Figure 34, the thermal conductivity half-life (τ i ) on the vertical axis, and the actual operating temperature (T i ) on the horizontal axis, and connect each point as a smooth curve, and the curve has an intersection with the horizontal line at 20,000 hours on the ordinate, and the intersection of the vertical line passing through this intersection with the horizontal axis is the rated temperature of the P40 test object (the rated temperature of the test object P40 in the name of thermal conductivity is 181).

[0393] [Table 145]

[0394] The advantageous technical effects of the above eight embodiments of the degradation life test method, algorithm and application of the new energy thermal management composite material of the present invention are as follows: (1) The maximum degradation test temperature reaches 298°C, reducing the laboratory degradation test time by 90% from over 1,000 hours; (2) It is suitable for predicting the degradation life of materials with three phases, i.e., solid, liquid, and gas, and breaks the limitation of GB / T 20028, ASTM G 166, ASTM G 169, ISO 2578, and UL 746B, which requires that the extended prediction temperature range be less than 0.8 times the difference between the highest and lowest test temperatures; (3) Suitable for evaluating or predicting the long-term practical service life of all polymer matrix composites; (4) Linear correlation coefficient R 2 is two "9" accuracy levels higher than GB / T 20028, ASTM G 166, ASTM G 169, ISO 2578, and UL 746B, making predictions more accurate. [Explanation of symbols]

[0395] 10.................................................. Nut 1................................................................. High temperature constant temperature chamber 11.................................................Transition diffusion direction of small molecules 12................................................................. Microelement cavitation 13.10................................................. Upper square clamping plate 13.11....................................................Screw hole A 13.12...................................................Through hole 13.13.....................................Protrusion reinforcement 13.20................................................ Lower square clamping plate 13.21....................................................Screw hole B 13.22....................................................Screw hole C 13.23.....................................Protrusion reinforcement 14.10..................................... Top overlap adhesive strip 14.20..................................... Lower overlap adhesive strip 15.1......................................................Upper positioning sheet A 15.2......................................................Lower positioning sheet A 15.3.......................................................Upper positioning sheet B 15.4....................................................... Lower positioning sheet B 16.10...................................................Top electrode 16.11...................................................Upper electrode head 16.12.......................................................... Upper electrode plate 16.20..................................... Lower electrode 16.21.................................... Lower electrode head 16.22.......................................................... Lower electrode plate 17.............................................................Insulating sheet that can withstand temperatures above 245°C 18................................................................Protected electrode 19.................................................Protective electrode 20................................................... Unprotected electrode 2.................................................................High temperature probe 21.................................................Setting screw (electrode head) 22.................................................. Nylon bolt 23.................................................................Insulation ring that can withstand temperatures above 245°C 3................................................................. Upper auxiliary elastic sheet 4................................................................. Irremovable combined specimen 4.1................................................................ Upper rigid plate 4.1A....................................................... Upper butt adhesive plate 4.2......................................................... Test Object 4.3.......................................................... Lower rigid plate 4.3A....................................................... Lower butt adhesive plate 5................................................................. Lower auxiliary elastic sheet 6................................................................. Cryogenic probe 7................................................................. Low temperature incubator 8.................................................Screw 9.................................................................Volatilization direction of low molecular weight substances a................................................................... year F.................................................Automatic force application by equipment -F................................................................Reaction force of the device itself t................................................................. Actual usage time δ 4.2 ......................................................... Thickness of the test object with the thermal conductivity meter

Claims

1. A degradation life test method and algorithm for new energy thermal management composite materials, comprising the steps of: preparing a test object as a standard test specimen from one or a combination of any two of an open-type specimen, a closed-type specimen, and a jig-compressed specimen; placing the standard specimen in at least four designated constant temperature environments, and further subjecting the standard specimen to at least one of moist heat, thermal shock, and thermal cycles for a designated time or cumulative number of cycles in each temperature environment; testing the physicochemical and electrical properties of the test object using the standard specimen or laminated composite test piece; and determining the actual measured values ​​of the physicochemical and electrical properties. and further fitting three constants in a dynamic correlation equation (2) of the 15 parameters; substituting the fitted constants into the dynamic correlation equation (2) to calculate new values ​​of the 15 parameters in an arbitrarily designated constant temperature environment; and substituting the new values ​​of the 15 parameters into equation (1) one by one to evaluate or predict the physicochemical properties and electrical properties of the test object at an arbitrarily designated time under at least one of conditions of moist heat, cold-heat shock, and cold-heat cycle for an arbitrarily designated time or cumulative number of cycles; The micro-vaporization expansion vibration equation (1) is expressed by the following equation: [Equation 1] Here, Equation (1) covers all of the physicochemical and electrical properties, and for the sake of brevity, it is not just a relational expression for one property, but is expressed as a general formula including 15 parameters that do not change over time. When evaluating or predicting any of the physicochemical and electrical properties, it is necessary to replace the parameters and symbols corresponding to the physicochemical and electrical properties in Equation (1) one by one, The parameters include the following in equation (1): 【number】 A total of 15 parameters are included, of which 14 are independent parameters and the remaining ΔP 1 are linearly related parameters, i.e., the parameters do not change with time but change with temperature; for simplicity, the symbol "Q" is used to represent any of the 15 parameters; The constants include three constants under each parameter "Q" name in the micro-vaporization expansion oscillation equation (1) that do not change with time or temperature, but only with the chemical composition of the test object; for simplicity, the three constants under each parameter name are represented by three letters "A, B, C"; when evaluating or predicting any of the physicochemical properties or electrical properties, they are substituted one by one for each parameter and its corresponding constant in the following kinetic correlation equation (2): [Equation 2] [In formula (2), Q - any of the 15 parameters in equation (1) at any temperature; A - the fitted value of the empirical constant K related to the activation energy of the multicomponent reaction and the activation energy of diffusion after superposition; B - fitted values ​​of empirical constants related to the superposition of multicomponent chemical reaction rates and diffusion rates, dimensionless; C - fitted value of the automorphic constant after Fourier series transformation related to the multicomponent activation energy K; T - the specified constant temperature in absolute temperature + 273.15 K. A method and algorithm for testing the degradation life of new energy thermal management composite materials.

2. Use of the degradation life test method and algorithm for a new energy thermal management composite material described in claim 1, wherein the degradation life test method and algorithm are used to evaluate or predict the physicochemical properties and electrical properties of a test object in an arbitrarily specified constant temperature environment for an arbitrarily specified time or cumulative number of cycles, or to evaluate or predict the half-life of any of the physicochemical properties and electrical properties of a test object in a specified constant temperature environment, or to evaluate or predict any of the physicochemical properties and electrical properties of a test object over a specified actual use time of 20,000 hours. and evaluating or predicting the rated temperature of the composite material; wherein the physicochemical properties and electrical properties also include at least one of color, density, thermal conductivity, oil separation rate, compression set rate, specific heat, hardness, tensile strength, elongation at break, butt tensile adhesive strength, lap shear adhesive strength, glass transition temperature, coefficient of linear expansion, dielectric breakdown strength, DC or AC leakage resistance, volume resistivity, dielectric constant, loss factor, oxygen index, flame retardancy, vacuum volatile matter, water absorption, mildew resistance, smoke density, smoke generation index, and gas toxicity index.

3. The degradation life test method and algorithm according to claim 1, characterized in that the composite material includes any one of a solid, fluid, and melt state of a polymer matrix composite material, or a mixture of any two states of a solid, fluid, and melt, or any one of rubber, plastic, fiber, and thermosetting material, and a composite thereof, or any one of an elastomer, an adhesive, a sealant, and a foam material, and a composite thereof.

4. 2. The degradation life test method and algorithm according to claim 1, wherein the test object includes a specimen of the composite material fabricated into a shape that complies with corresponding physical, chemical, and electrical property test standards.

5. The open-type specimen is characterized in that the test object is not coated, wrapped, sandwiched, or sealed using a material, packaging material, or container different from the chemical composition of the test object, and the test object is exposed to a degradation environment.

6. The method and algorithm for degradation life testing according to claim 1, characterized in that the closed specimen includes a material, packaging material, or container different from the chemical composition of the test object, and includes coating, wrapping, sandwiching, or sealing to isolate part or all of the surface area of ​​the test object from the degradation environment.

7. The compression specimen using the jig is formed by sandwiching the test object between at least two rigid plates, and adjusting the distance between the two rigid plates to a specified thickness, compression ratio, or pressure using a fixture; the shape of the edge contour of the rigid plate includes any of an arched line, a straight line, a broken line, or one defined by connecting any two head and tail ends of an arched line, a straight line, or a broken line; the dimensions of the rigid plate correspond to the size of the test object required for physical and chemical property and electrical property tests, and if the rigid plate is prone to warping deformation under compressive stress, one side of the rigid plate is 2. The degradation life test method and algorithm according to claim 1, wherein the warpage is countered by providing one or a combination of any two of the reinforcing materials.

8. 2. The degradation life test method and algorithm of claim 1, wherein the combined specimen includes a surface area of ​​the test object in which a portion of the surface area is in an open specimen state and another portion of the surface area is in a closed specimen state, or a jig-compressed specimen is made into a closed specimen state.

9. 2. The degradation life test method and algorithm of claim 1, wherein the specified constant temperature includes setting the constant temperature required for the test in at least one oven, drying chamber, or storage chamber at a temperature of 400°C or less within an allowable temperature measurement error range; or setting the constant temperature as an average temperature of the ratio of the area under the temperature curve to the corresponding time, with the temperature curve as the vertical axis and time as the horizontal axis.

10. 2. The degradation life test method and algorithm according to claim 1, wherein the moist heat includes controlling the relative humidity to a range of 5 to 100% by controlling the moisture content of any one of an air atmosphere, an oxidizing atmosphere, a reducing atmosphere, an inert gas atmosphere, or a mixed medium in an oven, a drying chamber, or a storage chamber in the specified constant temperature environment.

11. 2. The degradation life test method and algorithm according to claim 1, wherein the thermal shock includes: after a specified time has elapsed in a specified high-temperature environment, transferring the test object to a low-temperature environment at a specified temperature drop rate and allowing a specified time to elapse; or after a specified time has elapsed in a specified low-temperature environment, transferring the test object to a high-temperature environment at a specified temperature increase rate and allowing a specified time to elapse.

12. 2. The degradation life test method and algorithm according to claim 1, wherein the thermal cycles involve alternately transferring the test object between a designated high constant temperature environment and a designated low constant temperature environment according to a designated temperature drop rate and a designated temperature rise rate until a designated time has elapsed or a designated number of cumulative cycles has been reached; the alternate transfer is characterized in that the temperature curve is on the vertical axis and the time is on the horizontal axis, and the contour shape of the temperature curve includes any of a straight line, a broken line, and an arc line, or a loop formed by connecting two head and tail ends, or a wavy shape with ups and downs.

13. The degradation life test method, algorithm, and application thereof according to claim 1, characterized in that the specified time or cumulative number of cycles includes placing the standard specimen in a temperature-controlled oven, drying chamber, or storage room, and removing it from the oven, drying chamber, or storage room after a certain time or cumulative number of cycles has passed according to a given test procedure, and then placing it in another specified constant temperature environment.

14. 2. The method and algorithm for degradation life testing according to claim 1, wherein the laminated composite test piece includes attaching at least one layer of material or part having a known performance index and known dimensions to the upper and lower rigid plates of the compression specimen by the jig during a constant temperature process or when testing physicochemical and electrical properties, and using a measuring instrument to accurately measure the physicochemical and electrical properties.

15. 2. The degradation life test method and algorithm according to claim 1, wherein the actual measured values ​​include data on physicochemical and electrical properties measured using measuring instruments or equipment that meet standard requirements for physicochemical and electrical properties, and in accordance with the operations and conditions specified in the standards.

16. To fit the parameters, the measured values ​​(P) of the physicochemical and electrical properties are used as verification samples; they are increased or decreased in as small a step as possible using an electronic calculation program or the Parallax method, and input into Equation (1). The iterative process of the "Q" values ​​of 15 different parameters is repeated, and (P t ) calculated value; t 2. The degradation life test method and algorithm of claim 1, further comprising: when the standard deviation of the difference between the predicted value (P) and the actual measured value (P) converges to a minimum value, the "Q" of the corresponding 15 parameters is set as the optimal value; and when there are multiple optimal values ​​among the fitted values ​​of the 15 parameters due to the frequency doubling effect in mathematics, selecting only the 15 "Q" values ​​in a relatively small group closest to "1x" as the optimal parameters.

17. To fit the constants, different "C" values ​​are temporarily input, and the iteration process is repeated in formula (2). A graph is drawn with the logarithm of each of the 15 optimal parameter "Q" values ​​on the vertical axis and 1 / (T+C) on the horizontal axis. If the graph is close to a straight line connecting the points, "A, B, C" are the best-fit values; or, using the least squares method, electronic calculation program, or parallax method, the values ​​are increased or decreased in the smallest possible steps, and different "C" values ​​are input, and the iteration process is repeated in formula (2). The R output from the calculation program system is ２ 2. The degradation life test method and algorithm of claim 1, wherein the coefficients are: A, B, C, which correspond to the 15 parameters obtained, respectively, and the minimum boundary value of the "C" value is -273.

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