Coating rheological property testing method based on micro-fluidic chip

Abstract

The invention relates to the technical field of coating performance testing, and discloses a coating rheological property testing method based on a micro-fluidic chip, which comprises the following steps: providing the micro-fluidic chip on which a shear failure area and a dynamic evolution observation area which are communicated in sequence are integrated, an on-way programmable micro heater, a micro acoustic transducer and a microelectrode impedance spectroscopy array are cooperatively integrated in the dynamic evolution observation area; driving the coating sample to flow through the area at a preset volume flow rate; when a sample flows through the dynamic evolution observation area, acoustic signals and electrical impedance spectroscopy signals at different positions are synchronously collected. Through correlation analysis of evolution of the two characteristic parameters along with time and temperature, the technical problems that a real high-shear working condition cannot be simulated and multi-dimensional in-situ characterization is difficult to perform in the prior art are solved, and trace, rapid and high-resolution dynamic monitoring of the coating structure recovery process is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of paint performance test, in particular to a paint rheological property test method based on a microfluidic chip. BACKGROUND

[0002] As a complex colloidal suspension system, the rheological property of paint is a core factor determining its storage stability, application performance and final paint film appearance quality. In particular, the thixotropy of paint, i.e. the property of viscosity reduction under high shear and gradual recovery after shear removal, is crucial for achieving excellent application results. For example, in the process of spraying or brushing, etc. with high shear rate, the viscosity of paint needs to be rapidly reduced to facilitate atomization and flow; and after the end of construction, the viscosity needs to be appropriately recovered within a certain time to ensure that the paint film can fully flow to eliminate brush marks, while also having sufficient anti-sagging ability. Therefore, accurately characterizing the structure recovery dynamic process of paint after experiencing high shear damage has important guiding significance for the development and optimization of paint formulations.

[0003] In the prior art, a rotary rheometer is usually used to characterize the thixotropic behavior of paint. This kind of instrument can execute step shear experiments to simulate the shear-recovery process by applying controllable shear stress or strain and measuring the response of the material, and is a standard tool in current laboratory research.

[0004] However, the conventional rotary rheometer still faces some inherent limitations in simulating and analyzing the rheological behavior of paint in real industrial application scenarios. When simulating high shear applications, these instruments are limited by their mechanical structure and cannot reach the ultra-high shear rates commonly found in industrial processes, which makes the simulation of the initial structure damage of paint insufficient. More importantly, due to the mechanical inertia of the instrument itself, it takes an unavoidable response time to switch from a high shear state to a low shear or static state for observing the recovery process, which often leads to the information of the earliest, most rapid and critical stage of paint structure recovery being missed.

[0005] In addition, the traditional rheological measurement provides a macroscopic mechanical response parameter such as viscosity or modulus. It reflects the comprehensive effect of all microstructures inside the coating, but cannot effectively decouple the contributions from different sources. In the actual recovery process, the evolution dynamics of different microstructures may differ significantly, and such differences play a decisive role in the final performance, but the traditional method is difficult to provide in-depth, multi-dimensional in-situ insight. At the same time, the existing large instruments are not efficient and consume a large amount of samples in simulating non-isothermal conditions commonly seen in actual processes, and meeting the high-throughput screening requirements needed for modern formulation development. Therefore, developing a new technology that can more realistically simulate industrial conditions and can multi-dimensionally and high-resolution in-situ characterize the microstructure recovery process of the coating is a technical problem to be solved in the field. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a coating rheological property testing method based on a microfluidic chip, which solves the problem that the existing coating rheological testing technology is difficult to simulate real high-shear conditions and cannot in-situ, multi-dimensionally and high-time-resolution quantitatively characterize the dynamic recovery process of the microstructure inside the coating after shearing.

[0007] To achieve the above object, the present application is implemented by the following technical scheme: The present application provides a coating rheological property testing method based on a microfluidic chip in the first aspect.

[0008] The method comprises the following steps:

[0009] S1: providing a microfluidic chip, the microfluidic chip being integrated with a shear damage zone and a dynamic evolution observation zone connected in sequence; the dynamic evolution observation zone being integrated with an in-line programmable micro-heater array, a micro-acoustic transducer array and a micro-electrode impedance spectrum array along the flow direction thereof;

[0010] S2: driving a coating sample to flow through the shear damage zone and the dynamic evolution observation zone in sequence at a preset volume flow rate;

[0011] S3: starting the in-line programmable micro-heater array to establish a preset temperature distribution in the dynamic evolution observation zone;

[0012] S4: while the coating sample is flowing through the dynamic evolution observation zone, synchronously collecting acoustic signals and electrical impedance spectrum signals at different positions through the micro-acoustic transducer array and the micro-electrode impedance spectrum array;

[0013] S5: processing the acoustic signals into mechanical structure characteristic parameters characterizing the coating sample, and processing the electrical impedance spectrum signals into particle network characteristic parameters characterizing the coating sample;

[0014] S6: comprehensively characterizing the rheological property of the paint sample based on the changes of the mechanical structure characteristic parameters and the particle network characteristic parameters with position and temperature.

[0015] In a possible implementation, in the S2, the preset volume flow rate Q is set so as to make the wall shear rate The value can be determined by the following formula:

[0016]

[0017] wherein,

[0018] is the wall shear rate;

[0019] Q is the preset volume flow rate;

[0020] w d is the width of the shear breakdown zone;

[0021] h d is the height of the shear breakdown zone.

[0022] This step realizes the standardized reset of the shear history of the paint sample.

[0023] In a possible implementation, the method further comprises a coordinate conversion step: converting the position coordinate x in the dynamic evolution observation zone into a structure evolution time t(x) according to the following formula:

[0024]

[0025] wherein,

[0026] t(x) is the structure evolution time;

[0027] x is the position coordinate in the dynamic evolution observation zone;

[0028] A o is the cross-sectional area of the channel of the dynamic evolution observation zone;

[0029] Q is the preset volume flow rate. This step maps the measurement data in the spatial dimension to the time dimension.

[0030] In a possible implementation, in the S5, the specific process of processing the acoustic signal comprises: first, measuring the propagation speed V ac and the attenuation coefficient a ac of the shear wave in the paint sample through the micro acoustic transducer array; second, based on the propagation speed V ac and the attenuation coefficient aac calculating a complex shear modulus G of the material * :

[0031]

[0032] wherein,

[0033] G * is the complex shear modulus;

[0034] p is the density of the coating sample;

[0035] w ac is the angular frequency of the shear wave;

[0036] i is the imaginary unit.

[0037] Finally, the storage modulus G' is extracted from the real part of the complex shear modulus G * as the mechanical structure characteristic parameter.

[0038] In one possible implementation, the specific process of processing the electrical impedance spectrum signal in S5 includes: first, measuring the complex impedance spectrum Z * of the coating sample in a preset frequency range by the microelectrode impedance spectrum array; second, fitting the complex impedance spectrum Z * using a preset equivalent circuit model; and finally, extracting the network resistance R net from the fitting result as the particle network characteristic parameter.

[0039] In one possible implementation, S6 includes: correlating the variation curves of the storage modulus G' and the network resistance R net with the structure evolution time t(x), and determining the evolution asynchrony of the recovery processes of the two by comparing the characteristic parameters of the two curves.

[0040] The microfluidic chip is characterized in that a shear damage zone and a dynamic evolution observation zone are sequentially connected and communicated thereon; the dynamic evolution observation zone is a channel with a cross-sectional area greater than that of the shear damage zone, and three functional arrays are integrated in the channel along the flow direction of the channel:

[0041] a programmable micro-heater array along the channel, configured to establish a preset temperature distribution in the dynamic evolution observation zone;

[0042] a micro-acoustic transducer array, configured to collect acoustic signals of the coating sample when the coating sample flows through;

[0043] a microelectrode impedance spectrum array, configured to collect electrical impedance spectrum signals of the coating sample when the coating sample flows through.

[0044] In a possible implementation, the micro acoustic transducer array and the micro electrode impedance spectrum array are discretely arranged along a flow direction of the dynamic evolution observation area.

[0045] The application provides a paint rheological property test method based on a microfluidic chip.

[0046] 1. The application can obtain in-situ and real-time double information of energy storage modulus representing mechanical structure and network resistance representing particle network connectivity evolution over time, can reveal the internal correlation and asynchrony of the two microstructure evolution processes, and thus provides more profound insights into the thixotropic mechanism of the paint than a single rheological curve.

[0047] 2. The application can simulate key process procedures of the paint from high shear application to low shear flow leveling and solidification on a micro sample scale by arranging the shear damage area and the dynamic evolution observation area in sequence and in communication, and combining a programmable micro heater array. The method not only reproduces a controlled shear history, but also introduces a programmable temperature path, so that the test conditions are closer to actual application scenarios, and thus direct data of the paint rheological property evolution under complex working conditions can be obtained, which provides an effective basis for formula optimization and process design.

[0048] 3. The application integrates a complex rheological test procedure on an integrated chip based on microfluidic chip technology, significantly reduces the sample amount and reagent consumption required for a single test. At the same time, a complete structure evolution curve can be obtained by a continuous flow experiment through the space-time measurement principle, which greatly shortens the test period. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0051] Embodiment:

[0052] Please refer to the accompanying Figure 1 The embodiment of the application provides a paint rheological property test method based on a microfluidic chip, which comprises the following steps:

[0053] S1: providing a microfluidic chip, the microfluidic chip being integrated with a shear disruption zone and a dynamic evolution observation zone which are sequentially communicated; the dynamic evolution observation zone being integrated with an in-line programmable micro-heater array, a micro-acoustic transducer array and a micro-electrode impedance spectrum array along a flow direction thereof;

[0054] In the embodiment, a paint rheological property testing method based on a microfluidic chip is provided. The microfluidic chip is a physical carrier for carrying out all core steps of the testing method.

[0055] In a specific embodiment, the main structure of the microfluidic chip can be bonded by two layers of substrates. As a preferred solution, the upper substrate can be made of polydimethylsiloxane (PDMS) material with good optical transparency and elasticity, and the lower substrate can be made of glass with high flatness and excellent electrical insulation. The microchannel structure inside the chip can be formed in the PDMS substrate through a standard soft lithography process, and the functional electrical and thermal elements are integrated on the glass substrate through micro-nano processing. Finally, the two layers of substrates are aligned and bonded to form a closed microfluidic system.

[0056] Specifically, the microfluidic chip is integrated with a shear disruption zone and a dynamic evolution observation zone which are sequentially communicated.

[0057] The shear disruption zone is designed to apply a uniform and high enough shear field to the paint sample entering the chip to eliminate the unknown or inconsistent shear history carried by the sample itself, thereby providing a standardized and repeatable initial state for subsequent observation. To this end, the shear disruption zone is designed as a narrow channel with a predetermined and precise geometric size (width w d and height h d ). When the paint sample is driven through this region at a predetermined volume flow rate Q, the wall shear rate can be determined by the following relationship:

[0058]

[0059] By reasonably setting the channel size and volume flow rate, the wall shear rate can reach the order of magnitude of simulating high shear applications such as industrial spraying.

[0060] Following the shear disruption zone, the microfluidic chip is integrated with a dynamic evolution observation zone. The design purpose of this region is to provide a nearly static and low shear environment for the paint sample after the structure is disrupted, so as to observe the spontaneous recovery and evolution process of the internal microstructure. Therefore, the dynamic evolution observation zone is designed as a section with a cross-sectional area A oWide straight channel much larger than the shear fracture zone. When fluid enters this region from the shear fracture zone, its average flow velocity drops sharply, and the shear effect is weakened.

[0061] Since the sample flows at a constant volume flow rate Q in this region, a certain mapping relationship is established between the position coordinate x along the flow direction and the structural evolution time t(x) experienced since entering this region:

[0062]

[0063] This design converts spatial measurements into time observations, which is the basis for in-situ monitoring of dynamic processes.

[0064] In order to realize multi-dimensional and quantitative characterization of the structural evolution process, three different functional arrays are integrated along the flow direction in the dynamic evolution observation zone, which are programmable micro-heater array, micro-acoustic transducer array and micro-electrode impedance spectrum array.

[0065] Further, the programmable micro-heater array functions to simulate the application scenarios of the coating at different process temperatures and study the influence of temperature on the structural evolution dynamics. The array is composed of a series of independently addressable and controllable micro-resistance heating units, which are discretely arranged along the flow direction. Preferably, the heating units can be made of metal materials such as platinum through sputtering and peeling process. By applying independent control voltage to each heating unit, various preset temperature distributions can be accurately generated and maintained in the dynamic evolution observation zone, such as linear temperature gradient, stepwise heating / cooling curve, or programmed dynamic temperature profile simulating the real drying process.

[0066] Further, the micro-acoustic transducer array functions to non-perturbatively and in real time detect the mechanical structure characteristics of the coating sample, especially the changes in its viscoelasticity. The array is composed of multiple pairs of micro-electromechanical system transducers arranged along the flow direction, each pair containing a transmitter and a receiver. Preferably, the transducers can be made of piezoelectric materials such as aluminum nitride for efficient generation and detection of high-frequency shear waves. The working principle is that the transmitter generates a shear wave with angular frequency ω ac , which propagates in the flowing coating sample and is received by the receiver. By analyzing the changes of the received signal relative to the transmitted signal, the propagation speed V ac and attenuation coefficient α ac of the shear wave at this position can be accurately measured. These two physical quantities are directly related to the complex shear modulus G * of the material, which can be calculated through the following relationship:

[0067]

[0068] where p is the density of the paint sample, and i is the imaginary unit. The storage modulus G' can be obtained by separating the real part of the complex shear modulus, which directly quantifies the strength and elasticity of the mechanical network inside the paint. Therefore, the variation curve of G' obtained by in-line measurement intuitively reflects the recovery process of the mechanical structure.

[0069] Further, a microelectrode impedance spectrum array is used to monitor the formation and evolution of the conductive or semiconductive particle network inside the paint from the electrical dimension. The array is composed of a plurality of microelectrodes arranged along the flow direction, and preferably, interdigital electrodes or the like are made of metal materials such as gold with stable chemical properties. The working principle is that a weak alternating current field with a scanning frequency in a preset range is applied to each pair of electrodes, and the complex impedance response spectrum Z * of the system is measured. The paint is a suspension, and its electrical response is closely related to the dispersion, agglomeration and percolation network formation state of the particles inside. In order to extract the key parameters representing the state of the particle network from the original impedance spectrum data, an equivalent circuit model can be used for fitting analysis. For example, a typical model R s (R net ||CPE) has an impedance expression as follows:

[0070]

[0071] Through fitting, the network resistance R net can be analyzed from the impedance spectrum of each measurement position. The variation trend of the parameter directly reflects the formation and improvement process of the conductive path between the particles.

[0072] In summary, the microfluidic chip of the embodiment integrates the shear destruction zone and the dynamic evolution observation zone integrating the three functional arrays, so that the evolution data of the mechanical structure characteristics (represented by G') and the particle network characteristics (represented by R net ) of the paint sample along with the controlled shear history and the programmable temperature path over time can be obtained on a unified platform through one-time micro-sample testing. Through the correlation analysis of the two sets of evolution data, especially the analysis of the asynchronization of their evolution processes, deep insights into the performance of the paint formula in the application of leveling, anti-sagging and the like can be provided.

[0073] S2: driving the paint sample to flow through the shear destruction zone and the dynamic evolution observation zone in sequence at a preset volume flow rate;

[0074] In the embodiment, this step is not only the basis for realizing sample transportation, but more importantly, through the accurate control of the volume flow rate, the specific physical conditions and measurement framework required by the test method are directly constructed.

[0075] Specifically, the execution of this step relies on a peripheral driving system capable of providing a steady, pulsation-free fluid flow. As a preferred solution, a high-precision microsyringe pump or a pressure-controlled microfluidic injection system can be employed. The core technical requirement of this driving system is that it can maintain a constant volume flow rate Q with extremely high precision and repeatability throughout the entire test. This volume flow rate Q, as a key, user-settable independent variable prior to the experiment, directly determines the accuracy and reliability of all subsequent measurements.

[0076] When the driving system is started, the paint sample is pumped from the sample inlet into the microfluidic chip and first enters the shear disruption zone. The key significance of this step is that the pre-set volume flow rate Q, in combination with the specific geometry (width w d and height h d ) of the shear disruption zone, together generates a certain, high-intensity shear field in the channel. The wall shear rate experienced by the sample in this region can be characterized by the following relationship:

[0077]

[0078] wherein, is the wall shear rate; Q is the pre-set volume flow rate; w d is the width of the shear disruption zone; h d is the height of the shear disruption zone. Through this step, the weak physical network structure existing inside the paint sample due to thixotropy and formed during sample preparation and loading is fully and repeatedly disrupted. This ensures that any sample entering the subsequent observation zone has a standardized initial microstructure that is independent of its original state, laying the foundation for high-faith dynamic evolution observation.

[0079] After flowing through the shear disruption zone, the sample continues to advance under the driving of the same constant volume flow rate Q and enters the dynamic evolution observation zone.

[0080] Another key significance of this step is that the constant volume flow rate Q provides a time reference for in-situ measurement of dynamic processes. Since the channel cross-sectional area A o of the dynamic evolution observation zone is known, the position coordinate x of the sample in the flow direction x and the structural evolution time t(x) experienced by the sample since it entered the region establish an accurate, linear mapping relationship:

[0081]

[0082] wherein,

[0083] t(x) is the structural evolution time;

[0084] x is the position coordinate in the dynamic evolution observation zone;

[0085] A o is the cross-sectional area of the channel in the dynamic evolution observation zone;

[0086] Q is a preset volumetric flow rate.

[0087] It makes each independent measurement of the sensors discretely arranged along the flow direction in space equivalent to a snapshot of the state of the sample at a certain time point in the recovery process. By continuously collecting and processing the signals at different positions in space, the complete dynamic curve of the evolution of the microstructure characteristics of the sample over time can be reconstructed.

[0088] S3: Start the programmable micro-heater array along the flow direction to establish a preset temperature distribution in the dynamic evolution observation zone;

[0089] In this embodiment, starting the programmable micro-heater array along the flow direction to establish a preset temperature distribution in the dynamic evolution observation zone is a key step in the test method of the present application for introducing a controllable thermal field and realizing multi-physical field coupling analysis. This step aims to use temperature as an independent and accurately programmable variable to simulate the non-isothermal environment that the coating may experience in actual application and systematically study the influence of temperature on the evolution dynamics of the internal microstructure of the coating.

[0090] Specifically, the implementation of this step relies on a series of independently addressable micro-resistance heating units integrated on the substrate of the dynamic evolution observation zone. These heating units collectively form a programmable micro-heater array along the flow direction. As a preferred solution, the heating units can be precisely patterned on a glass substrate by a micro-nano processing technology using a metal material with a stable resistance temperature coefficient, such as platinum or gold. They are discretely arranged along the flow direction of the dynamic evolution observation zone, and each unit can be connected to an external control system through independent electrical pins.

[0091] The process of starting the array is performed by an external multi-channel precision power supply or a dedicated temperature control driving system. According to the target temperature distribution program preset by the user, the system applies an accurately calculated and independent driving voltage or current to each micro-heating unit in the array.

[0092] Its working principle is based on the Joule heating effect. When an electric current flows through a micro-heating unit with a certain resistance, the electric energy is converted into heat energy, thereby heating the unit and its adjacent area. The heat power P heat generated by a single heating unit can be characterized by the following formula:

[0093] P heat = I 2• R heater ;

[0094] where I is the current flowing through the heating unit, R heater is the resistance of the heating unit itself. By independently modulating the driving current or voltage of each heating unit, one can construct an arbitrary shape of stable spatial temperature profile along the flow direction on a macroscopic scale.

[0095] To ensure the accuracy and stability of the established temperature distribution, in one possible implementation, a miniature temperature sensor, such as a resistance temperature detector also made of platinum, can be co-integrated near each heating unit. The control system monitors the actual temperature at each location in real time and compares it with the preset value. Through proportional-integral-derivative control algorithm, the power applied to each heating unit is dynamically adjusted to accurately maintain the preset temperature distribution and offset disturbances such as heat dissipation caused by fluid flow.

[0096] Therefore, the fundamental purpose of this step of starting and establishing a preset temperature distribution is to superimpose the thermal field as a controllable experimental dimension on the flow field, acoustic field and electric field. When the coating sample flows through the dynamic evolution observation area, the internal structural evolution process is not only a function of time, but also a function of temperature. The data obtained by subsequent acoustic and electrical measurements naturally contain temperature-dependent information. The execution of this step enables the test method of the present application to expand from a single isothermal test to a multi-physical field characterization that can systematically obtain the coupling relationship between coating rheological properties and temperature in a single micro-experiment, thereby providing a solid data foundation for understanding and optimizing the application performance of coatings in complex temperature environments.

[0097] S4: While the coating sample flows through the dynamic evolution observation area, the acoustic signals and electrical impedance spectrum signals at different positions are synchronously collected by the miniature acoustic transducer array and the microelectrode impedance spectrum array;

[0098] In this embodiment, while the coating sample flows through the dynamic evolution observation area, the acoustic signals and electrical impedance spectrum signals at different positions are synchronously collected by the miniature acoustic transducer array and the microelectrode impedance spectrum array, which is the core link of the present application for performing measurements and obtaining raw data in the test method. This step aims to convert the microstructure state of the coating sample at a specific spatiotemporal coordinate point into quantifiable electrical signals belonging to different physical dimensions.

[0099] Specifically, the synchronous acquisition is realized by the central host computer and the multi-channel data synchronous acquisition system. The host computer is responsible for issuing trigger instructions to each measurement subsystem, and the data synchronous acquisition system is responsible for providing a unified time reference for all collected data. This cooperative working mechanism ensures that at any time, the acoustic signal and the electrical impedance spectrum signal collected for the same position on the flow path have a precise time correspondence.

[0100] When the paint sample flows through a certain position x in the dynamic evolution observation area under the driving of a constant volume flow rate Q i , the host computer simultaneously starts the acoustic and electrical measurement processes at that position.

[0101] On the one hand, for the collection of acoustic signals, the specific process is as follows: the high-frequency signal generator connected to the micro-acoustic transducer at position x i generates a preset angular frequency ω ac electrical excitation signal and applies it to the transmitter. The transmitter converts the electrical signal into mechanical vibration under the piezoelectric effect, thereby exciting a shear wave at the contact interface with the paint sample. The shear wave is received by the corresponding receiver after propagating a short distance in the paint sample. The receiver converts the received mechanical vibration back into an electrical signal through the inverse piezoelectric effect. The data acquisition system captures the complete waveforms of the transmitted and received signals and calculates the propagation speed V ac (x i ) and the attenuation coefficient α ac (x i ) of the shear wave at that position by comparing the phase difference and amplitude changes of the two signals. These two physical quantities constitute the original acoustic signal at that position, which provides direct input for subsequent calculation of the complex shear modulus G * representing the mechanical structure characteristics.

[0102] On the other hand, for the collection of electrical impedance spectrum signals, the specific process is as follows: the electrochemical workstation connected to the micro-electrode impedance spectrum array at the same position x i applies a small sinusoidal excitation voltage with a frequency scanning within the preset range [ω ei,min , ω ei,max ] on the electrode pair according to the preset program. At the same time, the workstation synchronously measures the current response signal flowing through the electrode. By analyzing the amplitude ratio and phase difference of the voltage and current at each frequency point ω ei , the workstation calculates the complex impedance value Z * (ω ei , x i ) at that frequency point.

[0103] Z * (ω ei,x i ) = Z re (ω ei ,x i )+i·Z im (ω ei ,x i );

[0104] Among them, Z re and Z im These represent the real and imaginary parts of the complex impedance, respectively, with i being the imaginary unit. After completing one full frequency scan, a set of discretized impedance spectrum data describing the electrical properties of the coating sample at that location can be obtained. This set of data is used for subsequent fitting of an equivalent circuit model and extraction of the network resistance R, which characterizes the particle network properties. net The original evidence was provided.

[0105] The acquisition of the aforementioned acoustic and electrical signals is continuous throughout the entire process of the coating sample flowing through the dynamic evolution observation zone. Since the micro-acoustic transducer array and microelectrode impedance spectroscopy array are discretized along the flow direction x, the system performs a dual measurement of the current structural evolution time point t(x) as the sample flows past each sensor location. Ultimately, the output of this step is a series of paired raw datasets associated with spatial location (i.e., evolution time) and temperature: {V ac (x i ),α ac (x i )} and {Z * (ω ei ,x i This step provides comprehensive and intrinsically linked raw experimental data for subsequent data processing and multiphysics coupling analysis.

[0106] S5: The acoustic signal is processed into parameters characterizing the mechanical structural properties of the coating sample, and the electrical impedance spectrum signal is processed into parameters characterizing the particle network properties of the coating sample.

[0107] In this embodiment, processing the acoustic signal into parameters characterizing the mechanical structural properties of the coating sample and processing the electrical impedance spectrum signal into parameters characterizing the particle network properties of the coating sample are key data processing steps in the testing method of this invention to extract core physical information from the original measurement data.

[0108] Firstly, the ultimate goal of acoustic signal processing is to obtain mechanical structural property parameters that can quantitatively characterize the strength and elasticity of the internal mechanical network of the coating sample. The process is as follows:

[0109] For any position x within the dynamic evolution observation area i The acquired raw acoustic signal, i.e., the propagation speed V of the shear wave.ac (x i ) and attenuation coefficient α ac (x i The first step in the process is to calculate the complex wave number k of the shear wave at that location. * Complex wavenumber k * From its actual part k r and the imaginary part k i constitute:

[0110] k * (x i )=k r (x i )+i·k i (x i );

[0111] Wherein, the real part k r The imaginary part k is related to the propagation speed. i Related to attenuation, the specific relationship is as follows:

[0112]

[0113] k i (x i )=α ac (x i );

[0114] In the formula, ω ac Let i be the known operating angular frequency of the shear wave, where i is the imaginary unit.

[0115] Subsequently, based on the viscoelastic theory of materials, the complex shear modulus G of the material is... * With complex wave number k * There exists a definite physical relationship between them. Through this relationship, the wave propagation characteristics can be mapped to the intrinsic mechanical properties of the material:

[0116]

[0117] Where ρ is the density of the paint sample, which can be input as a known parameter. Let k * Substituting the expression and expanding it, we can separate the real part of the complex shear modulus, i.e., the storage modulus G′, and the imaginary part, i.e., the loss modulus G″:

[0118]

[0119] In this embodiment, preferably, the energy storage modulus G is selected. ′As the final mechanical structure characteristic parameter. The reason is that G" directly represents the ability of the material to store elastic potential energy during deformation, which is a measure of the material's solid state characteristics, and thus can most directly reflect the strength of the mechanical network formed by the interaction of particles or polymers within the coating to resist deformation.

[0120] In parallel, the processing of the electrical impedance spectroscopy signal, the ultimate goal of which is to obtain a particle network characteristic parameter that can quantitatively characterize the connectivity of the conductive or semiconductive particle network within the coating. The process is as follows:

[0121] For the same position x i A set of complex impedance spectroscopy data Z * (ω ei ,x i ) covering multiple frequency points is collected. Due to the contribution of multiple factors such as solution, electrode interface, and particle network, it is difficult to directly interpret. Therefore, this step uses the equivalent circuit model fitting method to decouple these contributions. First, select an equivalent circuit model that can reasonably describe the physical and chemical processes of the coating system. As an preferred solution, a model containing solution resistance, network resistance, and interface capacitance elements can be used, for example, R s (R net ||CPE) model, whose total complex impedance expression is:

[0122]

[0123] Where, is the total complex impedance of the model; ω ei is the angular frequency of the impedance spectroscopy measurement; R s is the solution resistance in the equivalent circuit, representing the conductivity of the coating matrix itself; R net is the network resistance in the equivalent circuit, representing the resistance of the percolation network formed by the mutual contact of pigment and filler particles; CPE is a constant phase angle element used to describe the non-ideal capacitance behavior that usually exists, which is defined by parameters Y0 (a measure of capacitance size) and n.

[0124] Next, use numerical optimization algorithms such as nonlinear least squares to fit the experimental impedance spectroscopy data collected with the above model to determine a set of parameter values (R s , R net , Y0, n) that minimize the error between the model and the data.

[0125] In this embodiment, the network resistance R net is selected as the final particle network characteristic parameter from the set of parameters obtained from the fitting. The reason is that R netThis parameter is explicitly separated from the total impedance, and its value is directly related to the connectivity of the particle network: a well-connected, percolating particle network corresponds to a smaller R net value, and vice versa. Therefore, tracking R net as a function of position allows one to quantitatively characterize the formation and maturation of the particle network.

[0126] S6: Based on the changes of the mechanical structure characteristic parameters and the particle network characteristic parameters with position and temperature, the rheological properties of the paint sample are comprehensively characterized.

[0127] In this embodiment, based on the changes of the mechanical structure characteristic parameters and the particle network characteristic parameters with position and temperature, the rheological properties of the paint sample are comprehensively characterized, which is the final analysis and characterization step of the test method of the present application. The purpose of this step is to correlate the feature parameters extracted from different physical dimensions in the previous steps, thereby obtaining a deep understanding of the evolution mechanism of the internal microstructure of the paint beyond single-dimensional characterization.

[0128] Specifically, the aforementioned steps of the present method have produced two sets of core, synchronously acquired data sets. One is the mechanical structure characteristic parameter, preferably the storage modulus G', which changes with position x i and temperature T(x i ), and characterizes the construction process of the mechanical network inside the paint that resists deformation. The second is the particle network characteristic parameter, preferably the network resistance R net , which changes with the same position x i and temperature T(x i ), and characterizes the connectivity evolution process of the conductive percolation network formed by the pigment and filler particles inside the paint.

[0129] To achieve comprehensive characterization, the first step is to establish a unified analysis framework. The position coordinates x of all measurement points are converted to a unique structure evolution time t(x) using the following formula:

[0130]

[0131] where t(x) is the structure evolution time, x is the position coordinate, A o is the cross-sectional area of the channel of the dynamic evolution observation zone, and Q is the preset volumetric flow rate. Through this conversion, two key evolution curves can be obtained: the curve of the storage modulus as a function of time and temperature G'(t, T), and the curve of the network resistance as a function of time and temperature R net (t, T).

[0132] The core of comprehensive characterization lies in comparative and correlation analysis of these two evolution curves. In a specific embodiment, the normalized G'(t) and Rnet (t) plotted on the same time axis. By comparing the shape, slope and characteristic time of reaching steady state of the two curves, the intrinsic correlation between the mechanical network recovery and the particle network formation can be revealed.

[0133] For example, through this comprehensive characterization, it can be explicitly judged whether the evolution of the two network structures is out of sync. If the two curves are basically coincided on the time axis, it indicates that the enhancement of the mechanical network and the penetration of the particle network are highly coupled and synchronous processes. On the contrary, if the growth of one curve is significantly earlier or later than the other, it indicates that the evolution of the two microstructures is decoupled in dynamics, and there is a sequence.

[0134] In order to quantitatively describe this evolution out of sync, characteristic time parameters can be extracted from the two curves respectively. As a preferred scheme, the time required to reach half of its steady state value, i.e. the recovery half-life t 1 / 2,G ′ and By comparing the difference between the two characteristic time parameters , the lag or advance degree of the mechanical structure recovery relative to the particle network formation can be quantitatively characterized.

[0135] In addition, since the present application applies a preset temperature profile during the measurement, the comprehensive characterization inevitably contains the analysis of the temperature effect. By repeating the test under different preset temperature profiles, a series of temperature-dependent evolution curve families can be obtained. Further, the variation of the characteristic time parameters (such as t 1 / 2,G′ and ) with temperature can be analyzed. By plotting, for example, the Arrhenius plot of ln(1 / t 1 / 2 ) vs. 1 / T, the apparent activation energy controlling the mechanical network recovery and the particle network formation can be calculated respectively. By comparing the size of the two activation energies, the difference in temperature sensitivity of the two structural evolution processes can be revealed from the thermodynamic point of view.

[0136] In summary, the present step is not simply to present two independent sets of data, but to cross-compare and correlate them by placing them in a unified space-time and temperature framework, so as to realize the comprehensive characterization of the rheological properties of the coating. The final output is not a single rheological parameter, but a dynamic panoramic view of how the different microstructures inside the coating evolve over time, how they are related to each other, and how each responds to temperature after shear failure. This characterization provides a direct and mechanistic basis for in-depth understanding of the thixotropic mechanism of the coating and optimizing its leveling and sag resistance and other application properties.

[0137] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for testing rheological properties of paint based on a microfluidic chip, characterized in that, The method comprises the following steps: S1: providing a microfluidic chip, wherein a shear destruction zone and a dynamic evolution observation zone are integrated on the microfluidic chip in sequence, and a programmable micro-heater array, a micro-acoustic transducer array and a micro-electrode impedance spectrum array are integrated in the dynamic evolution observation zone along a flow direction of the dynamic evolution observation zone; S2: driving a coating sample to flow through the shear destruction zone and the dynamic evolution observation zone in sequence at a preset volume flow rate; S3: starting the programmable micro-heater array to establish a preset temperature distribution in the dynamic evolution observation zone; S4: while the coating sample flows through the dynamic evolution observation zone, synchronously collecting acoustic signals and electrical impedance spectrum signals at different positions through the micro-acoustic transducer array and the micro-electrode impedance spectrum array; S5: processing the acoustic signals into mechanical structure characteristic parameters of the coating sample, and processing the electrical impedance spectrum signals into particle network characteristic parameters of the coating sample; S6: comprehensively characterizing rheological properties of the coating sample based on changes of the mechanical structure characteristic parameters and the particle network characteristic parameters with positions and temperatures.

2. The method of claim 1, wherein, In the S1, the micro-acoustic transducer array and the micro-electrode impedance spectrum array are discretely arranged along the flow direction of the dynamic evolution observation zone.

3. The method of claim 1, wherein, In the S2, the preset volume flow rate, together with the width w d and the height h d of the shear destruction zone determines the wall shear rate experienced by the paint sample in this zone, which relationship is satisfied by: Wherein, for the wall shear rate; Q is the preset volume flow rate; w d w is the width of the shear fracture zone; h d is the height of the shear damage zone.

4. The method of claim 1, wherein, The method further comprises: converting a position coordinate x in the dynamic evolution observation zone into a structure evolution time t(x) according to the following formula: Wherein, t(x) is the structure evolution time; x is the position coordinate in the dynamic evolution observation zone; A o A is the cross-sectional area of the channel for the dynamic evolution observation zone; Q is the preset volume flow rate.

5. The method of claim 1, wherein, In the S5, the mechanical structure characteristic parameter is a storage modulus.

6. The method of claim 1, wherein, In the S5, the particle network characteristic parameter is a network resistance obtained by fitting the electrical impedance spectrum signals through an equivalent circuit model.

7. The method of claim 1, wherein, In the S3, the preset temperature distribution is at least one of a temperature gradient distribution, a step temperature distribution or a programmed dynamic temperature distribution.

8. The method of claim 1, wherein, The S6 comprises: determining evolution asynchrony of the recovery processes of the mechanical structure characteristic parameters and the particle network characteristic parameters by correlatively analyzing evolution processes of the mechanical structure characteristic parameters and the particle network characteristic parameters with the structure evolution time.

9. The method of claim 1, wherein, In the S4, collecting the acoustic signals specifically comprises: measuring a propagation speed and an attenuation coefficient of a shear wave in the coating sample through the micro-acoustic transducer array.

10. The method of claim 1, wherein, In the S4, collecting the electrical impedance spectrum signals specifically comprises: measuring a complex impedance spectrum of the coating sample in a preset frequency range through the micro-electrode impedance spectrum array.

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