A method for measuring thermal conductivity of nano-adsorbent material in moisture absorption state

Abstract

This invention provides a method for measuring the thermal conductivity of nano-adsorbent materials under hygroscopic conditions, relating to the field of thermal conductivity measurement technology for adsorbent materials. By establishing a dynamic closed-loop control system for humidity and water content, and combining it with the quantitative influence of light intensity on the thermal conductivity of materials, this invention innovatively achieves synchronous dynamic measurement and analysis of the thermal conductivity of nano-adsorbent materials under varying environments. This not only improves the accuracy and real-time performance of humidity control and precisely captures the regulatory mechanism of light-humidity coupling effect on heat conduction, but also realizes intelligent early warning and management of environmental risks by constructing an incremental light effect model and a two-dimensional damage map.

Description

Technical Field

[0001] This invention relates to the field of thermal conductivity measurement technology for adsorption materials, specifically a method for measuring the thermal conductivity of nano-adsorption materials under hygroscopic conditions. Background Technology

[0002] Nanomaterials, due to their excellent porous structure and hygroscopic properties, are widely used in building energy conservation, environmental control, and thermal management. However, their thermal conductivity is affected by the complex coupling effect of environmental humidity and light conditions, causing the thermal conductivity to dynamically change with the hygroscopic state and light intensity. This makes it difficult for traditional thermal conductivity measurement methods to accurately reflect the material's thermal conduction characteristics under actual operating conditions. In existing technologies, most measurement methods are designed for steady-state or single environmental factors, lacking dynamic monitoring and separation analysis of humidity change paths and light effects. Especially when simulating typical application scenarios such as the interaction between indoor air humidity and natural light, it is difficult to reveal the specific impact mechanism of photo-humidity coupling on thermal conductivity.

[0003] In the prior art, CN110361416A discloses a method for measuring the thermal conductivity of a hygroscopic fiber fabric material in its hygroscopic state. The method includes: S1, pre-conditioning the dried hygroscopic fiber fabric material until it reaches a hygroscopic equilibrium state, and measuring the moisture content α1; S2, further measuring the porosity φ and effective thermal conductivity λ, and calculating the thermal conductivity λsh of the fiber skeleton of the hygroscopic fiber fabric material; S3, continuing the hygroscopic treatment of the hygroscopic fiber fabric material until it reaches a state above hygroscopic equilibrium but below saturation, measuring the moisture content α2, and calculating the volume ratio φw of non-hygroscopic moisture; S4, calculating the thermal conductivity λeff at the moisture content α2 using a parallel conduction model. Although it can measure thermal conductivity, it focuses on calculating thermal conductivity in segments by porosity and moisture content under static moisture absorption equilibrium. The measurement process is relatively cumbersome and relies on traditional thermal measurement methods and physical model corrections. It lacks real-time monitoring and separation of the effects of dynamic humidity changes and light coupling, and cannot reflect the coupling effects caused by light conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for measuring the thermal conductivity of nano-adsorbent materials under hygroscopic conditions, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, comprising the following steps:

[0008] S1: The adsorbent material is placed under constant temperature conditions for a moisture absorption test. The humidity of the test environment and the moisture content of the adsorbent material during the moisture absorption process are collected in real time. At the same time, the thermal conductivity of the adsorbent material is also collected.

[0009] S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and perform a light source state switching operation when the moisture content of the adsorbent material reaches the preset moisture content node.

[0010] S3: Measure the thermal conductivity in the bright state and the thermal conductivity in the dark state before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state.

[0011] S4: Based on the dark state thermal conductivity, construct the relationship curve between thermal conductivity and humidity under no light conditions, and then combine the light intensity of the light source and the thermal conductivity deviation to generate the light effect increment surface;

[0012] S5: Generate the thermal conductivity of the adsorbent material under different environments based on the relationship curve and the incremental surface of the light effect, and issue an early warning when there is a risk in the environment.

[0013] Preferably, the logic for controlling the humidity of the test environment according to the humidity change path is as follows:

[0014] The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorbent material. Then, based on the relationship function and the required moisture absorption rate, a time-moisture content objective function is constructed.

[0015] The target moisture content of the adsorbent material is obtained based on the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is then adjusted using a PID algorithm.

[0016] Preferably, when adjusting the humidity of the test environment using the PID algorithm, a single-point retest process is also included, the specific logic of which is as follows:

[0017] When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the humidity of the test environment is adjusted using a PID algorithm.

[0018] When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is paused, and the control parameters in the PID algorithm are corrected. The correction formula is as follows:

[0019] ;

[0020] In the formula , These represent the proportional coefficient and integral time constant in the PID algorithm, respectively. , These represent the corrected proportional gain and the integral time constant, respectively. express The deviation between the real-time moisture content and the target moisture content at any given time. Indicates the deviation threshold. , Represents the empirical coefficient. ;

[0021] After correction, the humidity of the test environment is returned to the humidity node 10 seconds before exceeding the limit, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

[0022] Preferably, when adjusting the humidity of the test environment using a PID algorithm, a path accuracy verification is also included, the specific logic of which is as follows:

[0023] Define path reproducibility metrics:

[0024] ;

[0025] In the formula Indicates the reproducibility index. , They represent Real-time moisture content and target moisture content at any given time, superscript The index representing the moisture content node;

[0026] When satisfied ,or If the accuracy of the humidity change path is deemed insufficient, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

[0027] Preferably, the logic for calculating the thermal conductivity deviation caused by the light source condition is as follows:

[0028] When the real-time moisture content of the adsorbent material reaches any preset moisture content node, the humidity change process is paused, and the test environment is kept at a constant temperature and humidity.

[0029] The thermal conductivity of the adsorbent material under light conditions was collected and calibrated as the thermal conductivity in the light state.

[0030] After turning off the light source and waiting for the temperature in the test environment to stabilize, the thermal conductivity of the adsorbent material under no-light conditions was collected and calibrated as the dark-state thermal conductivity.

[0031] The difference between the thermal conductivity in the light state and the thermal conductivity in the dark state is defined as the thermal conductivity deviation.

[0032] Preferably, the relationship curve between thermal conductivity and humidity under no-light conditions is fitted using cubic spline interpolation, and the fitting function expression is:

[0033] ;

[0034] In the formula express The dark-state thermal conductivity at time t. express Constantly test the humidity of the environment. This represents the fitted function.

[0035] Preferably, the optical effect increment surface is a two-dimensional surface, generated using radial basis function interpolation, and its expression is:

[0036] ;

[0037] In the formula express The deviation of thermal conductivity at any given time Represents radial basis functions. express Light intensity at any given time.

[0038] Preferably, when issuing an early warning when there is an environmental risk, the rate of change of water content under light conditions and the rate of change of water content under no light conditions are calculated first.

[0039] The ratio of the rate of change of moisture content under light conditions to the rate of change of moisture content under no light conditions is defined as the light enhancement coefficient. The larger the light enhancement coefficient, the stronger the influence of light intensity on the moisture absorption rate, and the higher the environmental risk.

[0040] Preferably, an early warning is issued by constructing a two-dimensional damage map, which includes the relationship curve between thermal conductivity and humidity under no-light conditions and the incremental surface of light effect under light conditions.

[0041] The horizontal axis of the relationship curve represents the humidity of the test environment, and the vertical axis represents the dark-state thermal conductivity.

[0042] The input axis of the incremental optical effect surface is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation.

[0043] In the incremental surface of light effect, the region with a light enhancement coefficient exceeding 1.2 is designated as the medium influence region, and it is believed that the light intensity in this range has a certain impact on the thermal conductivity of the adsorption material.

[0044] Will satisfy:

[0045] ;

[0046] The area is designated as the strong influence zone, where the light intensity is considered to have a strong impact on the thermal conductivity of the adsorption material. An early warning is issued when the light intensity of the environment surrounding the adsorption material reaches this range. In the formula... Indicates the deviation in thermal conductivity. This represents the light enhancement coefficient.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This invention innovatively achieves synchronous dynamic measurement and analysis of the thermal conductivity of nano-adsorbent materials under varying environments by establishing a dynamic closed-loop control system for humidity and moisture content, combined with the quantitative influence of light intensity on the thermal conductivity of materials. This method not only improves the accuracy and real-time performance of humidity control and precisely captures the regulatory mechanism of light-humidity coupling on heat conduction, but also realizes intelligent early warning and management of environmental risks by constructing an incremental light effect model and a two-dimensional damage map. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0050] Figure 2 This is a surface plot showing the incremental optical effect of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0052] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0053] Example:

[0054] Please see Figures 1 to 2 The present invention provides a technical solution:

[0055] A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, comprising the following steps:

[0056] S1: The adsorbent material is placed under constant temperature conditions for a moisture absorption test. The humidity of the test environment and the moisture content of the adsorbent material during the moisture absorption process are collected in real time. At the same time, the thermal conductivity of the adsorbent material is also collected.

[0057] Specifically, the moisture absorption test can be conducted using a constant temperature and humidity sample test chamber. A coil can be installed inside the chamber and connected to an external constant temperature water bath to maintain a constant temperature during the test. Alternatively, the change in the thermal conductivity of the adsorbent material can be detected by placing the sensor probe of a thermal conductivity measuring instrument (such as a transient heat source method device) next to the adsorbent material. The humidity inside the test chamber can be controlled using nitrogen: nitrogen flows through a humidity generator before entering the sample test chamber to maintain the required air humidity. Changing the nitrogen flow rate yields different humidity levels. Furthermore, to simulate the effect of sunlight on the water vapor adsorption-desorption process and the coupling effect of the material's thermal conductivity, a light source with variable intensity and wavelength can be placed above the sample. This allows for different operating conditions to be provided to the adsorbent material by changing the light intensity and the humidity inside the test chamber.

[0058] Here, a constant temperature and humidity sample test chamber is used to achieve precise and dynamic control of temperature and humidity. Combined with nitrogen flow rate adjustment, humidity can be flexibly adjusted. Furthermore, a transient heat source method instrument is used to monitor changes in the thermal conductivity of the adsorbent material in real time. An adjustable light source is then superimposed to simulate sunlight, enabling simultaneous dynamic measurement of material properties under the coupled effects of humidity and light. Compared to traditional methods that only measure thermal conductivity in a steady-state or single-environment environment, this step offers high timeliness and multi-physics field coupling control capabilities, significantly improving the accuracy and realism of the measurement.

[0059] S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and perform a light source state switching operation when the moisture content of the adsorbent material reaches the preset moisture content node.

[0060] The logic for controlling the humidity of the test environment according to the humidity change path is as follows:

[0061] The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorbent material. Then, based on the relationship function and the required moisture absorption rate, a time-moisture content objective function is constructed.

[0062] In this step, humidity initially changes linearly with time, and the functional relationship between humidity and time can be expressed as:

[0063] ;

[0064] In the formula This represents the functional relationship between humidity and time.

[0065] After detecting the moisture content and humidity at different times, the relationship function between humidity and moisture content can be fitted. Assume the fitted function expression is as follows:

[0066] ;

[0067] In the formula , They represent Moisture content and humidity at any given time A fitting function representing the relationship between humidity and moisture content;

[0068] In other words, the objective function for time-moisture content can be expressed as:

[0069] ;

[0070] Understandably, the functional relationship between humidity and time initially increases linearly. This is to obtain humidity and moisture content data at different times in order to fit a function relating humidity and moisture content. After obtaining the function relating humidity and moisture content, the internal... It is no longer limited to linear changes, but can be determined according to the user's needs, thereby adjusting the relationship between time and moisture content, which means adjusting the moisture absorption rate of the adsorbent material.

[0071] The target moisture content of the adsorbent material is obtained based on the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is adjusted by the PID algorithm. The PID algorithm is a mature existing technology, and its specific calculation formula will not be elaborated here.

[0072] It is understandable that the moisture content of the adsorbent material (i.e., the amount of water adsorbed inside the material) is not a simple function of the ambient humidity, but rather a response to the ambient humidity over a certain time scale, exhibiting a certain lag and dynamic change characteristics. Here, to achieve precise control and dynamic measurement of the material's hygroscopic state, the ambient humidity is dynamically adjusted through a "humidity change path," and closed-loop control is achieved through real-time feedback of the moisture content.

[0073] When adjusting the humidity of the test environment using the PID algorithm, a single-point retest process is also set up, the specific logic of which is as follows:

[0074] When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the humidity of the test environment is adjusted using a PID algorithm.

[0075] When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is paused, and the control parameters in the PID algorithm are corrected. The correction formula is as follows:

[0076] ;

[0077] In the formula , These represent the proportional coefficient and integral time constant in the PID algorithm, respectively. , These represent the corrected proportional gain and the integral time constant, respectively. express The deviation between the real-time moisture content and the target moisture content at any given time. Indicates the deviation threshold. , Represents the empirical coefficient. .

[0078] As can be seen from the correction formula, when the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, it indicates that the current control error is large and the system response is insufficient. At this time, the proportional gain is amplified to enhance the controller's response and improve its ability to correct errors instantly. The amplification factor increases linearly with the deviation, and the increase is controlled by empirical parameters to prevent excessive amplification of the proportional gain from causing control oscillations. Integral action is beneficial for eliminating steady-state errors, but too small an integral time may lead to system oscillations, while too large an integral time will cause a slow system response. Therefore, the integral time constant is multiplied by a square root factor inversely proportional to the deviation. This reduces the integral time constant when the deviation is large, accelerating the integral response; and increases the integral time when the deviation is small, avoiding over-integration that could cause oscillations.

[0079] After correction, the humidity of the test environment is returned to the humidity node 10 seconds before exceeding the limit, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

[0080] When adjusting the humidity of the test environment using the PID algorithm, a path accuracy verification is also included. The specific logic is as follows:

[0081] Define path reproducibility metrics:

[0082] ;

[0083] In the formula Indicates the reproducibility index. , They represent Real-time moisture content and target moisture content at any given time, superscript The index representing the moisture content node;

[0084] When satisfied ,or If the accuracy of the humidity change path is deemed insufficient, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

[0085] In this step, by introducing a single-point retest mechanism and a full-path retest mechanism, the accumulation of errors caused by path deviation is effectively avoided, making the collected data more consistent with the preset dynamic path. This can prevent PID control failure or performance degradation caused by environmental disturbances, equipment nonlinearity or model errors, thereby ensuring the long-term stable and accurate operation of the humidity control system and improving the scientific nature and repeatability of the data.

[0086] S3: Measure the thermal conductivity in the bright state and the thermal conductivity in the dark state before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state.

[0087] The logic for calculating the thermal conductivity deviation caused by the light source condition is as follows:

[0088] When the real-time moisture content of the adsorbent material reaches any preset moisture content node, the humidity change process is paused, and the test environment is kept at a constant temperature and humidity.

[0089] The thermal conductivity of the adsorbent material under light conditions was collected and calibrated as the thermal conductivity in the light state.

[0090] After turning off the light source and waiting for the temperature in the test environment to stabilize, the thermal conductivity of the adsorbed material under no-light conditions is collected and calibrated as the dark-state thermal conductivity. The use of constant temperature and humidity environment and temperature stability judgment effectively avoids the impact of short-term temperature fluctuations caused by heat input or loss during the light source switching process on the measurement results, and improves the repeatability and reliability of the data.

[0091] The difference between the thermal conductivity in the light state and the thermal conductivity in the dark state is defined as the thermal conductivity deviation.

[0092] Measuring thermal conductivity in both the light and dark states separately is crucial for effectively separating the direct impact of light on thermal conductivity. By pausing humidity changes when the moisture content reaches a preset threshold, environmental humidity and temperature stability are ensured, thereby eliminating the interference of humidity variations and temperature disturbances on thermal conductivity. Measuring thermal conductivity under both light (light state) and dark (dark state) conditions accurately captures the changes in thermal conductivity caused by light, improving the signal-to-noise ratio and data accuracy.

[0093] S4: Construct the relationship curve between thermal conductivity and humidity under no-light conditions based on the dark state thermal conductivity, and then generate the light effect increment surface by combining the light intensity of the light source and the thermal conductivity deviation.

[0094] The relationship between thermal conductivity and humidity under no-light conditions was fitted using cubic spline interpolation. This curve reflects the fundamental law of thermal conductivity variation with ambient humidity (or moisture content) of nano-adsorbent materials under no-light conditions. Cubic spline interpolation ensures the first and second-order continuity of the fitted curve at data nodes, making it suitable for describing the nonlinear and smooth variation of the material's thermal conductivity with humidity. This curve reveals how the material's hygroscopic state independently affects its thermal conductivity and is core data for evaluating the material's fundamental thermophysical properties.

[0095] The fitting function expression for the relationship curve is:

[0096] ;

[0097] In the formula express The dark-state thermal conductivity at time t. express Constantly test the humidity of the environment. This represents the fitted function.

[0098] The optical effect increment surface is a two-dimensional surface, generated using radial basis function interpolation. The surface expression is as follows:

[0099] ;

[0100] In the formula express The deviation of thermal conductivity at any given time Represents radial basis functions. express Light intensity at any given time.

[0101] Radial basis function interpolation can efficiently handle multidimensional nonlinear relationships and is suitable for capturing the complex variation of thermal conductivity under the coupling of humidity and light intensity. The specific function type used (such as Gaussian function, multiple quadratic function, multiple harmonic spline function) can be determined based on expert experience. This surface describes the spatial distribution of the additional influence of light intensity and humidity on thermal conductivity, revealing the light-humidity coupling mechanism and its moderating effect on thermal conductivity.

[0102] In this step, by fitting the dark-state baseline curve and the illumination increment surface respectively, we make full use of multidimensional data to accurately capture the coupling effect of the two key environmental factors, humidity and illumination. This avoids the limitations of single-variable or linear models, separates the basic thermal conduction behavior from the illumination effect and models them clearly, making it easier to analyze the contribution of the two to the material properties separately, and realize the scientific analysis and visualization of the coupling mechanism.

[0103] S5: Generate the thermal conductivity of the adsorbent material under different environments based on the relationship curve and the light effect increment surface. That is, first obtain the dark state thermal conductivity under no light conditions based on humidity and the relationship curve, and then obtain the thermal conductivity deviation based on humidity, light intensity and the light effect increment surface. The sum of the dark state thermal conductivity and the thermal conductivity deviation is the light state thermal conductivity. At the same time, issue an early warning when there is a risk in the environment.

[0104] When issuing an early warning when there is an environmental risk, first calculate the rate of change of water content under light conditions and the rate of change of water content under no light conditions;

[0105] The ratio of the rate of change in moisture content under light conditions to that under lightless conditions is defined as the light enhancement coefficient. A larger light enhancement coefficient indicates a stronger influence of light intensity on the moisture absorption rate, and thus a higher environmental risk. In other words, when the light enhancement coefficient... The value indicates that light promotes the moisture absorption process; the larger the value, the more significant the effect of light on the moisture absorption rate.

[0106] Early warning is issued by constructing a two-dimensional damage map, which includes the relationship curve between thermal conductivity and humidity under no-light conditions and the incremental surface of light effect under light conditions.

[0107] The dual-dimensional damage map constructed here combines the basic thermal conductivity curve with the optical effect increment surface to form a two-dimensional or three-dimensional visualization. This visually represents the thermal conductivity of the nano-adsorption material under different combinations of humidity and light conditions. It not only shows the thermal performance state of the material but also reflects the "functional damage" or performance fluctuation trend caused by environmental changes.

[0108] The horizontal axis of the relationship curve represents the humidity of the test environment, and the vertical axis represents the dark-state thermal conductivity.

[0109] The input axis of the incremental surface of the light effect is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation.

[0110] In the incremental surface of light effect, the region with a light enhancement coefficient exceeding 1.2 is designated as the medium influence region, and it is believed that the light intensity in this range has a certain impact on the thermal conductivity of the adsorption material.

[0111] Will satisfy:

[0112] ;

[0113] The area is designated as the strong influence zone, where the light intensity is considered to have a strong impact on the thermal conductivity of the adsorption material. An early warning is issued when the light intensity of the environment surrounding the adsorption material reaches this range. In the formula... Indicates the deviation in thermal conductivity. This represents the light enhancement coefficient.

[0114] The moderate and strong influence zones here refer to areas in the damage spectrum where the increase in light effect or the light enhancement coefficient exceeds a preset threshold, indicating that under this combination of humidity and light intensity, light has an impact on the thermal conductivity of the material. In practical applications, the strong influence zone is a key area of ​​focus during design and monitoring, as it may indicate significant fluctuations in material performance and potential thermal management risks or performance degradation hazards, thus requiring early warning.

[0115] In this step, by simultaneously incorporating two key environmental variables, humidity and light intensity, an intuitive and scientific two-dimensional damage map is constructed using the basic thermal conductivity curve under no-light conditions and the incremental surface caused by light. This effectively captures the comprehensive impact of photo-humidity coupling on the thermal conductivity of materials, avoids the blind spots and misjudgments under single-factor analysis, and realizes the dynamic quantification and risk warning of the thermal conductivity of materials under the coupling of multiple environmental factors.

[0116] In this embodiment, the radial basis function for constructing the incremental optical effect surface is built using a combination of Gaussian radial basis function and exponential function, which can be specifically expressed as:

[0117] ;

[0118] In the formula , , , All of these are model parameters.

[0119] The humidity component uses a Gaussian radial basis function because the thermal conductivity of nano-adsorbent materials is most sensitive to humidity changes within a certain range (near the critical humidity). The Gaussian function can well simulate this peak response. Moreover, the effect of humidity on the thermal conductivity of materials usually has a "most sensitive region," and the "bell-shaped" curve of the Gaussian function precisely reflects this limitation and gradualness. The light intensity component uses an exponential response function because the effect of light intensity on the thermal conductivity deviation usually shows a rapid increase with increasing intensity, followed by a gradual saturation trend. The exponential decay function in the latter half can accurately simulate this nonlinear increasing and eventually stabilizing physical process, which is consistent with the kinetic characteristics of light-induced moisture absorption and thermal conductivity changes.

[0120] Based on this, the incremental surface of the light effect was fitted using 20 sets of measurement data under different humidity and light intensities. The measurement data are shown in the table below:

[0121] Table 1: Measurement Data

[0122]

[0123] The fitted image shows that the surface exhibits a typical "peak-valley" structure, reflecting the coupled regulatory effect of light and humidity. Under certain humidity levels (approximately 65%-75%), the influence of light is most significant, indicating that the material has the strongest photosensitive thermal conductivity in this humidity range. This helps to explore how to regulate the thermal properties of materials through humidity and light, and guide the design of functional materials.

[0124] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0125] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, characterized in that, The specific steps include: S1: The adsorbent material is placed under constant temperature conditions for a moisture absorption test. The humidity of the test environment and the moisture content of the adsorbent material during the moisture absorption process are collected in real time. At the same time, the thermal conductivity of the adsorbent material is also collected. S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and perform a light source state switching operation when the moisture content of the adsorbent material reaches the preset moisture content node. S3: Measure the thermal conductivity in the bright state and the thermal conductivity in the dark state before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state. S4: Based on the dark state thermal conductivity, construct the relationship curve between thermal conductivity and humidity under no light conditions, and then combine the light intensity of the light source and the thermal conductivity deviation to generate the light effect increment surface; S5: Generate the thermal conductivity of the adsorbent material under different environments based on the relationship curve and the incremental surface of the light effect, and issue an early warning when there is a risk in the environment.

2. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 1, characterized in that: The logic for controlling the humidity of the test environment according to the humidity change path is as follows: The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorbent material. Then, based on the relationship function and the required moisture absorption rate, a time-moisture content objective function is constructed. The target moisture content of the adsorbent material is obtained based on the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is then adjusted using a PID algorithm.

3. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 2, characterized in that: When adjusting the humidity of the test environment using the PID algorithm, a single-point retest process is also set up, the specific logic of which is as follows: When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the humidity of the test environment is adjusted using a PID algorithm. When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is paused, and the control parameters in the PID algorithm are corrected. The correction formula is as follows: ； In the formula , These represent the proportional coefficient and integral time constant in the PID algorithm, respectively. , These represent the corrected proportional gain and the integral time constant, respectively. express The deviation between the real-time moisture content and the target moisture content at any given time. Indicates the deviation threshold. , Represents the empirical coefficient. ; After correction, the humidity of the test environment is returned to the humidity node 10 seconds before exceeding the limit, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

4. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 3, characterized in that: When adjusting the humidity of the test environment using the PID algorithm, a path accuracy verification is also included. The specific logic is as follows: Define path reproducibility metrics: ； In the formula Indicates the reproducibility index. , They represent Real-time moisture content and target moisture content at any given time, superscript The index representing the moisture content node; When satisfied ,or If the accuracy of the humidity change path is deemed insufficient, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

5. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 1, characterized in that: The logic for calculating the thermal conductivity deviation caused by the light source condition is as follows: When the real-time moisture content of the adsorbent material reaches any preset moisture content node, the humidity change process is paused, and the test environment is kept at a constant temperature and humidity. The thermal conductivity of the adsorbent material under light conditions was collected and calibrated as the thermal conductivity in the light state. After turning off the light source and waiting for the temperature in the test environment to stabilize, the thermal conductivity of the adsorbent material under no-light conditions was collected and calibrated as the dark-state thermal conductivity. The difference between the thermal conductivity in the light state and the thermal conductivity in the dark state is defined as the thermal conductivity deviation.

6. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 5, characterized in that: The relationship curve between thermal conductivity and humidity under no-light conditions was fitted using cubic spline interpolation, and the fitting function expression is as follows: ； In the formula express The dark-state thermal conductivity at time t. express Constantly test the humidity of the environment. This represents the fitted function.

7. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 6, characterized in that: The optical effect increment surface is a two-dimensional surface, generated using radial basis function interpolation. The surface expression is as follows: ； In the formula express The deviation of thermal conductivity at any given time Represents radial basis functions. express Light intensity at any given time.

8. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 7, characterized in that: When issuing an early warning when there is an environmental risk, first calculate the rate of change of water content under light conditions and the rate of change of water content under no light conditions; The ratio of the rate of change of moisture content under light conditions to the rate of change of moisture content under no light conditions is defined as the light enhancement coefficient. The larger the light enhancement coefficient, the stronger the influence of light intensity on the moisture absorption rate, and the higher the environmental risk.

9. The method for measuring the thermal conductivity of a nano-adsorbent material under hygroscopic conditions according to claim 8, characterized in that: Early warning is issued by constructing a two-dimensional damage map, which includes the relationship curve between thermal conductivity and humidity under no light conditions and the incremental surface of light effect under light conditions. The horizontal axis of the relationship curve represents the humidity of the test environment, and the vertical axis represents the dark-state thermal conductivity. The input axis of the incremental optical effect surface is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation. In the incremental surface of light effect, the region with a light enhancement coefficient exceeding 1.2 is designated as the medium influence region, and it is believed that the light intensity in this range has a certain impact on the thermal conductivity of the adsorption material. Will satisfy: ； The area is designated as the strong influence zone, where the light intensity is considered to have a strong impact on the thermal conductivity of the adsorption material. An early warning is issued when the light intensity of the environment surrounding the adsorption material reaches this range. In the formula... Indicates the deviation in thermal conductivity. This represents the light enhancement coefficient.

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