A chloride-based multi-parameter humidity and conductivity comprehensive calculation method

Abstract

The application discloses a chloride-based multi-parameter humidity and conductivity comprehensive calculation method, comprising the following steps: S1, collecting basic parameter information of chloride materials, and performing relevant data arrangement, and comparing after entering a system; S2, then setting an environmental humidity change range, and obtaining moisture absorption and surface water layer thickness under different relative humidity conditions; S3, establishing a relevant coupling model, and considering ion mobility, charge transmission channel number and interface polarization effect of the chloride materials under different humidity; S4, introducing a multi-parameter regulation factor, correcting environmental temperature, pressure and material surface state, and obtaining correct data; the method has high application prospect and popularization value.

Description

Technical Field

[0001] This invention relates to the field of multi-parameter calculation technology for chlorides, specifically a comprehensive calculation method based on the multi-parameter humidity resistance and conductivity of chlorides. Background Technology

[0002] Currently, common methods for analyzing electrical conductivity and humidity mainly rely on experimental measurements or predictions using a single physical model. These methods typically suffer from low computational accuracy, strong parameter limitations, and poor adaptability. In particular, when dealing with complex ionic systems (such as chlorides), traditional models struggle to accurately reflect the material's electrical conductivity response under varying humidity conditions due to the significant contribution of chloride ions to electrical conductivity.

[0003] Chloride materials are widely used in practical engineering applications and have obvious humidity sensitivity and ionic conductivity. Therefore, how to comprehensively consider various factors such as ion concentration, humidity adsorption, and structural parameters in chloride materials to accurately model and calculate their conductivity has become a technical problem that urgently needs to be solved.

[0004] Currently, there is a lack of a comprehensive calculation method that integrates multiple parameter inputs, is applicable to chloride systems, and takes into account the relationship between humidity and conductivity response, which limits the in-depth application of this type of material. Summary of the Invention

[0005] This invention provides a multi-parameter humidity resistance and conductivity comprehensive calculation method based on chloride, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter humidity resistance and conductivity comprehensive calculation method based on chlorides, comprising the following steps:

[0007] S1. Collect basic parameter information of chloride materials, organize relevant data, and compare it after entering it into the system;

[0008] S2. Then set the range of ambient humidity changes and obtain the amount of moisture absorbed and the thickness of the surface water layer under different relative humidity conditions;

[0009] S3. Establish relevant coupling models, considering the ion mobility, number of charge transport channels and interfacial polarization effect of chloride materials under different humidity conditions.

[0010] S4. Introduce multi-parameter control factors to correct for ambient temperature, pressure, and material surface states, and obtain accurate data;

[0011] S5. Based on the above data, calculate the conductivity value of the chloride material under the target humidity condition, and output the conductivity versus humidity response curve.

[0012] According to the above technical solution, the chloride material in S1 includes inorganic salt chlorides, selected from one or more of sodium chloride, potassium chloride, magnesium chloride, and zinc chloride, and the initial particle size of the material is in the range of 10 nanometers to 10 micrometers, the particle size distribution conforms to the Gaussian distribution model, and the D50 particle size is preferably 100 nanometers to 1 micrometer.

[0013] According to the above technical solution, in step S2, the adsorption response behavior of the material in a gradually changing humidity environment is simulated by setting a segmented relative humidity change range.

[0014] The hygroscopic mass change of chloride materials was recorded in real time using the mass difference method or dynamic gas adsorption method, and normalized by combining the temperature compensation factor to obtain the unit hygroscopic amount at each humidity point.

[0015] According to the above technical solution, the moisture absorption calculation further includes a pore adsorption correction process for porous or nanoparticle chloride materials. By correcting the contribution of capillary condensation to electrolyte behavior, the thickness of the water film on a unit surface is estimated using the adsorption isotherm combined with the specific surface area of ​​the material, and the critical point for the formation of a continuous water layer is determined accordingly. The moisture absorption, water layer thickness and relative humidity data are stored synchronously for subsequent model input boundary condition setting.

[0016] According to the above technical solution, the coupling model in S3 includes a modeling structure based on a multi-physics field cooperative mechanism, which integrates three sub-modules: mass transfer, electric field drive, and surface charge accumulation.

[0017] The coupling effects between water molecule adsorption behavior, ion migration kinetics, and material interface polarization processes are described respectively.

[0018] The coupling calculation formula is as follows:

[0019] σ eff =σ0(1+k*RH

[0020] in:

[0021] σ eff The equivalent electrical conductivity of the material at a humidity RH ().

[0022] σ0 is the initial electrical conductivity of the material in a dry state;

[0023] k is the humidity sensitivity coefficient, obtained by experiment or model fitting, which reflects the degree of material response to humidity changes;

[0024] RH represents the relative humidity of the current environment.

[0025] According to the above technical solution, the multi-parameter control factors in S4 include ambient temperature, air pressure, and the surface state of the material, which are used to further correct the calculation results. The temperature factor is used to compensate for the difference in ion activity caused by temperature changes, the air pressure factor is used to adjust the adsorption behavior of water vapor on the material surface, and the surface state refers to the roughness, porosity, and presence of coating on the material surface, which affect the residence time of water and the ion movement path.

[0026] According to the above technical solution, after obtaining the original parameters, the system of S4 will automatically call a set of correction rules to apply the above-mentioned regulatory factors to the preliminary data results.

[0027] According to the above technical solution, the calculation step in S5 is based on the data obtained after the aforementioned processing, performs conductivity calculation under the target humidity condition, and outputs the response relationship curve between conductivity and humidity. The curve is generated by calculating the conductivity value at different humidity points step by step, and then using software to generate a continuous image.

[0028] According to the above technical solution, in S5, the user can judge whether the material is suitable for use in a specific humidity environment based on the curve. The response curve can also serve as the basis for material screening and sensor design, assisting researchers in quickly identifying high-performance chloride materials.

[0029] According to the above technical solution, the conductivity-humidity response curve output in S5 can be exported as a standardized visual graphic interface, which is compatible with material performance evaluation systems, intelligent humidity sensor development platforms, and new environmentally adaptable material screening databases, so as to realize data sharing and engineering application transformation.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. By collecting and organizing the basic parameters of chloride materials, a systematic classification and modeling of the properties of various inorganic salt chlorides was achieved, providing a good data foundation for the standardized calculation of subsequent models. At the same time, by setting a segmented relative humidity variation range and combining the moisture absorption data measured by the mass difference method or dynamic gas adsorption method, the adsorption response behavior of the material under the gradual change of humidity can be quantified. Furthermore, by fitting the moisture absorption isotherm through the surface physical adsorption theory, the characterization accuracy of the material's water absorption behavior is effectively improved.

[0032] 2. Taking into full account the influence of ambient temperature, pressure and material surface condition on conductivity, a multi-parameter control factor is introduced to dynamically correct the preliminary calculation data, which effectively solves the problem of inaccurate prediction under actual working conditions by traditional models. The system can automatically call correction rules to avoid manual intervention and improve the overall level of intelligence.

[0033] 3. The output conductivity and humidity response curves can be presented intuitively in a graphical interface, which facilitates material performance evaluation and comparison. At the same time, it can be connected to various practical application scenarios such as material screening systems and humidity sensor development platforms. This curve not only helps users determine the applicable environment of materials, but also serves as a database to support engineering design, intelligent screening and product optimization, and has good data sharing and engineering transformation value.

[0034] In summary, the method of the present invention improves the accuracy of conductivity modeling of chloride materials, simplifies the calculation process, and enhances the visualization and usability of the results, thus having high application prospects and promotional value. Attached Figure Description

[0035] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0036] In the attached diagram:

[0037] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0038] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0039] Example: Figure 1 As shown, the present invention provides a technical solution: a multi-parameter humidity resistance and conductivity comprehensive calculation method based on chlorides, comprising the following steps:

[0040] S1. Collect basic parameter information of chloride materials, organize relevant data, and compare it after entering it into the system;

[0041] S2. Then set the range of ambient humidity changes and obtain the amount of moisture absorbed and the thickness of the surface water layer under different relative humidity conditions;

[0042] S3. Establish relevant coupling models, considering the ion mobility, number of charge transport channels and interfacial polarization effect of chloride materials under different humidity conditions.

[0043] S4. Introduce multi-parameter control factors to correct for ambient temperature, pressure, and material surface states, and obtain accurate data;

[0044] S5. Based on the above data, calculate the conductivity value of the chloride material under the target humidity condition, and output the conductivity versus humidity response curve.

[0045] According to the above technical solution, the chloride material in S1 includes inorganic salt chlorides, selected from one or more of sodium chloride, potassium chloride, magnesium chloride, and zinc chloride, and the initial particle size of the material is in the range of 10 nanometers to 10 micrometers, the particle size distribution conforms to the Gaussian distribution model, and the D50 particle size is preferably 100 nanometers to 1 micrometer.

[0046] According to the above technical solution, in S2, the adsorption response behavior of the material in a gradually changing humidity environment is simulated by setting a segmented relative humidity change range.

[0047] The hygroscopic mass change of chloride materials was recorded in real time using the mass difference method or dynamic gas adsorption method, and normalized by combining the temperature compensation factor to obtain the unit hygroscopic amount at each humidity point.

[0048] The moisture absorption calculation further includes a pore adsorption correction process for porous or nanoparticle chloride materials. By correcting the contribution of capillary condensation to electrolyte behavior, the thickness of the water film per unit surface is estimated using adsorption isotherms combined with the specific surface area of ​​the material, and the critical point for the formation of a continuous water layer is determined accordingly. The moisture absorption, water layer thickness and relative humidity data are stored synchronously for subsequent model input boundary condition settings.

[0049] According to the above technical solution, the coupling model in S3 includes a modeling structure based on a multi-physics field cooperative mechanism, which integrates three sub-modules: mass transfer, electric field drive, and surface charge accumulation.

[0050] The coupling effects between water molecule adsorption behavior, ion migration kinetics, and material interface polarization processes are described respectively.

[0051] The coupling calculation formula is as follows:

[0052] σ eff =σ0(1+k*RH)

[0053] in:

[0054] σ eff The equivalent electrical conductivity of the material at a humidity RH (relative humidity, in decimal form);

[0055] σ0 is the initial electrical conductivity of the material in the dry state (RH=0);

[0056] k is the humidity sensitivity coefficient, obtained by experiment or model fitting, which reflects the degree of material response to humidity changes;

[0057] RH is the relative humidity of the current environment (e.g., 0.45 means 45%).

[0058] To improve the model's adaptability and accuracy, the system automatically switches to a nonlinear response mechanism when the RH is high (RH>0.75) or when encountering high-porosity materials: based on surface physical adsorption theory (such as BET adsorption isotherms), combined with the actual measured water film thickness and specific surface area, a nonlinear adsorption-conductivity mapping relationship is established, the critical point of water layer continuity is calculated, and its influence on interfacial charge polarization is estimated. For porous materials with significant capillary condensation behavior, this method introduces a capillary water layer correction factor to nonlinearly adjust the standard conductivity to more closely approximate the material's actual behavior in high-humidity environments.

[0059] The humidity sensitivity coefficient k was obtained by experimentally measuring the conductivity of chloride materials under different relative humidities and performing linear regression fitting using the least squares method, or by inferring back through supervised learning training in a machine learning model built based on multi-parameter input.

[0060] Two paths are provided for determining the value of k.

[0061] First, the relationship between σ_eff and RH was determined experimentally, and the least squares method was used to perform linear regression to fit the k value;

[0062] Secondly, a regression model is constructed through machine learning (such as support vector regression SVR or artificial neural network ANN), using multiple parameters such as humidity, particle size, and porosity as input variables to fit the mapping relationship between the k value and material properties.

[0063] Some chloride materials may exhibit a decreasing conductivity trend under high humidity due to interruption of ion pathways or insulation of interfacial water film. In this case, k is negative. The model can automatically determine the sign of k based on sample data and adjust the conductivity response direction to avoid misjudgment.

[0064] During operation, the following behavior occurs:

[0065] 1. Sodium chloride nanoparticles with an average particle size of 100 nm were selected, and their conductivity σ0 under dry conditions was measured to be 1.5 × 10⁻⁶ at 25 °C. -5 The conductivity (S / cm) was measured under RH conditions of 0.2, 0.4, 0.6, and 0.8, and the results showed conductivity values ​​of 1.5, 1.3, 1.1, and 0.9 × 10⁻⁶, respectively. -5 The conductivity S / cm shows that the conductivity decreases with increasing humidity. The linear regression fit yields k = -0.75, and the average error between the model prediction and the measured value is 3.2%, indicating that the model is suitable for this type of negatively correlated response material.

[0066] 2. High-porosity (approximately 55%) magnesium chloride porous microspheres were selected, with an initial conductivity σ0 of 4.0 × 10⁻⁶. -6With S / cm and RH set to 0.3, 0.5, and 0.7, the corresponding hygroscopic mass and conductivity were tested. After obtaining the critical water layer thickness using BET fitting, the model was corrected, and then the conductivity was predicted using this method. The goodness of fit R0 was obtained. 2 With an accuracy exceeding 0.98, compared to the traditional static adsorption model, this method controls the error within 2.7% in the humidity-sensitive section, significantly improving accuracy.

[0067] Comparing the calculation errors under multiple RH environments, it was found that the prediction deviation of the traditional model was generally higher than 15%, while the error after correction by the proposed method was lower than 5%. This shows that the proposed multi-parameter control and simplified modeling strategy has higher prediction accuracy and adaptability under actual working conditions.

[0068] According to the above technical solution, the multi-parameter control factors in S4 include ambient temperature, air pressure, and the surface state of the material, which are used to further correct the calculation results to improve the accuracy of the data. The temperature factor is used to compensate for the difference in ion activity caused by temperature changes, the air pressure factor is used to adjust the adsorption behavior of water vapor on the material surface, and the surface state refers to the roughness, porosity, and presence of coatings on the material surface, all of which affect the residence time of water and the ion movement path.

[0069] After acquiring the raw parameters, the S4 system will automatically call a set of correction rules to apply the above-mentioned control factors to the preliminary data results, thereby more closely reflecting the actual conductivity of the material under actual working conditions.

[0070] This correction process can effectively improve the generalization ability of the model, making the calculation results applicable not only to ideal experimental environments, but also to conductivity prediction in practical engineering application scenarios.

[0071] According to the above technical solution, the calculation step in S5 is based on the data obtained after the aforementioned processing. It performs conductivity calculation under the target humidity condition and outputs the response curve between conductivity and humidity. The curve is generated by calculating the conductivity value at different humidity points step by step and then using software to generate a continuous image, which can intuitively reflect the trend of material conductivity changing with humidity.

[0072] To improve the adaptability of the prediction model to actual operating conditions, the system integrates a set of multi-parameter correction rules based on physical mechanisms. These rules cover the effects of ambient temperature, air pressure, and material surface roughness or porosity on ion migration and moisture adsorption behavior, thereby dynamically correcting the initially calculated conductivity results.

[0073] Temperature correction employs a mobility model based on the Arrhenius equation, expressed as follows:

[0074]

[0075] Where μ T Let T be the ion mobility at temperature T, μ0 be the mobility at a reference temperature (typically 298 K), Ea be the activation energy for migration of the material, and B be the Boltzmann constant. The corresponding conductivity correction formula is:

[0076] σT=σ0·(μT / μ0)

[0077] This model can effectively compensate for the enhanced or suppressed ion movement caused by temperature changes and is applicable to most chloride systems.

[0078] Regarding pressure correction, considering the influence of air pressure on the moisture adsorption balance and water layer thickness formation, a pressure-sensitive correction coefficient βP is introduced, and the correction model is as follows:

[0079] σP=σT·(1+βP·(P-P0))

[0080] Where P is the current ambient pressure, P0 is the standard atmospheric pressure (101.325 kPa), and βP is obtained by experimental fitting. This method is applicable to the prediction and adjustment of conductivity under high-altitude, closed container, or pressurized environments.

[0081] After obtaining the original material parameters and environmental variables, the above correction rules can be automatically invoked, and the corrected equivalent conductivity value can be dynamically output through modular calculation, ensuring that the prediction error is controlled within 5% under variable environmental conditions.

[0082] In S5, users can use the curve to determine whether a material is suitable for use in a specific humidity environment, such as whether it can maintain stable electrical conductivity under high humidity or dry conditions. This response curve can also serve as a basis for material screening and sensor design, helping researchers quickly identify high-performance chloride materials, reduce the workload of actual testing, and improve R&D efficiency.

[0083] The conductivity-humidity response curve output by S5 can be exported as a standardized visual graphic interface, which can be adapted to material performance evaluation systems, intelligent humidity sensor development platforms, or new environmentally adaptable material screening databases to achieve data sharing and engineering application transformation.

[0084] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A chloride-based multi-parameter humidity and conductivity integrated calculation method, characterized in that: Comprise the following steps: S1, collect the basic parameter information of chloride material, and carry out related data arrangement, enter the system and compare; S2, then set the humidity range, obtain the moisture absorption and surface water layer thickness under different relative humidity conditions; S3, establish the relevant coupling model, consider the ion mobility of chloride material under different humidity, the number of charge transmission channel and the interface polarization effect; S4, introduce the multi-parameter control factor, correct the environmental temperature, pressure and material surface state, and obtain the correct data; S5, according to the above data, calculate the conductivity value of chloride material under the target humidity condition, and output the conductivity and humidity response curve; The coupling model in S3 includes modeling structure based on multi-physical field synergistic mechanism, which integrates mass transfer, electric field driving and surface charge accumulation three sub-modules; The mutual coupling effect between water molecule adsorption behavior, ion migration mechanics and material interface polarization process is described respectively; The coupling calculation formula is as follows: In order to improve the adaptability and precision of the model, the system will automatically switch to nonlinear response mechanism when RH > 0.75 or high porosity material is encountered: based on the surface physical adsorption theory, combined with the actual measured water film thickness and specific surface area, the nonlinear adsorption-conductivity mapping relationship is established, the water layer continuous critical point is calculated and its influence on interface charge polarization is estimated, for porous materials with significant capillary condensation behavior, this method introduces capillary water layer correction factor to nonlinearly adjust the standard conductivity, so as to be closer to the real behavior of the material in high humidity environment; Equivalent conductivity of the material at humidity RH; G0 is the initial conductivity of the material in dry state at RH = 0; The humidity sensitivity coefficient is obtained from experiments or model fitting, and reflects the degree of response of the material to humidity changes. RHcurrent is the relative humidity of the current environment; The multi-parameter control factor in S4 includes environmental temperature, gas pressure and material surface state, which is used for further correction of the calculation results. The temperature factor is used to compensate for the different ion activities caused by temperature change. The gas pressure factor is used to adjust the adsorption behavior of water vapor on the material surface. The surface state refers to the roughness, porosity and whether there is coating on the material surface, which affects the residence time of water and the ion movement path. The chloride material in S1 includes inorganic salt chlorides, which are selected from one or more of sodium chloride, potassium chloride, magnesium chloride and zinc chloride, and the initial particle size of the material is in the range of 10 nanometers to 10 microns. The particle size distribution conforms to the Gaussian distribution model, and the D50 particle size is 100 nanometers to 1 micrometer.

2. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, In S2, the segmented relative humidity variation interval is set to simulate the adsorption response behavior of the material in the humidity environment changing step by step; 3. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, The mass difference method or dynamic gas adsorption method is used to record the moisture absorption mass change of chloride material in real time, and the temperature compensation factor is used for normalization processing to obtain the unit moisture absorption at each humidity point. The moisture absorption calculation further includes a pore adsorption correction process for porous or nanoparticle chloride materials. By correcting the contribution of capillary condensation to electrolyte behavior, the water film thickness on the unit surface is calculated using the adsorption isotherm combined with the specific surface area of the material, and the critical point of continuous water layer formation is determined accordingly. The moisture absorption, water layer thickness and relative humidity data are stored synchronously for subsequent model input boundary condition setting.

4. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 3, characterized in that, ​ 5. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, The system of S4 automatically calls a set of correction rules to apply the above-mentioned regulation factors to the preliminary data results after obtaining the original parameters.

6. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, The calculation step in S5 is based on the data obtained after the foregoing processing, and the conductivity is calculated under the target humidity condition, and the response curve between the conductivity and the humidity is output, which is generated in the form of a continuous image by gradually calculating the conductivity values at different humidity points and then using software.

7. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, In S5, the user can determine whether the material is suitable for the use requirements in a specific humidity environment according to the curve, and the response curve can also be used as a basis for material screening and sensor design to assist researchers in quickly identifying chloride materials with excellent performance.

8. The chloride-based multi-parameter humidity and conductivity integrated calculation method according to claim 1, characterized in that, The conductivity-humidity response curve output in S5 can be derived into a standardized visual graphical interface, which is suitable for material performance evaluation systems, intelligent humidity-sensitive sensor development platforms, and new environmental adaptability material screening databases, and realizes data sharing and engineering application transformation.

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