A nano-particle filled dielectric equivalent capacitance testing system and method based on a triboelectric nanogenerator

Abstract

The present invention relates to a nanoparticle-filled dielectric equivalent capacitance test system and method based on a triboelectric nanogenerator, belonging to the field of micro-nano sensing. The system includes a TENG, a test circuit, and a nanoparticle capacitance module to be measured; the nanoparticle capacitance module to be measured includes multiple C NP1 and multiple C NPi is the equivalent capacitance of the particle aggregates, which are connected in parallel according to different types n, and then connected in series according to different numbers N of the same type; the method includes a TENG internal capacitance spectrum analysis method and a nanoparticle equivalent capacitance identification method. The present invention can provide a nanoparticle equivalent capacitance test circuit with a simple structure and quickly and effectively obtain the dispersion characterization of nanoparticles.

Description

Technical Field

[0001] The present invention belongs to the field of micro-nano sensing, and relates to a nano-particle filled dielectric equivalent capacitance test system and method based on a triboelectric nanogenerator. Background Art

[0002] With the continuous rapid development and expanding application fields of nanofluids and nanocomposites, the applications of nanoparticles have played the advantages of their own characteristics in the fields of electronics, heat transfer, tribology, medicine, environmental science, etc. The characterization of their dispersibility plays a crucial role in nanofluids and nanocomposites. According to the electric double layer theory, the distribution of nanoparticles in a solution can be regarded as a series of capacitance aggregations. However, the research on measuring the equivalent capacitance using the electric double layer model of nanoparticles to characterize the particle dispersibility has not been carried out yet. Therefore, it is necessary to explore the test circuit and method for the equivalent capacitance of nanoparticles.

[0003] Currently, the tests for nanoparticle dispersibility mainly adopt indirect methods such as the potentiometric method, transmission ratio method, and direct methods such as dynamic light scattering method and electron microscope scanning method. These indirect methods mainly evaluate by measuring the surface potential magnitude of particles or absorption spectra, and are only suitable for dilute solutions, with certain limitations. In addition, the direct measurement methods also have disadvantages such as poor result reproducibility and great destructiveness for particle size distribution characterization. The test of the equivalent circuit of nanoparticles filled in a dielectric based on a triboelectric nanogenerator has the advantages of simple structure, easy operation, short sample test time, high test efficiency, high adaptability, good result reproducibility, etc. It can also be further used to analyze various interaction potentials of nanoparticles in the nanofluid system, providing key parameters for quantitatively calculating multi-scale processes such as colloidal phase change, fluid heat transfer, dynamic wetting, and self-assembly. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a nano-particle filled dielectric equivalent capacitance test system and method based on a triboelectric nanogenerator, which uses the TENG as a constant charge source, stably outputs signals and performs spectral parameter analysis based on the output voltage and transferred charge quantity signals, and then uses an optimization algorithm to realize the identification of the equivalent capacitance of nanoparticles.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a nano-particle filled dielectric equivalent capacitance test system based on a triboelectric nanogenerator, including: TENG, electrometer, and optimal load matching impedance R L ;

[0007] The TENG is a tubular liquid-solid friction nanogenerator, the main body of which includes a circular hose fixed on a base and a conductive electrode attached to the circular hose. Deionized water is injected into the circular hose, and nanoparticles are distributed in the deionized water. The TENG is used as a constant charge power source to stably output a constant charge amount, which is easier to couple with the equivalent capacitance of the nanoparticles.

[0008] In the deionized water, the same type of nanoparticles form multiple aggregates, each of which has an equivalent capacitance C NPi ; The equivalent capacitances between the same nanoparticle aggregates are connected in series, while the equivalent capacitances between different nanoparticles are connected in parallel;

[0009] The geometric equivalent capacitance C0, the internal resistance R0 of the annular hose and the equivalent capacitance C0 of each agglomerate Npi The equivalent internal capacitance C of TENG in ;

[0010] The electrometer is used to measure the transferred charge of TENG and the L The open circuit voltage after series connection is calculated, and the equivalent internal capacitance C of TENG is calculated based on the measured open circuit voltage and transferred charge. in .

[0011] Furthermore, the TENG is a tubular liquid-solid friction nanogenerator, the main body of which includes a circular hose, a liquid friction medium, nanoparticles and a conductive electrode terminal; the geometric equivalent capacitance of the circular hose is C0, measured by a capacitance resistance meter. The internal resistance of the circular hose is R0, which is generally matched with the optimal load matching impedance R L Equal, determined in advance through experimental testing with TENG power supply.

[0012] Furthermore, the electrical connection method of the tubular liquid-solid friction nanogenerator to test the nanofluid sample is that the two ends of the conductive electrode of the tubular friction nanogenerator body are connected in parallel to a voltmeter and in series to an ammeter via an external optimal matching impedance.

[0013] Further, the material of the annular hose includes, but is not limited to, the following materials: perfluoroethylene propylene, polychloroprene, polyisobutylene, polyoxymethylene, polyethylene adipate, polydiallyl phthalate, polyethanol butyral, styrene propylene copolymer, polyamide, polyimide, melamine formaldehyde, polycarbonate, chloroprene rubber, natural rubber, cellulose, ethyl cellulose, cellulose acetate;

[0014] Further, the selection of the liquid friction medium includes but is not limited to the following materials: deionized water, ethyl acetate, n-propyl acetate, sodium hydroxide solution, potassium hydroxide solution, sodium carbonate solution, sodium bicarbonate solution, potassium carbonate solution, ammonia water, mineral insulating oil, vegetable insulating oil, synthetic grease oil, furfural, etc.;

[0015] Further, the selection of the nanoparticles includes but is not limited to any one of the following materials: silica, zinc oxide, aluminum nitride, aluminum oxide, barium calcium zirconate titanate, barium titanate, bismuth oxide, bismuth tungstate, calcium carbonate, aluminum hydroxide, carbon nanotubes, cerium oxide, cobalt manganese ferrite, copper oxide, calcium copper titanate, etc.

[0016] Further, the material of the conductive electrode terminal is selected from metals or alloys; wherein the metals include copper, titanium, chromium, selenium, iron, manganese, molybdenum, tungsten or vanadium; the alloys include tin alloy, cadmium alloy, bismuth alloy, indium alloy, gallium alloy, tungsten alloy, molybdenum alloy, niobium alloy or tantalum alloy.

[0017] On the other hand, the present invention provides a method for testing the equivalent capacitance of a nanoparticle-filled dielectric based on a triboelectric nanogenerator, and the method specifically includes the following steps:

[0018] S1: Establish a nanoparticle equivalent capacitance model through an electric double layer model, and introduce a tubular liquid-solid triboelectric nanogenerator TENG as a constant charge power source to achieve stable output of voltage and transferred charge amount;

[0019] S2: Establish a capacitance circuit model for measuring and evaluating the equivalent capacitance of nanoparticles by considering the circuit parameters of the TENG structure itself and the nanofluid parameters;

[0020] S3: TENG internal capacitance spectrum analysis method: Obtain the frequency domain information of the internal capacitance through the spectrum of the output signal;

[0021] S4: Identification method of equivalent capacitance in nanofluid: Use quantum genetic algorithm and local optimization Levenberg-Marquardt algorithm for parameter identification, so as to calculate the equivalent capacitance value of the particles.

[0022] Further, in step S3, the TENG internal capacitance spectrum analysis method is: simultaneously collect the time series signals of the ammeter and the voltmeter, record the time domain waveforms of Q l and V c , and use the fast Fourier method to calculate the spectrum information of the signal, that is, C in in the frequency domain; wherein, Q l and V c are the transferred charge amount and the voltage across the internal capacitance C in respectively, and C in is the equivalent internal capacitance of the TENG.

[0023] Further, in step S4, the identification method of the equivalent capacitance in the nanofluid is as follows: The known parameters are C0, R0, and the experimental measurement value of C in , and the parameters to be determined are C NPi and N i . Therefore, the parameter identification of the nanofluid is transformed into a nonlinear programming problem, and an algorithm that first performs global optimization and then local optimization is adopted to find the optimal solution. The goal of the solution is to minimize the error between the calculated value and the experimental value of |C in |. Finally, the distribution of the equivalent capacitance values of the nanoparticles and the number of types of particle aggregates in the sample are obtained. Among them, C NPi is the equivalent capacitance of the i-th type of aggregate; N i is the series order of the equivalent capacitance of the i-th type of aggregate.

[0024] Further, step S4 specifically includes: Using the least squares method to establish an optimization objective function f, that is, to ensure that the error between the actual measurement value and the model calculation value of the internal capacitance |C in | is minimized;

[0025]

[0026] Among them, C′ Actual and C″ A ″ ctual are the actual measurement values of the real part and the imaginary part respectively, and C′ Model and C″ M ″ odel are the model calculation values of the real part and the imaginary part respectively; By iteratively calculating C′ Model and C″ M ″ odel according to the real part C′ and the imaginary part C" of the complex capacitance under each branch, when the optimization objective function f approaches the minimum value of 0 under a certain accuracy, N i and C NPi are the optimal solution sets that meet the requirements;

[0027] Regarding the high-dimensional nonlinearity of the objective function f, the solution process has high requirements for the selection of the initial value of the algorithm and its convergence ability. To avoid the problems of serious initial value dependence, easy to fall into local convergence, and low computational efficiency or search accuracy when a single algorithm performs multi-parameter nonlinear optimization, the present invention adopts a fusion algorithm combining the genetic algorithm and the Levenberg-Marquardt algorithm to solve the optimization model of parameter identification; First, use the genetic algorithm for global optimization to determine the circuit order n and initially tighten the feasible region, and then use the Levenberg-Marquardt algorithm to achieve precise local optimization within the feasible region given by the genetic algorithm to obtain the parameter identification result of C NP .

[0028] The beneficial effects of the present invention are as follows: The circuit of the test system of the present invention has the advantages of simple structure, easy operation, shortening the sample test time, and improving the test efficiency. Furthermore, detailed information on the equivalent capacitance of particles in nanofluids can be obtained, which helps to provide key parameters for the multi-scale processes of nanofluids in multi-physical fields.

[0029] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. Brief Description of the Drawings

[0030] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0031] Figure 1 is the test circuit for the internal capacitance C in of TENG;

[0032] Figure 2 is the equivalent capacitance circuit model for identifying nanofluid parameters;

[0033] Figure 3 is the structure and working schematic diagram of a tubular liquid-solid triboelectric nanogenerator;

[0034] Figure 4 is the internal capacitance calculation result of the measurement circuit model;

[0035] Figure 5 is the calculation method for the equivalent capacitance of nanoparticles in nanofluids;

[0036] Figure 6 is the result of the equivalent capacitance of nanoparticles; Detailed Embodiments

[0037] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention schematically, and the following embodiments and the features in the embodiments can be combined with each other without conflict.

[0038] Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams rather than actual diagrams, and should not be construed as a limitation on the present invention; in order to better illustrate the embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0039] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the attached drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the attached drawings are only for illustrative purposes and should not be construed as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0040] Please refer to Figures 1 to 6 , the present invention provides a nano-particle filled medium equivalent capacitance test system based on a tubular liquid-solid triboelectric nanogenerator. The present invention can be specifically applied to measuring the equivalent capacitance of nano-particles in nanofluids, and further used to analyze various interaction potentials of nano-particles in the nanofluid system, providing key parameters for quantitatively calculating multi-scale processes such as colloidal phase transition, fluid heat transfer, dynamic wetting, and self-assembly. The core idea of the present invention is to establish a capacitance branch of nano-particles through an electric double layer model, and introduce TENG as a constant charge power source to achieve stable voltage and transferred charge output. A capacitance circuit model for measuring and evaluating the equivalent capacitance of nano-particles is established by considering the circuit parameters of the structure itself and the nanofluid parameters. The frequency-domain information of the internal capacitance is obtained from the spectrum of the output signal, and then the quantum genetic algorithm and the local optimization Levenberg-Marquardt algorithm are used for parameter identification to calculate the equivalent capacitance value of the particles. The applicable range of nano-particles in the test nanofluid is wide, including but not limited to: various conductive types, semi-conductive types, insulating types, and nanofluids under other liquid media.

[0041] Figure 1 The test circuit showing the internal capacitance C in of TENG. As Figure 1 shown, in the equivalent circuit model of TENG and nanofluid, C in is mainly calculated according to the spectral information of the measured V c and Q l , and varies with different nanofluid samples.

[0042] Figure 2Shown is an equivalent capacitance circuit model for identifying nanofluid parameters. C0 is measured by a capacitance resistor meter and is approximately 20 mF. Similarly, the maximum matching load impedance values of 10 MΩ are taken for both R0 and R L in the circuit. As Figure 2 shown, for the C NP part of the nanofluid, the series order of the equivalent capacitances of different types of aggregates and the equivalent capacitance of the same type of aggregates is mainly considered. According to the refined circuit model, assuming that the total volume of the nanoparticles remains constant, the relationship between different types of capacitances satisfies Equation (1).

[0043]

[0044] It can be seen from the circuit that the series circuit of C NPi corresponds to the equivalent capacitance of different types of aggregates. For the equivalent circuit model with N branches, the port admittance value is

[0045]

[0046] Furthermore, according to the definition of the complex capacitance of the dielectric, Equation (3) can be obtained as

[0047]

[0048] According to Equation (3), the expressions for the real part C′ and the imaginary part C″ of the complex capacitance under each branch can be obtained as shown in Equations (4) and (5) respectively.

[0049]

[0050] Finally, according to the spectral information of the measured signal, the modulus value of the internal capacitance value C in is

[0051]

[0052] In Equations (2)-(6), ω is the frequency, C in is the internal capacitance of the TENG, C0 is the geometric equivalent capacitance of the pipe material, R0 is the internal resistance of the pipe material, and C NPi is the equivalent capacitance of the i-th type of aggregate. N i is the series order of the equivalent capacitance of the i-th type of aggregate, and obviously it is proportional to the relative content of the i-th type of aggregate. It can be found from Equations (4)-(6) that the currently known parameters are the experimental measurement values of C0, R0, and C in , and the undetermined parameters are C NPi and N i , so the parameter identification of the nanofluid is transformed into a non-linear programming problem, and an algorithm of first global and then local is adopted for optimization. The goal of the solution is to minimize the error between the calculated value and the experimental value of |C in |, and finally calculate C NPand N i .

[0053] Figure 3 The structure and working principle of the liquid-solid friction nanogenerator as a constant charge source are shown. As shown in the left figure, the liquid-solid friction nanogenerator consists of three parts: FEP tube, liquid medium, and copper electrode. The FEP tube is selected as an electronegative material with good flexibility and hydrophobicity. The tubular liquid-solid friction nanogenerator is installed on a base to fix the tubular liquid-solid friction nanogenerator. Here, we designed 7 pairs of electrodes, the adjacent electrodes are different positive and negative electrodes, and the spacing between adjacent electrodes can be optimized and adjusted. In addition, the entire power generation process of TENG is described in detail in the right figure. In the initial state, after the FEP inner tube and the injected liquid medium are fully in contact, according to the triboelectric series, it can be seen that the inner surface of the FEP tube is negatively charged and the liquid medium is positively charged. According to the principle of charge conservation, the number of negative charges on the inner wall of the FEP tube is equal to the number of positive charges in the liquid. When the liquid medium moves to the right, asymmetric charges will be generated between the conductive electrodes on the left and right sides to form a potential difference, thereby driving electrons to flow from one electrode to another through the external circuit, and a current signal will be generated at this time. When the liquid is located in the middle of the left and right electrodes, the left and right electrodes are balanced and no potential difference is generated. Finally, when the liquid flows to the right electrode and completely covers the right electrode, the current direction is reversed. At this point, a complete power generation cycle of TENG is completed.

[0054] Figure 4 The internal capacitance calculation result of the measurement circuit. According to the voltage across the capacitor and the transferred charge signal calculated by TENG output, the C of different samples is calculated through the spectrum information. in . And C in The data was processed by mean filtering to obtain a clear difference signal. The figure shows the change of internal capacitance component with frequency at different ultrasound times. It can be found that there is a maximum value at the base frequency and it gradually decreases with frequency. As the ultrasound time increases, the internal capacitance component generally shows a downward trend. The frequency in the figure is an electrical parameter calculated by fast Fourier transform.

[0055] Figure 5 is the parameter identification method of the equivalent capacitance of nanoparticles in nanofluids. From the above equations (4) and (5), it can be seen that C in The real and imaginary parts of C NPi and N i Therefore, this paper selects C inThe C′ and C″ of the data are used as reference data for parameter identification. It is also explained that the essence of parameter identification of this circuit model is a multi-parameter non-linear fitting problem, and then this problem can be converted into a corresponding multi-parameter non-linear optimization model for solution. For this problem, in this embodiment, the least square idea is adopted to establish an optimization objective function f, as shown in Equation (7), that is, to ensure that the error between the actual measured value and the model calculated value of the internal capacitance |C in | is minimized.

[0056]

[0057] In the formula, C′ Actual and C″ Actual are the actual measured values of the real part and the imaginary part respectively, and C′ Model and C″ Model are the model calculated values of the real part and the imaginary part respectively. By iteratively calculating C′ Model and C″ M ″ odel according to the above formulas (4) and (5), when the solution of the objective function f approaches the minimum value of 0 under the condition of meeting a certain accuracy, N i and C NPi are the optimal solution sets that meet the requirements. For the high-dimensional non-linear function of the optimization objective, the solution process has high requirements for the selection of the initial value of the algorithm and the convergence ability. To avoid the problems of serious initial value dependence, easy to fall into local convergence, and low calculation efficiency or search accuracy when a single algorithm is used for multi-parameter non-linear optimization, therefore, in this embodiment, a fusion algorithm combining genetic algorithm and Levenberg-Marquardt algorithm is used to solve the optimization model of parameter identification. First, the genetic algorithm is used for global optimization to determine the circuit order n and initially tighten the feasible region, and then the Levenberg-Marquardt algorithm is used to achieve accurate local optimization within the feasible region given by the genetic algorithm to obtain the parameter identification result of C NP .

[0058] Figure 6 are the measurement results of the equivalent capacitance at different ultrasonic times. After analyzing the spectral signals of the output voltage and transferred charge of the measured sample according to the above process, the C NP values of different samples can be obtained. In this example, SiO2 nanofluid with a volume fraction of 0.01% is used for electrical measurement at different ultrasonic times. By analyzing the spectral information of the output signal and introducing an algorithm for parameter identification. Within 0 to 60 minutes of the experiment, as the ultrasonic time increases, the distribution range of the equivalent capacitance value of the nanoparticles gradually shifts to the left and the value becomes smaller.

[0059] In this embodiment, the preparation methods of each module are also provided. First, the specific manufacturing method of the frequency-variable liquid-solid triboelectric nanogenerator for measuring the equivalent capacitance of nanoparticles is described. The hose of the TENG is selected as an FEP tube with an inner diameter of 4 mm and a thickness of 4 mm. First, it is cut into lengths for 7 pairs of electrodes, and 7 pairs of conductive copper tapes (5 cm) with adhesiveness on the back are pasted onto the surface of the FEP tube in an orderly decreasing manner according to the interval ratio from 1 to 8, forming a frequency-variable multi-gate structure. The electrode length can be adjusted to the required length (2, 3, 4, 5, and 6 cm), and it is only necessary to ensure that the length of the liquid medium is the same as that of the copper electrode. In addition, the TENG is installed and fixed on the base of the acrylic plate, where adjacent copper electrodes are opposite electrodes, and each section of the copper electrode is cross-connected.

[0060] Preparation of nanofluid: First, a certain amount of deionized water and a specific amount of nanoparticles are mixed and added to a beaker to prepare nanoparticle solutions with different volume fractions. Then, ultrasonic dispersion is performed for different times using an ultrasonic cell disruptor, and finally, nanofluid samples with different dispersibilities are obtained.

[0061] Measurement of electrical output: The open-circuit voltage and transferred charge of the TENG device are measured by a Keithley 6514 system electrometer. The measurement signal is input under a high-speed data acquisition system controlled by LabView.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A nanoparticle-filled dielectric equivalent capacitance testing system based on a triboelectric nanogenerator, characterized in that: The system includes: a TENG, an electrometer, and an optimal load matching impedance R L ;; The TENG is a tubular liquid-solid friction nanogenerator. The main body of the generator includes an annular flexible hose fixed on a base and a conductive electrode pasted on the annular flexible hose. Deionized water is injected into the annular flexible hose, and nanoparticles are distributed in the deionized water. The TENG serves as a constant charge power source for stably outputting a constant amount of charge and is coupled with the equivalent capacitance of the nanoparticles. In the deionized water, nanoparticles of the same type form multiple aggregates, and each aggregate has an equivalent capacitance C NPi ; the equivalent capacitances between the same nanoparticle aggregates are in series, and the equivalent capacitances between different nanoparticles are in parallel; The geometric equivalent capacitance C0 of the circular hose, the internal resistance R0, and the equivalent capacitance C of each aggregate Npi constitute the equivalent internal capacitance C of the TENG in ; The electrometer is used to measure the transferred charge of the TENG and the open-circuit voltage after the TENG and R L are connected in series, and calculate the equivalent internal capacitance C of the TENG according to the measured open-circuit voltage and transferred charge in .

2. A method for testing the equivalent capacitance of a nanoparticle-filled dielectric based on a triboelectric nanogenerator, characterized in that: The method specifically includes the following steps: S1: Establish an equivalent capacitance model of nanoparticles through the electric double layer model, and introduce a tubular liquid-solid triboelectric nanogenerator (TENG) as a constant charge power source to achieve stable voltage and transferred charge output; the tubular liquid-solid triboelectric nanogenerator (TENG) includes an annular flexible tube fixed on a base and a conductive electrode pasted on the annular flexible tube, deionized water is injected into the annular flexible tube, and nanoparticles are distributed in the deionized water; the equivalent capacitance model of the nanoparticles includes that nanoparticles of the same type form multiple aggregates, and each aggregate has an equivalent capacitance C NPi , the equivalent capacitances between the same nanoparticle aggregates are in series, and the equivalent capacitances between different nanoparticles are in parallel, finally forming the equivalent capacitance C of the nanoparticles NP ; S2: Establish a capacitive circuit model for measuring and evaluating the equivalent capacitance of nanoparticles by considering the circuit parameters of the TENG in combination with the nanofluid parameters; the capacitive circuit model includes the optimal load matching impedance R L , the geometric equivalent capacitance C0 of the circular hose, the internal resistance R0 of the circular hose, and the equivalent capacitance C of the nanoparticles NP , where C0, R0, and C NP are connected in parallel and then connected in series with R L ; the geometric equivalent capacitance C0 of the circular hose, the internal resistance R0, and the equivalent capacitance C of each aggregate NPi constitute the equivalent internal capacitance C in of the TENG: where ω is the frequency, N i is the series order of the equivalent capacitance of the i-th type of aggregate, and n is the number of types of nanoparticle aggregates; Express the real part of C in as: Express the imaginary part of C in as follows: S3: TENG internal capacitance spectrum analysis method: simultaneously collect the timing signals of the ammeter and the voltmeter, and record Q l and V c time-domain waveforms, and use the fast Fourier method to calculate the spectrum information of the signals, that is, C in in the frequency domain; where Q l and V c are the transferred charge amount and the equivalent internal capacitance C in at both ends of respectively; S4: Identification method for equivalent capacitance in nanofluid: Given parameters C0, R0 and the experimental measurement value of C in , and the parameters to be determined are C NPi and N i . Therefore, the parameter identification of nanofluid is transformed into a non-linear programming problem, and an algorithm that first performs global optimization and then local optimization is adopted to find the optimal solution. The goal of the solution is to minimize the error between the calculated value and the experimental value of |C in |. Finally, the distribution of the equivalent capacitance values of nanoparticles and the number of types of particle aggregates in the sample are obtained; the quantum genetic algorithm and the local optimization Levenberg-Marquardt algorithm are used for parameter identification, so as to calculate the equivalent capacitance values of the particles.

3. The method according to claim 2, wherein: Step S4 specifically includes: establishing an optimization objective function f using the least squares method, that is, ensuring that the error between the actual measured value and the model calculated value of the internal capacitance |C in | is minimized; where C′ Actual and C″ Actual are the actual measured values of the real part C′ and the imaginary part C″ respectively, and C′ Model and C″ Model are the model calculated values of the real part C′ and the imaginary part C″ respectively; by iteratively calculating C′ Model and C″ Model according to the real part C′ and the imaginary part C″ of the complex capacitance under each branch, when the objective function f approaches the minimum value of 0 under the condition of meeting a certain accuracy, N i and C NPi are the optimal solution sets that meet the requirements; For the high-dimensional non-linearity of the objective function f, a fusion algorithm combining the genetic algorithm and the Levenberg-Marquardt algorithm is used to solve the optimization model of parameter identification. First, the genetic algorithm is used for global optimization to determine the circuit order n and preliminarily tighten the feasible region. Then, the Levenberg-Marquardt algorithm is used to achieve precise local optimization within the feasible region given by the genetic algorithm to obtain the parameter identification result of C NP .

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