Single-walled carbon nanotube film, preparation method, system and application

Abstract

The invention relates to the technical field of single-walled carbon nanotube films, and provides a single-walled carbon nanotube film, a preparation method, a system and application, the preparation method is used for preparing the single-walled carbon nanotube film, and the preparation method comprises the following steps: obtaining a single-walled carbon nanotube layer; basic parameters of the multiple coating chambers are obtained, and the preset material layer thickness of each functional material layer is obtained; constructing a constraint function and constructing an optimization function for each coating chamber; and obtaining an optimal control decision parameter of the coating chamber according to the constraint function and the optimization function, controlling a corresponding working parameter of the coating chamber according to the optimal control decision parameter, and sequentially arranging a plurality of functional material layers on the single-walled carbon nanotube layer. The prepared single-walled carbon nanotube film has the characteristics of high strength, high conductivity, lightness and thinness, excellent flexibility, chemical stability and the like of a traditional single-walled carbon nanotube film, endows various other materials with excellent performance, and is suitable for being applied to the fields of flexible sensors, composite materials and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of single-walled carbon nanotube films, and in particular to a single-walled carbon nanotube film, a preparation method, a system and applications. Background Art

[0002] Carbon nanotubes, also known as buckytubes, are a unique one-dimensional quantum material with radial dimensions measured in nanometers and axial dimensions measured in micrometers, and are essentially sealed at both ends. Carbon nanotubes are primarily composed of several to dozens of coaxial circular tubes of hexagonally arranged carbon atoms, which can be thought of as rolled-up graphene sheets. Based on the number of graphene layers, carbon nanotubes can be categorized as single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs). During the initial formation of multi-walled nanotubes, the spaces between the layers easily become traps for various defects, resulting in the walls of multi-walled nanotubes often being riddled with small, hole-like defects. Compared to multi-walled nanotubes, single-walled nanotubes have a narrower diameter distribution, fewer defects, and greater uniformity.

[0003] Carbon nanotube film is a macroscopic thin film material with a thickness ranging from single-atom molecule to micrometer and millimeter scales, formed by a large number of carbon nanotubes entangled and connected with each other. It is an important part of the field of carbon nanotube research and has the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability. It has made certain progress in its application research in the fields of electronic power, energy storage, intelligent sensing, composite materials, aerospace, etc.

[0004] Although research on the application of carbon nanotube films in the fields of electronic power, energy storage, etc. has made certain progress, the requirements for material performance in these fields are constantly increasing. The demand for composite materials that can combine two or more advantageous properties in a new material is becoming more and more urgent. Traditional carbon nanotube films are generally unable to meet the above requirements. Therefore, it is necessary to develop a single-walled carbon nanotube film that can achieve multifunctional integration based on traditional single-walled carbon nanotube films, so that it can simultaneously combine two or more advantageous properties to meet the increasingly high performance requirements of composite materials in the fields of electronic power, energy storage, etc. Summary of the Invention

[0005] Based on this, in order to solve the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials with two or more excellent properties simultaneously in fields such as electronic power and energy storage, the present invention provides a single-walled carbon nanotube film, a preparation method, a system and an application. By sequentially arranging a plurality of functional material layers on the single-walled carbon nanotube layer, it can endow excellent properties of various other materials on the basis of the high strength, high conductivity, thinness, excellent flexibility and chemical stability of traditional single-walled carbon nanotube films, and is suitable for applications in fields such as flexible sensors and composite materials. The specific technical solutions are as follows:

[0006] A single-walled carbon nanotube film, which comprises:

[0007] A single-walled carbon nanotube layer;

[0008] A plurality of functional material layers, which are sequentially arranged on the single-walled carbon nanotube layer;

[0009] Wherein, the single-walled carbon nanotube layer and the plurality of functional material layers are sequentially arranged from bottom to top.

[0010] By sequentially arranging a plurality of functional material layers on the single-walled carbon nanotube layer, the single-walled carbon nanotube film can endow excellent properties of various other materials on the basis of the high strength, high conductivity, thinness, excellent flexibility and chemical stability of traditional single-walled carbon nanotube films, and is suitable for applications in fields such as flexible sensors and composite materials. It solves the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials with two or more excellent properties simultaneously in fields such as electronic power and energy storage.

[0011] Preferably, the single-walled carbon nanotube layer is obtained by applying a single-walled carbon nanotube solution on a metal substrate, and the metal substrate is a copper substrate, a gold substrate or a silver substrate.

[0012] Preferably, the functional material is a combination of one or more of nano metal materials, metal oxides, metal hydroxides, metal sulfides, metal salts, and semiconductor materials.

[0013] The present invention also provides a method for preparing a single-walled carbon nanotube film for preparing the single-walled carbon nanotube film, which comprises the following steps:

[0014] After pre-treating the metal substrate, the single-walled carbon nanotube is arranged on the pre-treated metal substrate to obtain a single-walled carbon nanotube layer;

[0015] Obtain the basic parameters of multiple coating chambers corresponding to the multiple functional material layers, the preset material layer thickness of each functional material layer, and the functional material layer thickness correction value, where the multiple functional material layers correspond one-to-one to the multiple coating chambers;

[0016] Construct a constraint function, and for each coating chamber, construct an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the functional material layer thickness correction value;

[0017] Obtain the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, control the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially arrange the multiple functional material layers on the single-walled carbon nanotube layer.

[0018] The single-walled carbon nanotube film prepared according to the preparation method, on the basis of having the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of the traditional single-walled carbon nanotube film, endows excellent properties of various other materials, and is suitable for applications in fields such as flexible sensors and composite materials. It solves the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials in the fields of electronic power, energy storage, etc. to have two or more superior properties at the same time.

[0019] Preferably, the specific method for constructing the constraint function includes the following steps:

[0020] Obtain the maximum sputtering power and the minimum sputtering power of the target in the coating chamber;

[0021] Obtain the maximum transfer speed and the minimum transfer speed of the coating chamber;

[0022] According to the preset total material layer thickness and the preset total thickness deviation value of the multiple functional material layers, obtain the maximum value and the minimum value of the functional material layer thickness correction value;

[0023] Construct a constraint function according to the maximum sputtering power, the minimum sputtering power, the maximum transfer speed, the minimum transfer speed, the maximum value and the minimum value of the functional material layer thickness correction value.

[0024] Preferably, the optimization function F(X) = F(D, P, V, ΔD);

[0025] Among them, D represents the preset material layer thickness, P represents the sputtering power of the target in the coating chamber, V represents the transfer speed of the coating chamber, ΔD represents the functional material layer thickness correction value, F(X) represents the minimum thickness error under the control decision parameter X, and F(X) = F(D, P, V, ΔD) represents finding the optimal control decision parameter X to minimize F(X).

[0026] Preferably, after the metal substrate is pretreated, the single-walled carbon nanotubes are disposed on the pretreated metal substrate. The specific method for obtaining the single-walled carbon nanotube layer includes the following steps:

[0027] The metal substrate is ultrasonically cleaned and vacuum dried;

[0028] A single-walled carbon nanotube solution is prepared, and the single-walled carbon nanotube solution is applied to the surface of the metal substrate that has been ultrasonically cleaned and vacuum dried by any one of the immersion deposition method, the drop coating method, the dip coating method, and the doctor blade method, thereby obtaining a single-walled carbon nanotube layer.

[0029] Preferably, the specific method for preparing the single-walled carbon nanotube solution includes the following steps:

[0030] The single-walled carbon nanotube powder is dissolved in an organic solvent;

[0031] Under ultrasonic assistance, the single-walled carbon nanotube powder in the organic solvent is uniformly dispersed to obtain the single-walled carbon nanotube solution.

[0032] The present invention also provides a single-walled carbon nanotube thin film preparation system for implementing the single-walled carbon nanotube thin film preparation method as described above, which includes:

[0033] A pretreatment module for pretreating the metal substrate and then disposing the single-walled carbon nanotubes on the pretreated metal substrate to obtain a single-walled carbon nanotube layer;

[0034] A parameter acquisition module for acquiring the basic parameters of a plurality of coating chambers corresponding to a plurality of the functional material layers, the preset material layer thickness of each of the functional material layers, and the functional material layer thickness correction value, and the plurality of the functional material layers correspond to the plurality of the coating chambers one by one;

[0035] A function construction module for constructing a constraint function, and for each coating chamber, constructing an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the functional material layer thickness correction value;

[0036] The optimal control decision parameter acquisition module is used to obtain the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, control the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially dispose the plurality of functional material layers on the single-walled carbon nanotube layer.

[0037] Correspondingly, the present invention also provides an application of the single-walled carbon nanotube thin film prepared according to the single-walled carbon nanotube thin film preparation method as described above, which applies the single-walled carbon nanotube thin film to a flexible sensor or a composite material. Brief Description of the Drawings

[0038] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0039] Figure 1 is a schematic diagram of the overall process for preparing a single-walled carbon nanotube film in an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of the process for obtaining a single-walled carbon nanotube layer in an embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of the process for constructing a constraint function in an embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of the process for preparing a single-walled carbon nanotube solution in an embodiment of the present invention.

[0043] Figure 5 is a schematic diagram of the overall structure of a single-walled carbon nanotube film preparation system in an embodiment of the present invention. Detailed Description of the Embodiments

[0044] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with its embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the protection scope of the present invention.

[0045] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only implementation.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0047] The "first" and "second" described in the present invention do not represent specific quantities and sequences, but are only used for name distinction.

[0048] Before specifically describing the embodiments of the present invention, a brief introduction to the prior art will be given first.

[0049] Carbon nanotubes, also known as buckytubes, are one-dimensional quantum materials with a special structure. Their radial size is on the nanometer scale, and their axial size is on the micrometer scale. Both ends of the tube are basically sealed. Carbon nanotubes are mainly composed of several to dozens of coaxial circular tubes formed by carbon atoms arranged in a hexagonal pattern. The distance between layers is fixed, and they can be divided into three types according to the different orientations of the carbon hexagons along the axis: zigzag, armchair, and helical. Among them, the helical carbon nanotubes have chirality, while the zigzag and armchair carbon nanotubes do not have chirality.

[0050] Carbon nanotubes can be regarded as being curled from graphene sheets. Therefore, they can be divided into single-walled carbon nanotubes (or single-layer carbon nanotubes, Single-walled Carbon nanotubes, SWCNTs) and multi-walled carbon nanotubes (or multi-layer carbon nanotubes, Multi-walled Carbon nanotubes, MWCNTs) according to the number of graphene sheets. When multi-walled tubes start to form, it is easy for the space between layers to become trap centers and capture various defects. Therefore, the tube walls of multi-walled tubes are usually covered with small hole-like defects. Compared with multi-walled tubes, single-walled tubes have a smaller distribution range of diameter sizes, fewer defects, and higher uniformity.

[0051] Carbon nanotube films are macroscopic film materials with a thickness ranging from single-atom molecules to micrometers or millimeters formed by a large number of carbon nanotubes intertwined and connected. It is an important part of the research field of carbon nanotubes and has characteristics such as high strength, high conductivity, lightness, excellent flexibility, and chemical stability. Certain progress has been made in the applied research in the fields of electronics, energy storage, intelligent sensing, composite materials, aerospace, etc.

[0052] Although certain progress has been made in the applied research of carbon nanotube films in the fields of electronics and energy storage, the requirements for material properties in these fields are constantly increasing. Especially for composite materials with two or more superior performance advantages, for example, on the basis of requiring light weight and high strength, the composite material is required to have good electrical conductivity, or on the basis of requiring flexibility, the composite material is required to have good electromagnetic shielding performance, or on the basis of requiring high electrical conductivity, the composite material is required to have stable chemical properties. So far, there have been few reports and applications of multifunctional carbon nanotube film composite materials based on single-walled carbon nanotubes.

[0053] In view of the problem that traditional carbon nanotube films generally cannot meet the increasing requirements for composite materials with two or more excellent performance advantages in fields such as electronic power and energy storage, it is necessary to develop a single-walled carbon nanotube film based on the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of carbon nanotubes, which can achieve multi-functional integration and have two or more excellent performance advantages at the same time, so as to meet the increasing performance requirements for composite materials in fields such as electronic power, energy storage, composite materials and intelligent flexible sensors.

[0054] An embodiment of the present invention provides a single-walled carbon nanotube film, which includes a single-walled carbon nanotube layer and a plurality of functional material layers sequentially arranged on the single-walled carbon nanotube layer. Among them, the single-walled carbon nanotube layer and the plurality of functional material layers are sequentially arranged from bottom to top, or the metal substrate, the single-walled carbon nanotube layer and the plurality of functional material layers can also be sequentially arranged from top to bottom.

[0055] Specifically, the single-walled carbon nanotube solution can be first applied on the metal substrate to obtain the single-walled carbon nanotube layer. The metal substrate includes, but is not limited to, a copper substrate, a gold substrate or a silver substrate. The functional materials include, but are not limited to, one or a combination of several of nano metal materials, metal oxides, metal hydroxides, metal sulfides, metal salts, and semiconductor materials.

[0056] The thickness of the single-walled carbon nanotube layer is between 1 μm and 15 μm, preferably between 5 and 10 μm. For the single-walled carbon nanotube layer, the metal substrate can be first ultrasonically cleaned and vacuum dried, then the single-walled carbon nanotube solution is prepared, and then the single-walled carbon nanotube solution is applied on the surface of the metal substrate that has been ultrasonically cleaned and vacuum dried by any one of the immersion deposition method, the drop coating method, the dip coating method, and the doctor blade method to obtain the single-walled carbon nanotube layer.

[0057] For each of the functional material layers, the thickness is between 1 μm and 20 μm, and a functional material layer with uniform thickness can be obtained by chemical vapor deposition or magnetron sputtering.

[0058] The functional material properties of the plurality of functional material layers can be the same or different. For example, the material property of one of the functional material layers can be one of nano metal copper, iron, nickel or aluminum, and the material properties of the other functional material layers include, but are not limited to, gold, silver and copper. In this way, the prepared single-walled carbon nanotube film simultaneously has the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of single-walled carbon nanotubes, as well as the excellent electromagnetic shielding and conductive properties of the functional material layer.

[0059] Preferably, the multiple functional material layers sequentially include a first shielding layer, a semiconductor layer, and a second shielding layer from bottom to top. An insulating transition layer is further provided between the single-walled carbon nanotube layer and the first shielding layer, between the first shielding layer and the semiconductor layer, and between the semiconductor layer and the second shielding layer. The insulating transition layer can be a PVA (polyvinyl alcohol) fiber layer. The first shielding layer and the second shielding layer are made of metals with electromagnetic shielding properties such as copper, aluminum, nickel, iron, cobalt, etc. The semiconductor layer is made of semiconductor materials such as silicon (Si), germanium (Ge), selenium (Se), boron (B), tellurium (Te), antimony (Sb), etc. In this way, the prepared single-walled carbon nanotube film simultaneously has the characteristics of high strength, high conductivity, lightness, excellent flexibility, and chemical stability of single-walled carbon nanotubes, as well as the electromagnetic shielding and conductivity of the first shielding layer, the second shielding layer, and the third shielding layer, and the semiconductor physical properties such as thermosensitivity, piezoresistivity, or photosensitivity of the semiconductor layer.

[0060] Of course, for the thickness and material properties of the multiple functional material layers, they can be appropriately adjusted according to the actual application situation to meet the requirements of specific application fields. An insulating transition layer and / or a surface protection layer (the function is to keep the surface of the surface functional material layer intact) can be provided between the multiple functional material layers.

[0061] By sequentially arranging multiple functional material layers on the single-walled carbon nanotube layer, the single-walled carbon nanotube film can endow excellent properties of various other materials on the basis of having the characteristics of high strength, high conductivity, lightness, excellent flexibility, and chemical stability of the traditional single-walled carbon nanotube film, and is suitable for applications in fields such as flexible sensors and composite materials. It solves the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials in the fields of electronic power, energy storage, etc., which require two or more superior performance advantages at the same time.

[0062] As Figure 1 shown, the present invention also provides a method for preparing a single-walled carbon nanotube film for preparing the single-walled carbon nanotube film, which includes the following steps:

[0063] S1, after preprocessing the metal substrate, set the single-walled carbon nanotubes on the preprocessed metal substrate to obtain a single-walled carbon nanotube layer.

[0064] Specifically, as Figure 2 shown, the specific method for setting the single-walled carbon nanotubes on the preprocessed metal substrate after preprocessing the metal substrate to obtain a single-walled carbon nanotube layer includes the following steps:

[0065] S11, perform ultrasonic cleaning and vacuum drying on the metal substrate;

[0066] S12. Prepare a single-walled carbon nanotube solution and apply the single-walled carbon nanotube solution onto the surface of the metal substrate that has been ultrasonically cleaned and vacuum dried using any one of the methods of immersion deposition, drop coating, dip coating, or blade coating to obtain a single-walled carbon nanotube layer.

[0067] As Figure 4 shown, the specific method for preparing the single-walled carbon nanotube solution includes: S121. Dissolve the single-walled carbon nanotube powder in an organic solvent, where the organic solvent includes but is not limited to absolute ethanol and n-butanol; S122. Under ultrasonic assistance, uniformly disperse the single-walled carbon nanotube powder in the organic solvent to obtain the single-walled carbon nanotube solution.

[0068] The metal substrate includes but is not limited to a copper substrate, a gold substrate, or a silver substrate. The functional material includes but is not limited to one or a combination of several of nano metal materials, metal oxides, metal hydroxides, metal sulfides, metal salts, and semiconductor materials.

[0069] The thickness of the single-walled carbon nanotube layer is between 5 - 10 μm. Since the pretreatment of the metal substrate and the preparation of the single-walled carbon nanotube solution are conventional technical means in the art, they will not be elaborated here.

[0070] S2. Obtain the basic parameters of multiple coating chambers corresponding to multiple functional material layers, the preset material layer thickness of each functional material layer, and the functional material layer thickness correction value. The multiple functional material layers correspond one-to-one with the multiple coating chambers.

[0071] For each functional material layer, it corresponds to a coating chamber. Each coating chamber is configured with a corresponding functional material target, and the corresponding functional material layer is deposited based on the magnetron sputtering process. The basic parameters include but are not limited to the target sputtering power and the transfer speed of the coating chamber.

[0072] By adjusting the target sputtering power and the transfer speed of the coating chamber, a functional material layer with a preset material layer thickness can be obtained.

[0073] If an insulating transition layer is also provided between the single-walled carbon nanotube layer and the functional material layer, and between the functional material layers, then before the first coating chamber and between adjacent two coating chambers, there are also insulating transition chambers for applying the insulating transition layer between the single-walled carbon nanotube layer and the first functional material layer, and between adjacent two functional material layers and functional material layers respectively. In this insulating transition chamber, the insulating transition layer can be applied between the single-walled carbon nanotube layer and the first functional material layer, and between adjacent two functional material layers and functional material layers respectively by spraying. For the insulating transition layer, it at least includes a spraying mechanism for spraying the insulating transition layer material powder or solution, such as a spray gun, etc.

[0074] One of the purposes of setting the insulating transition layer is to maintain the performance independence between the single-walled carbon nanotube layer and the multiple functional material layers, avoid interference between performances, and at the same time can isolate and protect the single-walled carbon nanotube layer and the multiple functional material layers to ensure the integrity of their functions.

[0075] By setting the insulating transition chamber and the multiple coating chambers, it is more convenient to prepare the single-walled carbon nanotube film. Since the coating chamber and the insulating transition layer are on a continuous production line, the production efficiency of the single-walled carbon nanotube film can also be improved. That is to say, through the preparation device, it can not only obtain a single-walled carbon nanotube film with characteristics such as high strength, high conductivity, thinness, excellent flexibility and chemical stability of the traditional single-walled carbon nanotube film, and endow it with excellent properties of a variety of other materials and is suitable for applications in fields such as flexible sensors and composite materials, but also improve the production efficiency and quality.

[0076] S3. Construct a constraint function, and for each of the coating chambers, construct an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the functional material layer thickness correction value.

[0077] S4. Obtain the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, control the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially set the multiple functional material layers on the single-walled carbon nanotube layer.

[0078] Preferably, the optimization function is expressed as F(X)=F(D,P,V,ΔD); where D represents the preset material layer thickness, P represents the sputtering power of the target in the coating chamber, V represents the transfer speed of the coating chamber, ΔD represents the functional material layer thickness correction value, F(X) represents the minimum thickness error under the control decision parameter X, and F(X)=F(D,P,V,ΔD) represents finding the optimal control decision parameter X to minimize F(X).

[0079] During the actual operation of the coating chamber, the sputtering power of the target will be affected by factors such as power supply voltage fluctuations and target purity, resulting in the inconsistency between the actual sputtering power and the preset sputtering power, that is, a certain error occurs, which leads to the error between the preset coating thickness and the actual coating thickness. Since the single-walled carbon nanotube film includes multiple functional material layers, if the errors of each functional material layer and the errors of the multiple functional material layers are not corrected, it will not only cause the thickness error of a single functional material layer and affect the physical properties of a single functional material layer, but also, with the accumulation of errors after each functional material layer is coated, it is possible to cause a large error in the multiple functional material layers and even the entire single-walled carbon nanotube film, reducing its comprehensive performance as a composite material.

[0080] By constructing the optimization function to correct the thickness of each functional material layer and obtain the optimal control decision parameters of the optimized coating chamber, the thickness error of the functional material layer can be reduced, the physical properties of a single functional material layer and the comprehensive properties of the single-walled carbon nanotube film can be improved, and the problem of large errors in multiple functional material layers and even the entire single-walled carbon nanotube film caused by the accumulation of errors after coating each functional material layer can be avoided.

[0081] Specifically, when the preset material layer thickness of each functional material layer is fixed, based on the optimization function F(X) = F(D, P, V, ΔD) and the optimization iteration method, the sputtering power of the target in the coating chamber and the conveying speed are continuously changed and adjusted to minimize the thickness error of the functional material layer corresponding to the coating chamber. More specifically, the thickness error is the thickness error between the predicted material layer thickness and the preset material layer thickness, and can be obtained based on the finite element simulation method or according to the historical operation data of the coating chamber for coating the corresponding functional material layer (including but not limited to the preset coating thickness and the actual coating thickness, as well as the thickness error between the preset coating thickness and the actual coating thickness and the corresponding target sputtering power and conveying speed). The sputtering power of the target, the corresponding preset material layer thickness and the actual material layer thickness (i.e., the material layer thickness actually measured after coating based on the sputtering power of the target) data at a certain conveying speed are obtained, a prediction curve function is constructed based on the sputtering power of the target, the corresponding preset material layer thickness and the actual material layer thickness at a certain conveying speed, and finally the corresponding predicted material layer thickness is obtained according to the prediction curve function. To reduce the computational amount, a conveying speed interval can also be set, that is, according to the set maximum conveying speed and minimum conveying speed, the conveying speed range [V min , V max is divided into multiple conveying speed intervals, and each conveying speed interval corresponds to a prediction curve function.

[0082] For the first coating chamber on the continuous production line, the thickness correction value of the functional material layer in the corresponding optimization function F(X) = F(D, P, V, ΔD) is zero, that is, the optimization function of the first coating chamber is F(X) = F(D, P, V). After obtaining the optimal control decision parameters X and the corresponding thickness error of this coating chamber based on the optimization function F(X) = F(D, P, V, ΔD) corresponding to the first coating chamber, in the order of the production line process, the optimization functions F(X) = F(D, P, V, ΔD) of the remaining several coating chambers are obtained one by one in sequence.

[0083] Obtaining the curve function between the target sputtering power and the predicted thickness at a certain transfer speed, and calculating the difference between the predicted thickness and the preset thickness based on the function curve. Taking this difference as the thickness error between the preset thickness and the actual thickness of the functional material layer has the advantage of eliminating the measurement of the actual thickness of the functional material layer corresponding to each coating chamber, thereby improving production efficiency.

[0084] For the thickness correction value of the functional material layer in the optimization function of a certain coating chamber, it can be understood as a thickness correction parameter related to the average value of the thickness errors of the functional material layers corresponding to several coating chambers before this coating chamber. The purpose of setting a thickness correction value for the optimization function of each coating chamber is to avoid large errors in multiple functional material layers and even the overall single-walled carbon nanotube film caused by the accumulation of errors after coating multiple functional material layers continuously.

[0085] Considering the influence of the thickness of the functional material layer on its performance, as a preferred technical solution, the thickness correction value of the functional material layer in the optimization function of a certain coating chamber where λ represents the thickness error adjustment coefficient, set by technicians, generally 1.0, n represents the total number of several coating chambers before a certain coating chamber, and Δd i represents the thickness error of the functional material layer corresponding to the i-th coating chamber before a certain coating chamber. It can be understood that based on the optimization function F(X) = F(D, P, V, ΔD), the preset thickness D of the functional material layer is D = D + ΔD.

[0086] Considering the influence of the thickness of the functional material layer on its performance, the thickness error adjustment coefficient λ is introduced, and based on the thickness error adjustment coefficient and the thickness errors of the functional material layers corresponding to several coating chambers before a certain coating chamber, by limiting the thickness correction value of the functional material layer, it can be ensured that the preset thickness of the functional material layer corrected according to the thickness correction value of the functional material layer is within a suitable range. That is to say, according to the formula of the thickness correction value of the functional material layer Taking into account the thickness errors of the functional material layers corresponding to several coating chambers before a certain coating chamber can avoid large errors in multiple functional material layers and even the overall single-walled carbon nanotube film caused by the accumulation of errors after coating multiple functional material layers continuously, and ensure the performance of each functional material layer.

[0087] For the thickness error adjustment coefficient, preferably, its obtaining method is as follows: First, for each coating chamber, when the preset material layer thickness is known, the initial thickness error under the optimal control decision parameter X is found according to the optimization function F(X) = F(D, P, V), and then according to the formula the thickness error adjustment coefficient of a certain coating chamber is obtained; where Δd' represents the initial thickness error of a certain coating chamber, and Δdmax It represents the maximum initial thickness error corresponding to several coating chambers in front of a certain coating chamber. For the thickness correction value of the functional material layer in the optimization function of the first coating chamber, it can be set to 0. Here, the optimization function F(X) = F(D, P, V) represents finding the optimal control decision parameter X and minimizing the initial thickness error F(X).

[0088] Formula The function of the formula is to adjust the thickness error adjustment coefficient λ of a certain coating chamber according to the initial thickness error corresponding to several coating chambers in front of a certain coating chamber and the initial thickness error of a certain coating chamber, so as to avoid that due to the excessive thickness error of the functional material layer corresponding to several coating chambers in front of a certain coating chamber, the thickness correction value of the functional material layer in the optimization function of a certain coating chamber is too large, affecting the performance of the functional material layer after optimizing the control decision parameter.

[0089] Considering the influence of the thickness of the functional material layer on its performance, as a preferred technical solution, the thickness correction value of the functional material layer in the optimization function of the i-th coating chamber where λ i represents the thickness error adjustment coefficient of the i-th coating chamber, which is set by the technical personnel and is generally 1.0. n represents the total number of several coating chambers in front of a certain coating chamber, and Δd i represents the thickness error of the functional material layer corresponding to the i-th coating chamber in front of a certain coating chamber. It can be understood that based on the optimization function F(X) = F(D, P, V, ΔD i ), the preset thickness D of the functional material layer = D + ΔD i .

[0090] Preferably, the thickness error adjustment coefficient of the i-th coating chamber η represents the material characteristic factor, which can be understood as a non-linear combination of refractive index and extinction coefficient. V real represents the real-time coating rate of the i-th coating chamber, and V nom represents the theoretical coating rate of the i-th coating chamber. Δh represents the cumulative thickness error of several coating chambers before the i-th coating chamber, h' represents the allowable error threshold, and α represents the adaptive gain coefficient, which can be dynamically calculated through the LSTM network.

[0091] Specifically, the material characteristic factor η is used to comprehensively reflect the characteristics of the functional material and reflect the sensitivity of the material to the coating rate. It usually includes the refractive index and extinction coefficient of the material. By considering the optical characteristics of the material, it ensures that the thickness error adjustment coefficient can adapt to the coating requirements of different materials, thereby improving the accuracy of coating thickness control.

[0092] The material characteristic factor η = n1 can be obtained through the dynamic weight allocation of the refractive index n1 and the extinction coefficient k 2 -k 2 , compensating for the influence of material characteristic fluctuations on the coating process.

[0093] The real-time coating rate reflects the actual coating rate at the current moment. By real-time monitoring the coating rate and dynamically adjusting the thickness error adjustment coefficient, the thickness control during the coating process can be made more precise. For the real-time coating rate, it can be obtained by weighted fusion of multi-modal signals through the attention mechanism, combining X-ray fluorescence thickness measurement data and plasma emission spectroscopy. The attention mechanism weighting is expressed as where w i represents the dynamic weight of the i-th process parameter in the feature space, with a value range of (0, 1). The sum of the weights is ensured to be 1 through the softmax function, which is used to quantify the influence degree of each sensor parameter in the coating chamber on the coating thickness correction; x i contains engineering parameters of three types of data sources:

[0094] 1. X-ray fluorescence thickness measurement data: real-time film thickness, historical thickness deviation, thickness fluctuation variance.

[0095] 2. Plasma emission spectroscopy: characteristic spectral line intensity, spectral peak full width at half maximum, spectral line offset.

[0096] 3. Substrate temperature field distribution: temperature gradient, temperature mean, heat flux density. f(x i ) = β·ReLU(W g ·x i +b g ), where β, W g , b g represent the process parameter sensitivity coefficient, learnable weight matrix, and bias term respectively.

[0097] Here, the attention mechanism weighting formula realizes the intelligent weighted fusion of coating process parameters through a combination of physical parameters and data-driven methods.

[0098] The theoretical coating rate is generally the ideal coating rate set according to process requirements and experience. As a reference standard, it is used to compare and correct the real-time coating rate, thereby calculating the thickness error adjustment coefficient.

[0099] The cumulative thickness error can be understood as the cumulative error between the actual coating thickness and the target thickness in several coating chambers before the i-th coating chamber during the coating process; through the cumulative thickness error, the thickness error during the coating process can be more comprehensively reflected, which is helpful for long-term thickness control and adjustment. For the cumulative thickness error, an improved ARIMA-GRU model can be used to process the spatio-temporal correlation of the error and achieve three-step-ahead prediction. Specifically, E t+1 = φ1E t + θ1ε t + σ(W g [h t , C t ).

[0100] Formula E t+1 = φ1E t + θ1ε t + σ(W g [h t , G t ) integrates the linear time series characteristics of the ARIMA model and the non-linear spatio-temporal feature extraction ability of the GRU network, and specifically includes three parts: 1. The autoregressive term φ1E t , which inherits the memory of the ARIMA model for historical errors; 2. The moving average term θ1ε t , which reflects the cumulative effect of historical random errors; 3. The GRU correction term σ(W g [h t , C t ), which captures spatio-temporal correlation features through a neural network.

[0101] Among them, E t represents the thickness error value at the current moment, which characterizes the deviation between the actual thickness of the coating layer and the target value, and is obtained in real time through an on-line thickness measurement sensor (such as X-ray fluorescence); φ1 represents the autoregressive coefficient, which reflects the influence weight of historical errors on the next moment and is obtained through ARIMA model parameter estimation. Its value range is usually (-1, 1), and the larger the absolute value, the stronger the persistence of the error; θ1 represents the moving average coefficient, which characterizes the contribution degree of historical random noise to the current prediction and can be solved by the maximum likelihood estimation method; ε t represents the white noise error term, which follows a normal distribution with a mean of 0 and a variance of σ 2 , representing the random disturbance that cannot be modeled in the process; h t represents the hidden state vector of the GRU network, which dynamically captures the long-term dependence relationship of the time series through a gating mechanism (update gate and reset gate). Its dimension depends on the GRU network structure design (such as 64 dimensions or 128 dimensions); C tRepresents the spatio-temporal context matrix, which is used to integrate process parameter time series (such as coating rate, vacuum degree, sputtering power, etc.), spatial correlation features (state correlation weights between coating chamber groups extracted by the Graph Attention Network (GAT)), and environmental parameters (such as substrate temperature field distribution, chamber gas flow state); W g Represents the trainable weight matrix, which is used to perform feature fusion on the hidden state h t and the context C t ; σ(·) represents the Sigmoid function (σ(x) = 1 / (1 + e -x )) and is used to map the linearly combined features to the interval (0, 1) as the gain coefficient of the GRU correction term, which can suppress the influence of outliers on the prediction. The σ(·) function can effectively suppress the prediction deviation caused by sensor noise.

[0102] Specifically, for the formula E t+1 = φ1E t + θ1ε t + σ(W g [h t , G t ), its spatio-temporal correlation modeling process is as follows:

[0103] 1. ARIMA baseline prediction: Generate a preliminary error prediction through φ1E t + θ1ε t ;

[0104] 2. GRU feature extraction: Use historical process data to train the GRU network to generate h t ;

[0105] 3. Context fusion: Weightedly fuse h t with the real-time process parameter C t through W g ;

[0106] 4. Nonlinear correction: Perform spatio-temporal feature compensation on the ARIMA prediction result through σ(·).

[0107] Compared with the traditional ARIMA fixed parameters, the formula E t+1 = φ1E t + θ1ε t + σ(W g [h t , G t ) realizes the dynamic coupling of process parameters and spatial states through W g . It has the dual characteristics of fusing minute-level process fluctuations (GRU) and hourly-level trend changes (ARIMA). In the prediction of coating thickness, it reduces the residual variance by 38.2% compared with the single ARIMA model, and the MAE (Mean Absolute Error) of the three-step-ahead prediction is controlled within ±1.5 nm.

[0108] The allowable error threshold can be understood as a pre-set allowable range of thickness error (such as ±5nm), that is, within this range, the coating thickness can be considered qualified. It serves as a threshold in the thickness error adjustment coefficient, which determines the response threshold of the adjustment coefficient. It can help control the adjustment range of the thickness error adjustment coefficient and avoid instability caused by excessive adjustment.

[0109] The adaptive gain coefficient is used to adjust the thickness error adjustment coefficient's strength, making it more flexible and intelligent, adapting to varying coating conditions and environmental changes. The adaptive gain coefficient can be generated using either an LSTM network or the MIT algorithm. In the MIT algorithm, the adaptive gain coefficient is optimized using gradient descent to ensure a balance between error convergence speed and stability. In the LSTM network, the optimal gain is predicted by learning nonlinear relationships from historical process data. In the MIT algorithm, the adaptive gain coefficient is updated in real time based on the gradient calculation of the generalized error.

[0110] The method of generating the adaptive gain coefficient by the MIT rule includes: m (t)-y(t) gradient information, real-time update of adaptive gain coefficient Among them, γ represents the preset learning rate, which is used to ensure the error convergence speed and stability balance, y m (t) and y(t) represent the outputs of the reference model and the controlled object, respectively.

[0111] The method of generating an adaptive gain coefficient by an LSTM network includes: designing a lightweight LSTM network, inputting real-time process parameters such as coating rate and temperature, and outputting an adaptive gain coefficient α = LSTM (V real ,T',P'), compensate for the physical model error through residual learning and improve the generalization ability under complex working conditions. T' and P' represent the substrate material temperature and the coating chamber pressure (or the vacuum degree of the coating chamber) respectively.

[0112] The thickness error adjustment coefficient formula is as follows The structure is described as follows:

[0113] 1. Dynamic feedback closed loop. V real and V nom The ratio of Δh and h' constitutes the proportional link, and the ratio of Δh and h' constitutes the integral link, forming a PI control structure; the integral saturation problem is suppressed through the nonlinear mapping of the tanh function.

[0114] 2. Multi-scale regulation mechanism. Microscale: η and V real Real-time adjustment of coating rate compensation; macro scale: Δh and α manage long-term cumulative errors.

[0115] In summary, the thickness error adjustment coefficient of the i-th coating chamber By comprehensively considering multiple factors such as material properties, real-time coating rate, theoretical coating rate, cumulative thickness error, operating error threshold, and adaptive gain coefficient, precise control and optimization of the thickness of the functional material layer during the coating process are achieved.

[0116] In summary, the single-walled carbon nanotube film prepared according to the preparation method has, on the basis of the characteristics of traditional single-walled carbon nanotube films such as high strength, high conductivity, lightness, excellent flexibility, and chemical stability, excellent properties of various other materials, and is suitable for applications in fields such as flexible sensors and composite materials. It solves the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials in fields such as electronic power and energy storage, which require two or more superior performance advantages at the same time.

[0117] As a preferred technical solution, in step S3, as Figure 3 shown, the specific method for constructing the constraint function includes the following steps:

[0118] S31, obtain the maximum sputtering power and the minimum sputtering power of the target material in the coating chamber.

[0119] S32, obtain the maximum transfer speed and the minimum transfer speed of the coating chamber.

[0120] For the maximum sputtering power, the minimum sputtering power, the maximum transfer speed, and the minimum transfer speed, they can all be set by technicians and will not be elaborated here.

[0121] S33, according to the preset total thickness of the material and the preset total thickness deviation value of multiple functional material layers, obtain the maximum value and the minimum value of the thickness correction value of the functional material layer.

[0122] Specifically, the preset total thickness deviation value can be expressed as ±ε. Assuming that the total number of multiple functional material layers is m, the maximum value of the thickness correction value of the functional material layer is ΔD max = +ε / m, and the maximum value of the thickness correction value of the functional material layer is ΔD min = -ε / m.

[0123] S34, according to the maximum sputtering power P max 、the minimum sputtering power P min 、the maximum transfer speed V max 、the minimum transfer speed V min 、the maximum value ΔD of the thickness correction value of the functional material layer max and the minimum value ΔD min construct a constraint function.

[0124] For the target sputtering power among the optimal control decision parameters, its corresponding constraint function is

[0125] For the transfer speed among the optimal control decision parameters, its corresponding constraint function is

[0126] For the correction value of the functional material layer thickness among the optimal control decision parameters, its corresponding constraint function is

[0127] By constructing the constraint function, it is possible to limit the correction value of the functional material layer thickness and the sputtering power and transfer speed in the optimal control decision parameters obtained according to the optimization function, and avoid the problem of excessive thickness error between the preset total thickness of the plurality of functional material layers and the actual total thickness of the material layers caused by the excessive or too small correction value of the functional material layer thickness, the sputtering power of the target in the coating chamber, and the transfer speed, so as to ensure the overall performance of the single-walled carbon nanotube film.

[0128] The present invention also provides a single-walled carbon nanotube film preparation system for implementing the single-walled carbon nanotube film preparation method as described above, as Figure 5 shown, which includes a pretreatment module, a parameter acquisition module, a first optimization parameter construction module, and an optimal control decision parameter acquisition module.

[0129] The pretreatment module is used to pretreat the metal substrate and then set the single-walled carbon nanotubes on the pretreated metal substrate to obtain a single-walled carbon nanotube layer; the parameter acquisition module is used to acquire the basic parameters of a plurality of coating chambers corresponding to the plurality of functional material layers, the preset material layer thickness of each functional material layer, and the correction value of the functional material layer thickness, and the plurality of functional material layers correspond to the plurality of coating chambers one by one.

[0130] The function construction module is used to construct a constraint function, and for each coating chamber, construct an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the correction value of the functional material layer thickness; the optimal control decision parameter acquisition module is used to obtain the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, and control the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially set the plurality of functional material layers on the single-walled carbon nanotube layer.

[0131] For each of the functional material layers, its thickness is between 1 μm and 20 μm, and a functional material layer with uniform thickness can be obtained by chemical vapor deposition or magnetron sputtering.

[0132] The functional material properties of multiple said functional material layers can be the same or different. For example, the material property of one of the functional material layers can be one of nano copper, iron, nickel or aluminum, and the material properties of the other functional material layers include but are not limited to gold, silver and copper. In this way, the prepared single-walled carbon nanotube film simultaneously has the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of single-walled carbon nanotubes, as well as the excellent electromagnetic shielding and conductive properties of the functional material layers.

[0133] That is to say, the single-walled carbon nanotube film prepared according to the said preparation system, on the basis of having the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of the traditional single-walled carbon nanotube film, endows excellent properties of various other materials, and is suitable for applications in fields such as flexible sensors and composite materials. It solves the problem that traditional carbon nanotube films usually cannot meet the increasing requirements for composite materials in fields such as electronic power and energy storage, which require two or more superior performance advantages at the same time.

[0134] As a preferred technical solution, multiple said functional material layers sequentially include a first shielding layer, a semiconductor layer and a second shielding layer from bottom to top. Insulating transition layers are also provided between the single-walled carbon nanotube layer and the first shielding layer, between the first shielding layer and the semiconductor layer, and between the semiconductor layer and the second shielding layer. The insulating transition layer can be a PVA (polyvinyl alcohol) fiber layer. The first shielding layer and the second shielding layer are made of metals with electromagnetic shielding properties such as copper, aluminum, nickel, iron, cobalt, etc. The semiconductor layer is made of semiconductor materials such as silicon (Si), germanium (Ge), selenium (Se), boron (B), tellurium (Te), antimony (Sb), etc. In this way, the prepared single-walled carbon nanotube film simultaneously has the characteristics of high strength, high conductivity, lightness, excellent flexibility and chemical stability of single-walled carbon nanotubes, as well as the electromagnetic shielding and conductive properties of the first shielding layer, the second shielding layer and the third shielding layer, and the semiconductor physical properties such as thermosensitivity, piezoresistivity or photosensitivity of the semiconductor layer.

[0135] Therefore, the said preparation system at least further includes multiple coating chambers corresponding to multiple said functional material layers, and insulating transition chambers provided before the first coating chamber and between adjacent two coating chambers for applying the insulating transition layer between the single-walled carbon nanotube layer and the first functional material layer and between adjacent two functional material layers and functional material layers respectively. And the coating chambers and the insulating transition layer are on a continuous production line.

[0136] By providing the insulating transition chamber and the plurality of coating chambers, it is more convenient to prepare the single-walled carbon nanotube film. Since the coating chambers and the insulating transition layer are on a continuous production line, the production efficiency of the single-walled carbon nanotube film can also be improved. That is to say, through the preparation device, not only can a single-walled carbon nanotube film with the characteristics of high strength, high conductivity, light weight, excellent flexibility and chemical stability of a traditional single-walled carbon nanotube film be obtained, and excellent properties of a variety of other materials be imparted, and it is suitable for applications in fields such as flexible sensors and composite materials, but also the production efficiency and quality can be improved.

[0137] Correspondingly, the present invention also provides an application of a single-walled carbon nanotube film prepared according to the single-walled carbon nanotube film preparation method described above, which applies the single-walled carbon nanotube film to a flexible sensor or a composite material.

[0138] By applying the single-walled carbon nanotube film to a flexible sensor or a composite material, and utilizing the characteristics of high strength, high conductivity, light weight, excellent flexibility and chemical stability of the single-walled carbon nanotube and the excellent electromagnetic shielding and conductive properties of the functional material layer of the prepared single-walled carbon nanotube film, the increasingly high performance requirements of the flexible sensor or composite material field for thin film materials can be met.

[0139] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0140] The above-described embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A single-walled carbon nanotube film, characterized in that, The single-walled carbon nanotube film includes: A single-walled carbon nanotube layer; Multiple functional material layers, which are sequentially arranged on the single-walled carbon nanotube layer; Wherein, the single-walled carbon nanotube layer and the multiple functional material layers are sequentially arranged from bottom to top.

2. The single-walled carbon nanotube film according to claim 1, wherein The single-walled carbon nanotube layer is obtained by applying a single-walled carbon nanotube solution on a metal substrate, and the metal substrate is a copper substrate, a gold substrate or a silver substrate.

3. The single-walled carbon nanotube film according to claim 2, wherein The functional material is one or a combination of several of nano metal materials, metal oxides, metal hydroxides, metal sulfides, metal salts, semiconductor materials.

4. A method for preparing a single-walled carbon nanotube film, which is used to prepare the single-walled carbon nanotube film according to any one of claims 1-3, characterized in that, The method for preparing the single-walled carbon nanotube film includes the following steps: After pre-treating the metal substrate, arranging single-walled carbon nanotubes on the pre-treated metal substrate to obtain a single-walled carbon nanotube layer; Obtaining the basic parameters of multiple coating chambers corresponding to the multiple functional material layers, the preset material layer thickness of each functional material layer, and the functional material layer thickness correction value, and the multiple functional material layers correspond to the multiple coating chambers one by one; Constructing a constraint function, and for each coating chamber, constructing an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the functional material layer thickness correction value; Obtaining the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, controlling the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially arranging the multiple functional material layers on the single-walled carbon nanotube layer.

5. The method for preparing a single-walled carbon nanotube film according to claim 4, wherein, The specific method for constructing the constraint function includes the following steps: Obtaining the maximum sputtering power and the minimum sputtering power of the target in the coating chamber; Obtaining the maximum transfer speed and the minimum transfer speed of the coating chamber; According to the preset total material thickness and the preset total thickness deviation value of the multiple functional material layers, obtaining the maximum value and the minimum value of the functional material layer thickness correction value; Constructing a constraint function according to the maximum sputtering power, the minimum sputtering power, the maximum transfer speed, the minimum transfer speed, the maximum value and the minimum value of the functional material layer thickness correction value.

6. The method for preparing a single-walled carbon nanotube film according to claim 5, characterized in that, The optimization function F(X) = F(D, P, V, ΔD); Wherein, D represents the preset material layer thickness, P represents the sputtering power of the target in the coating chamber, V represents the transfer speed of the coating chamber, ΔD represents the functional material layer thickness correction value, F(X) represents the minimum thickness error under the control decision parameter X, and F(X) = F(D, P, V, ΔD) represents finding the optimal control decision parameter X to minimize F(X).

7. The method for preparing a single-walled carbon nanotube film according to claim 6, characterized in that, The specific method for arranging single-walled carbon nanotubes on the pre-treated metal substrate to obtain a single-walled carbon nanotube layer after pre-treating the metal substrate includes the following steps: Performing ultrasonic cleaning and vacuum drying on the metal substrate; Preparing a single-walled carbon nanotube solution, and applying the single-walled carbon nanotube solution on the surface of the metal substrate that has been ultrasonically cleaned and vacuum dried by any one of the immersion deposition method, the drop coating method, the dip coating method, and the doctor blade method to obtain a single-walled carbon nanotube layer.

8. The method for preparing a single-walled carbon nanotube film according to claim 7, wherein, The specific method for preparing the single-walled carbon nanotube solution includes the following steps: Dissolve the single-walled carbon nanotube powder in an organic solvent; Under ultrasonic assistance, uniformly disperse the single-walled carbon nanotube powder in the organic solvent to obtain the single-walled carbon nanotube solution.

9. A single-walled carbon nanotube film preparation system for implementing the single-walled carbon nanotube film preparation method according to any one of claims 4-8, characterized in that, The single-walled carbon nanotube thin film preparation system includes: A pretreatment module for pretreating a metal substrate and then setting single-walled carbon nanotubes on the pretreated metal substrate to obtain a single-walled carbon nanotube layer; A parameter acquisition module for acquiring the basic parameters of multiple coating chambers corresponding to multiple functional material layers, the preset material layer thickness of each functional material layer, and the functional material layer thickness correction value, where the multiple functional material layers correspond to the multiple coating chambers one by one; A function construction module for constructing a constraint function and, for each coating chamber, constructing an optimization function according to the preset material layer thickness of the corresponding functional material layer, the basic parameters of the coating chamber, and the functional material layer thickness correction value; An optimal control decision parameter acquisition module for obtaining the optimal control decision parameters of the coating chamber according to the constraint function and the optimization function, controlling the working parameters of the corresponding coating chamber according to the optimal control decision parameters, and sequentially setting the multiple functional material layers on the single-walled carbon nanotube layer.

10. Use of a single-walled carbon nanotube film prepared by the method for preparing a single-walled carbon nanotube film according to any one of claims 4-8, characterized in that Apply the single-walled carbon nanotube thin film to a flexible sensor or a composite material.

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