Multifunctional sensing composite film and preparation method thereof

Abstract

The invention relates to a multifunctional sensing composite film and a preparation method thereof. The multifunctional sensing composite film comprises a polyimide film; a micro-nano structure graphene layer, wherein the micro-nano structure graphene layer is a multi-stage micro-nano structure formed by performing magnetic control picosecond laser micromachining on the polyimide film; the chemical modification layer covers the surface of the micro-nano structure graphene layer; the embedded electric heating copper wire structure is embedded into the polyimide film, and the arrangement mode of the embedded electric heating copper wire structure is determined through multi-objective optimization of a non-dominated sorting genetic algorithm and a convolutional neural network agent model. According to the method disclosed by the invention, the energy-saving and lightweight multifunctional composite film can be rapidly prepared, so that the composite film has high super-hydrophobic self-cleaning performance, high temperature resistance and anti-icing and deicing performance are ensured, the anti-icing and deicing energy consumption of equipment is greatly reduced, and anti-pollution and self-cleaning are kept.

Description

Technical Field

[0001] The present disclosure relates to the technical field of functional composite materials, and in particular to a multifunctional sensing composite film and a preparation method thereof. Background Art

[0002] With the application of equipment resistant to severe cold and high temperature climates in extreme scenarios such as firefighting, polar scientific expeditions, aerospace, etc., the demand for multifunctional sensing composite films in extreme environments (such as high temperature, low temperature icing, and high pollution) is increasing. The self-cleaning performance may be affected by high and low temperatures and lose anti-pollution properties, resulting in a decrease in anti-icing performance, so it is difficult for these three properties to coexist at the same time. At present, anti-icing and de-icing systems are the main protection systems. Existing active de-icing includes mechanical de-icing, electrothermal de-icing and pneumatic de-icing. Passive anti-icing mainly prepares super-hydrophobic self-cleaning surfaces, and methods include chemical vapor deposition, electrospray deposition and laser micro-nano manufacturing.

[0003] Chemical vapor deposition requires high experimental temperatures and expensive equipment, while electrospray deposition takes a significant amount of time to prepare the solution and requires extended post-processing, complicating the manufacturing process. Laser micro-nanofabrication, on the other hand, enables patterned and controllable microstructures. This is a straightforward, simple, and rapid method for growing graphene directly on the surface of high-temperature resistant polyimide films. Graphene's high thermal conductivity and stable structure help prevent surface icing.

[0004] However, in extremely low-temperature environments, active electric heating technology is still needed for de-icing. However, the current width and spacing of the electric heating wires lack rational design. In order to achieve uniform heating, the pursuit of a denser copper wire arrangement leads to increased power consumption and weight, which is not conducive to environmental protection and wearability. Manual parameter debugging is inefficient and it is difficult to simultaneously meet multiple objectives such as temperature uniformity, power density, and lightweight. Therefore, how to optimize and prepare this multifunctional sensing composite film based on multi-objective optimization is an urgent problem to be solved. Summary of the Invention

[0005] To address the low efficiency of manual parameter adjustment of traditional composite films and the difficulty in simultaneously meeting multiple requirements such as temperature uniformity, power density, and lightweight, this disclosure proposes a multifunctional sensing composite film and its preparation method to solve the above problems.

[0006] According to one aspect of the present disclosure, there is provided a multifunctional sensing composite film, comprising:

[0007] Polyimide film;

[0008] A micro-nanostructured graphene layer, wherein the micro-nanostructured graphene layer is a multi-level micro-nanostructure formed by performing magnetron picosecond laser micromachining on a polyimide film;

[0009] A chemical modification layer covering the surface of the micro-nanostructured graphene layer;

[0010] The embedded electric heating copper wire structure is embedded in the polyimide film, and its arrangement is determined by multi-objective optimization of a non-dominated sorting genetic algorithm and a convolutional neural network agent model.

[0011] Preferably, the polyimide film is subjected to magnetron picosecond laser micromachining, comprising: placing a magnet horizontally below the polyimide film, so that the magnetic flux lines of the magnet pass vertically through the surface of the polyimide film.

[0012] Preferably, the polyimide film is subjected to magnetron picosecond laser micromachining to form a multi-level micro-nano structure, including: the polyimide film is subjected to magnetron picosecond pulse laser processing to form a conical micron structure and a "fog"-like graphene capillary nanostructure.

[0013] Preferably, the chemical modification layer comprises methyl nonafluoroisobutyl ether, methyl nonafluorobutyl ether and an acrylate-containing substance, and the chemical modification layer covers the surface of the micro-nanostructured graphene layer. After modifying the micro-nanostructured graphene layer, a micron-scale conical structure and a granular nanostructure are formed on its surface.

[0014] Preferably, the embedded electric heating copper wire structure (2) is a serpentine path circuit designed based on experiment-simulation-multi-objective optimization.

[0015] Preferably, the experiment-simulation-multi-objective optimization design includes:

[0016] Establish a simulation model through multi-physics field coupling simulation software and optimize and adjust the model parameters;

[0017] Obtain temperature difference, power density, unit mass and temperature field distribution data on the surface of polyimide film;

[0018] Construct a three-dimensional data set containing regional thermal distribution characteristic maps and establish a database for multi-objective optimization design.

[0019] Preferably, the experiment-simulation-multi-objective optimization design further includes:

[0020] Establish a multi-objective optimization model for the input parameter space of line width and line spacing, surface temperature difference, power density, and unit mass;

[0021] Generate Pareto solution sets through non-dominated sorting genetic algorithm and convolutional neural network agent model.

[0022] Preferably, the experiment-simulation-multi-objective optimization design further includes: outputting the optimal electrode arrangement scheme based on the Pareto solution set using an entropy weight-artificial hybrid weight allocation method, the specific steps of which are as follows:

[0023] The objective weight of the entropy weight method is calculated based on the information entropy of the Pareto solution set;

[0024] Set manual experience weights based on engineering requirements;

[0025] Calculate the hybrid weight according to the objective weight of the entropy weight method and the artificial experience weight;

[0026] The Pareto solution set is sorted according to the hybrid weight using the approximate ideal solution sorting method, and the optimal electrode arrangement scheme is output.

[0027] According to one aspect of the present disclosure, a method for preparing a multifunctional sensing composite film is also provided, comprising the following steps:

[0028] Manufacturing an electrically heated polyimide film according to an optimal electrode arrangement scheme, wherein the optimal electrode arrangement scheme is determined by multi-objective optimization using a non-dominated sorting genetic algorithm and a convolutional neural network agent model;

[0029] Multi-level micro-nanostructures were formed by magnetron picosecond laser micromachining of polyimide films;

[0030] A chemical modification reagent is added dropwise onto the surface of the graphene structure of the multi-level micro-nano structure to form a chemical modification layer.

[0031] Preferably, a method for preparing a multifunctional sensing composite film comprises the following steps:

[0032] Prepare several electrically heated polyimide films with different line widths and line spacings, and test their temperature characteristics;

[0033] Based on the temperature characteristic data, a simulation model is established using multi-physics field coupling simulation software, and the consistency between the model and the experiment is verified;

[0034] Through large sample simulation, surface temperature difference, power density, unit mass and temperature field distribution data are collected to construct a three-dimensional data set;

[0035] The three-dimensional data set is input into a non-dominated sorting genetic algorithm and a convolutional neural network agent model to output an optimal electrode arrangement scheme.

[0036] Compared with the prior art, the beneficial effects of the present disclosure are:

[0037] 1) The present invention not only has high superhydrophobic self-cleaning properties, but also ensures high temperature resistance and anti-icing performance, greatly reducing the power consumption required for anti-icing. In addition, the electric heating film designed based on "experiment-simulation-multi-objective optimization" takes into account the comprehensive consideration of the weight and heating performance of the film, and is suitable for the needs of extreme environments.

[0038] 2) The serpentine path electric heating wire proposed in this paper, based on the "experiment-simulation-multi-objective optimization" design, overcomes the difficulties of increased energy consumption, excessive surface temperature difference and increased weight caused by unreasonable design. It also combines NSGA-II with the CNN convolutional neural network proxy model, which is more efficient than traditional full simulation optimization. It proposes an entropy weight-artificial hybrid weight distribution method to balance objective data laws and engineering experience.

[0039] 3) The method of chemically modified magnetron picosecond green light pulse laser processing of polyimide proposed in the present disclosure has lower manufacturing costs, higher manufacturing efficiency, and is simple and easy to implement compared to other methods.

[0040] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0041] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0043] Figure 1 A schematic diagram showing the overall structure of a multifunctional sensing composite film;

[0044] Figure 2 A schematic diagram of an electrically heated polyimide film based on "experiment-simulation-multi-objective optimization" design is shown;

[0045] Figure 3 The CNN convolutional neural network structure diagram in Example 2 is shown;

[0046] Figure 4 The NSGA-II optimization flow chart in Example 2 is shown;

[0047] Figure 5 A schematic diagram of a device using magnetically controlled picosecond pulse laser processing technology in Example 3 is shown;

[0048] Figure 6 The surface scanning electron microscope (SEM) image of the graphene composite film prepared in Example 3 is shown;

[0049] Figure 7 This is a cross-sectional SEM image of the graphene composite film prepared in Example 3 of the present disclosure;

[0050] Figure 8 This is a surface SEM image of the modified graphene composite film prepared in Example 3 of the present disclosure;

[0051] Figure 9 This is a cross-sectional SEM image of the modified graphene composite membrane prepared in Example 3 of the present disclosure.

[0052] Reference numerals

[0053] 1. Sample tank; 2. Electric heating copper wire; 3. Chemical modification layer; 4. Magnet; 5. Processing tank; 6. Picosecond green pulse laser. DETAILED DESCRIPTION

[0054] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0055] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0056] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0057] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] Based on the above ideas, this embodiment provides a multifunctional sensing composite film, the overall structure of which is shown in FIG. Figure 1 As shown, including:

[0061] Polyimide film 1;

[0062] A micro-nanostructured graphene layer, wherein the micro-nanostructured graphene layer is a multi-level micro-nanostructure formed by performing magnetron picosecond laser micromachining on a polyimide film 1;

[0063] A chemically modified layer 3, covering the surface of the micro-nanostructured graphene layer, forming a super-hydrophobic, self-cleaning and anti-icing composite functional layer;

[0064] The embedded electric heating copper wire structure 2 is embedded in the polyimide film 1, and its arrangement is determined by multi-objective optimization of a non-dominated sorting genetic algorithm and a convolutional neural network agent model.

[0065] In this embodiment, magnetron picosecond laser micromachining is performed on the polyimide film 1 , including: placing a magnet 4 horizontally below the polyimide film 1 so that the magnetic flux of the magnet 4 passes vertically through the surface of the polyimide film 1 .

[0066] Among them, the processing parameters of the magnetron picosecond green light pulse laser 6 include: magnetic field intensity of 70.0mt, laser wavelength of 532nm, laser power of 5.0W, laser frequency of 100.0Khz, scanning speed of 60.0mm / s, scanning line spacing of 0.008mm, repeated processing times of 2 times, and processing direction of XY bidirectional.

[0067] In this embodiment, a polyimide film 1 was subjected to magnetron picosecond laser micromachining to form a multi-level micro-nanostructure. The structure includes a conical microstructure and a "fog" graphene capillary nanostructure. The polyimide film 1 has a thickness of 40.0 to 100.0 microns. The microstructures are 6.0 to 9.0 microns wide and 17.0 to 20.0 microns high, with a contact angle of 26.7°.

[0068] Furthermore, the chemically modified layer 3 covers the surface of the micro-nanostructured graphene layer. After modifying the micro-nanostructured graphene layer, micron-scale conical structures and granular nanostructures are formed on its surface. The micron-scale structures are 6.0 to 9.0 microns wide and 17.0 to 20.0 microns high. The nanoscale particles have a diameter of 10.0 to 200.0 nm, a contact angle of 163.1°, and a rolling angle of 1.8°. The chemically modified layer 3 contains methyl nonafluoroisobutyl ether, methyl nonafluorobutyl ether, and an acrylate-containing substance, and the coating thickness is less than 100.0 nm.

[0069] In this embodiment, the embedded electric heating copper wire structure 2 is a serpentine path circuit based on experiment-simulation-multi-objective optimization design, with a film thickness of 0.1 mm, a line thickness of 0.1 mm, a line width of 0.1 mm-3.0 mm, and a line spacing of 0.1 mm-3.0 mm.

[0070] Furthermore, the experiment-simulation-multi-objective optimization design includes: establishing a simulation model based on the experiment through the multi-physics field coupling simulation software COMSOL, and optimizing and adjusting the parameters of the model; comparing the experimental data, adjusting the model parameters for optimization, changing the line width and line spacing of the electric heating copper wire 2, and obtaining the temperature difference ΔT (℃) and power density P (℃) on the surface of the polyimide film 1. d (W / cm 2 ), unit mass m u (g / cm 2 ) and temperature field distribution data to collect large sample data; construct a three-dimensional data set including regional thermal distribution characteristic maps, and establish a database for multi-objective optimization design.

[0071] The multi-objective optimization design uses a combination of NSGA-II and a CNN convolutional neural network agent model to replace time-consuming simulation to establish the relationship between input and output. The input parameters include the width of the electric heating copper wire 2, and the output is the optimal electrode arrangement scheme based on entropy weight-artificial hybrid weight distribution.

[0072] The multi-objective optimization objective function includes minimizing the surface temperature difference, minimizing the power density and minimizing the unit mass, which can be expressed as:

[0073] f1=ΔT=max(T i )-min(T i ),

[0074]

[0075] f3=m u =(2ρ 膜 t 膜 A 膜 +ρ 铜 t 铜 A 铜 ) / A 膜 ,

[0076] Where, T i is the surface temperature, R is the resistance, A is the area, ρ is the density, t is the thickness, and V is the input voltage.

[0077] Furthermore, the experiment-simulation-multi-objective optimization design also includes: establishing a multi-objective optimization model of the input parameter space of line width and line spacing and surface temperature difference, power density, and unit mass; generating a Pareto solution set through a non-dominated sorting genetic algorithm and a convolutional neural network agent model.

[0078] Furthermore, the experiment-simulation-multi-objective optimization design further includes: outputting the optimal electrode arrangement scheme based on the Pareto solution set using an entropy weight-artificial hybrid weight allocation method, the specific steps of which are as follows:

[0079] The objective weights of the entropy weight method are calculated based on the information entropy of the Pareto solution set; the artificial experience weights are set according to engineering requirements; the hybrid weights are calculated based on the objective weights of the entropy weight method and the artificial experience weights, so as to balance the objective data rules and engineering experience; the Pareto solution set is sorted according to the hybrid weights using the approximate ideal solution sorting method, and the optimal electrode arrangement scheme is output.

[0080] The objective weight of the approximate ideal solution sorting method based on information entropy is expressed as:

[0081]

[0082] The subjective weight based on engineering requirements is expressed as:

[0083]

[0084] The mixing weight is expressed as:

[0085]

[0086] Where α, β and γ are subjective weighted distribution coefficients, e j is the information entropy.

[0087] Example 2

[0088] The present disclosure provides a method for preparing a multifunctional sensing composite film, comprising the following steps:

[0089] An electrically heated polyimide film 1 is manufactured according to the optimal electrode arrangement scheme; a multi-level micro-nano structure is formed by performing magnetron picosecond laser micromachining on the polyimide film 1; and a chemical modification agent is added to the surface of the graphene structure of the multi-level micro-nano structure to form a chemical modification layer 3.

[0090] Furthermore, the method for preparing the multifunctional sensing composite film further comprises the following steps:

[0091] Several electrically heated polyimide films 1 with different line widths and line spacings were prepared, and their temperature characteristic data were tested. Based on the temperature characteristic data, a simulation model was established using multi-physics field coupling simulation software, and the consistency between the model and the experiment was verified. Through large-sample simulation, surface temperature difference, power density, unit mass, and temperature field distribution data were collected to construct a three-dimensional data set. The three-dimensional data set was input into a non-dominated sorting genetic algorithm and a convolutional neural network agent model to output an optimal electrode arrangement scheme.

[0092] In this embodiment, the design method of the electrically heated polyimide film 1 based on "experiment-simulation-multi-objective optimization" includes the following steps:

[0093] S21. Select 4-6 electrically heated polyimide films 1 of the same size with different line spacing and line widths, and monitor the temperature changes after the same voltage is input.

[0094] Electrically heated polyimide film 1 Figure 2 As shown, it is composed of a polyimide film 1 of equal thickness on the outside sandwiching a serpentine copper heating wire in the middle.

[0095] Select 4-6 electric heating polyimide films 1 with different copper wire widths and copper wire spacings, input different voltages on one electric heating polyimide film 1, and use a thermal imaging instrument to record the surface temperature difference ΔT (℃), power density P d (W / cm 2 ), unit mass m u (g / m 2 ) and the temperature field distribution cloud map; input the same voltage as the previous electric heating polyimide film 1 on different electric heating polyimide films 1, and use a thermal imaging instrument to record the surface temperature changes over time, and record the surface temperature difference ΔT (℃), power density P d (W / cm 2 ) and unit mass m u (g / m 2 ).

[0096] S22. Use COMSOL software to establish modeling and simulation based on experimental data to build a reliable model that is consistent with the experiment.

[0097] The temperature rise of the selected electric heating film is simulated according to the process of importing the geometric model - defining material properties - setting domains, boundary conditions, loads and constraints - dividing the finite element mesh - solving - testing, and matching it with the experiment, and finally confirming a reliable simulation model that is consistent with the experiment.

[0098] S23, change the line width and line spacing parameters of the electric heating copper wire 2, record the surface temperature difference ΔT (℃), power density P d (W / cm2 ), unit mass m u (g / cm 2 ) to collect large sample data.

[0099] Using a reliable simulation model, by repeatedly changing the copper wire width and the spacing between the copper wires, the surface temperature of the electric heating film changes over time and the power usage are simulated and the data is recorded to collect large sample data.

[0100] S24. Use the NSGA-II algorithm combined with the CNN convolutional neural network proxy model framework, input large sample data, and output the predicted optimal electrode arrangement plan. The specific steps are as follows:

[0101] Step 1: Based on the simulation data, the independent variables copper wire width and wire spacing are Z-score normalized, and the dependent variables surface temperature difference, power density, and unit mass are Min-Max normalized.

[0102] Step 2: Build a multi-task prediction model, build a CNN neural network architecture and train it. The CNN convolutional neural network structure is as follows: Figure 3 As shown. The input layer in the figure includes line width and line spacing; according to the information bottleneck theory, the dimension of the first hidden layer should be the square of the input dimension. The first fully connected layer in the shared hidden layer includes 64 nodes, and according to the tower structure principle, the second connected layer includes 32 nodes. The output branch includes 3 nodes, namely surface temperature difference, power density, and unit mass. Adaptive learning rate: The initial value is 0.001 and decreases by 20% every 10 epochs. The loss function is:

[0103] L total =0.5L ΔT +0.3L power +0.2L mass ,

[0104] Where, L ΔT is the loss term of surface temperature difference, L power is the loss term of probability density, L mass is the loss term per unit mass.

[0105] Step 3: Implementation of the NSGA-II multi-objective optimization method, which simulates binary crossover and polynomial mutation, and sets the process constraints as:

[0106] s≥0.5ω

[0107] Where s is the line spacing and ω is the line width. Short circuits are prevented to limit the maximum current density. The top 10% of individuals are selected every five generations for high-precision COMSOL simulation to generate data for incremental model training.

[0108] The NSGA-II optimization flow chart (including the agent model interaction mechanism) is attached. Figure 4 The multi-objective optimization process includes objective function setting and constraint determination. The objective function setting maximizes the adaptation to engineering requirements by minimizing surface temperature, minimizing power density, and minimizing unit mass. The constraint determination achieves safety assurance through process constraints and limiting maximum current density. The multi-objective algorithm is solved under the conditions of maximizing adaptation to engineering requirements and safety assurance. The entropy weight-artificial hybrid weight decision process specifically includes the following steps:

[0109] Initial population generation: Generate an initial population based on the design parameter space (such as copper wire width and wire spacing), with a population size of N, and each individual represents an electrode arrangement scheme; agent model training: Input the parameters of the initial population into the pre-trained CNN agent model to predict multi-target outputs (surface temperature difference ΔT, power density, unit mass), replacing time-consuming high-precision COMSOL simulation; population evaluation: Includes non-dominated sorting, crowding calculation, elite retention, and crossover and mutation to generate new populations; high-precision verification and incremental learning: Select the top 10% of individuals every 5 generations for high-precision COMSOL simulation to verify the agent model prediction results, and add new data to the training set to perform incremental training on the CNN model to improve prediction accuracy; hybrid weight decision: In the Pareto front solution set, the entropy weight-artificial hybrid weight distribution method is used to determine the optimal solution; output the optimal solution: Comprehensively evaluate the Pareto solution set based on the hybrid weight, and output the optimal electrode arrangement parameters (such as a wire width of 1.3mm and a wire spacing of 1.3mm) to meet the temperature difference, power consumption and lightweight requirements.

[0110] Step 4: Output the Pareto frontier and adopt the entropy weight-artificial hybrid weight allocation method, including the objective weight of the entropy weight TOPSIS method obtained by calculating the information entropy of the Pareto solution set, and the artificial experience weight subjectively allocated based on engineering requirements. Finally, the two form a hybrid weight to balance the objective data rules and engineering experience, determine the optimal solution selection mechanism and output the optimal solution. In this embodiment, α = 0.6, β = 0.3, γ = 0.1.

[0111] The objective weight of the approximate ideal solution sorting method based on information entropy is expressed as:

[0112]

[0113] The subjective weight based on engineering requirements is expressed as:

[0114]

[0115] The mixing weight is expressed as:

[0116]

[0117] From this embodiment, it can be seen that the line spacing is 1.3mm, the line width is 1.3mm, and the unit mass is 7.8g / cm 2 , power density is 0.2W / cm 2 , the surface temperature difference is 3.5℃; when the power density is 0.71W / cm 2 , the surface temperature difference is 13.2℃.

[0118] Example 3

[0119] This embodiment provides a method for processing a polyimide film 1 using a chemically modified magnetron picosecond green pulse laser 6, comprising the following steps:

[0120] S31. Before laser preparation, the polyimide film 1 is cleaned with deionized water (conductivity ≤ 0.1 μs / cm) and anhydrous ethanol (content ≥ 99.7%, density 0.789-0.791 g / mL).

[0121] S32 , processing the cleaned polyimide film 1 with a magnetron picosecond green pulse laser 6 .

[0122] Schematic diagram of processing technology device Figure 5 As shown, the polyimide film 1 needs to be fixed to the processing groove 5 or plate with double-sided tape to ensure that the surface is evenly processed; a magnet 4 with a magnetic field strength of 70.0mt is placed under the processing groove 5 or plate, and the surface area of ​​the magnet 4 is ensured to be larger than the laser processing area; the laser is a picosecond green light pulse solid laser with a wavelength of 532nm and a laser power of 5W; the scanning line spacing is 0.008mm, and when processing in the x direction, the laser power is 100Khz, the scanning rate is 60mm / s, the idle running speed is 50mm / s, and the number of repeated processing times is 2; when processing in the y direction, the laser power is 120Khz, the scanning rate is 45mm / s, the idle running speed is 50mm / s, and the number of repeated processing times is 2.

[0123] The surface scanning electron microscope SEM image of the graphene composite film obtained after processing is as follows: Figure 6 As shown in Figure 2, a hollow structure can be vaguely seen on the surface, while the surface is covered with a graphene capillary structure similar to "fog"; the cross-sectional SEM image of the graphene composite film is shown in Figure 2. Figure 7 As shown, the "mist"-like nanographene capillaries are distributed on the top and side of the cone with a clearly visible nanoscale layered structure. The cone-shaped microstructure is 6.0 to 9.0 microns wide, 17.0 to 20.0 microns high, and has a contact angle of 26.7°, showing hydrophilicity.

[0124] S33. Drop a chemical modification reagent onto the surface with the graphene structure and wait for about 10 seconds.

[0125] When 3M's Novec1702 electronic coating droplets were placed on the graphene surface prepared on S2 and waited for 10 seconds, it showed superhydrophobicity with a contact angle of 163.0° and a rolling angle of 1.8°. However, after chemical modification on the surface of the non-processed polyimide film 1, the contact angle was only 97.1°.

[0126] The surface SEM image of the modified graphene composite film is shown in Figure 2. Figure 8 As shown in Figure 2, its morphology shows that it is attached with more particle structures and there are no graphene capillaries. The coating wraps the capillaries and forms particles. The cross-sectional SEM image of the modified graphene composite membrane is shown in Figure 2. Figure 9 As shown, nanoparticle structure can be seen on the surface of the pointed cone. The micron-scale structure is 6.0 to 9.0 microns wide and 17.0 to 20.0 microns high. The diameter of the nanoparticles is 10.0 to 200.0 nm.

[0127] The high temperature resistance is tested on a heating platform, the low temperature environment is ensured in a high-precision high and low constant temperature test chamber, and the anti-icing performance is tested on a semiconductor cooling table.

[0128] The test results show that after heating at 200°C, the surface contact angle is still greater than 160°C and the contact angle is less than 5°C. The dust on the surface can be easily removed by water, and the surface still has self-cleaning properties. In a -15°C refrigeration environment, on a -15°C refrigeration platform, the power density is 0.71W / cm 2 The freezing delay time of a 5 μL droplet was increased from 9.0 s to 274.1 s; the input power density was 0.71 W / cm 2 When the current is 0.01, the deicing time is extended from 26.5s to 32.0s, which is only extended by 3.5s.

[0129] The formula for calculating electric energy consumption is:

[0130]

[0131] Where, is the average power consumed per second during the freezing and de-icing cycle, P d is the power density, A 膜 is the area of ​​the membrane, τ m is the time required for electric heating to melt ice, and τ is the total time for ice formation and ice melting.

[0132] This embodiment shows that after adding the ice sensor, electric heating can be used to remove ice after ice forms, saving about 86% of electric energy.

[0133] Based on the description of the above embodiments, it can be seen that the embodiments of the present disclosure can achieve the following technical effects:

[0134] 1) The present invention not only has high superhydrophobic self-cleaning properties, but also ensures high temperature resistance and anti-icing performance, greatly reducing the power consumption required for anti-icing. In addition, the electric heating film designed based on "experiment-simulation-multi-objective optimization" takes into account the comprehensive consideration of the weight and heating performance of the film, and is suitable for the needs of extreme environments.

[0135] 2) The serpentine path electric heating wire proposed in this paper, based on the "experiment-simulation-multi-objective optimization" design, overcomes the difficulties of increased energy consumption, excessive surface temperature difference and increased weight caused by unreasonable design. It also combines NSGA-II with the CNN convolutional neural network proxy model, which is more efficient than traditional full simulation optimization. It proposes an entropy weight-artificial hybrid weight distribution method to balance objective data laws and engineering experience.

[0136] 3) The method of chemically modified magnetron picosecond green light pulse laser processing of polyimide proposed in the present disclosure has lower manufacturing costs, higher manufacturing efficiency, and is simple and easy to implement compared to other methods.

[0137] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technical improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A multifunctional sensing composite film, characterized in that: include: Polyimide film (1); A micro-nanostructured graphene layer, wherein the micro-nanostructured graphene layer is a multi-level micro-nanostructure formed by performing magnetron picosecond laser micromachining on a polyimide film (1); A chemically modified layer (3) covering the surface of the micro-nanostructured graphene layer; The embedded electric heating copper wire structure (2) is embedded in the polyimide film (1), and its arrangement is determined by multi-objective optimization of a non-dominated sorting genetic algorithm and a convolutional neural network agent model.

2. The multifunctional sensing composite film according to claim 1, characterized in that: The polyimide film (1) is subjected to magnetron picosecond laser micromachining, comprising: placing a magnet (4) horizontally below the polyimide film (1) so that the magnetic flux of the magnet (4) vertically passes through the surface of the polyimide film (1).

3. The multifunctional sensing composite film according to claim 1, characterized in that: The polyimide film (1) is subjected to magnetron picosecond laser micromachining to form a multi-level micro-nano structure, comprising: the polyimide film (1) is subjected to magnetron picosecond pulse laser processing to form a conical micron structure and a mist-like graphene capillary nanostructure.

4. The multifunctional sensing composite film according to claim 1, characterized in that: The chemical modification layer (3) comprises methyl nonafluoroisobutyl ether, methyl nonafluorobutyl ether and an acrylate-containing substance. The chemical modification layer (3) covers the surface of the micro-nanostructured graphene layer. After modifying the micro-nanostructured graphene layer, a micron-scale conical structure and a granular nanostructure are formed on the surface of the micro-nanostructured graphene layer.

5. The multifunctional sensing composite film according to claim 1, characterized in that: The embedded electric heating copper wire structure (2) is a serpentine path circuit based on experiment-simulation-multi-objective optimization design.

6. The multifunctional sensing composite film according to claim 5, characterized in that: The experiment-simulation-multi-objective optimization design includes: Establish a simulation model through multi-physics field coupling simulation software and optimize and adjust the model parameters; Obtaining temperature difference, power density, unit mass and temperature field distribution data on the surface of the polyimide film (1); Construct a three-dimensional data set containing regional thermal distribution characteristic maps and establish a database for multi-objective optimization design.

7. The multifunctional sensing composite film according to any one of claims 5 or 6, characterized in that: The experiment-simulation-multi-objective optimization design also includes: Establish a multi-objective optimization model for the input parameter space of line width and line spacing, surface temperature difference, power density, and unit mass; Generate Pareto solution sets through non-dominated sorting genetic algorithm and convolutional neural network agent model.

8. The multifunctional sensing composite film according to claim 7, characterized in that: The experiment-simulation-multi-objective optimization design further includes: outputting the optimal electrode arrangement scheme based on the Pareto solution set using an entropy weight-artificial hybrid weight allocation method, the specific steps of which are as follows: The objective weight of the entropy weight method is calculated based on the information entropy of the Pareto solution set; Set manual experience weights based on engineering requirements; Calculate the hybrid weight according to the objective weight of the entropy weight method and the artificial experience weight; The Pareto solution set is sorted according to the hybrid weight using the approximate ideal solution sorting method, and the optimal electrode arrangement scheme is output.

9. The method for preparing the multifunctional sensing composite film according to claim 1, wherein: The steps include: An electrically heated polyimide film (1) is manufactured according to an optimal electrode arrangement scheme, wherein the optimal electrode arrangement scheme is determined by multi-objective optimization using a non-dominated sorting genetic algorithm and a convolutional neural network agent model; A multi-level micro-nano structure is formed by performing magnetron picosecond laser micromachining on a polyimide film (1); A chemical modification agent is added dropwise onto the surface of the graphene structure of the multi-level micro-nano structure to form a chemical modification layer (3).

10. The method for preparing the multifunctional sensing composite film according to claim 9, characterized in that: The steps include: Prepare several electrically heated polyimide films (1) with different line widths and line spacings, and test their temperature characteristic data; Based on the temperature characteristic data, a simulation model is established using multi-physics field coupling simulation software, and the consistency between the model and the experiment is verified; Through large sample simulation, surface temperature difference, power density, unit mass and temperature field distribution data are collected to construct a three-dimensional data set; The three-dimensional data set is input into a non-dominated sorting genetic algorithm and a convolutional neural network agent model to output an optimal electrode arrangement scheme.

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