Method for structure optimization of t3c2tx-mxene materials

Abstract

The application provides a structure optimization method and system of T3C2Tx-MXene material, the method comprises the following steps: obtaining a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model is a multilayer structure, and layer thickness attributes and interlayer thickness attributes of the T3C2Tx-MXene material particle model are selected; a four-terminal network equivalent method is used as an objective function of a particle swarm algorithm, the selected layer thickness attributes and interlayer thickness attributes are applied to the objective function, and the optimization ranges of layer thickness d1 and interlayer thickness d2 in the objective function are respectively set, so that the optimal reflectivity under a specific frequency range is obtained, and the T3C2Tx-Mxene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity are obtained. The method can effectively improve the wave absorption performance of the multilayer Ti3C2Tx material by optimizing two-phase media.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of structure optimization of T3C2Tx-MXene wave-absorbing materials, and particularly relates to a structure optimization method of T3C2Tx-MXene materials. BACKGROUND

[0002] Ti3C2Tx-MXene is a new type of graphene-like two-dimensional material, and its special structure and physical and chemical properties make it show excellent performance and potential application prospect in the field of electromagnetic wave absorbing materials.

[0003] With the continuous development of information technology, wireless electronic devices are becoming lighter and more portable, and the harm of electromagnetic waves is becoming more and more significant, becoming a new source of pollution. The electromagnetic interference generated by electronic devices will have harmful effects on equipment, human body and environment. The main countermeasure against the harm of electromagnetic waves is to develop high-performance microwave absorbing and electromagnetic shielding materials. Therefore, in recent years, the research on electromagnetic wave radiation absorbing and interference materials has been increasing. Developing wave-absorbing materials with light quality, thin thickness, wide absorbing frequency band and good environmental stability performance has become the main requirement for wave-absorbing materials. Among them, the traditional wave-absorbing materials have the problems of large density and narrow absorbing frequency width, which limit their application. For example, among many traditional wave-absorbing materials, their wave-absorbing performance, wave-transmitting property and wave-absorbing frequency width cannot well meet the current development requirements. At present, MXene, as a new type of two-dimensional nanomaterial, has attracted widespread attention and has been applied to the field of electromagnetic wave absorbing and shielding. Among MXene materials, the two-dimensional layered Ti3C2Tx-MXene is the most widely used, which has been applied in the fields of energy storage, catalysis, sensors, shielding and wave-absorbing due to its excellent electrochemical performance, mechanical performance, magnetic performance and thermal conductivity performance. In the field of microwave wave-absorbing, Ti3C2Tx has high conductivity and dielectric performance due to its two-dimensional layered structure, which can cause conductive loss and dielectric loss of electromagnetic waves in the material, thereby having good microwave absorbing performance. However, the high conductivity of MXene leads to high interface reflection and poor impedance matching. Therefore, in order to improve the impedance matching and electromagnetic attenuation capability of the material, the wave-absorbing material can be optimized and designed through interlayer structure optimization method, thereby better studying the wave-absorbing problem of multilayer composite board.

[0004] In the current research system, in the research on the wave-absorbing optimization problem of Ti3C2Tx-MXene materials, most of them are to optimize the design of Ti3C2Tx-MXene materials in the direction of material wave-absorbing. For example, Fe3O4 magnetic material is introduced into Ti3C2Tx-MXene material to form a composite material to optimize the impedance matching, so as to improve the wave-absorbing performance. There is still a lot of exploration space in the structure wave-absorbing optimization of Ti3C2Tx-MXene materials. SUMMARY

[0005] Based on this, the purpose of this invention is to propose a structural optimization method for T3C2Tx-MXene materials, so as to improve the microwave absorption performance of multilayer Ti3C2Tx-MXene materials through a novel structural optimization method.

[0006] This invention proposes a method for structural optimization of T3C2Tx-MXene materials, the method comprising:

[0007] Obtain a T3C2Tx-MXene material particle model, wherein the T3C2Tx-MXene material particle model is a multilayer structure, and select the layer thickness attribute and interlayer thickness attribute of the T3C2Tx-MXene material particle model;

[0008] The four-terminal network equivalent method is used as the objective function of the particle swarm optimization algorithm. The selected layer thickness attribute and the interlayer thickness attribute are applied to the objective function. The optimization range of layer thickness d1 and interlayer thickness d2 in the objective function are set respectively to obtain the optimal reflectivity in a specific frequency range. The T3C2Tx-Mxene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity are then obtained.

[0009] In summary, by combining the particle swarm optimization (PSO) algorithm with the four-terminal network equivalent method and applying it to the optimization of microwave absorption in a multilayer Ti3C2Tx-MXene structure, and based on experimental characterization of the material structure, the number of material layers and interlayer thickness were determined. The four-terminal network equivalent method was used as the objective function of the PSO algorithm. Initial parameter factors were substituted into the algorithm program, and after the maximum number of iterations, the optimal reflectivity within the given frequency range was obtained. The corresponding Ti3C2Tx-MXene material thickness d1 and interlayer thickness d2 at this optimal reflectivity were also determined. The PSO algorithm effectively optimizes the reflectivity of multilayer Ti3C2Tx-MXene microwave absorbing materials. Under the condition of the material's electromagnetic parameters, its microwave absorption performance was calculated. Furthermore, based on the four-terminal network equivalent method, the microwave absorption performance of the material was optimized by changing its microstructure.

[0010] In a preferred embodiment of the present invention, the step of using the four-terminal network equivalent method as the objective function of the particle swarm optimization algorithm, applying the selected layer thickness attribute and the interlayer thickness attribute to the objective function, and setting the optimization range of layer thickness d1 and interlayer thickness d2 in the objective function respectively to obtain the optimal reflectivity in a specific frequency range, and obtaining the T3C2Tx-Mxene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity includes:

[0011] The layer thickness d1 and the interlayer thickness d2 of the material are taken as optimization variables to divide the T3C2Tx-Mxene material into a plurality of particles in the optimization ranges corresponding to the layer thickness d1 and the interlayer thickness d2 respectively, and the objective function R is defined according to the four-terminal network equivalent method:

[0012]

[0013] wherein fitness() represents the fitness function, D min ≤d1, d2≤D max , D min and D max are the minimum and maximum values of d1 and d2 respectively.

[0014] In the preferred embodiment of the present application, the four-terminal network equivalent method is taken as the objective function of the particle swarm algorithm, and the selected layer thickness attribute and interlayer thickness attribute are applied to the objective function, and the optimization ranges of the layer thickness d1 and the interlayer thickness d2 in the objective function are set respectively to obtain the optimal reflectivity in a specific frequency range, and the step of obtaining the thickness d1 and the interlayer thickness d2 of the T3C2Tx-Mxene material corresponding to the optimal reflectivity further comprises:

[0015] The parameters d1 and d2 in the optimization model are fitted and optimized by using the particle swarm algorithm, and various parameter factors of the particle swarm algorithm are initialized, the dimension is set to 2, the particle number is set to 20, the dynamic parameter 1, and the learning factors c1 and c2 are [0.5, 2.5];

[0016] The positions of 20 particles are initialized, and the velocities of 20 particles are initialized, the position data of the particles are substituted into the objective function R to calculate the self-adaptation value of each particle, and the self-optimal position of each particle is obtained based on the self-adaptation value, and the global optimal position is obtained according to the self-optimal position;

[0017] The particle positions in the particle swarm are constantly updated and solved from the initial position coordinates by using the iterative algorithm, and it is judged whether the current iteration number reaches the preset maximum cycle number;

[0018] If the current iteration number does not reach the preset maximum cycle number, the self-adaptation value of each particle is repeatedly calculated;

[0019] If the current iteration number does not reach the preset maximum cycle number, the global optimal position of the particle is recorded, and the cycle is ended.

[0020] In the preferred embodiment of the present application, the positions of the 20 particles are initialized, and the velocities of the 20 particles are initialized, the position data of the particles are substituted into the objective function R to calculate the self-adaptation value of each particle, and the self-optimal position of each particle is obtained based on the self-adaptation value, and the step of obtaining the global optimal position according to the self-optimal position comprises:

[0021] The velocity of the particle is calculated according to the following formula:

[0022]

[0023] wherein v i,d represents the velocity of the i-th particle; x i，d represents the position of the i-th particle, v （i-1）,d represents the velocity of the i-1-th particle, represents the self-optimal position of the i-th particle, represents the global optimal position;

[0024] The position of the particle is calculated according to the following formula:

[0025]

[0026] wherein x （i-1），d represents the position of the i-th particle.

[0027] In the preferred embodiment of the present application, the step of continuously updating and solving the particle position in the particle swarm from the initial position coordinates by using the iterative algorithm comprises:

[0028] Each layer of the T3C2Tx-MXene material is regarded as a four-terminal network, and n dielectric layers are sequentially stacked, which is equivalent to the cascade of n four-terminal networks, as shown in the following formula:

[0029]

[0030] wherein E, F, G and H are parameters in the transfer matrix in the equivalent calculation process, the transfer matrix parameters of the i-th equivalent network are E i , F i , G i , H i , ch() represents cosh(), that is, the hyperbolic cosine function, sh() represents sinh(), that is, the hyperbolic sine function, and λ0 is the wavelength of an electromagnetic wave. i d is the thickness of the i-th layer, ε0 is the relative permittivity of free space, µ0 is the relative permeability of free space, ε i represents the relative permittivity of the i-th layer, µ i represents the relative permeability of the i-th layer, and γ idenotes the transmission factor (also called phase factor) of the i-th layer, Z i denotes the wave impedance of the i-th layer, j denotes the imaginary part of a complex number;

[0031] The optimal reflectivity is calculated according to the following formula:

[0032]

[0033] wherein RL denotes the optimal reflectivity, and Z0 is the intrinsic impedance of free space.

[0034] Another aspect of the present application also provides a structure optimization system of T3C2Tx-MXene material, the system comprises:

[0035] The optimization variable locking module is configured to obtain a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model is a multi-layer structure, and layer thickness attributes and interlayer thickness attributes of the T3C2Tx-MXene material particle model are selected.

[0036] The structure optimization execution module is configured to use a four-terminal network equivalent method as an objective function of a particle swarm algorithm, apply the selected layer thickness attributes and interlayer thickness attributes to the objective function, and set optimization ranges of layer thickness d1 and interlayer thickness d2 in the objective function, respectively, to obtain an optimal reflectivity in a specific frequency range, and obtain T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity.

[0037] In a preferred embodiment of the present application, the structure optimization execution module comprises:

[0038] The objective function construction unit is configured to use layer thickness d1 and interlayer thickness d2 of the material as optimization variables, divide the T3C2Tx-MXene material into a plurality of particles in the optimization ranges corresponding to layer thickness d1 and interlayer thickness d2, respectively, and define the objective function R according to the four-terminal network equivalent method as:

[0039]

[0040] wherein fitness() denotes a fitness function, D min ≤d1、d2≤D max , D min and D max are minimum and maximum values of d1 and d2, respectively.

[0041] In a preferred embodiment of the present application, the structure optimization execution module further comprises:

[0042] The initialization unit is used to fit the parameters d1 and d2 in the optimization model using the particle swarm optimization algorithm. It initializes various parameter factors of the particle swarm optimization algorithm, setting the dimension to 2, the number of particles to 20, the dynamic parameter to 1, and the learning factors c1 and c2 to [0.5, 2.5].

[0043] The global optimal position acquisition unit is used to initialize the positions and velocities of 20 particles, substitute the particle position data into the objective function R to calculate the self-fit value of each particle, obtain the self-optimal position of each particle based on the self-fit value, and obtain the global optimal position based on the self-optimal position.

[0044] The iteration count detection unit is used to continuously update the particle positions in the particle swarm from the initial position coordinates using an iterative algorithm, and to determine whether the current iteration count has reached the preset maximum number of iterations.

[0045] The iterative repetition unit is used to repeatedly calculate the fitness value of each particle if the current iteration number has not reached the preset maximum number of cycles.

[0046] The global optimal position recording unit is used to record the global optimal position of the particle and end the loop if the current iteration number has not reached the preset maximum number of loops.

[0047] In a preferred embodiment of the present invention, the global optimal position acquisition unit further includes:

[0048] The velocity calculation subunit is used to calculate the particle velocity according to the following formula:

[0049]

[0050] Among them, v i,d x represents the velocity of the i-th particle; i，d v represents the position of the i-th particle. （i-1）,d This represents the velocity of the (i-1)th particle. Let represent the optimal position of the i-th particle. Indicates the globally optimal position;

[0051] The position calculation subunit is used to calculate the particle's position according to the following formula:

[0052]

[0053] Where, x （i-1），d This represents the position of the i-th particle.

[0054] In a preferred embodiment of the present invention, the iteration count detection unit further includes:

[0055] The four-terminal equivalent execution subunit regards each layer of the T3C2Tx-MXene material as a four-terminal network, and the n medium layers are sequentially stacked to be equivalent to the cascade of n four-terminal networks, as shown in the following formula:

[0056]

[0057] Wherein, E, F, G, H are parameters in the equivalent calculation process transfer matrix, the transfer matrix parameters of the i-th equivalent network are E i , F i , G i , H i , ch() represents cosh(), that is, the hyperbolic cosine function, sh() represents sinh(), that is, the hyperbolic sine function, and λ0 is the wavelength of the electromagnetic wave.d i is the thickness of the i-th layer, ε0 is the relative dielectric constant of free space, µ0 is the relative permeability of free space, ε i represents the relative dielectric constant of the i-th layer, µ i represents the relative magnetic permeability of the i-th layer, γ i represents the transmission factor (also called phase factor) of the i-th layer, Z i is the wave impedance of the i-th layer, and j represents the imaginary part of the complex number.

[0058] The optimal reflectivity solving subunit is used to calculate the optimal reflectivity according to the following formula:

[0059]

[0060] Wherein, RL represents the optimal reflectivity, and Z0 is the intrinsic impedance of free space.

[0061] Additional aspects and advantages of the application will be partially given in the following description, partially will become obvious from the following description, or will be understood by the embodiments of the application. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The flowchart of the structure optimization method of the T3C2Tx-MXene material according to the first embodiment of the application is shown in the figure;

[0063] Figure 2 The calculation example of the 50-layer multilayer T3C2Tx-MXene material at 18 GHz in Example 1 is shown in the figure: (a) TEM image of 20 layers of T3C2Tx-MXene material, (b) particle state position change, (c) algorithm convergence process;

[0064] Figure 3(a) TEM image of 100 layers of T3C2Tx-MXene material in Example 2, (b) particle state position change, (c) algorithm convergence process;

[0065] Figure 4 (a) TEM image of 200 layers of T3C2Tx-MXene material in Example 3, (b) particle state position change, (c) algorithm convergence process;

[0066] Figure 5 (a) shown is the result of selecting the optimized 50-layer multi-layer T3C2Tx-MXene material in Example 1 (frequency-reflectivity-interlayer thickness d1, d2 relationship diagram); Figure 5 (b) shown is a comparison chart of reflectivity data after multi-layer structure optimization (target functions are four-terminal network equivalent method and transmission line method, respectively) and before multi-layer structure optimization;

[0067] Figure 6 is the optimization result of the 50-layer multi-layer T3C2Tx-MXene material in Example 1;

[0068] Figure 7 is a structural schematic diagram of a structure optimization system of a T3C2Tx-MXene material according to the second embodiment of the present application.

[0069] The following specific embodiments will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0070] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The drawings show several embodiments of the present application. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing the specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0072] Please refer to Figure 1 , shown is a flowchart of a structure optimization method of a T3C2Tx-MXene material in the first embodiment of the present application, which comprises steps S01 to S02, wherein:

[0073] Step S01: Obtain a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model is a multi-layer structure, and layer thickness properties and interlayer thickness properties of the T3C2Tx-MXene material particle model are selected;

[0074] It should be noted that the T3C2Tx-MXene material can be a multi-layer structure of 50 layers, 100 layers, 150 layers, etc.

[0075] Step S02: The four-terminal network equivalent method is used as the objective function of the particle swarm algorithm, the selected layer thickness properties and interlayer thickness properties are applied to the objective function, and the optimization ranges of the layer thickness d1 and the interlayer thickness d2 in the objective function are set respectively to obtain the optimal reflectivity in a specific frequency range, and the T3C2Tx-MXene material thickness d1 and the interlayer thickness d2 corresponding to the optimal reflectivity are obtained.

[0076] Specifically, first, the layer thickness d1 and the interlayer thickness d2 of the material are taken as optimization variables, so as to divide the T3C2Tx-MXene material into a plurality of particles in the optimization ranges corresponding to the layer thickness d1 and the interlayer thickness d2 respectively, so as to optimize them, and according to the four-terminal network equivalent method formula, in order to obtain the maximum electromagnetic wave absorption loss, the objective function R is defined as:

[0077]

[0078] Wherein, fitness() represents the fitness function, D min ≤d1、d2≤D max , D min and D max are the minimum and maximum values of d1 and d2 respectively.

[0079] Then, the four-terminal network equivalent method is used as the objective function and connected with the particle swarm optimization algorithm, and the program is compiled into MATLAB for running, and the specific process is as follows:

[0080] The particle swarm algorithm is used to fit and optimize the parameters d1 and d2 in the model, and the particle swarm algorithm parameters are initialized, the dimension is set to 2, the particle number is set to 20, the dynamic parameter 1, and the learning factors c1 and c2 are [0.5, 2.5];

[0081] The positions of 20 particles are initialized, and the velocities of 20 particles are initialized, the position data of the particles are substituted into the objective function R to calculate the self-adaptation value of each particle, and the global optimal position is obtained based on the self-adaptation value of each particle, and the global optimal position is obtained based on the self-adaptation value of each particle;

[0082] The process of initializing the positions of 20 particles is as follows:

[0083] R1=(d1),

[0084] R2= (d1, d2),

[0085] …

[0086] R20=( d1,d2,…,d1,d2);

[0087] The process of initializing the velocities of 20 particles is as follows:

[0088] v1=(vd1)

[0089] v2=( vd1,vd2)

[0090] …

[0091] v20=( vd1,vd2,…,vd1,vd2)

[0092] The particle position in the particle swarm is continuously updated from the initial position coordinates by using an iterative algorithm to solve, and it is judged whether the current iteration number reaches the preset maximum loop number.

[0093] Specifically, the velocity of the particle is calculated according to the following formula:

[0094]

[0095] wherein v i,d represents the velocity of the i-th particle; x i，d represents the position of the i-th particle, v （i-1）,d represents the velocity of the i-1-th particle, represents the optimal position of the i-th particle itself, represents the global optimal position;

[0096] The position of the particle is calculated according to the following formula:

[0097]

[0098] wherein x （i-1），d represents the position of the i-th particle.

[0099] Each layer of the T3C2Tx-MXene material is regarded as a four-terminal network, and n dielectric layers are sequentially stacked, which is equivalent to the cascade of n four-terminal networks, as shown in the following formula:

[0100]

[0101] Where E, F, G, and H are the parameters in the transfer matrix of the equivalent computation process, and the transfer matrix parameter of the i-th equivalent network is E. i F i G i H i ch() represents cosh(), which is the hyperbolic cosine function, sh() represents sinh(), which is the hyperbolic sine function, and λ0 is the wavelength of the electromagnetic wave. d i Let εi be the thickness of the i-th layer, ε0 be the relative permittivity of free space, and µ0 be the relative permeability of free space. i µ represents the relative permittivity of the i-th layer. i γ represents the relative permeability of the i-th layer. i Z represents the transmission factor (also called phase factor) of the i-th layer. i Let be the wave impedance of the i-th layer, and j be the imaginary part of the complex number;

[0102] The optimal reflectivity is calculated using the following formula:

[0103]

[0104] Where RL represents the optimal reflectivity and Z0 is the intrinsic impedance in free space.

[0105] If the current iteration count has not reached the preset maximum number of iterations, then the fitness value of each particle is recalculated.

[0106] If the current iteration count has not reached the preset maximum number of iterations, record the global optimal position of the particle and end the loop.

[0107] It should be noted that in the current research system, the research on the wave absorption optimization problem of Ti3C2Tx-MXene material is mostly through optimizing the design of Ti3C2Tx-MXene material in the direction of material wave absorption. For example, by introducing Fe3O4 magnetic material into Ti3C2Tx-MXene material to form a composite material to optimize impedance matching, the effect of improving wave absorption performance is achieved. There is still a lot of room for exploration in the structure of Ti3C2Tx-MXene material wave absorption optimization. Two-dimensional layered Ti3C2Tx material has a special sheet structure, and in the case of multiple sheet Ti3C2Tx stacked (generally after etching), there is an air layer in the sheet Ti3C2Tx, which makes two-dimensional layered Ti3C2Tx material a multilayer material with two-phase media separated from each other. By optimizing the structure of the multilayer material, the wave absorption performance of the multilayer material can be improved. For multilayer Ti3C2Tx material, optimizing the thickness of the two-phase medium (Ti3C2Tx material layer and air layer) to improve the wave absorption performance of the multilayer Ti3C2Tx material is a very feasible method.

[0108] Therefore, based on this, the present application proposes a structure optimization algorithm for multilayer Ti3C2Tx-MXene wave absorbing material, which combines particle swarm optimization (PSO) with four-terminal network equivalent method and applies it to Ti3C2Tx-MXene multilayer structure wave absorption optimization. Based on the experimental characterization of the material structure, the number of layers and the interlayer thickness of the material are selected, the four-terminal network equivalent method is used as the objective function of the particle swarm optimization algorithm, the initial parameter factor is introduced into the algorithm program, and after the maximum number of iterations, the optimal reflectivity in the given frequency range is obtained, and the corresponding material Ti3C2Tx thickness d1 and interlayer thickness d2 under this optimal reflectivity are obtained. Under the particle swarm optimization algorithm for multilayer Ti3C2Tx-MXene wave absorbing material, the reflectivity of the material can be effectively optimized, the wave absorption performance can be calculated under the condition of material electromagnetic parameters, and the wave absorption performance of the material can be optimized by changing the microstructure of the material based on the four-terminal network equivalent method. In addition, in the results of this optimization algorithm, the values of the simulated reflectivity before and after optimization are compared, and the optimized reflectivity is lower, showing better wave absorption performance; at the same time, the optimization results of the traditional transmission line method and the four-terminal network equivalent method as the objective function are compared, and due to the advantage of the four-terminal network equivalent method in calculating the superposition of different types of media, it can be well applied to the calculation of the wave absorption performance of MXene multilayer material, and the results show that the optimization result of the four-terminal network equivalent method as the objective function has better wave absorption performance.

[0109] Example 1

[0110] S1: Select a 50-layer multilayer T3C2Tx-MXene material particle model, and the electron microscope image is as followsFigure 2 (a) shown, and the layer thickness d1, the interlayer thickness d2 and the number of layers of the material are selected;

[0111] S2: The four-terminal network equivalent method is used as the objective function of the particle swarm algorithm, the selected d1, d2 and the number of layers of the multi-layer material particle in S1 are applied to the objective function, and the optimization range of d1 and d2 in the result optimization function is selected;

[0112] S3: The particle swarm algorithm program with the objective function of the four-terminal network equivalent method is compiled into MATLAB and run;

[0113] S3-1: The particle swarm algorithm parameters are initialized, the dimension is 2, the population size is 20, the dynamic parameter is 1, and the learning factor is [0.5, 2.5];

[0114] S3-2: The four-terminal network equivalent method is used as the objective function and the function image is drawn;

[0115] S3-3: The iterative algorithm is used to update the particle position in the particle swarm from the initial position coordinates;

[0116] S3-4: The optimization algorithm selects the optimal position according to the advantages and disadvantages of the fitness value, then updates the position according to the speed of each iteration, and the optimal result is reflected in the objective function, and finally the optimized reflectivity and the corresponding material thickness d1 and interlayer thickness d2 are obtained;

[0117] S3-5: The optimization algorithm will stop calculating and output the optimal solution after reaching the maximum number of iterations, if the output results d1 and d2 are not within the optimization range, it will not meet the requirements, and the program will return to step S3-3 to recalculate.

[0118] S4: Data analysis is performed on the output optimization results, and the particle state position change and convergence process in the output results are shown in Figure 2 (b, c).

[0119] Example 2:

[0120] S1: A 100-layer multi-layer T3C2Tx-MXene material particle model is selected, and the electron microscope image is shown in Figure 3 (a) shown, and the layer thickness d1, the interlayer thickness d2 and the number of layers of the material are selected;

[0121] S2: The four-terminal network equivalent method is used as the objective function of the particle swarm algorithm, the selected d1, d2 and the number of layers of the multi-layer material particle in S1 are applied to the objective function, and the optimization range of d1 and d2 in the result optimization function is selected;

[0122] S3: The particle swarm algorithm program with the objective function of the four-terminal network equivalent method is compiled into MATLAB and run;

[0123] S3-1: initialize various parameter factors of the particle swarm algorithm, dimension 2, population size 20, dynamic parameter 1, learning factor [0.5, 2.5];

[0124] S3-2: take the four-terminal network equivalent method as the objective function and draw a function image;

[0125] S3-3: use an iterative algorithm to constantly update the particle position in the particle swarm from the initial position coordinates to solve;

[0126] S3-4: the optimization algorithm will select the optimal position according to the pros and cons of the fitness value, then update the position according to the speed of each iteration, the optimal result will be reflected in the objective function, and finally the optimized reflectivity and corresponding material thickness d1 and interlayer thickness d2 are obtained;

[0127] S3-5: the optimization algorithm will stop calculating and output the optimal solution after reaching the maximum number of iterations, if the output results d1 and d2 are not within the optimization range, it will not meet the requirements, and the program will return to step S3-3 to recalculate.

[0128] S4: analyze the output optimization results, and the particle state position change and convergence process in the output results are shown in Figure 3 (b, c).

[0129] Example 3:

[0130] S1: select a 200-layer multilayer T3C2Tx-MXene material particle model, the electron microscope image is shown in Figure 4 (a), and select the layer thickness d1, interlayer thickness d2 and number of layers of the material;

[0131] S2: take the four-terminal network equivalent method as the objective function of the particle swarm algorithm, apply the selected d1, d2 and number of layers of the multilayer material particles in the objective function, and select the optimization range of d1 and d2 in the result optimization function;

[0132] S3: compile the particle swarm algorithm program with the objective function as the four-terminal network equivalent method into MATLAB and run it;

[0133] S3-1: initialize various parameter factors of the particle swarm algorithm, dimension 2, population size 20, dynamic parameter 1, learning factor [0.5, 2.5];

[0134] S3-2: take the four-terminal network equivalent method as the objective function and draw a function image;

[0135] S3-3: use an iterative algorithm to constantly update the particle position in the particle swarm from the initial position coordinates to solve;

[0136] S3-4: The optimization algorithm will select the optimal position according to the pros and cons of the fitness value, then update the position according to the speed of each iteration, and the optimal result will be reflected in the objective function, and finally the optimized reflectivity and the corresponding material thickness d1 and interlayer thickness d2 are obtained;

[0137] S3-5: The optimization algorithm will stop calculating and output the optimal solution after reaching the maximum number of iterations, and if the output results d1 and d2 are not within the optimization range, it will not meet the requirements, and the program will return to step S3-3 to recalculate.

[0138] S4: Data analysis is performed on the output optimization results, and the particle state position change and convergence process in the output results are shown in Figure 4 (b, c).

[0139] In the above three cases, according to the output optimization results, the more the number of layers, the smaller the absolute value of reflectivity, indicating that the wave absorption performance of the material decreases. In the case of 50 layers of material, the reflectivity of the optimization result changes in the frequency range of 2-18GHz as shown in Figure 5 (a), it can be seen that in the range of 2-18GHz, the absolute value of reflectivity increases, and the average is-97.85dB, and at 18GHz, the minimum reflectivity value is 104.71dB. In the comparison chart of optimization result data in Figure 5 (b), the absolute value of reflectivity of the multi-layer structure before optimization (COMSOL simulation calculation) gradually decreases in the frequency range of 2-18GHz, and the average is about-16.36dB, and at 2GHz, the minimum reflectivity is-19.95dB; in the optimization calculation, the reflectivity calculated by the transmission line method as the objective function gradually decreases in the frequency range of 2-18GHz, and the average is-66.43dB, and at 2GHz, the minimum reflectivity is-76.91dB; however, in the comparison chart of three calculation results in Figure 5 (b), it can be seen that the reflectivity after structure optimization is lower than that before optimization, almost four times, indicating that this structure optimization method can well reduce the reflectivity and improve the wave absorption performance of the material; and the four-terminal network equivalent method as the objective function of optimization calculation has lower reflectivity than the transmission line method, as shown in the comparison Figure 5 (b), in terms of average reflectivity, four-terminal network equivalent optimization is about-30dB lower than transmission line optimization. In addition, the optimization result data in Example 1 is plotted as a scatter plot as shown in Figure 6 .

[0140] Please refer to Figure 7 , which is a structure schematic diagram of a structure optimization system of a T3C2Tx-MXene material in the second embodiment of the present application, which comprises:

[0141] The optimization variable locking module 10 is configured to obtain a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model being a multi-layer structure, and select layer thickness attributes and interlayer thickness attributes of the T3C2Tx-MXene material particle model;

[0142] The structure optimization execution module 20 is configured to use a four-terminal network equivalent method as a target function of a particle swarm algorithm, apply the selected layer thickness attributes and interlayer thickness attributes to the target function, and set optimization ranges of layer thickness d1 and interlayer thickness d2 in the target function respectively, so as to obtain optimal reflectivity in a specific frequency range, and obtain T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity.

[0143] Further, the structure optimization execution module 20 further comprises:

[0144] The target function construction unit is configured to use layer thickness d1 and interlayer thickness d2 of the material as optimization variables, divide the T3C2Tx-MXene material into a plurality of particles in the optimization ranges of layer thickness d1 and interlayer thickness d2 respectively, and define the target function R according to the four-terminal network equivalent method as:

[0145]

[0146] wherein fitness() represents a fitness function, D min ≤d1、d2≤D max , D min and D max are minimum and maximum values of d1 and d2 respectively.

[0147] The initialization unit is configured to use a particle swarm algorithm to fit parameters d1 and d2 in the optimization model, initialize various parameter factors of the particle swarm algorithm, set the dimension as 2, the number of particles as 20, the dynamic parameter 1, and the learning factors c1 and c2 as [0.5, 2.5];

[0148] The global optimal position acquisition unit is configured to initialize positions of 20 particles, initialize speeds of the 20 particles, substitute the position data of the particles into the target function R to calculate self-adaptive values of each particle, obtain self-optimal positions of each particle based on the self-adaptive values, and acquire a global optimal position according to the self-optimal positions;

[0149] Further, the global optimal position acquisition unit further comprises:

[0150] The speed calculation subunit is configured to calculate the speed of the particle according to the following formula:

[0151]

[0152] wherein v i,d represents the i-th particle velocity; x i，d represents the position of the i-th particle, v （i-1）,d represents the i-1-th particle velocity, represents the i-th particle's own optimal position, represents the global optimal position;

[0153] a position calculation subunit configured to calculate the position of the particle according to the following formula:

[0154]

[0155] wherein x （i-1），d represents the position of the i-th particle.

[0156] an iteration number detection unit configured to use an iteration algorithm to constantly update the position of the particle in the particle swarm from the initial position coordinates to solve, and to determine whether the current iteration number reaches a preset maximum loop number;

[0157] Further, the iteration number detection unit further comprises:

[0158] a four-terminal equivalent execution subunit configured to regard each layer of the T3C2Tx-MXene material as a four-terminal network, and to stack n dielectric layers in turn to be equivalent to a cascade of n four-terminal networks, as shown in the following formula:

[0159]

[0160] wherein E, F, G, and H are parameters in the transfer matrix in the equivalent calculation process, and the transfer matrix parameters of the i-th equivalent network are E i , F i , G i , and H i , ch() represents cosh(), i.e., the hyperbolic cosine function, sh() represents sinh(), i.e., the hyperbolic sine function, and λ0is the wavelength of the electromagnetic wave. i d is the thickness of the i-th layer, ε0is the relative permittivity of free space, µ0is the relative permeability of free space, ε i represents the relative permittivity of the i-th layer, µ i represents the relative permeability of the i-th layer, γ i represents the transmission factor (also referred to as the phase factor) of the i-th layer, Z i is the wave impedance of the i-th layer, and j represents the imaginary part of a complex number.

[0161] an optimal reflectivity solving subunit configured to calculate the optimal reflectivity according to the following formula:

[0162]

[0163] wherein RL represents the optimal reflectivity and Z0 is the intrinsic impedance of free space.

[0164] The repeating iteration unit is configured to, if the current iteration number does not reach the preset maximum loop number, repeat the calculation of the self-adaptive value of each particle.

[0165] The global optimal position recording unit is configured to, if the current iteration number does not reach the preset maximum loop number, record the global optimal position of the particle and end the loop.

[0166] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of ordered instructions for implementing logical functions, which can be embodied in any computer readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instruction execution systems, apparatus or devices. For the purpose of this specification, the "computer readable medium" can be any device that can contain a storage, communication, propagation or transmission of a program for use by or in conjunction with an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0167] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program is printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic conversion of the obtained program into a computer readable medium, and then storing the program in a computer memory if necessary.

[0168] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques can be used to implement the hardware used to implement the described functions: discrete logic circuitry having logic gates for implementing logic functions upon data signals, application specific integrated circuits having logic gates for implementing the logic functions on data signals, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0169] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0170] The above-described embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for structure optimization of T3C2Tx-MXene material, characterized in that, The method comprises: obtaining a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model being a multi-layer structure, and selecting layer thickness attributes and interlayer thickness attributes of the T3C2Tx-MXene material particle model; applying the four-terminal network equivalent method as a target function of a particle swarm algorithm, and applying the selected layer thickness attributes and interlayer thickness attributes to the target function, and setting optimization ranges of layer thickness d1 and interlayer thickness d2 in the target function respectively to obtain optimal reflectivity in a specific frequency range, and obtaining T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity; the four-terminal network equivalent method specifically comprises: each layer of the T3C2Tx-MXene material is regarded as a four-terminal network, n medium layers are sequentially stacked, and are equivalent to the cascade of n four-terminal networks, as shown in the following formula: Wherein, E, F, G, H are parameters in the equivalent calculation process transfer matrix, the transfer matrix parameters of the i th equivalent network are E i , F i , G i , H i , ch() represents cosh(), that is, the hyperbolic cosine function, sh() represents sinh(), that is, the hyperbolic sine function, λ0 is the wavelength of electromagnetic wave; d i is the thickness of the i th layer, ε0 is the relative dielectric constant of free space, µ0 is the relative permeability of free space, ε i represents the relative dielectric constant of the i th layer, µ i represents the relative magnetic conductivity of the i th layer, γ i represents the transmission factor (also called phase factor) of the i th layer, Z i is the wave impedance of the i th layer, j represents the imaginary part of the complex number.

2. The method of structure optimization of T3C2Tx-MXene material according to claim 1, wherein, the step of applying the four-terminal network equivalent method as a target function of a particle swarm algorithm, and applying the selected layer thickness attributes and interlayer thickness attributes to the target function, and setting optimization ranges of layer thickness d1 and interlayer thickness d2 in the target function respectively to obtain optimal reflectivity in a specific frequency range, and obtaining T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity comprises: layer thickness d1 and interlayer thickness d2 of the material are taken as optimization variables, the T3C2Tx-MXene material is divided into a plurality of particles in the optimization ranges corresponding to layer thickness d1 and interlayer thickness d2 respectively, and the target function R is defined according to the four-terminal network equivalent method: where fitness() represents a fitness function, D min ≤ d1, d2≤ D max , D min and D max are the minimum and maximum values of d1, d2, respectively.

3. The method of claim 2, wherein the T3C2Tx-MXene material is characterized by, the step of applying the four-terminal network equivalent method as a target function of a particle swarm algorithm, and applying the selected layer thickness attributes and interlayer thickness attributes to the target function, and setting optimization ranges of layer thickness d1 and interlayer thickness d2 in the target function respectively to obtain optimal reflectivity in a specific frequency range, and obtaining T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity further comprises: parameters d1 and d2 in the optimization model are fitted and optimized by using a particle swarm algorithm, various parameter factors of the particle swarm algorithm are initialized, the dimension is set to 2, the number of particles is set to 20, dynamic parameter 1, and learning factors c1 and c2 are [0.5, 2.5]; positions of 20 particles are initialized, speeds of the 20 particles are initialized, position data of the particles are substituted into the target function R to calculate self-adaptive values of each particle, self-optimal positions of each particle are obtained based on the self-adaptive values, and a global optimal position is obtained according to the self-optimal positions; an iterative algorithm is used to update and solve the particle positions in the particle swarm from initial position coordinates, and it is judged whether the current iteration number reaches a preset maximum cycle number; if the current iteration number does not reach the preset maximum cycle number, the self-adaptive values of each particle are repeatedly calculated; if the current iteration number does not reach the preset maximum cycle number, the global optimal position of the particle is recorded, and the cycle is ended.

4. The method of structure optimization of T3C2Tx-MXene material according to claim 3, wherein, The step of initializing the positions of the 20 particles, initializing the velocities of the 20 particles, substituting the position data of the particles into the objective function R to calculate the self-adaptation value of each particle, obtaining the self-optimal position of each particle based on the self-adaptation value, and obtaining the global optimal position according to the self-optimal position comprises: The velocity of the particle is calculated according to the following formula: wherein, denotes the i-th particle velocity; denotes the position of the i-th particle, denotes the i-1-th particle velocity, denotes the i-th particle's own optimal position, denotes the global optimal position; The position of the particle is calculated according to the following formula: wherein, represents the position of the i-th particle.

5. A system for structure optimization of T3C2Tx-MXene materials, characterized in that, The system comprises: The optimization variable locking module is configured to obtain a T3C2Tx-MXene material particle model, the T3C2Tx-MXene material particle model being a multi-layer structure, and select layer thickness attributes and interlayer thickness attributes of the T3C2Tx-MXene material particle model; The structure optimization execution module is configured to use a four-terminal network equivalence method as an objective function of a particle swarm algorithm, apply the selected layer thickness attributes and interlayer thickness attributes to the objective function, and set optimization ranges of layer thickness d1 and interlayer thickness d2 in the objective function respectively to obtain optimal reflectivity under a specific frequency range, and obtain T3C2Tx-MXene material thickness d1 and interlayer thickness d2 corresponding to the optimal reflectivity. The four-terminal network equivalence method specifically comprises: Each layer of the T3C2Tx-MXene material is regarded as a four-terminal network, n dielectric layers are sequentially stacked, and the n four-terminal networks are equivalent to a cascade, as shown in the following formula: Wherein, E, F, G, H are parameters in the equivalent calculation process transfer matrix, the transfer matrix parameters of the i th equivalent network are E i , F i , G i , H i , ch() represents cosh(), that is, the hyperbolic cosine function, sh() represents sinh(), that is, the hyperbolic sine function, λ0 is the wavelength of electromagnetic wave; d i is the thickness of the i th layer, ε0 is the relative dielectric constant of free space, µ0 is the relative permeability of free space, ε i represents the relative dielectric constant of the i th layer, µ i represents the relative magnetic permeability of the i th layer, γ i represents the transmission factor (also called phase factor) of the i th layer, Z i is the wave impedance of the i th layer, j represents the imaginary part of the complex number.

6. The system for structure optimization of T3C2Tx-MXene materials of claim 5, wherein, The structure optimization execution module comprises: The objective function construction unit is configured to use layer thickness d1 and interlayer thickness d2 of the material as optimization variables, divide the T3C2Tx-MXene material into a plurality of particles in the optimization ranges of layer thickness d1 and interlayer thickness d2 respectively, and define the objective function R according to the four-terminal network equivalence method as: where fitness() represents a fitness function, D min ≤ d1, d2≤ D max , D min and D max are the minimum and maximum values of d1, d2, respectively.

7. The system for structure optimization of T3C2Tx-MXene materials of claim 6, wherein, The structure optimization execution module further comprises: The initialization unit is configured to use a particle swarm algorithm to fit parameters d1 and d2 in the optimization model, initialize various parameter factors of the particle swarm algorithm, set the dimension to 2, the number of particles to 20, the dynamic parameter 1, and the learning factors c1 and c2 to [0.5, 2.5]; The global optimal position acquisition unit is configured to initialize the positions of the 20 particles, initialize the velocities of the 20 particles, substitute the position data of the particles into the objective function R to calculate the self-adaptation value of each particle, obtain the self-optimal position of each particle based on the self-adaptation value, and obtain the global optimal position according to the self-optimal position; The iteration number detection unit is configured to use an iteration algorithm to constantly update and solve the particle position in the particle swarm from the initial position coordinates, and determine whether the current iteration number reaches a preset maximum loop number. The repeated iteration unit is configured to repeatedly calculate the self-adaptation value of each particle if the current iteration number does not reach the preset maximum loop number. The global optimal position recording unit is configured to record the global optimal position of the particle and end the loop if the current iteration number does not reach the preset maximum loop number.

8. The system for structural optimization of T3C2Tx-MXene materials of claim 7, wherein, The global optimal position acquisition unit further comprises: The velocity calculation subunit is configured to calculate the velocity of the particle according to the following formula: wherein, denotes the i-th particle velocity; denotes the position of the i-th particle, denotes the i-1-th particle velocity, denotes the i-th particle's own optimal position, denotes the global optimal position; A position calculation subunit is configured to calculate the position of the particle according to the following formula: wherein, represents the position of the i-th particle.

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