Modified graphene-based epoxy coating, coating, preparation method of modified graphene-based epoxy coating and anti-corrosion application of modified graphene-based epoxy coating

Abstract

The invention discloses a modified graphene-based epoxy coating, a coating and a preparation method and anti-corrosion application of the modified graphene-based epoxy coating, and belongs to the technical field of epoxy coatings. The modified graphene-based epoxy coating comprises an epoxy coating substrate liquid and modified graphene, preferably, the modified graphene is prepared from graphene and polydimethylsiloxane. The organic paint and the coating prepared by the invention can greatly improve the resistance to marine corrosion, and can provide advantages for ship body corrosion protection.

Description

Technical Field

[0001] The present invention relates to and belongs to the technical field of epoxy coatings, and in particular to a modified graphene-based epoxy coating, a coating, a preparation method thereof, and anti-corrosion applications. Background Art

[0002] Epoxy coating is one of the most economical and effective ways to protect metals from corrosive media, but the shortcomings of epoxy coatings, such as high brittleness, easy microporous cracks, and poor wear resistance, reduce its anti-corrosion effect. In order to improve the anti-corrosion performance of epoxy coatings, epoxy coatings must be modified. Graphene-based materials are widely concerned in the field of anti-corrosion due to their unique structure, large specific surface area, good water-blocking and oxygen-isolating properties, ion impermeability and chemical stability. They are a good choice for improving the anti-corrosion performance of epoxy coatings. However, graphene oxide has poor compatibility with epoxy resin, which leads to stacking, migration, and sliding of graphene oxide in epoxy coatings. It cannot effectively isolate the invasion of corrosive media such as water molecules, oxygen, and chloride ions, which limits its application in the field of anti-corrosion.

[0003] At present, loading nano-inorganic particles, silane coupling agent modification and polymer modification are common methods to improve the dispersion of graphene-based materials in resins. By combining different methods to modify graphene-based materials together, not only can the dispersion of graphene-based materials in resins be effectively improved, but also new functions can be given to the coating, which can significantly improve the anti-corrosion performance of the coating. Summary of the invention

[0004] The present invention provides a modified graphene-based epoxy coating, a coating, a preparation method thereof, and anti-corrosion applications, to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the first aspect of the present invention is to disclose a modified graphene-based epoxy coating, characterized in that the modified graphene-based epoxy coating comprises an epoxy coating base liquid and modified graphene.

[0006] Furthermore, the modified graphene includes graphene-based materials and polydimethylsiloxane.

[0007] Furthermore, the graphene-based material is a graphene-based raw material modified by organosilane;

[0008] Preferably, the graphene-based raw material is at least one of graphene and graphene oxide;

[0009] Preferably, the organosilane is at least one of 3-aminopropyltriethoxysilane and 3-mercaptopropyltrimethoxysilane;

[0010] Preferably, the polydimethylsiloxane is at least one of trimethyl-terminated polydimethylsiloxane, hydroxy-terminated polydimethylsiloxane and bis-3-methacryloxypropylated tetramethyldisiloxane;

[0011] Preferably, the catalyst used in modifying graphene is 1,8-diazabicyclo[5.4.0]undec-7-ene.

[0012] Furthermore, the epoxy coating base liquid includes epoxy resin and xylene.

[0013] Furthermore, the weight ratio of the graphene-based raw material to the organosilane in the organosilane-modified graphene-based material is (0.2-1):1;

[0014] Preferably, the weight ratio of the modified graphene-based material, polydimethylsiloxane and the catalyst in the modified graphene is 1:(2-8):0.0001;

[0015] Preferably, the weight ratio of epoxy resin to xylene in the epoxy coating base liquid is 8:(1-3) by weight;

[0016] Preferably, the weight ratio of the epoxy coating base liquid to the modified graphene is 4.5:(0.025-0.1) in parts by weight.

[0017] The second aspect of the present invention is to disclose a method for preparing the modified graphene-based epoxy coating according to the first aspect, comprising the following steps:

[0018] S01, preparing modified graphene;

[0019] S02, mixing the modified graphene prepared in S01 with the epoxy coating base liquid to obtain the modified graphene-based epoxy coating.

[0020] Furthermore, in S01, the graphene-based raw material is added to anhydrous ethanol, uniformly dispersed by ultrasonic treatment for 20-30 minutes, and kept at 60°C for 30-60 minutes; then, organic silane is added, and the modified graphene-based material is obtained after reflux reaction at 60°C for 6-12 hours, centrifuged, washed with ethanol and deionized water, and dried; then, the modified graphene-based material, polydimethylsiloxane and catalyst are dispersed in chloroform solvent, liquid nitrogen freezing and deoxygenation cycle are performed, and the reaction is carried out at 40°C in a nitrogen atmosphere for 18 hours, and the modified graphene is obtained by evaporation and separation.

[0021] The third aspect of the present invention is to disclose a modified graphene-based epoxy coating prepared by the modified graphene-based epoxy coating described in the first aspect, and the raw materials for preparing the modified graphene-based epoxy coating include the modified graphene-based epoxy coating and a curing agent.

[0022] The fourth aspect of the present invention is to disclose a method for preparing the modified graphene-based epoxy coating according to the third aspect, comprising adding 1 part by weight of a curing agent to 8 parts by weight of the modified graphene-based epoxy coating, stirring for 10 minutes, vacuum degassing, applying the coating on the alloy surface, and curing to obtain the modified graphene-based epoxy coating;

[0023] Preferably, the alloy surface is pretreated by grinding and ultrasonic cleaning with acetone, anhydrous ethanol and deionized water;

[0024] Preferably, the alloy is an aluminum alloy or carbon steel;

[0025] Preferably, in S02, the magnetic stirring time is 6-8 hours, the curing agent is polyamide 650, and the modified graphene-based epoxy coating is obtained by curing for 2 days at room temperature.

[0026] The fifth aspect of the present invention is to disclose the application of the modified graphene-based epoxy coating described in the third aspect in ship corrosion protection.

[0027] Furthermore, tiny embedded sensors are added to the sample coatings to conduct compatibility tests between the sensors and the coatings. The sensors have signal processing circuits or interfaces to convert sensor signals into digital signals that can be transmitted via wires or wirelessly. They can be connected to external monitoring equipment via wires or wirelessly to form a sensor network to monitor temperature, stress and other parameters in real time. The sensor data is preprocessed, and the received data is processed and analyzed to extract useful information such as temperature distribution and stress change trends. Based on the data analysis results, the coating process parameters are optimized to improve the coating quality and performance. The optimized process parameters are fed back to the control strategy module to continuously improve the coating process and evaluate the state of the alloy surface.

[0028] The monitoring data is backed up and stored regularly, and when abnormal data is monitored, an alarm or early warning mechanism is triggered.

[0029] Based on historical data and experimental data, a mathematical model between coating process parameters (temperature, pressure) and alloy surface parameters (temperature, stress) is established. Using machine learning or optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), the temperature and pressure in the coating process are dynamically adjusted according to real-time monitoring data to minimize temperature fluctuations and stress concentration on the alloy surface. The adjusted temperature and pressure parameters are fed back to the coating equipment to achieve real-time control. According to the data analysis results and process optimization requirements, the algorithm and control strategy are continuously improved to form a closed-loop control.

[0030] Furthermore, a high-precision mathematical model is established to describe the complex relationship between coating process parameters (such as temperature and pressure) and alloy surface parameters (such as temperature distribution and stress state): based on physical laws and experimental data, a multivariable nonlinear equation describing the relationship between coating process parameters and alloy surface parameters is established, and a deep neural network (such as convolutional neural network, recurrent neural network, etc.) or a hybrid model (such as a combination of neural network and physical model) is used to fit and optimize the multivariable nonlinear equation to improve the prediction accuracy of the model.

[0031] Furthermore, an advanced optimization algorithm is designed to dynamically adjust the coating process parameters according to the real-time monitored alloy surface parameters to achieve the optimization of coating quality and performance: the coating quality and performance are taken as optimization targets, and a multi-objective optimization problem is established. A genetic algorithm is used for global search, and a set of coating process parameters are randomly generated as the initial population. Each individual represents a set of specific process parameters (such as temperature, pressure, etc.). The fitness of each individual is evaluated according to the actual effect of the coating process (such as coating quality, stress distribution, etc.). This usually requires an evaluation function or experimental data to support it. According to the fitness value, excellent individuals are selected for crossover (combining the gene fragments of two individuals to generate new individuals) and mutation (randomly changing certain gene values ​​of individuals to increase diversity). The above process is repeated until the predetermined number of iterations is reached or a satisfactory solution is found. At this point, the genetic algorithm has searched one or more potential optimization solution spaces.

[0032] Furthermore, the search results of the genetic algorithm are locally optimized by using a reinforcement learning algorithm. In the solution space searched by the genetic algorithm, a strategy is defined, which determines how to adjust the current coating process parameters to find a better solution. The coating process parameters are regarded as states, and possible adjustments (such as increasing or decreasing temperature, pressure, etc.) are regarded as actions. A reward function is designed to evaluate the effect of each action (i.e., the adjusted coating process parameters). The higher the reward value, the closer the adjusted parameters are to the optimal solution. A reinforcement learning algorithm (such as Q-learning, policy gradient, etc.) is used to find the action sequence that maximizes the reward value through continuous trial and error and learning. In the solution space searched by the genetic algorithm, a reinforcement learning algorithm is used to perform a local search to find the optimal coating process parameters.

[0033] In order to dynamically adjust the coating process parameters, a strategy update formula based on reinforcement learning is designed. This formula will update the strategy based on the current state (coating process parameters), action (parameter adjustment) and reward (coating effect). Assume that the current state is s t , the action taken is a t , the reward is r t , the next state is s t+1, the strategy is π(a|s) (indicating the probability of taking action a in state s). Use the Q-learning algorithm to update the Q value (i.e. the value of the state-action pair):

[0034] Q(s t , a t )←Q(s t , a t )+α[r t +γmax a’ Q(s t+1 , a)-Q(s t , a t )]

[0035] Among them, α is the learning rate, which determines the coverage of new information over old information; γ is the discount factor, which determines the importance of future rewards; a′ is the next possible action. Based on the Q value, the strategy π can be updated:

[0036] π(a|s)∝exp(βQ(s,a))

[0037] Among them, β is a temperature parameter used to control the "greedy" degree of the strategy (i.e., it is more inclined to choose actions with high Q values).

[0038] The coating process parameters (such as temperature, pressure, etc.) are quantified into discrete or continuous state and action spaces, and a reward function is designed based on the coating effect. Then, the above formula is used for iterative learning until the optimal coating process parameters are found.

[0039] Compared with the prior art, the present invention provides a modified graphene-based epoxy coating, coating, preparation method and anti-corrosion application thereof, which has the following beneficial effects:

[0040] (1) The present invention proposes a long-lasting anti-corrosion paint and coating for the marine environment, which can significantly improve the resistance to marine corrosion. The modified graphene-based material prepared by the present invention exhibits a higher water contact angle, promotes the peeling between graphene-based materials, and avoids the agglomeration and stacking of graphene-based materials. At the same time, the modification improves the compatibility of graphene-based materials with epoxy resin. The prepared modified graphene-based epoxy coating exhibits stronger mechanical properties and better salt spray corrosion resistance;

[0041] (2) The graphene-based raw materials in the thiol-modified graphene-based materials themselves have extremely high specific surface area and excellent barrier properties, and can form a "maze-like" shielding structure to effectively inhibit the infiltration, penetration and diffusion of corrosive media. The thiol-modified graphene-based materials can further enhance this physical barrier property and tightly combine with the epoxy coating through chemical bonding and other methods to form a denser and more stable anti-corrosion layer; and the graphene-based raw materials have good electrical conductivity and can form a conductive network to provide better electrochemical protection. The thiol modification improves the compatibility and dispersibility of the graphene-based raw materials and the epoxy coating, thereby maintaining the conductivity and cathodic protection of the coating and reducing the occurrence of corrosion reactions; graphene itself has high strength and high toughness, and adding it to the epoxy coating can significantly improve the coating. The strength and toughness of the coating can be improved by thiol modification. The thiol modification further enhances the interaction between graphene and the epoxy coating, thereby improving the overall mechanical properties of the coating. The addition of graphene can improve the wear resistance of the coating, making it more durable. At the same time, the anti-ultraviolet properties of graphene can also give the coating a certain weather resistance, reducing the aging and peeling of the coating caused by ultraviolet light. The dispersibility of graphene in the coating has an important influence on its anti-corrosion performance. The thiol modification can improve the surface properties of graphene, making it easier to disperse evenly in the epoxy coating, thereby avoiding the occurrence of agglomeration. Through thiol modification, more functional groups can be introduced to enhance the interaction between graphene and the epoxy coating, which helps to form a more stable and uniform coating structure and improve the overall performance of the coating.

[0042] (3) Through the catalytic action of polydimethylsiloxane and DBU, the thiol functional groups on the surface of graphene are modified, so that the dispersibility of graphene in the epoxy coating is significantly improved. The stable dispersibility helps to reduce the porosity of the coating and enhance the density of the coating, thereby improving the anti-corrosion effect of the coating, and can block the penetration of corrosive media such as water, oxygen, and chloride ions in the environment, thereby slowing down or preventing the corrosion process of Q235 carbon steel;

[0043] (4) Adding a curing agent to the modified graphene-based epoxy coating and stirring, vacuum degassing, brushing on the alloy surface, and curing to obtain the modified graphene-based epoxy coating makes it very important to control the operating parameters such as temperature and pressure. Tiny embedded sensors are introduced into the sample to monitor the temperature, stress and other parameters of the alloy surface in real time, which is convenient for timely adjustment and unified intelligent industrial process, realizing fully automated feedback control, and ensuring the stability and controllability of the coating process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The Raman spectra of the modified graphene oxide samples at various stages prepared in Example 1;

[0045] Figure 2Optical microscope photos of the cross-section of the epoxy coating prepared in Example 1: (a and b) are modified graphene oxide-based epoxy coatings; (c and d) are pure epoxy resin coatings;

[0046] Figure 3 Anticorrosion performance test of the epoxy coating prepared in Example 1: (a) Nyquist plot, (b) Bode plot and (c) Tafel plot;

[0047] Figure 4 This is a graph showing the results of the salt spray chamber test of the epoxy coating prepared in Example 1;

[0048] Figure 5 Anticorrosion performance test of the epoxy coatings prepared in Example 1 and Example 2: (a) Nyquist plot, (b) Bode plot and (c) Tafel plot;

[0049] Figure 6 Anticorrosion performance test of the epoxy coatings prepared in Example 1 and Examples 3-6: (a) Nyquist plot and (b) Tafel plot;

[0050] Figure 7 This is a graph showing the salt spray chamber test results for the epoxy coating prepared in Comparative Example 1. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] In addition, in the present invention, the descriptions such as "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions and technical features between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] Unless otherwise specified, the examples and comparative examples are parallel tests with the same components, component contents, preparation steps and preparation parameters. The experimental methods in the following examples are conventional methods unless otherwise specified. The test materials used in the following examples are analytical reagents (AR) unless otherwise specified. Parts are parts by weight unless otherwise specified, and the materials are purchased from commercial channels.

[0054] Graphene oxide was purchased from Macklin, G864820, Cas No. 7782-42-5, purity>99%; graphene was purchased from SigmaAldrich, powder, conductivity>10 3 S / m, CAS number: 1034343-98-0; bisphenol A epoxy resin (E-51) was purchased from Hubei Kewode Chemical Co., Ltd., with an epoxy value of 0.53eq / 100g; trimethyl-terminated polydimethylsiloxane with a molecular weight of 4000, hydroxy-terminated polydimethylsiloxane with a molecular weight of 500, and bis-3-methylpropyleneoxypropylated tetramethyldisiloxane with a molecular weight of 800, all of which were purchased from SigmaAldrich; polyamide 650 was purchased from Aladdin, CAS number: 63428-84-2, amine value: 200-240mgKOH / g.

[0055] Example 1

[0056] 1) By weight, 100 parts of graphene oxide powder are added to 200 parts of anhydrous ethanol, ultrasonically dispersed for 30 minutes, and then kept warm in an oil bath at 60°C for 30 minutes. 0.2 parts of 3-mercaptopropyltrimethoxysilane are slowly added under stirring, followed by reflux at 60°C for 8 hours. After the reaction system is cooled, centrifuge at 12000r / min for 10 minutes to separate the thiol-modified graphene-based material. Add 200 parts of anhydrous ethanol, ultrasonically disperse for 30 minutes to wash away the residual 3-mercaptopropyltrimethoxysilane, and repeat the centrifugation and washing process three times. The powder material is freeze-dried in a cold trap at -43°C for 12 hours to obtain a thiol-modified graphene-based material.

[0057] 2) By weight, 100 parts of thiol-modified graphene-based materials, 600 parts of bis-3-methylpropenyloxypropylated tetramethyldisiloxane and 0.01 parts of 1,8-diazabicyclo[5.4.0]undec-7-ene are dispersed in 8 parts of chloroform solvent. The mixed solution is placed in a Schlenk bottle, and after three deoxygenation cycles of liquid nitrogen freezing-vacuum extraction-nitrogen filling, the mixed solution is reacted at 40°C for 18h. Finally, the chloroform solvent is removed by a rotary evaporator at 60°C to separate the modified graphene.

[0058] 3) Grind Q235 carbon steel with 800, 1200 and 2000 grit sandpaper respectively, rinse with deionized water and anhydrous ethanol respectively, and dry in an air drying oven for later use.

[0059] 4) By weight, 4.0 parts of epoxy resin, 0.5 parts of xylene and 0.1 parts of modified graphene were mixed, and magnetic stirring was performed for 6 hours to obtain a modified graphene-based epoxy coating.

[0060] 5) By weight, 8 parts of modified graphene-based epoxy coating were added to 1 part of polyamide 650, stirred for 10 minutes, and then vacuum-defoamed and applied to the surface of carbon steel. The modified graphene-based epoxy coating was cured for 2 days at room temperature to obtain the modified graphene-based epoxy coating.

[0061] Example 2

[0062] The difference from Example 1 is that an equal weight portion of graphene is used to replace the graphene oxide in Example 1 to prepare the modified graphene-based epoxy coating and coating, and the rest is consistent with Example 1.

[0063] Example 3

[0064] The difference from Example 1 is that the weight portion of modified graphene used in preparing the modified graphene-based epoxy coating is 0.12, and the rest is consistent with Example 1.

[0065] Example 4

[0066] The difference from Example 1 is that the weight portion of modified graphene used in preparing the modified graphene-based epoxy coating is 0.075, and the rest is consistent with Example 1.

[0067] Example 5

[0068] The difference from Example 1 is that the weight portion of modified graphene used in preparing the modified graphene-based epoxy coating is 0.05, and the rest is consistent with Example 1.

[0069] Example 6

[0070] The difference from Example 1 is that the weight portion of modified graphene used in preparing the modified graphene-based epoxy coating is 0.025, and the rest is consistent with Example 1.

[0071] Example 7

[0072] The difference from Example 1 is that the weight portion of polydimethylsiloxane used in preparing the modified graphene is 200, and the rest is consistent with Example 1.

[0073] Example 8

[0074] The difference from Example 1 is that the weight portion of polydimethylsiloxane used in preparing the modified graphene is 800, and the rest is consistent with Example 1.

[0075] Example 9

[0076] The difference from Example 1 is that the weight portion of xylene used in preparing the modified graphene-based epoxy coating is 1.5, and the rest is consistent with Example 1.

[0077] Example 10

[0078] The difference from Example 1 is that the weight part of xylene used in preparing the modified graphene-based epoxy coating is 1, and the rest is consistent with Example 1.

[0079] Embodiment 11

[0080] The difference from Example 1 is that 3-aminopropyltriethoxysilane in equal parts by weight is used to replace the 3-mercaptopropyltrimethoxysilane in Example 1, and the rest is the same as Example 1.

[0081] Example 12

[0082] The difference from Example 1 is that an equal weight portion of trimethyl-terminated polydimethylsiloxane is used to replace the bis-3-methacryloxypropylated tetramethyldisiloxane in Example 1, and the rest is the same as Example 1.

[0083] Embodiment 13

[0084] The difference from Example 1 is that an equal weight portion of hydroxyl-terminated polydimethylsiloxane is used to replace the bis-3-methacryloxypropylated tetramethyldisiloxane in Example 1, and the rest is the same as Example 1.

[0085] Embodiment 14

[0086] Tiny embedded sensors (such as temperature sensors, stress sensors, etc.) are added to the sample coating prepared in Example 1. Temperature sensors such as thermistors, thermocouples, etc., and stress sensors such as strain gauges, piezoresistive sensors, etc., will be tightly combined with the coating after curing. Compatibility tests between the sensors and the coating are performed to ensure that the sensors will not chemically react in the coating or affect the performance of the coating. The sensor has a signal processing circuit or interface to convert the sensor signal into a digital signal that can be transmitted through a wire or wirelessly. It can be connected to an external monitoring device through a wire or wirelessly to form a sensor network to monitor in real time the temperature, stress and other parameters when the coating is applied to the alloy to form a coating. The sensor data is preprocessed, including filtering, denoising, calibration, etc., to improve the accuracy and reliability of the data. The received data is processed and analyzed to extract useful information such as temperature distribution, stress change trend, etc. Based on the data analysis results, the coating process parameters are optimized to improve the coating quality and performance. The optimized process parameters are fed back to the control strategy module to continuously improve the coating process and evaluate the state of the alloy surface.

[0087] The monitoring data is backed up and stored regularly to prevent data loss or damage. According to the preset thresholds or conditions, when abnormal data is monitored, an alarm or early warning mechanism is triggered so that timely measures can be taken to deal with it.

[0088] Based on historical data and experimental data, a mathematical model between coating process parameters (temperature, pressure) and alloy surface parameters (temperature, stress) is established. Using machine learning or optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), the temperature and pressure in the coating process are dynamically adjusted according to real-time monitoring data to minimize temperature fluctuations and stress concentration on the alloy surface. The adjusted temperature and pressure parameters are fed back to the coating equipment to achieve real-time control. According to the data analysis results and process optimization requirements, the algorithm and control strategy are continuously improved to form a closed-loop control.

[0089] Embodiment 15

[0090] Establish a high-precision mathematical model to describe the complex relationship between coating process parameters (such as temperature and pressure) and alloy surface parameters (such as temperature distribution and stress state): Based on physical laws and experimental data, establish a multivariable nonlinear equation describing the relationship between coating process parameters and alloy surface parameters, and use deep neural networks (such as convolutional neural networks, recurrent neural networks, etc.) or hybrid models (such as a combination of neural networks and physical models) to fit and optimize the multivariable nonlinear equation to improve the prediction accuracy of the model.

[0091] Design an advanced optimization algorithm that can dynamically adjust the coating process parameters according to the real-time monitored alloy surface parameters to achieve the optimization of coating quality and performance: take the coating quality and performance as the optimization target and establish a multi-objective optimization problem. Use a genetic algorithm for global search, randomly generate a set of coating process parameters as the initial population, each individual represents a specific set of process parameters (such as temperature, pressure, etc.), and evaluate the fitness of each individual according to the actual effect of the coating process (such as coating quality, stress distribution, etc.). This usually requires an evaluation function or experimental data to support it. According to the fitness value, select excellent individuals for crossover (combining the gene fragments of two individuals to generate new individuals) and mutation (randomly changing certain gene values ​​of individuals to increase diversity). Repeat the above process until the predetermined number of iterations is reached or a satisfactory solution is found. At this point, the genetic algorithm has searched one or more potential optimization solution spaces.

[0092] At the same time, the search results of the genetic algorithm are locally optimized using a reinforcement learning algorithm. In the solution space searched by the genetic algorithm, a strategy is defined that determines how to adjust the current coating process parameters to find a better solution. The coating process parameters are regarded as states, and possible adjustments (such as increasing or decreasing temperature, pressure, etc.) are regarded as actions. A reward function is designed to evaluate the effect of each action (i.e., the adjusted coating process parameters). The higher the reward value, the closer the adjusted parameters are to the optimal solution. A reinforcement learning algorithm (such as Q-learning, policy gradient, etc.) is used to find the action sequence that maximizes the reward value through continuous trial and error and learning. In the solution space searched by the genetic algorithm, a reinforcement learning algorithm is used to perform a local search to find the optimal coating process parameters.

[0093] In order to dynamically adjust the coating process parameters, a strategy update formula based on reinforcement learning is designed. This formula will update the strategy based on the current state (coating process parameters), action (parameter adjustment) and reward (coating effect). Assume that the current state is s t , the action taken is a t , the reward is r t , the next state is s t+1 , the strategy is π(a|s) (indicating the probability of taking action a in state s). Use the Q-learning algorithm to update the Q value (i.e. the value of the state-action pair):

[0094] Q(s t , a t )←Q(s t , a t )+α[r t +γmax a’ Q(s t+1 , a)-Q(s t , a t )]

[0095] Among them, α is the learning rate, which determines the coverage of new information over old information; γ is the discount factor, which determines the importance of future rewards; a′ is the next possible action. Based on the Q value, the strategy π can be updated:

[0096] π(a|s)∝exp(βQ(s,a))

[0097] Among them, β is a temperature parameter used to control the "greedy" degree of the strategy (i.e., it is more inclined to choose actions with high Q values).

[0098] The coating process parameters (such as temperature, pressure, etc.) are quantified into discrete or continuous state and action spaces, and a reward function is designed based on the coating effect. Then, the above formula is used for iterative learning until the optimal coating process parameters are found.

[0099] Comparative Example 1

[0100] The difference between Comparative Example 1 and Example 1 is that the modification of steps (1) to (2) is not performed, and the graphene oxide is directly added to the epoxy resin to form an unmodified graphene oxide epoxy coating on the surface of carbon steel Q235, and the other conditions are the same.

[0101] Performance Testing

[0102] 1. Raman spectroscopy test: The modified graphene prepared in Example 1 was subjected to Raman spectroscopy test. The test results are as follows: Figure 1 shown.

[0103] Depend on Figure 1 It can be seen that the graphite peak (G peak) and disordered peak (D peak) of pure graphene oxide, thiol-modified graphene oxide, unmodified graphene oxide and modified graphene oxide all appear at 1590 cm -1 1340cm -1 The number of graphene oxide layers is related to the position of the G peak. As the number of layers increases, the G peak moves toward the lower wavenumber direction. The G peaks of pure graphene oxide, thiol-modified graphene oxide, unmodified graphene oxide with catalyst, and modified graphene oxide are 1592 cm-1, 1593 cm-2, and 1597 cm-3, respectively. -1 , 1599cm -1 , 1605cm -1 and 1599cm -1 Therefore, the number of graphene oxide layers can be reduced by thiol modification and polydimethylsiloxane modification. This is because the modification can reduce the agglomeration of graphene oxide and can also greatly improve the dispersion uniformity of graphene oxide-based materials in organic solvents.

[0104] 2. Optical microscope test: The modified graphene-based coating prepared in Example 1 was subjected to a cross-sectional optical microscope test. The test results are as follows: Figure 2 shown.

[0105] Depend on Figure 2 a and Figure 2 b shows that the interface of the epoxy coating filled with modified graphene exhibits an obvious loose porous structure. This modified graphene is evenly dispersed in the epoxy resin, so that its flake structure can cut the epoxy resin into different areas. The corrosive medium needs to go through a tortuous path to reach the metal surface, which is equivalent to increasing the thickness of the coating and enhancing its barrier properties. In pure epoxy coating ( Figure 2 c and Figure 2 In d), the corrosive medium can quickly pass through the defects to reach the metal surface, resulting in poor barrier properties.

[0106] 3. Corrosion resistance test: The modified graphene-based coating prepared in Example 1 was subjected to a corrosion resistance test. The test results are as follows: Figure 3 shown.

[0107] Depend on Figure 3 a and Figure 3 b. Comparing the Nyquist plots under different immersion times, the electrochemical impedance of the sample gradually increases with the addition of graphene oxide and modified graphene oxide. The electrochemical impedance of the epoxy coating filled with pure graphene oxide is one order of magnitude higher than that of the pure epoxy coating, while the electrochemical impedance of the epoxy coating filled with modified oxyalkylene-based materials is 3 orders of magnitude higher. The electrochemical impedance reflects the ease with which the coating is corroded. This result shows that the filling of modified oxyalkylene-based materials greatly enhances the corrosion resistance of the epoxy coating.

[0108] Depend on Figure 3 c It can be seen that the corrosion current of the epoxy coating filled with modified oxyalkylene-based materials is smaller than that of the pure epoxy coating, indicating that the modification and filling of graphene oxide can improve the physical barrier effect of the coating, effectively block the invasion of corrosive media, reduce the generation of conductive channels, significantly reduce the corrosion current of the coating, and improve the corrosion resistance of the coating.

[0109] 4. Accelerated corrosion performance test: The modified graphene-based coating prepared in Example 1 was subjected to a salt spray box accelerated corrosion performance test. The test results are as follows: Figure 4 shown.

[0110] Depend on Figure 4 It can be seen that with a cycle of 24h salt spray and 24h drying, the carbon steel surface coated with modified graphene-based epoxy coating presents a smooth milky yellow color. After 10 cycles of salt spray, the coating surface is still smooth milky yellow. After 40 cycles, the coating color turns gray and the carbon steel does not rust. The salt spray test results show that modified graphene-based epoxy coating can effectively protect carbon steel from corrosion in marine salt spray environment and has good long-term stability.

[0111] 5. Electrochemical related tests: The epoxy coatings prepared in Example 1 and Example 2 were subjected to electrochemical related tests. The test results are as follows: Figure 5 shown.

[0112] Depend on Figure 5 a and Figure 5 b It can be seen that the epoxy coatings prepared with modified graphene and graphene oxide exhibit similar electrochemical impedance, indicating that their anti-corrosion properties are similar. Figure 5 In the polarization curve of c, the two samples also show similar corrosion overpotential and corrosion current. It can be seen that graphene and graphene oxide have similar anti-corrosion properties as filled epoxy coatings.

[0113] Electrochemical tests were performed on the epoxy coatings prepared in Example 1 and Examples 3-6. The test results are as follows: Figure 6 shown.

[0114] Depend on Figure 6 It can be seen that as the addition ratio of modified graphene oxide increases, the impedance of the coating increases and the corrosion current gradually decreases. This result proves that the barrier effect of uniformly dispersed modified graphene in the coating on corrosive media increases with the increase in the addition amount. When the addition ratio increases to 2.5%, the resistance decreases and the corrosion current increases. This may be because when the amount of graphene in the coating system increases to a certain extent, the colloidal balance is broken, agglomeration occurs, and the anti-corrosion performance is reduced.

[0115] 6. Anti-loss performance test: The coatings prepared in Example 1 and Comparative Example 1 were tested for anti-loss performance. The test results are as follows: Figure 7 shown.

[0116] Depend on Figure 7 It can be seen that the color of the epoxy coating filled with unmodified graphene has faded after the fifth salt spray cycle, showing a rough surface, but no rust has occurred. With the increase of salt spray cycles, the corrosion of carbon steel has become more and more serious. After the 20th cycle, the entire sample has been fully corroded. Through experimental comparison, the modification of graphene can greatly improve the corrosion resistance of epoxy coatings.

[0117] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. If these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A modified graphene-based epoxy coating, characterized in that the modified graphene-based epoxy coating comprises an epoxy coating base liquid and modified graphene.

2. The modified graphene-based epoxy coating according to claim 1, characterized in that: The modified graphene includes a graphene-based material and polydimethylsiloxane.

3. The modified graphene-based epoxy coating according to claim 2, characterized in that: The graphene-based material is a graphene-based raw material modified by organosilane; Preferably, the graphene-based raw material is at least one of graphene and graphene oxide; Preferably, the organosilane is at least one of 3-aminopropyltriethoxysilane and 3-mercaptopropyltrimethoxysilane; Preferably, the polydimethylsiloxane is at least one of trimethyl-terminated polydimethylsiloxane, hydroxy-terminated polydimethylsiloxane and bis-3-methacryloxypropylated tetramethyldisiloxane; Preferably, the catalyst used in modifying graphene is 1,8-diazabicyclo[5.4.0]undec-7-ene.

4. The modified graphene-based epoxy coating according to claim 1, characterized in that: The epoxy coating base liquid comprises epoxy resin and xylene.

5. The modified graphene-based epoxy coating according to claim 3, characterized in that: The weight ratio of the graphene-based raw material to the organosilane in the organosilane-modified graphene-based material is (0.2-1):1; Preferably, the weight ratio of the modified graphene-based material, polydimethylsiloxane and the catalyst in the modified graphene is 1:(2-8):0.0001; Preferably, the weight ratio of epoxy resin to xylene in the epoxy coating base liquid is 8:(1-3) by weight; Preferably, the weight ratio of the epoxy coating base liquid to the modified graphene is 4.5:(0.025-0.1) in parts by weight.

6. A modified graphene-based epoxy coating according to any one of claims 1 to 5, characterized in that: The preparation method of the modified graphene-based epoxy coating comprises the following steps: S01, preparing modified graphene; S02, mixing the modified graphene prepared in S01 with the epoxy coating base liquid to obtain the modified graphene-based epoxy coating.

7. The method for preparing the modified graphene-based epoxy coating according to claim 6, characterized in that: In the S01, the graphene-based raw material is added to anhydrous ethanol, ultrasonically dispersed for 20-30 minutes, and kept at 60°C for 30-60 minutes; then, organic silane is added, and the modified graphene-based material is obtained after the reaction is kept at 60°C for 6-12 hours, centrifuged, washed with ethanol and deionized water, and dried; then, the modified graphene-based material, polydimethylsiloxane and catalyst are dispersed in chloroform solvent, liquid nitrogen freezing and deoxygenation cycle are performed, and the reaction is carried out at 40°C in a nitrogen atmosphere for 18 hours, and the modified graphene is separated by evaporation.

8. A modified graphene-based epoxy coating prepared from the modified graphene-based epoxy coating according to any one of claims 1 to 5, characterized in that: The raw materials for preparing the modified graphene-based epoxy coating include the modified graphene-based epoxy coating and a curing agent.

9. A method for preparing a modified graphene-based epoxy coating according to claim 8, characterized in that: By weight, add 1 part by weight of a curing agent to 8 parts by weight of the modified graphene-based epoxy coating, stir for 10 minutes, remove bubbles in a vacuum, apply to the alloy surface, and cure to obtain a modified graphene-based epoxy coating; Preferably, the alloy surface is pretreated by grinding and ultrasonic cleaning with acetone, anhydrous ethanol and deionized water; Preferably, the alloy is an aluminum alloy or carbon steel; Preferably, in S02, the magnetic stirring time is 6-8 hours, the curing agent is polyamide 650, and the modified graphene-based epoxy coating is obtained by curing for 2 days at room temperature.

10. Use of the modified graphene-based epoxy coating according to any one of claims 1 to 5 in ship corrosion protection.

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