Polymer dispersion quality detection method and system based on electrical impedance spectroscopy analysis

Abstract

The application provides a polymer dispersion quality detection method and system based on electrical impedance spectroscopy analysis, which comprises the following steps: first, injecting the polymer dispersion to be detected into a microfluidic test environment, collecting complex impedance data while applying a thermal cycle excitation, and calculating the characteristic area of the temperature-impedance phase angle closed track curve. Combining the characteristic area with the basic conductivity, the tortuosity coefficient representing the entanglement state of the polymer chain is calculated using the porous medium permeation model. Impedance scanning is performed at a constant temperature, and the non-linear fluctuation characteristics of the electrochemical group time delay curve are analyzed to quantitatively obtain the micro-mediamedia non-uniformity degree representing the dispersion uniformity. The dispersion is graded in quality by combining the tortuosity coefficient and the micro-mediamedia non-uniformity degree. The application has the effect of accurately detecting the microstructure state of the polymer liquid.

Description

Technical Field

[0001] This invention belongs to the field of electrical impedance spectroscopy analysis technology, specifically relating to a method and system for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis. Background Technology

[0002] Polymer dispersions are crucial basic materials in many industrial fields such as coatings, adhesives, inks, cosmetics, and biopharmaceuticals. Their stability and uniformity directly determine the performance and reliability of the final product. Therefore, accurate quality testing of polymer dispersions is a core aspect of production process control. Currently, commonly used quality testing methods mainly include measuring macroscopic physicochemical properties such as viscosity, solid content, and pH value, as well as measuring average particle size and particle size distribution using techniques such as dynamic light scattering.

[0003] However, the performance of polymer dispersions depends not only on the size of the dispersed phase particles, but also, to a large extent, on the coiling, stretching, and entanglement of the polymer chains themselves, as well as the uniformity of their spatial distribution in the dispersion medium. Theoretically, two batches of dispersions with identical viscosity and average particle size may exhibit significant differences in film-forming properties, stability, or reactivity because one batch has highly aggregated and entangled polymer chains, while the other is in a stretched and uniformly dispersed state. Traditional macroscopic detection methods cannot perceive or distinguish between these two different microstructures; instead, they average out the effects of these two states, resulting in similar detection results and ultimately leading to significant deviations in the final test results. Summary of the Invention

[0004] This invention provides a method and system for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis, in order to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis, the method comprising the following steps: A microfluidic testing environment including a temperature control module and a multi-electrode impedance probe was constructed, and the polymer dispersion to be tested was injected into the microfluidic testing environment; A preset rate of heating and cooling thermal cycling excitation is applied to the polymer dispersion to be tested. At the same time as the heating and cooling thermal cycling excitation is applied, an AC test signal with a single characteristic frequency is applied, and complex impedance data that varies with temperature is continuously collected. Based on complex impedance data, a closed trajectory curve with temperature as the horizontal axis and impedance phase angle as the vertical axis is established, and the characteristic area enclosed by the closed trajectory curve is calculated. The characteristic area characterizes the degree of structural rearrangement dissipation of the polymer dispersion under thermal shock. The basic conductivity of the polymer dispersion to be tested was determined, and the tortuosity coefficient of the carrier migration path was calculated using a porous media permeation model in combination with the characteristic area to quantify the coiling and entanglement state of polymer chains at the microscopic level. The polymer dispersion to be tested was kept at a preset constant temperature and impedance scanning was performed. The derivative of the impedance phase angle with respect to the angular frequency was calculated to obtain the electrochemical group time delay curve. Nonlinear fluctuation characteristics were extracted from the electrochemical group time delay curve, and the degree of microscopic media nonhomogeneity inside the polymer dispersion under test was quantified by calculating the root mean square value of the nonlinear fluctuation characteristics. The quality classification and screening of the polymer dispersions under test are carried out by combining the tortuosity coefficient and the degree of microscopic media non-homogeneity.

[0006] Optionally, the determination of the basic conductivity of the polymer dispersion to be tested, and the calculation of the tortuosity coefficient of the carrier migration path for quantifying the coiling and entanglement state of polymer chains at the microscopic level using a porous media permeation model in combination with the characteristic area, includes the following steps: The polymer dispersion to be tested is placed in a preset standard temperature environment and the DC resistance value is measured by a multi-electrode impedance probe. The DC resistance value is converted into the basic conductivity based on the geometric constant of the microfluidic test environment. A virtual porous medium model is constructed based on the solid content and density parameters of the polymer dispersion to be tested, and the conductive polymer network in the polymer dispersion to be tested is equivalent to the fluid channel in the virtual porous medium model. We obtain instrument constants that characterize the microfluidic testing environment and the geometry of the multi-electrode impedance probe, and construct a modified Kozeny-Carman equation that includes characteristic area, fundamental conductivity, and instrument constants. The characteristic area and basic conductivity are substituted into the modified Kozeny-Carman equation for solution, and the dimensionless value obtained is used as the tortuosity coefficient of the carrier migration path in the microscopic coiled and entangled state of polymer chains.

[0007] Optionally, constructing the modified Kozeny-Carman equation, which includes the characteristic area, fundamental conductivity, and instrument constant, includes the following steps: A standard conductive polymer solution with a known standard tortuosity coefficient was selected as a calibration sample and injected into the microfluidic testing environment. The calibration sample was tested under the same heating and cooling thermal cycling excitation conditions as the polymer dispersion to be tested, and the calibration characteristic area and calibration basic conductivity of the calibration sample were obtained. The standard tortuosity coefficient, calibration characteristic area, and calibration baseline conductivity are substituted into the pre-defined general form of the Kozeny-Carman equation as known quantities. The undetermined coefficients in the general form of the Kozeny-Carman equation are solved by reverse operation, and the solved undetermined coefficients are determined as instrument constants. By fixing the determined instrument constants in the general form of the Kozeny-Carman equations, the modified Kozeny-Carman equations are generated.

[0008] Optionally, the modified Kozeny-Carman equation is specifically expressed as follows: ; in, This is the tortuosity coefficient of the carrier migration path; This is the instrument constant; The effective conductive porosity calculated in the virtual porous medium model; Based on the fundamental conductivity; The characteristic area; This is the preset dimensional balance coefficient; Boltzmann's constant; This is the highest process temperature during thermal cycling. The term is an exponential function term with the natural logarithm as its base. This exponential function term is a thermodynamic dissipation correction term used to describe the dynamic hindering effect of structural rearrangement dissipation energy on electron transport paths.

[0009] Optionally, the step of maintaining the polymer dispersion to be tested at a preset constant temperature and performing impedance scanning, and calculating the derivative of the impedance phase angle with respect to the angular frequency to obtain the electrochemical group time delay curve includes the following steps: When the heating and cooling thermal cycle excitation ends and the polymer dispersion to be tested is in a preset constant temperature state, the control impedance analyzer generates a logarithmic scan signal from the start frequency to the end frequency. Discrete impedance phase angle data points of the polymer dispersion response under logarithmic scan signal are collected to form a frequency-phase angle data sequence; The first negative derivative of discrete impedance phase angle data points with respect to angular frequency in the frequency-phase angle data sequence is calculated using the finite difference method to obtain discrete group delay numerical points. Connect the group delay numerical points in frequency order to generate a continuous electrochemical group delay curve.

[0010] Optionally, the step of extracting the nonlinear fluctuation characteristics from the electrochemical group time delay curve and quantifying the degree of microscopic media heterogeneity within the polymer dispersion under test by calculating the root mean square value of the nonlinear fluctuation characteristics includes the following steps: A polynomial fitting algorithm was used to fit the global trend of the electrochemical group time delay curve to obtain a baseline curve characterizing the ideal uniform dispersion state. The electrochemical group time delay curve is subtracted from the baseline curve point by point. After removing the linear trend component in the electrochemical group time delay curve, the extracted residual sequence is used as the nonlinear fluctuation feature. Calculate the sum of squares of all data points in the residual sequence, divide the sum of squares by the total number of data points to obtain the mean square value, and take the square root of the mean square value to obtain the root mean square value of the nonlinear fluctuation characteristics. The root mean square value is defined as a quantitative indicator of the degree of non-uniformity of the micro-medium through linear mapping.

[0011] Optionally, the step of applying a preset rate of heating and cooling thermal cycling excitation to the polymer dispersion under test, and simultaneously applying an AC test signal of a single characteristic frequency while applying the heating and cooling thermal cycling excitation, and continuously acquiring complex impedance data that changes with temperature, includes the following steps: A preset rate of heating and cooling thermal cycling excitation is applied to the polymer dispersion to be tested, and the heating rate and cooling rate of the heating and cooling thermal cycling excitation are set to be greater than the thermal relaxation rate of the polymer chain, so that the polymer dispersion to be tested is always in a non-thermal equilibrium state during the thermal cycling process. The heating and cooling thermal cycle excitation is configured as a triangular wave temperature distribution, with the lower limit set to room temperature and the upper limit set to the film-forming process temperature of the polymer dispersion to be tested. A target high-frequency value that can penetrate the electric double layer and reflect the intergranular conductivity is selected as a single characteristic frequency. Within each time step of the triangular wave temperature distribution pattern, the transmission and response signals of the AC test signal with a single characteristic frequency are synchronously triggered. The acquired response signal is parsed into complex impedance data containing real and imaginary parts, and the complex impedance data is timestamped with the temperature value at the current time step.

[0012] Optionally, the step of establishing a closed trajectory curve with temperature as the horizontal axis and impedance phase angle as the vertical axis based on complex impedance data, and calculating the characteristic area enclosed by the closed trajectory curve, includes the following steps: The temperature value and impedance phase angle from the complex impedance data collected during the heating process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the heating trajectory line. The temperature value and impedance phase angle from the complex impedance data collected during the cooling process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the cooling trajectory line. The starting point of the heating trajectory line and the ending point of the cooling trajectory line are closed, and the ending point of the heating trajectory line is aligned with the starting point of the cooling trajectory line to form a closed trajectory curve. The area of ​​the geometric region enclosed by the closed trajectory curve in the temperature-phase angle coordinate system is calculated using the trapezoidal numerical integration algorithm and used as the characteristic area.

[0013] In a second aspect, the present invention also provides a polymer dispersion quality detection system based on electrical impedance spectroscopy analysis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis as described in any one of the first aspects.

[0014] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis according to any one of the first aspects.

[0015] The beneficial effects of this invention are: This invention obtains the impedance phase angle-temperature closed-loop trajectory curve, characterizing the degree of structural rearrangement dissipation under thermal perturbation, by applying thermal cycling excitation in a microfluidic environment and simultaneously acquiring complex impedance data. By combining dynamic thermal response test results (characteristic area) with steady-state electrical parameters (basic conductivity) through a porous media permeation model, the tortuosity coefficient, a microscopic physical quantity directly reflecting the three-dimensional spatial conformation and entanglement state of polymer chains, is decoupled and calculated. Simultaneously, by analyzing the nonlinear fluctuation characteristics of the electrochemical group time delay curve at isothermal conditions, another key indicator orthogonal to the tortuosity coefficient and characterizing the spatial uniformity of the dispersed phase, is obtained: the degree of microscopic media inhomogeneity. Finally, by comprehensively evaluating these two physically significant microstructural parameters—the tortuosity coefficient and the degree of microscopic media inhomogeneity—a deep quantitative characterization of the quality of polymer dispersions is achieved, thereby significantly improving the accuracy of comprehensive detection and evaluation of polymer dispersions. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a polymer dispersion quality detection method based on electrical impedance spectroscopy analysis in one embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the temperature-phase angle closed trajectory curve of the experimental sample in one embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0019] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0020] Figure 1 This is a schematic flowchart of a polymer dispersion quality detection method based on electrical impedance spectroscopy analysis in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps. For example Figure 1 As shown, the method for quality detection of polymer dispersions based on electrical impedance spectroscopy disclosed in this invention specifically includes the following steps: S101. Construct a microfluidic testing environment that includes a temperature control module and a multi-electrode impedance probe, and inject the polymer dispersion to be tested into the microfluidic testing environment.

[0021] This study constructs a microfluidic testing environment comprising a temperature control module and a multi-electrode impedance probe. A microfluidic chip with micron-scale fluid channels is fabricated using a combination of soft photolithography and microelectromechanical processing (MEMS) technology. The confined micro-geometric space within this chip allows for precise control of the fluid state. The microchannels are typically designed with a high aspect ratio rectangular cross-section to ensure laminar flow at low Reynolds numbers, eliminating interference from turbulence in electrochemical measurements. Miniaturized interdigitated electrode arrays or multi-point arrays of electrodes, made of chemically stable inert metal materials, are integrated into the test circuit using a four-electrode connection method. The two outer electrodes serve as current excitation terminals, and the two inner electrodes as voltage sensing terminals. This configuration effectively eliminates contact resistance and double-layer polarization effects at the electrode-solution interface, allowing direct acquisition of the intrinsic impedance response of the bulk solution. The temperature control module is tightly integrated with the substrate of the microfluidic chip, utilizing the Peltier effect to achieve rapid heat pumping and removal. Together with an embedded micro-temperature sensor, it forms a closed-loop feedback control system, ensuring that the polymer dispersion under test reaches the preset thermal equilibrium or dynamic temperature distribution in a very short time. Driven by a precision injection pump, the polymer dispersion is smoothly injected into the test environment, filling the sensing area above the electrodes. The conductive polymer chains in the liquid undergo orientation adjustment beforehand during the high-shear injection process. Subsequently, under the stable mechanical and thermal boundary conditions provided by the microfluidic cavity, it is ready to undergo subsequent thermoelectric coupling testing. This environment construction method significantly reduces sample volume and improves the repeatability and signal-to-noise ratio of the test results.

[0022] S102. Apply a pre-set rate of heating and cooling thermal cycling excitation to the polymer dispersion to be tested. While applying the heating and cooling thermal cycling excitation, apply an AC test signal with a single characteristic frequency and continuously collect complex impedance data that changes with temperature.

[0023] The process involves applying a pre-set rate of heating and cooling thermal cycling to the polymer dispersion under test, thereby disrupting the metastable thermodynamic equilibrium of the polymer chains at room temperature. External thermal energy input forces the polymer chain segments to move and rearrange their conformation. The applied temperature change is not constant heating, but rather a continuous temperature-changing scan in the form of a triangular wave, with the temperature change rate set faster than the timescale of the polymer chains' natural thermal relaxation, ensuring the polymer network is always in a non-equilibrium dynamic evolution process. Simultaneously with this thermal perturbation, a sinusoidal alternating current signal with a single characteristic frequency is continuously emitted into the fluid. This frequency must be chosen to penetrate the double-layer capacitance shielding of the electrode surface, directly acting on the grain boundaries and polymer chain network within the dispersion. With linear temperature increases and decreases, the complex impedance characteristics of the dispersion drift significantly. The data acquisition system synchronously records the response signal at a high sampling rate and analyzes it as real resistance and imaginary reactance. Utilizing Ohm's law and the principles of complex number operations, this process captures in real time the microscopic dynamics of polymer chains untangling, stretching, re-coiling, and anisotropic expansion or contraction of aggregates under thermal shock. Changes in viscosity, carrier activation energy, and chain segment spatial configuration caused by temperature variations are sensitively reflected in fluctuations in the complex impedance values, thus generating a time-temperature-impedance multidimensional data stream containing rich thermodynamic information.

[0024] S103. Based on the complex impedance data, establish a closed trajectory curve with temperature as the horizontal axis and impedance phase angle as the vertical axis, and calculate the characteristic area enclosed by the closed trajectory curve. The characteristic area characterizes the degree of structural rearrangement dissipation of the polymer dispersion under thermal shock.

[0025] During the heating phase, the polymer chains undergo endothermic motion, altering the conductive network structure, and the phase angle exhibits a specific trajectory of change with temperature. In the subsequent cooling phase, if the polymer chains can reversibly recover to their initial conformation, the cooling trajectory should perfectly coincide with the heating trajectory. However, actual polymer dispersions often possess structural defects or agglomeration, causing the chain rearrangement during cooling to lag behind the rate of temperature change, or forming an irreversible metastable structure. This results in the cooling trajectory separating from the heating trajectory, forming a closed hysteresis loop in the coordinate system. The area of ​​the geometric region enclosed by this closed trajectory curve, known as the characteristic area, is calculated using a numerical integration algorithm. This area physically corresponds directly to the total energy lost by the tested polymer dispersion within a thermal cycle due to internal molecular chain friction, structural rearrangement lag, and conformational entropy change. A larger characteristic area indicates a higher degree of irreversibility of the material's internal microstructure under thermal shock, poorer structural recovery ability, and more severe hidden defects or entanglements.

[0026] S104. Determine the basic conductivity of the polymer dispersion to be tested, and calculate the tortuosity coefficient of the carrier migration path to quantify the coiling and entanglement state of polymer chains at the microscopic level using a porous media permeation model in combination with the characteristic area.

[0027] First, the macroscopic fundamental conductivity of the dispersion was measured at standard temperature using DC or low-frequency AC methods. This value reflects the average transport capacity of charge carriers in the material. Then, the concept of a virtual porous medium was introduced, equating the conductive polymer chain network in the dispersion to fluid channels in a porous medium, while the insulating solvent or additives were considered as the framework. Using the modified Kozeny-Carman equation, thermodynamic dissipation information was incorporated into the geometric parameter calculations to calculate the tortuosity coefficient of the charge carrier migration path. The characteristic area was used as a thermodynamic correction factor through an exponential term to dynamically weight the geometric tortuosity calculated solely based on conductivity. As the characteristic area increases, the exponential term increases significantly, resulting in a larger calculated tortuosity coefficient. Physically, this can be explained by the fact that due to the large dissipation from structural rearrangement, charge carriers encounter more energy barriers and path deflections during transport, making the actual transport path much longer than a straight distance. This method enables the calculation of the microscopic coiling and entanglement state of polymer chains through electrical measurements alone, without the need for electron microscopy imaging.

[0028] S105. The polymer dispersion to be tested is kept at a preset constant temperature and impedance scanning is performed. The derivative of the impedance phase angle with respect to the angular frequency is calculated to obtain the electrochemical group time delay curve.

[0029] In this process, under isothermal conditions, the impedance analysis equipment outputs a series of sinusoidal signals of different frequencies on a logarithmic scale over a very wide frequency range, and the impedance phase angle response of the dispersion to each frequency signal is recorded point by point. Then, numerical differentiation techniques are used to calculate the first derivative of the phase angle with respect to the angular frequency, thereby obtaining the electrochemical group delay. Group delay physically describes the time delay difference experienced by electrical signal components of different frequencies as they pass through the test medium. For an ideal homogeneous medium, the group delay curve should be a smooth straight line or exhibit a monotonically changing trend; however, for polymer dispersions containing microscopic non-homogeneous structures, aggregates, phase interfaces, and chain segment entanglements of different sizes have different dielectric relaxation time constants. These microstructures induce local charge accumulation and release at specific frequency points, leading to nonlinear abrupt changes in the phase angle. By transforming this phase change in the frequency domain into a group delay curve in the time domain, this difference in microscopic dielectric relaxation can be greatly amplified, transforming the originally imperceptible minute fluctuations in the impedance spectrum into significant peak-valley characteristics on the group delay curve.

[0030] S106. Extract the nonlinear fluctuation characteristics from the electrochemical group time delay curve, and quantify the degree of microscopic medium nonhomogeneity inside the polymer dispersion under test by calculating the root mean square value of the nonlinear fluctuation characteristics.

[0031] Since the group delay curve itself may contain a baseline trend determined by electrode polarization or the overall dielectric constant, direct analysis of the raw data is insufficient to extract purely structural inhomogeneous information. Therefore, a baseline curve reflecting the group delay variation under ideal homogeneous conditions is first constructed using polynomial fitting or moving average algorithms. Then, the measured group delay curve is subtracted point-by-point from this baseline to extract the residual sequence. This residual sequence visually depicts the fluctuation of the actual signal around the ideal baseline, with each fluctuation corresponding to a microscopic inhomogeneous region or defect point within the dispersion. To uniformly quantify these chaotic fluctuation signals, the root mean square (RMS) value is calculated by taking the square root of the average of the sum of squares of all data points in the residual sequence. This value serves as a single statistical indicator, its magnitude directly proportional to the roughness of the group delay curve, thus mapping the degree of inhomogeneity of the microscopic medium within the polymer dispersion under test. The lower the root mean square value, the more uniform the distribution of components inside the dispersion and the better the dispersion of polymer chains. Conversely, a significant increase in the root mean square value indicates the presence of a large number of aggregates, gel particles or phase separation phenomena of different sizes in the liquid phase, resulting in drastic fluctuations in dielectric properties at the microscale.

[0032] S107. Combine the tortuosity coefficient and the degree of microscopic media non-uniformity to classify and screen the polymer dispersions to be tested.

[0033] The aforementioned steps yielded a tortuosity coefficient describing the efficiency of carrier transport paths and an index describing the degree of non-uniformity describing the consistency of the material's internal structure. These two parameters, from completely different but complementary dimensions, construct a quality evaluation coordinate system for polymer dispersions. In practical applications, dual threshold standards can be set: for example, for high-performance transparent conductive film applications, the dispersion must simultaneously meet the requirements of low tortuosity (meaning extended polymer chains and good conductivity) and low non-uniformity (meaning low haze and no defects after film formation); while for antistatic coating applications where conductivity requirements are not high, the tortuosity restriction can be appropriately relaxed, but non-uniformity still needs to be controlled to ensure the coating's appearance. Based on the comprehensive score of these two indicators, the tested products can be classified into different grades such as superior, good, qualified, and defective. This grading method can not only identify obvious deterioration products, but also sensitively capture microstructural degradation (such as early agglomeration or chain coiling) that cannot be detected by conventional viscosity or solid content tests. This guides the optimization of production processes, such as adjusting the amount of dispersant or changing homogenization conditions, and ultimately achieves refined control and screening of polymer dispersion product quality.

[0034] In one embodiment, the determination of the basic conductivity of the polymer dispersion to be tested, and the calculation of the tortuosity coefficient of the carrier migration path for quantifying the coiling and entanglement state of polymer chains at the microscopic level using a porous media permeation model in combination with the characteristic area, includes the following steps: The polymer dispersion to be tested is placed in a preset standard temperature environment and the DC resistance value is measured by a multi-electrode impedance probe. The DC resistance value is converted into the basic conductivity based on the geometric constant of the microfluidic test environment. A virtual porous medium model is constructed based on the solid content and density parameters of the polymer dispersion to be tested, and the conductive polymer network in the polymer dispersion to be tested is equivalent to the fluid channel in the virtual porous medium model. We obtain instrument constants that characterize the microfluidic testing environment and the geometry of the multi-electrode impedance probe, and construct a modified Kozeny-Carman equation that includes characteristic area, fundamental conductivity, and instrument constants. The characteristic area and basic conductivity are substituted into the modified Kozeny-Carman equation for solution, and the dimensionless value obtained is used as the tortuosity coefficient of the carrier migration path in the microscopic coiled and entangled state of polymer chains.

[0035] In this embodiment, a high-precision temperature control device is used to strictly lock the thermal environment within the microfluidic test chamber at a standard reference temperature (e.g., 25 degrees Celsius) to completely eliminate interference from thermal noise caused by temperature fluctuations and differences in molecular thermal motion on baseline measurements. Subsequently, a multi-electrode impedance probe system integrated at the bottom of the flow channel is activated. This system operates on the principle of Kelvin four-wire detection. One pair of electrodes on the outer side injects a constant, weak direct current or low-frequency alternating current into the dispersion to establish a stable electric field within the solution. The other pair of electrodes on the inner side uses a high-impedance voltmeter to sense the potential difference across a specific region of the fluid in real time. This separated measurement architecture effectively shields against unpredictable contact resistance and double-layer polarization impedance at the electrode-solution interface, ensuring that the obtained values ​​accurately reflect the bulk resistance characteristics of the solution. A pure DC resistance value is obtained. Subsequently, based on the precise geometric parameters pre-designed and fabricated according to the microfluidic testing environment, including the effective test length of the microchannel... and the cross-sectional area through which the fluid flows Numerical transformation is performed using physical definitions. The calculation formula follows... ,in This is the required basic conductivity.

[0036] In traditional porous media models, the ease with which fluids pass through the pores of a solid framework is used to describe the media structure. However, in the virtual model constructed in this scheme, since the conductive polymer chains in the dispersion under test are the only carriers of electron transport, the three-dimensional conductive network formed by the interconnection of these polymer chains is equivalent to the effective fluid channels in the porous media. Conversely, non-conductive solvent molecules (such as water) and insulating additives in the dispersion are regarded as solid frameworks or dead pores that hinder electron flow. Based on this physical assumption, the key geometric quantities in the model need to be calculated using the known physicochemical parameters of the polymer dispersion. The mass percentage solid content of the liquid under test, the bulk density of the conductive polymer, and the density of the solvent are obtained. By applying the principle of volume conservation, the mass fraction is converted into a volume fraction, thereby calculating the effective conductive porosity in the virtual model. This parameter directly quantifies the proportion of the space occupied by the conductive phase that can allow charge carriers to migrate freely in a unit volume of dispersion. Through this equivalent modeling, the originally abstract and difficult-to-measure microscopic polymer chain distribution state is successfully transformed into a geometric topological problem that can be described by classical flow mechanics equations, thus allowing the spatial curling state of microscopic chain segments to be inferred from macroscopic electrical parameters.

[0037] Before constructing the modified Kozeny-Carman equation, the influence of unavoidable dimensional tolerances during the micro / nano fabrication of microfluidic chips on the measurement results must be eliminated. Therefore, a specific calibration procedure is required: a standard solution with known standard tortuosity is used for testing, and the instrument constant characterizing the geometry of the specific testing environment is solved through inverse calculation. Once determined, the constant becomes an inherent property of the testing system. Subsequently, considering the structural rearrangement of polymer fluids under thermal fields, the classical Kozeny-Carman equation is thermodynamically modified. Traditional equations only consider static porosity and specific surface area, failing to describe the conformational adjustments of polymer chains with temperature changes. Therefore, this approach introduces an exponential correction term based on characteristic area into the equation. The modified equation organically combines the macroscopic fundamental conductivity, the effective conductive porosity representing the proportion of geometric channels, the instrument constant representing the geometric shape factor, and the characteristic area representing thermodynamic structural dissipation. This modified equation not only inherits the ability of the classical model to describe the geometric resistance of porous media, but also introduces the concept of an energy barrier through an exponential term. That is, the greater the rearrangement dissipation of the polymer chains, the higher the energy barrier that electron transport needs to overcome, and the more severe the equivalent path tortuosity. Next, the characteristic area representing the degree of structural rearrangement dissipation obtained through integration in previous thermal cycling tests, and the basic conductivity measured under standard conditions, are input as dynamic variables into the pre-constructed mathematical model. Simultaneously, the calibrated instrument constants and the calculated effective conductive porosity are used as fixed parameters, and the dimensionless value obtained from the solution is used as the tortuosity coefficient of the carrier migration path in the microscopic coiled and entangled states of the polymer chains.

[0038] In one embodiment, constructing a virtual porous media model based on the solid content and density parameters of the polymer dispersion to be tested, and equating the conductive polymer network in the polymer dispersion to the fluid channels in the virtual porous media model, includes the following steps: A three-dimensional discretized voxel grid space is constructed based on the microscale characteristics of the polymer dispersion to be tested, and the solid content parameter is converted into the space occupancy probability of the conductive nodes in the voxel grid space. Based on the average radius of gyration of the polymer chains in the polymer dispersion to be tested, a connectivity threshold between conductive nodes is set. Monte Carlo simulation is performed in the voxel grid space according to the connectivity threshold to generate a random conductive network distribution that conforms to the space occupancy probability. The fractal permeation algorithm is used to traverse the distribution of random conductive networks, identify and extract the skeleton of infinite permeation clusters that run through both ends of the voxel grid space, and remove dead-end clusters that have not formed a path in the distribution of random conductive networks. The volume percentage of the infinite permeable cluster skeleton in the voxel mesh space is calculated, and the volume percentage is defined as the effective conductive porosity of the virtual porous medium model. The spatial topology of the infinite permeable cluster skeleton is mapped to the fluid channels in the virtual porous medium model to eliminate the interference of non-conductive dead zones on the tortuosity calculation.

[0039] In this implementation, the scale resolution of the voxel grid, i.e., the side length of a single cubic voxel, needs to be determined first. The side length is chosen to be slightly smaller than the monomer length or feature unit size of the polymer chain to ensure that the discretization model can capture the structural details at the molecular level with sufficient precision. A three-dimensional mesh matrix containing N voxel units is constructed. Next, the solid content data of the polymer dispersion is processed. Since the macroscopic solid content is usually given as a mass fraction, it needs to be converted to a volume fraction by combining the density of the conductive polymer and the solvent density. The volume fraction is statistically directly equivalent to the probability that any voxel unit is occupied by a conductive polymer. In the initialized all-zero mesh space, each voxel node is assigned a state value based on this probability: a random number between 0 and 1 is generated; if the random number is less than the probability, the voxel node is marked as conductive (assigned a value of 1); otherwise, it is marked as insulating (assigned a value of 0).

[0040] However, merely having voxel space occupancy is insufficient; it's essential to determine whether adjacent or nearby conductive voxels have truly formed an electrical pathway. Therefore, it's necessary to introduce the average radius of gyration, a term from polymer physics describing the effective spatial extent of polymer chains coiling into clusters in a solvent. In the discretized voxel space, the average radius of gyration is converted into a connectivity threshold distance in units of voxel edge length. During Monte Carlo simulations, the algorithm traverses each voxel node marked as conductive and searches for other conductive nodes within its surrounding radius. If the Euclidean distance between two conductive nodes is less than or equal to the connectivity threshold, a valid electronic transition or inter-chain contact is determined between the two nodes, thus establishing a virtual edge connection between them. This determination process simulates the random collisions and entanglements of polymer chains under thermal motion. As the simulation progresses, thousands of isolated conductive voxels connect to each other through this distance determination rule, gradually aggregating into clusters of varying sizes. This method not only considers the spatial distribution of matter but also introduces molecular-scale effects, making the generated random conductive network no longer a simple geometric stacking but a topological structure with clear physical connectivity, truly reflecting the complex inter-chain interaction network inside the dispersion. After generating a random network containing a large number of connections, there are actually many isolated islands or dead-end structures. Although these structures are composed of conductive polymers, they are either isolated and suspended without being connected to the main trunk, or connected to the main trunk at one end and disconnected at the other, failing to form a complete current path from the microfluidic inlet to the outlet.

[0041] To eliminate these invalid components, an efficient fractal percolation algorithm based on breadth-first search or depth-first search is employed. The algorithm starts from all conductive nodes in the voxel grid space, defined as a boundary plane at the current inflow end, and recursively traverses and marks them inwards along established connected edges. Only connected paths that eventually extend to and reach the relative boundary plane defined as the current outflow end are considered part of the infinite percolation cluster. All conductive nodes not included in this path, even if they are themselves conductive, are identified as dead-end clusters that contribute nothing to macroscopic transport and are eliminated or marked as invalid. After percolation screening, the total number of effective conductive voxels belonging to the infinite percolation cluster skeleton in the voxel grid space is counted. This number is divided by the total number of equivalent voxels in the grid space; the resulting ratio is the corrected effective conductive porosity. Unlike the nominal porosity calculated solely based on solid content, the corrected effective conductive porosity strictly eliminates dead zones that exist but contribute nothing to conductivity; therefore, its value is often lower than the nominal value. Subsequently, the spatial morphology of the purified infinite permeable cluster framework was directly mapped to the fluid channel concept in the virtual porous media model. When calculating tortuosity using the Kozeny-Carman equation, the corrected effective conductive porosity was used instead of the original solid content data. This mathematically eliminated the dilution effect of artificially inflated porosity caused by ineffective dead ends on the tortuosity calculation, ensuring that the calculated tortuosity coefficient purely reflects the geometric curvature of the effective conductive pathway, rather than being averaged by ineffective substances. This mapping achieves a logical closed loop between macroscopic electrochemical detection and microscopic topological structure analysis, resulting in a high degree of physical confidence in the final quality evaluation index.

[0042] In one implementation, constructing the modified Kozeny-Carman equation, which includes the characteristic area, fundamental conductivity, and instrument constant, comprises the following steps: A standard conductive polymer solution with a known standard tortuosity coefficient was selected as a calibration sample and injected into the microfluidic testing environment. The calibration sample was tested under the same heating and cooling thermal cycling excitation conditions as the polymer dispersion to be tested, and the calibration characteristic area and calibration basic conductivity of the calibration sample were obtained. The standard tortuosity coefficient, calibration characteristic area, and calibration baseline conductivity are substituted into the pre-defined general form of the Kozeny-Carman equation as known quantities. The undetermined coefficients in the general form of the Kozeny-Carman equation are solved by reverse operation, and the solved undetermined coefficients are determined as instrument constants. By fixing the determined instrument constants in the general form of the Kozeny-Carman equations, the modified Kozeny-Carman equations are generated.

[0043] In this embodiment, the standard solution is an industrial standard or a laboratory-made high-purity reference material that has undergone rigorous physicochemical characterization and possesses high homogeneity and stability. The average coil state and microstructure of its internal polymer chains have been calibrated using authoritative methods such as small-angle X-ray scattering or cryo-transmission electron microscopy, thus obtaining a recognized standard tortuosity coefficient. This standard sample is injected into the microfluidic testing environment to be calibrated at a constant flow rate using a precision micropump, ensuring complete fluid filling of the flow channel without air bubbles, so that the physical state of the test area reaches the ideal calibration conditions. Although the tortuosity of the standard sample is a known static parameter, in actual testing, dynamic thermo-electric coupling testing is required to extract the corresponding electrochemical characterization quantity. Strictly following the experimental protocol for the unknown sample to be tested, a completely consistent temperature control curve (including heating rate, cooling rate, and temperature extremes) and AC test frequency are applied. During the thermal cycling process, the complex impedance data of the standard sample is recorded in real time, and a temperature-impedance phase angle trajectory curve is plotted. The area enclosed by the closed trajectory is calculated using numerical integration, i.e., the calibration characteristic area. Simultaneously, its fundamental conductivity was measured under standard isothermal conditions. By maintaining a high degree of consistency in the test conditions, it was ensured that the systematic errors contained in the calibration data were completely identical to the error background when testing unknown samples in the future.

[0044] The pre-defined Kozeny-Carman equation is a semi-empirical formula describing the resistance of fluid (equivalent to electric current) through a porous medium. Its general form includes physical quantities describing the microstructure of the medium and undetermined coefficients describing the geometric characteristics of the test system. In conventional applications, the system geometry is often assumed to be idealized, but this assumption can introduce significant errors in high-precision microfluidic testing. Therefore, an algebraic substitution method is used to fill in the corresponding variable positions in the general form of the equation with the known standard tortuosity coefficient, the measured calibration feature area, and the measured calibration baseline conductivity. The only remaining unknown in the equation is the instrument constant, which characterizes the microfluidic testing environment and the geometry of the multi-electrode impedance probe. Then, using algebraic transformation rules, terms containing undetermined coefficients are moved to one side of the equation, and all known numerical terms are moved to the other side, allowing the specific values ​​of the instrument constant to be calculated. Finally, the determined instrument constant is fixed in the general form of the Kozeny-Carman equation to generate the modified Kozeny-Carman equation, which is expressed as follows: ; in, This is the tortuosity coefficient of the carrier migration path; This is the instrument constant; The effective conductive porosity calculated in the virtual porous medium model; Based on the fundamental conductivity; The characteristic area; This is the preset dimensional balance coefficient; Boltzmann's constant; This is the highest process temperature during thermal cycling. This is an exponential function term with the natural logarithm as the base. The square root term reflects the theoretical path tortuosity derived from porosity under ideal static geometry; while the exponential term acts as a huge amplification factor, correcting the theoretical value according to the size of the characteristic area. If there is a large amount of coiling, entanglement, or agglomeration inside the liquid being tested, leading to a significant increase in the thermal hysteresis characteristic area, the exponential term will increase exponentially, thus greatly increasing the final calculated tortuosity coefficient. The final obtained... The value is a dimensionless pure numerical value. The closer the value is to 1, the closer the carrier migration path is to a straight line and the more extended the polymer chain is. A value much greater than 1 directly reveals that the polymer chain is in a poor state of high curling and entanglement at the microscopic level.

[0045] In one embodiment, the polymer dispersion to be tested is maintained at a preset constant temperature and impedance scanning is performed. The electrochemical group time delay curve is obtained by calculating the derivative of the impedance phase angle with respect to the angular frequency, including the following steps: When the heating and cooling thermal cycle excitation ends and the polymer dispersion to be tested is in a preset constant temperature state, the control impedance analyzer generates a logarithmic scan signal from the start frequency to the end frequency. Discrete impedance phase angle data points of the polymer dispersion response under logarithmic scan signal are collected to form a frequency-phase angle data sequence; The first negative derivative of discrete impedance phase angle data points with respect to angular frequency in the frequency-phase angle data sequence is calculated using the finite difference method to obtain discrete group delay numerical points. Connect the group delay numerical points in frequency order to generate a continuous electrochemical group delay curve.

[0046] In this embodiment, after undergoing severe thermal shock, the chain segment structure within the polymer dispersion has reached a new equilibrium or sub-equilibrium state based on its own thermal stability characteristics. At this point, the temperature control system is locked at the standard test temperature (e.g., 25°C), and after the temperature fluctuations have completely decayed, the frequency sweep mode of the impedance analyzer is activated. Unlike single-frequency testing, the frequency sweep signal covers an extremely wide frequency domain, spanning from low frequencies (e.g., 10Hz, corresponding to slow ion diffusion processes) to high frequencies (e.g., 10MHz, corresponding to fast electronic polarization processes). Because different polarization mechanisms in polymer materials (e.g., interfacial polarization, dipole orientation, chain segment motion) have different characteristic time constants, resonance responses only occur within specific frequency windows. To uniformly capture these timescale characteristics spanning several orders of magnitude, the frequency variation step size of the scan signal must be set to a logarithmic distribution, i.e., the same number of data points are collected within each order of magnitude frequency band. This logarithmic scanning method ensures that there are sufficiently dense sampling points in the low-frequency region to resolve slow diffusion behavior, while also effectively capturing fast relaxation processes in the high-frequency region, thereby constructing a broadband excitation field that can comprehensively reflect the microscopic dynamic characteristics of the material.

[0047] When a sinusoidal AC scanning signal is applied to a dispersion, the charged particles (ions, electrons) and dipole moments within the liquid will reciprocate or reorient under the drive of the electric field. Due to the viscoelasticity of the dispersion and the hindering effect of its microstructure, the response of the charges often lags behind the change in the driving electric field. This time lag is manifested as the phase angle in signal analysis. The high-speed digital signal processor inside the impedance analyzer uses quadrature demodulation technology to calculate the phase difference between the response current and the excitation voltage at each frequency point in real time. As the scanning frequency advances point by point, the system continuously records a series of discrete data pairs. , Let be the angular frequency of the i-th sampling point. This corresponds to the phase angle. Then, the first negative derivative of the discrete impedance phase angle data points with respect to the angular frequency in the frequency-phase angle data sequence can be calculated using the finite difference method, yielding discrete group delay numerical points. In physics, group delay is defined as the first negative derivative of the phase spectrum with respect to the angular frequency, characterizing the time delay experienced by the envelope of a wave packet signal containing multiple frequency components as it passes through a medium.

[0048] For the collected discrete data sequences, since direct continuous differentiation is not possible, numerical differentiation methods must be used. Central difference or forward / backward difference algorithms are selected. For the i-th data point in the sequence, the local slope is calculated using information from its neighboring points. The specific calculation formula is approximately as follows: ,in This is the estimated discrete group delay value at that frequency point. On the original phase angle curve, minute structural inhomogeneities may only appear as gentle bulges or slight inflections that are difficult to detect with the naked eye. However, once differential operations are performed, these subtle slope changes are transformed into dramatic fluctuations or sharp peaks in the group delay value. Next, each calculated discrete group delay value is plotted in a Cartesian coordinate system as the ordinate and the corresponding angular frequency as the abscissa. Then, using spline interpolation or piecewise linear connection methods, these discrete points are smoothly connected to form a continuous curve trajectory. This curve is called the electrochemical group delay spectrum. For homogeneous and structurally perfect polymer dispersions, their group delay curves usually exhibit a smooth, monotonous, and regular geometric shape, similar to a flat baseline; while for samples with quality problems (such as agglomeration, aging, impurities), their curves will exhibit messy spikes, abrupt peaks, or violent oscillations in specific frequency ranges. This graphical output method greatly lowers the threshold for data interpretation, allowing quality inspectors to quickly and intuitively judge the uniformity of the internal microstructure of the sample by simply observing the flatness of the curve, without having to delve into complex electrochemical principles.

[0049] In one embodiment, extracting the nonlinear fluctuation characteristics from the electrochemical group time delay curve and quantifying the degree of microscopic media heterogeneity within the polymer dispersion to be tested by calculating the root mean square value of the nonlinear fluctuation characteristics includes the following steps: A polynomial fitting algorithm was used to fit the global trend of the electrochemical group time delay curve to obtain a baseline curve characterizing the ideal uniform dispersion state. The electrochemical group time delay curve is subtracted from the baseline curve point by point. After removing the linear trend component in the electrochemical group time delay curve, the extracted residual sequence is used as the nonlinear fluctuation feature. Calculate the sum of squares of all data points in the residual sequence, divide the sum of squares by the total number of data points to obtain the mean square value, and take the square root of the mean square value to obtain the root mean square value of the nonlinear fluctuation characteristics. The root mean square value is defined as a quantitative indicator of the degree of non-uniformity of the micro-medium through linear mapping.

[0050] In this embodiment, the group delay data measured in practice often contains two parts of information: one part is the overall trend determined by electrode polarization, bulk solution conductivity, and dielectric constant, which reflects the macroscopic properties of the material and manifests as a smooth curve that monotonically changes with frequency; the other part is the local nonlinear fluctuation caused by micro-agglomeration, phase separation, or impurities, which is precisely the quality defect feature that needs to be extracted. Using the least squares principle, polynomial functions of order 3 to 5 are selected. To approximate the experimentally measured group delay data point sequence, the polynomial coefficients are iteratively optimized to make the fitted curve closely resemble the original data in the sense of minimizing the overall mean square error. This smooth curve generated by the fitting is physically considered the baseline, representing the group delay response trajectory that an ideal polymer dispersion, assuming no microscopic local defects and absolutely uniform component distribution, should have. After obtaining the baseline curve representing the macroscopic background, a computer algorithm is used to traverse the original measurement data sequence. For each frequency point, its actual measured group delay value is read. And calculate the theoretical value of the baseline function at that frequency point. Then, the subtraction operation is performed. Thus, the residual value at that point is obtained. Arranging the residual values ​​at all frequency points in sequence creates a residual sequence. Each non-zero value in this residual sequence directly corresponds to a tiny spike or protrusion on the original curve, physically mapping the microscopic inhomogeneities present within the dispersion, such as nanoscale aggregates, local entanglement of polymer chains, or discontinuities in solvent distribution. Through this subtraction operation, weak defect signals that were originally difficult to discern in a macroscopic background are explicitly extracted and become independent analytical objects.

[0051] For residual sequences containing both positive and negative values, to prevent the positive and negative biases from canceling each other out and thus masking the true fluctuation amplitude, each residual value in the sequence is first squared to convert it into a non-negative energy value. Next, all squared values ​​are summed and divided by the total number of data points in the sequence to obtain the mean square value. Finally, the square root of this mean square value is taken to obtain the root mean square value (RMS) of the nonlinear fluctuation characteristics. Calculating the RMS value is equivalent to calculating the effective value or energy density of the AC component in the group delay signal. As a single scalar indicator, the RMS value is extremely sensitive to capturing the overall fluctuation intensity of the residual sequence. Whether the fluctuation is a violent oscillation concentrated in a certain frequency band or a subtle spike distributed across the entire frequency band, it will contribute to the final RMS value through the sum of squares. Finally, an empirical database is established based on a large amount of historical test data, specific threshold ranges are set, and a linear mapping function is established to directly correlate the RMS value domain with the quality evaluation dimension of the degree of microscopic medium inhomogeneity. This quantitative definition transforms the originally abstract and elusive microscopic dispersion state into a digital indicator that can be directly read, compared, and has its pass / fail standards set. It not only eliminates subjective errors caused by human observation of spectra but also provides data support for real-time quality monitoring and the removal of substandard products on automated production lines, ensuring the consistency and reliability of each batch of polymer dispersion products at the microscopic level.

[0052] In one embodiment, applying a preset rate of heating and cooling thermal cycling excitation to the polymer dispersion to be tested, and simultaneously applying an AC test signal of a single characteristic frequency while applying the heating and cooling thermal cycling excitation, and continuously acquiring complex impedance data that varies with temperature, includes the following steps: A preset rate of heating and cooling thermal cycling excitation is applied to the polymer dispersion to be tested, and the heating rate and cooling rate of the heating and cooling thermal cycling excitation are set to be greater than the thermal relaxation rate of the polymer chain, so that the polymer dispersion to be tested is always in a non-thermal equilibrium state during the thermal cycling process. The heating and cooling thermal cycle excitation is configured as a triangular wave temperature distribution, with the lower limit set to room temperature and the upper limit set to the film-forming process temperature of the polymer dispersion to be tested. A target high-frequency value that can penetrate the electric double layer and reflect the intergranular conductivity is selected as a single characteristic frequency. Within each time step of the triangular wave temperature distribution pattern, the transmission and response signals of the AC test signal with a single characteristic frequency are synchronously triggered. The acquired response signal is parsed into complex impedance data containing real and imaginary parts, and the complex impedance data is timestamped with the temperature value at the current time step.

[0053] In this embodiment, at the microscopic level, it takes a certain amount of time for the polymer chain to adjust from one conformation to another equilibrium conformation with lower energy; this time characteristic is called the thermal relaxation time. The corresponding rate is the thermal relaxation rate. If the heating or cooling rate is extremely slow (quasi-static process), the polymer chains have sufficient time to adjust their conformation in real time to adapt to the current temperature. The system will always remain in thermodynamic equilibrium, and the hysteresis effect caused by structural defects will not be exposed. Therefore, this scheme sets the heating and cooling rates of the temperature control system to be significantly greater than... This rapid thermal shock causes the rearrangement of polymer chains to lag behind the changes in the temperature field, resulting in a metastable state where the chain segments are in a state of stress freezing or forced motion. It is in this non-equilibrium state that the tiny agglomerations, entanglements, or phase separation structures within the dispersion become significant obstacles to chain segment motion, thus amplifying its dependence on thermal history.

[0054] Specifically, a target temperature curve that linearly changes over time is designed using a programmable temperature controller: starting from room temperature in the laboratory environment, the temperature is linearly increased to the predetermined maximum temperature with a constant positive slope, and then immediately decreased linearly back to the predetermined maximum temperature with the same magnitude of negative slope, thus forming a standard isosceles triangular waveform on the time axis. The linear temperature change in the form of a triangular wave ensures that the magnitude of the thermal driving force remains constant throughout the entire test cycle, eliminating additional nonlinear interference caused by fluctuations in the temperature change rate. Then, a target high-frequency value that can penetrate the electric double layer and reflect the intergranular conductivity is selected as a single characteristic frequency, and a nanometer-thick electric double layer capacitor will naturally form at the interface between the electrode and the solution. If the test is conducted at a low frequency, this huge electric double layer capacitor will generate extremely high impedance, acting like a wall to shield the external electric field, so that the test signal mainly reflects the interface properties rather than the bulk properties of the solution. To overcome this shielding effect, based on the dielectric relaxation spectrum characteristics of the polymer dispersion, a fixed frequency point in the high-frequency region (usually in the range of 100kHz to 10MHz) is specifically selected as the test carrier. At this high frequency, the double-layer capacitor exhibits extremely low capacitive reactance, approximating a short-circuit conducting state, thus allowing the alternating electric field to smoothly penetrate the electrode interface and directly act on the interior of the dispersion. Simultaneously, the wavelength and energy characteristics of electromagnetic waves in this frequency band are precisely sensitive enough to detect micron- and submicron-scale conductive grains and their grain boundary structures. Choosing a single fixed frequency instead of full-frequency scanning ensures sufficiently high temporal resolution for data acquisition during rapid temperature changes, enabling the capture of transient temperature responses.

[0055] During the thermal cycling process, the entire time axis is divided into countless tiny time slices or steps. Each time a new time step is entered, the main control computer immediately sends a trigger command to the impedance analyzer, instructing it to transmit a sinusoidal excitation waveform with a predetermined characteristic frequency to the electrode system. Because the propagation and response speed of the electrical signal is much faster than the macroscopic thermal conduction speed due to temperature changes, this instantaneous electrochemical measurement can be considered as being completed within a quasi-isothermal transient slice. Simultaneously with signal transmission, a high-speed data acquisition card is activated, recording the current response waveform flowing through the dispersion and the voltage waveform across the electrodes at an extremely high sampling rate. The acquired raw voltage and current signals are processed by a digital phase-sensitive detector or a fast Fourier transform algorithm, and demodulated into complex impedance values. The real part The imaginary part represents the resistance component, reflecting energy dissipation and the state of the conductive path; The reactance component represents the energy storage and dielectric polarization characteristics. Simultaneously, the temperature control system records the real-time temperature reading within the microfluidic cavity at the moment of measurement. The data processing software uses high-precision timestamps to strictly bind the complex impedance obtained from the i-th measurement to the temperature at the time of its occurrence, merging them into a single data tuple. .

[0056] In one embodiment, establishing a closed trajectory curve with temperature as the horizontal axis and impedance phase angle as the vertical axis based on complex impedance data, and calculating the characteristic area enclosed by the closed trajectory curve includes the following steps: The temperature value and impedance phase angle from the complex impedance data collected during the heating process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the heating trajectory line. The temperature value and impedance phase angle from the complex impedance data collected during the cooling process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the cooling trajectory line. The starting point of the heating trajectory line and the ending point of the cooling trajectory line are closed, and the ending point of the heating trajectory line is aligned with the starting point of the cooling trajectory line to form a closed trajectory curve. The area of ​​the geometric region enclosed by the closed trajectory curve in the temperature-phase angle coordinate system is calculated using the trapezoidal numerical integration algorithm and used as the characteristic area.

[0057] In this embodiment, the subset corresponding to the linear temperature rise stage is selected from the complete thermal cycling dataset. For each sampling point in this subset, the recorded real-time temperature is extracted as the independent variable, and the impedance phase angle obtained through analytical calculation is extracted as the dependent variable. The phase angle reflects the degree of deviation of the material from purely resistive properties (0 degrees) to capacitive properties (-90 degrees). In a two-dimensional rectangular coordinate system, with the temperature axis as the X-axis and the phase angle axis as the Y-axis, the data pairs are plotted one by one and connected in chronological order to form a continuous curve. This heating trajectory physically records the entire process in which, as heat energy is injected, the polymer chains inside the dispersion gradually gain kinetic energy, overcome intermolecular forces, and begin to untangle, expand, or disintegrate aggregates. The slope of the curve directly corresponds to the temperature range in which the microstructure undergoes drastic changes. Similarly, the temperature value and impedance phase angle in the complex impedance data collected during the cooling process in the heating and cooling thermal cycling excitation are used as the abscissa and ordinate, respectively, to plot the cooling trajectory. Theoretically, if a polymer dispersion is an ideal thermodynamically reversible system without permanent structural damage or chemical changes, then the cooling trajectory should perfectly coincide with the previous heating trajectory. However, actual inferior or aged samples often have severe microscopic defects, causing the polymer chains to fail to return to their initial coiled conformation during rapid cooling, or to fall into a local minimum trap of some metastable state. Therefore, the plotted cooling trajectory usually deviates from the heating trajectory, and the magnitude and shape of this deviation characterize the degree of loss of the memory effect of the material's internal structure and the irreversible accumulation process of thermal damage.

[0058] In actual testing, due to heat conduction delays or sampling discreteness, the starting point of heating and the ending point of cooling may have slight numerical discrepancies. To perform subsequent area integration calculations, this shape must be geometrically closed. Using linear interpolation or endpoint forced closure algorithms, the endpoint of the heating curve is connected to the starting point of the cooling curve (theoretically the same point), and the endpoint of the cooling curve is connected back to the starting point of the heating curve. This forms a closed loop, called a thermal hysteresis loop. This closed loop is similar to a hysteresis loop in magnetism or a stress-strain hysteresis loop in mechanics; the existence of the loop directly indicates that the system has not recovered its state after undergoing a complete thermodynamic cycle. The shape, width, orientation, and degree of twist of the loop comprehensively reflect the frictional losses, conformational rearrangement resistance, and irreversible evolution of the microstructure of the polymer chains under thermally driven conditions. To accurately calculate the area enclosed by the aforementioned closed irregular curve, the composite trapezoidal rule in numerical analysis is used. The measurement range on the temperature axis is divided into N small temperature intervals. .

[0059] For each infinitesimal interval, calculate the absolute value of the difference in phase angle between the heating trajectory and the cooling trajectory. This is used as the height of the infinitesimal trapezoid, multiplied by the interval width. This yields the area contribution of the small region. The summation of the areas of all intervals is approximated by the following formula: The integral-derived characteristic area represents the cumulative sum of irreversible phase angle shifts caused by a unit temperature change within a thermal cycle, essentially quantifying the energy dissipated by structural rearrangement of the polymer dispersion under thermal shock. A smaller value indicates a closer overlap between the heating and cooling trajectories, a more stable material structure, and better thermal reversibility; conversely, a large value signifies severe, irreversible structural collapse or aggregation within the material, resulting in significant quality deterioration.

[0060] In one embodiment, to verify the effectiveness of the present invention in distinguishing differences in the microstructure and grading the quality of polymer dispersions, a complete microfluidic impedance testing system was constructed, and poly(3,4-ethylenedioxythiophene) / polystyrene sulfonate (PEDOT:PSS) was selected as a typical conductive polymer dispersion for the experiment. The experiment aims to achieve accurate detection of the dispersion quality by quantifying the structural rearrangement dissipation (characteristic area) and microscopic media inhomogeneity (group delay fluctuation) under thermodynamic non-equilibrium conditions.

[0061] Three different states of PEDOT:PSS aqueous dispersions were selected as test samples for the experiment: Sample A: Newly synthesized, with a solid content of 1.2%, filtered through a 0.45μm filter membrane, exhibiting good polymer chain stretching.

[0062] Sample B: Stored at room temperature for 3 months, solid content 1.2%, no macroscopic precipitation, but slight chain curling may exist at the microscopic level.

[0063] Sample C: Accelerated aging at 60℃ for 72 hours to simulate the agglomeration state after long-term storage, with a solid content of 1.2%.

[0064] The test environment was constructed as follows: a polydimethylsiloxane microfluidic chip was fabricated using soft lithography, with channel dimensions of 20 mm long, 2.0 mm wide, and 100 μm deep. A glass substrate with an integrated interdigitated gold electrode array (IDE) was bonded to the bottom of the channel. The electrode fingers had a width and spacing of 50 μm, with 50 pairs of fingers, forming a four-electrode impedance probe system to effectively eliminate the influence of electrode polarization impedance. A Peltier thermoelectric cooler (semiconductor cooling chip) was attached to the bottom of the microfluidic chip, and a PT100 temperature sensor was used to achieve closed-loop temperature control with an accuracy of ±0.1℃. Impedance signal acquisition was performed using a high-performance precision impedance analyzer (such as a Keysight E4990A), connected to a host computer for data processing.

[0065] Before conducting formal testing, the system was first calibrated to determine the instrument constants in the corrected Kozeny-Carman equations. Select a known standard tortuosity coefficient. The standard PEDOT:PSS solution was used as the calibration sample.

[0066] Step 2.1: Inject the calibration sample into the microfluidic chip and measure the DC resistance at a constant temperature of 25°C. Based on the microfluidic channel geometry (length L = 2 cm, cross-sectional area A = 0.002 cm²), calculate the calibration baseline conductivity. .

[0067] Step 2.2: Apply a heating and cooling thermal cycle of 10℃ / min (25℃ to 120℃), plot the closed trajectory curve at a frequency of 1MHz, and calculate the calibration feature area by trapezoidal integration. .

[0068] Step 2.3: Set parameters: dimensional balance coefficient Boltzmann constant Maximum process temperature (120℃), the effective conductive porosity is estimated to be 0.015 based on a solid content of 1.2% and a polymer density.

[0069] Step 2.4: Solve using the inverse operation of the modified Kozeny-Carman equation. Substituting the known quantities above, since the exponential term is close to 1 under standard sample conditions (because the heat dissipation of the standard sample is extremely low, the design minimizes the impact of normalization on this term during calibration), the instrument constant can be calculated. This constant will be fixed for calculations on all subsequent test samples.

[0070] Samples A, B, and C were sequentially injected into the cleaned microfluidic chip, and a thermal cycling excitation (25°C) was performed, identical to the calibration process. (120℃, speed 10℃ / min), frequency locked at 1MHz, test results are as follows: Figure 2 As shown.

[0071] Sample A test: During the heating process, the phase angle linearly changed from -15° to -5°; during the cooling process, due to the high flexibility of the molecular chains, it could recover quickly, and the cooling trajectory highly overlapped with the heating trajectory, forming an extremely narrow closed hysteresis loop. The characteristic area was calculated through integration. .

[0072] Sample B test: During heating, the phase angle changed from -22.0° to -10.0°; during cooling, the polymer chains exhibited a certain degree of hysteresis recovery, and the cooling trajectory clearly separated from the heating trajectory, forming a medium-sized spindle-shaped closed loop. The characteristic area was calculated by integration. .

[0073] Sample C test: During heating, the aggregated molecular chains absorb heat and untangle, causing significant fluctuations in the phase angle; during cooling, due to irreversible conformational collapse of the molecular chains, they cannot return along the original path, resulting in a severe deviation between the cooling and heating trajectories, forming a large hysteresis loop. The characteristic area is calculated through integration. .

[0074] The baseline conductivity of each sample at 25℃ was measured. The tortuosity coefficient of the carrier migration path is calculated by substituting the characteristic area into the modified Kozeny-Carman equation.

[0075] For sample A: Substituting into the formula, since... The value is relatively small, and the exponential correction term is close to 1.1, resulting in the final calculation. This value is close to 1, indicating that the electron transport path is close to a straight line and the network structure is excellent.

[0076] For sample B: , The heat dissipation correction term is moderate, and the calculation yields... (The path is slightly winding).

[0077] For sample C: Due to low conductivity and (480.7) is extremely large, and the exponential term increases significantly, reflecting the hindering effect of heat dissipation on the transmission path. The final calculation yields... This value is much greater than 1, indicating that electrons need to be transported in an extremely convoluted and entangled polymer network, with a tortuous path.

[0078] After the thermal cycling was completed, the temperature was kept constant at 25°C, and logarithmic frequency scanning was performed on each sample from 10 Hz to 10 MHz.

[0079] Data processing: Acquiring phase angle Data, computation group latency .

[0080] Nonlinear feature extraction: The group delay curve of sample A is smooth, monotonically changes with frequency, and has minimal residuals to the 5th-order polynomial fitting baseline. The root mean square (RMS) value of the residual sequence is calculated, yielding... .

[0081] Sample B: Slight nonlinear fluctuations appear in the mid-frequency range (1kHz-10kHz), indicating the presence of a small number of microscopic inhomogeneities. Residual .

[0082] The group delay curve of sample C exhibits significant spike-like fluctuations in the 1kHz to 100kHz frequency band, which is due to local dielectric relaxation inconsistencies caused by internal micro-agglomerations. After baseline removal, the residual fluctuations are severe, and the calculated values ​​are... .

[0083] Based on the above experimental data, the following quality grading standards and test result table are established: Table 1: Quality Grading Standards and Test Results

[0084] As can be seen from this embodiment, Sample A has a low tortuosity coefficient and uniform medium, and is judged to be a first-class product, suitable for the preparation of high-performance transparent electrodes; Sample B is judged to be a second-class product in all indicators; Although Sample C is not completely precipitated in appearance, its characteristic area and tortuosity coefficient show that its internal microstructure has been severely deteriorated (excessive coiling and aggregation of polymer chains), and it is judged to be a substandard product. The method proposed in this invention can organically combine the thermodynamic hysteresis effect with geometric topological parameters through the modified Kozeny-Carman equation, and compared with the single conductivity test, it can detect the microscopic quality defects of polymer dispersions earlier and more sensitively.

[0085] This invention also discloses a polymer dispersion quality detection system based on electrical impedance spectroscopy analysis, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis as described above.

[0086] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0087] The memory can be an internal storage unit of a computer device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) provided on the computer device. Furthermore, the memory can be a combination of internal storage units and external storage devices of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0088] The present invention also discloses a computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis described in any of the above embodiments.

[0089] The computer program can be stored in a machine-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The machine-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the machine-readable medium includes, but is not limited to, the above-mentioned components.

[0090] The polymer dispersion quality detection method based on electrical impedance spectroscopy analysis described in the above embodiments is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0091] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0092] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis, characterized in that, Includes the following steps: A microfluidic testing environment including a temperature control module and a multi-electrode impedance probe was constructed, and the polymer dispersion to be tested was injected into the microfluidic testing environment; A preset rate of heating and cooling thermal cycling excitation is applied to the polymer dispersion to be tested. At the same time as the heating and cooling thermal cycling excitation is applied, an AC test signal with a single characteristic frequency is applied, and complex impedance data that varies with temperature is continuously collected. Based on complex impedance data, a closed trajectory curve is constructed with temperature as the horizontal axis and impedance phase angle as the vertical axis. The characteristic area enclosed by the closed trajectory curve is then calculated. Characterizes the degree of structural rearrangement dissipation of the polymer dispersion under thermal shock; The basic conductivity of the polymer dispersion to be tested was determined, and the tortuosity coefficient of the carrier migration path was calculated using a porous media permeation model in combination with the characteristic area to quantify the coiling and entanglement state of polymer chains at the microscopic level. The polymer dispersion to be tested was kept at a preset constant temperature and impedance scanning was performed. The derivative of the impedance phase angle with respect to the angular frequency was calculated to obtain the electrochemical group time delay curve. Nonlinear fluctuation characteristics were extracted from the electrochemical group time delay curve, and the degree of microscopic media nonhomogeneity inside the polymer dispersion under test was quantified by calculating the root mean square value of the nonlinear fluctuation characteristics. The quality classification and screening of the polymer dispersions under test are carried out by combining the tortuosity coefficient and the degree of microscopic media non-homogeneity.

2. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 1, characterized in that, The determination of the basic conductivity of the polymer dispersion to be tested, and the calculation of the tortuosity coefficient of the carrier migration path for quantifying the coiling and entanglement state of polymer chains at the microscopic level using a porous media permeation model in combination with the characteristic area, includes the following steps: The polymer dispersion to be tested is placed in a preset standard temperature environment and the DC resistance value is measured by a multi-electrode impedance probe. The DC resistance value is converted into the basic conductivity based on the geometric constant of the microfluidic test environment. A virtual porous medium model is constructed based on the solid content and density parameters of the polymer dispersion to be tested, and the conductive polymer network in the polymer dispersion to be tested is equivalent to the fluid channel in the virtual porous medium model. We obtain instrument constants that characterize the microfluidic testing environment and the geometry of the multi-electrode impedance probe, and construct a modified Kozeny-Carman equation that includes characteristic area, fundamental conductivity, and instrument constants. The characteristic area and basic conductivity are substituted into the modified Kozeny-Carman equation for solution, and the dimensionless value obtained is used as the tortuosity coefficient of the carrier migration path in the microscopic coiled and entangled state of polymer chains.

3. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 2, characterized in that, The construction of the modified Kozeny-Carman equation, which includes characteristic area, fundamental conductivity, and instrument constant, comprises the following steps: A standard conductive polymer solution with a known standard tortuosity coefficient was selected as a calibration sample and injected into the microfluidic testing environment. The calibration sample was tested under the same heating and cooling thermal cycling excitation conditions as the polymer dispersion to be tested, and the calibration characteristic area and calibration basic conductivity of the calibration sample were obtained. The standard tortuosity coefficient, calibration characteristic area, and calibration baseline conductivity are substituted into the pre-defined general form of the Kozeny-Carman equation as known quantities. The undetermined coefficients in the general form of the Kozeny-Carman equation are solved by reverse operation, and the solved undetermined coefficients are determined as instrument constants. By fixing the determined instrument constants in the general form of the Kozeny-Carman equations, the modified Kozeny-Carman equations are generated.

4. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 3, characterized in that, The modified Kozeny-Carman equation is specifically represented as follows: ； in, This is the tortuosity coefficient of the carrier migration path; This is the instrument constant; The effective conductive porosity calculated in the virtual porous medium model; Based on the fundamental conductivity; The characteristic area; This is the preset dimensional balance coefficient; Boltzmann's constant; This is the highest process temperature during thermal cycling. The term is an exponential function term with the natural logarithm as its base. This exponential function term is a thermodynamic dissipation correction term used to describe the dynamic hindering effect of structural rearrangement dissipation energy on electron transport paths.

5. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 1, characterized in that, The process of maintaining the polymer dispersion to be tested at a preset constant temperature and performing impedance scanning to calculate the derivative of the impedance phase angle with respect to the angular frequency to obtain the electrochemical group time delay curve includes the following steps: When the heating and cooling thermal cycle excitation ends and the polymer dispersion to be tested is in a preset constant temperature state, the control impedance analyzer generates a logarithmic scan signal from the start frequency to the end frequency. Discrete impedance phase angle data points of the polymer dispersion response under logarithmic scan signal are collected to form a frequency-phase angle data sequence; The first negative derivative of discrete impedance phase angle data points with respect to angular frequency in the frequency-phase angle data sequence is calculated using the finite difference method to obtain discrete group delay numerical points. Connect the group delay numerical points in frequency order to generate a continuous electrochemical group delay curve.

6. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 1, characterized in that, The extraction of nonlinear fluctuation characteristics from the electrochemical group time delay curve, and the quantification of the degree of microscopic media heterogeneity within the polymer dispersion to be tested by calculating the root mean square value of the nonlinear fluctuation characteristics, includes the following steps: A polynomial fitting algorithm was used to fit the global trend of the electrochemical group time delay curve to obtain a baseline curve characterizing the ideal uniform dispersion state. The electrochemical group time delay curve is subtracted from the baseline curve point by point. After removing the linear trend component in the electrochemical group time delay curve, the extracted residual sequence is used as the nonlinear fluctuation feature. Calculate the sum of squares of all data points in the residual sequence, divide the sum of squares by the total number of data points to obtain the mean square value, and take the square root of the mean square value to obtain the root mean square value of the nonlinear fluctuation characteristics. The root mean square value is defined as a quantitative indicator of the degree of non-uniformity of the micro-medium through linear mapping.

7. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 1, characterized in that, The process of applying a preset rate of heating and cooling thermal cycling excitation to the polymer dispersion under test, and simultaneously applying an AC test signal of a single characteristic frequency while continuously acquiring complex impedance data that varies with temperature, includes the following steps: A preset rate of heating and cooling thermal cycling excitation is applied to the polymer dispersion to be tested, and the heating rate and cooling rate of the heating and cooling thermal cycling excitation are set to be greater than the thermal relaxation rate of the polymer chain, so that the polymer dispersion to be tested is always in a non-thermal equilibrium state during the thermal cycling process. The heating and cooling thermal cycle excitation is configured as a triangular wave temperature distribution, with the lower limit set to room temperature and the upper limit set to the film-forming process temperature of the polymer dispersion to be tested. A target high-frequency value that can penetrate the electric double layer and reflect the intergranular conductivity is selected as a single characteristic frequency. Within each time step of the triangular wave temperature distribution pattern, the transmission and response signals of the AC test signal with a single characteristic frequency are synchronously triggered. The acquired response signal is parsed into complex impedance data containing real and imaginary parts, and the complex impedance data is timestamped with the temperature value at the current time step.

8. The method for quality detection of polymer dispersions based on electrical impedance spectroscopy analysis according to claim 1, characterized in that, The process of establishing a closed trajectory curve with temperature as the horizontal axis and impedance phase angle as the vertical axis based on complex impedance data, and calculating the characteristic area enclosed by the closed trajectory curve, includes the following steps: The temperature value and impedance phase angle from the complex impedance data collected during the heating process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the heating trajectory line. The temperature value and impedance phase angle from the complex impedance data collected during the cooling process in the heating and cooling thermal cycle excitation are used as the abscissa and ordinate respectively to plot the cooling trajectory line. The starting point of the heating trajectory line and the ending point of the cooling trajectory line are closed, and the ending point of the heating trajectory line is aligned with the starting point of the cooling trajectory line to form a closed trajectory curve. The area of ​​the geometric region enclosed by the closed trajectory curve in the temperature-phase angle coordinate system is calculated using the trapezoidal numerical integration algorithm and used as the characteristic area.

9. A polymer dispersion quality detection system based on electrical impedance spectroscopy analysis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the polymer dispersion quality detection method based on electrical impedance spectroscopy analysis according to any one of claims 1 to 8.

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