阅读说明：本技术 一种导电聚合物和氧化石墨烯界面黏附性能的预测方法 (Prediction method for interface adhesion performance of conductive polymer and graphene oxide ) 是由 毕可东 胡斌 于 2021-07-27 设计创作，主要内容包括：本发明提供一种导电聚合物和氧化石墨烯界面黏附性能的预测方法,包括：步骤10)构建导电聚合物模型；步骤20)构建氧化石墨烯模型；步骤30)对导电聚合物模型和氧化石墨烯模型进行组合,得到黏附体系模型；步骤40)对黏附体系模型进行分子动力学模拟计算,计算得到导电聚合物-氧化石墨烯体系的总势能；步骤50)计算得到导电聚合物的势能和氧化石墨烯的势能,结合导电聚合物的势能、氧化石墨烯的势能以及导电聚合物-氧化石墨烯体系的总势能,得到导电聚合物和氧化石墨烯之间的黏附性能。本发明导电聚合物和氧化石墨烯界面黏附性能的预测方法,得到导电聚合物和氧化石墨烯的相互作用能,从而预测氧化石墨烯与导电聚合物之间的黏附性能。(The invention provides a method for predicting the adhesion performance of an interface of a conductive polymer and graphene oxide, which comprises the following steps: step 10), constructing a conductive polymer model; step 20), constructing a graphene oxide model; step 30) combining the conductive polymer model and the graphene oxide model to obtain an adhesion system model; step 40) performing molecular dynamics simulation calculation on the adhesion system model to obtain the total potential energy of the conductive polymer-graphene oxide system; and 50) calculating the potential energy of the conductive polymer and the potential energy of the graphene oxide, and combining the potential energy of the conductive polymer, the potential energy of the graphene oxide and the total potential energy of the conductive polymer-graphene oxide system to obtain the adhesion performance between the conductive polymer and the graphene oxide. According to the prediction method of the interface adhesion performance of the conductive polymer and the graphene oxide, the interaction energy of the conductive polymer and the graphene oxide is obtained, so that the adhesion performance between the graphene oxide and the conductive polymer is predicted.)

1. A method for predicting the adhesion performance of an interface of a conductive polymer and graphene oxide is characterized by comprising the following steps:

step 10), constructing a conductive polymer model;

step 20), constructing a graphene oxide model;

step 30) combining the conductive polymer model and the graphene oxide model to obtain an adhesion system model;

step 40) performing molecular dynamics simulation calculation on the adhesion system model to obtain the total potential energy of the conductive polymer-graphene oxide system;

and 50) calculating the potential energy of the conductive polymer and the potential energy of the graphene oxide, and combining the potential energy of the conductive polymer, the potential energy of the graphene oxide and the total potential energy of the conductive polymer-graphene oxide system to obtain the adhesion performance between the conductive polymer and the graphene oxide.

2. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 1, wherein the step 10) specifically comprises:

constructing a compound monomer model, constructing a polymer single chain by using the compound monomer model, and constructing and forming a conductive polymer model by using the polymer single chain.

3. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 2, wherein the step 10) further comprises: optimizing the conductive polymer model.

4. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 1, wherein the step 20) specifically comprises:

and constructing a single-layer graphene model, and randomly selecting carbon atoms on the surface of the single-layer graphene to connect with functional groups to obtain the graphene oxide model.

5. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 4, wherein the step 20) further comprises: and optimizing the graphene oxide model.

6. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 1, wherein the step 30) specifically comprises:

and connecting the graphene oxide model with the conductive polymer model, and contacting the graphene surface connected with the functional group with the side surface of the conductive polymer to form an adhesion system model.

7. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 1, wherein the step 40) specifically comprises:

performing geometric configuration optimization on the adhesion system model by using a steepest gradient descent method, wherein the time step is set to be 1 femtosecond according to the size and the structure of the adhesion system model;

carrying out thermodynamic equilibrium relaxation on the adhesion system model under a regular ensemble and an isothermal isobaric ensemble, and regulating and controlling the adhesion system model in a reasonable and stable interval; performing simulated stress relief annealing at the temperature of 300K to 500K; under the isothermal and isobaric ensemble, the target temperature was set at 303K, the simulation duration was set at 1 nanosecond, the atmospheric pressure was set along the x-axis and along the y-axis, and the vacuum state was set along the z-axis.

8. The method for predicting the interfacial adhesion property of the conductive polymer and the graphene oxide according to claim 1, wherein the step 50) specifically comprises:

the adhesion property between the conductive polymer and the graphene oxide was calculated using formula (1):

E_inter＝E_total-(E_polymer+E_FG) Formula (1)

In the formula, E_interIndicating the adhesion between the conductive polymer and graphene oxide, E_totalRepresents the total potential energy of the conductive polymer-graphene oxide system, E_polymerRepresents the potential energy of the conductive polymer, E_FGRepresents the potential energy of graphene oxide.

Technical Field

The invention belongs to the technical field of high molecular materials, and particularly relates to a prediction method of the adhesion performance of a conductive polymer and graphene oxide interface.

Background

The composite material of the graphene and the conductive polymer has optical transparency and excellent electrical property and mechanical property, and can overturn the fields of surface coating, electrowetting display and the like. Wettability of a material refers to the interaction of the surface of the material with water, which is generally stable and invariant in the absence of external influences, and can be divided into hydrophilic and hydrophobic properties. Depending on the application of the material, it may be desirable to select between hydrophilic and hydrophobic properties. Taking the electrowetting display as an example, the hydrophilic property of the display material is enhanced under the action of an external circuit. Unlike conventional bulk materials, graphene can achieve surface wetting property control by changing electron density or functional group oxidation. The electron density of graphene leads to changes in surface adhesion, which determines the interaction of graphene with hydrophilic and hydrophobic molecules, playing an important role in the construction of graphene-based chemical and biological sensors.

The molecular dynamics simulation method can solve the force borne by each atom in the system by calculating the interaction potential between atoms, solve a Newton dynamics equation set by a numerical method, and simulate and predict the motion rule of each atom in the system along with the time. Important parameter changes of a simulation system, including conformational change, potential energy change and thermodynamic characteristics, can be obtained in the simulation process, and the motion tracks and positions of all atoms can be displayed on a femtosecond time scale.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the method for predicting the interface adhesion performance of the conductive polymer and the graphene oxide is provided, and the interaction energy of the conductive polymer and the graphene oxide is obtained, so that the adhesion performance between the graphene oxide and the conductive polymer is theoretically predicted.

In order to solve the above technical problems, the present invention provides a method for predicting the adhesion performance of an interface between a conductive polymer and graphene oxide, comprising the following steps:

step 10), constructing a conductive polymer model;

step 20), constructing a graphene oxide model;

step 30) combining the conductive polymer model and the graphene oxide model to obtain an adhesion system model;

step 40) performing molecular dynamics simulation calculation on the adhesion system model to obtain the total potential energy of the conductive polymer-graphene oxide system;

and 50) calculating the potential energy of the conductive polymer and the potential energy of the graphene oxide, and combining the potential energy of the conductive polymer, the potential energy of the graphene oxide and the total potential energy of the conductive polymer-graphene oxide system to obtain the adhesion performance between the conductive polymer and the graphene oxide.

As a further improvement of the embodiment of the present invention, the step 10) specifically includes:

constructing a compound monomer model, constructing a polymer single chain by using the compound monomer model, and constructing and forming a conductive polymer model by using the polymer single chain.

As a further improvement of the embodiment of the present invention, the step 10) further includes: optimizing the conductive polymer model.

As a further improvement of the embodiment of the present invention, the step 20) specifically includes:

and constructing a single-layer graphene model, and randomly selecting carbon atoms on the surface of the single-layer graphene to connect with functional groups to obtain the graphene oxide model.

As a further improvement of the embodiment of the present invention, the step 20) further includes: and optimizing the graphene oxide model.

As a further improvement of the embodiment of the present invention, the step 30) specifically includes:

and connecting the graphene oxide model with the conductive polymer model, and contacting the graphene surface connected with the functional group with the side surface of the conductive polymer to form an adhesion system model.

As a further improvement of the embodiment of the present invention, the step 40) specifically includes:

performing geometric configuration optimization on the adhesion system model by using a steepest gradient descent method, wherein the time step is set to be 1 femtosecond according to the size and the structure of the adhesion system model;

carrying out thermodynamic equilibrium relaxation on the adhesion system model under a regular ensemble and an isothermal isobaric ensemble, and regulating and controlling the adhesion system model in a reasonable and stable interval; performing simulated stress relief annealing at the temperature of 300K to 500K; under the isothermal and isobaric ensemble, the target temperature was set at 303K, the simulation duration was set at 1 nanosecond, the atmospheric pressure was set along the x-axis and along the y-axis, and the vacuum state was set along the z-axis.

As a further improvement of the embodiment of the present invention, the step 50) specifically includes:

the adhesion property between the conductive polymer and the graphene oxide was calculated using formula (1):

E_inter＝E_total-(E_polymer+E_FG) Formula (1)

In the formula, E_interIndicating the adhesion between the conductive polymer and graphene oxide, E_totalRepresents the total potential energy of the conductive polymer-graphene oxide system, E_polymerRepresents the potential energy of the conductive polymer, E_FGRepresents the potential energy of graphene oxide.

Compared with the prior art, the technical scheme of the invention has the following beneficial effects: according to the method for predicting the interface adhesion performance of the conductive polymer and the graphene oxide, provided by the embodiment of the invention, a conductive polymer and graphene oxide adhesion system model is established and formed by establishing a conductive polymer model and a graphene oxide model, molecular dynamics simulation calculation is carried out, the distribution state of a surface chain segment of the conductive polymer under the action of the graphene oxide is simulated and predicted, and the interface adhesion performance of the conductive polymer is predicted by calculating the energy change of the conductive polymer on the surface of the graphene oxide. The method can be used for predicting and comparing the interaction potential energy of the graphene oxide substituted by different functional groups and the conductive polymer, determining the graphene oxide with the best adhesion performance, and laying a theoretical foundation for expanding the engineering application of the conductive polymer.

Drawings

FIG. 1 is a flowchart illustrating a method for predicting the adhesion between an interface of a conductive polymer and graphene oxide according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a graphene oxide structure labeled with a linking functional group position;

fig. 3(a) is an axial view of an amino-functionalized graphene, and fig. 3(b) is a front view of an amino-functionalized graphene;

FIG. 4 is a schematic diagram of a polythiophene single chain model;

FIG. 5 is a model of adhesion of polythiophene to graphene oxide;

fig. 6 is a schematic diagram of the adhesion performance of graphene oxide with different types of functional groups and polythiophene.

Detailed Description

The technical solution of the present invention will be explained in detail below.

The embodiment of the invention provides a method for predicting the adhesion performance of an interface of a conductive polymer and graphene oxide, as shown in fig. 1, comprising the following steps:

step 10), constructing a conductive polymer model;

step 20), constructing a graphene oxide model;

step 30) combining the conductive polymer model and the graphene oxide model to obtain an adhesion system model;

step 40) performing molecular dynamics simulation calculation on the adhesion system model to obtain the total potential energy of the conductive polymer-graphene oxide system;

and 50) calculating the potential energy of the conductive polymer and the potential energy of the graphene oxide, and combining the potential energy of the conductive polymer, the potential energy of the graphene oxide and the total potential energy of the conductive polymer-graphene oxide system to obtain the adhesion performance between the conductive polymer and the graphene oxide.

According to the method, the total potential energy of the adhesion system model is simulated and calculated by constructing the adhesion system model, the strength of the adhesion function is represented by the potential energy difference between the total potential energy of the adhesion system and the potential energy of related individuals, and compared with the method for testing the adhesion function of the adhesion system by adopting a tensile mechanics method, the method provided by the embodiment of the invention does not need to be tested and measured, is convenient and rapid to obtain the adhesion performance, and is time-saving and labor-saving. The method provided by the embodiment of the invention can be used for predicting the interaction potential energy of the graphene oxide substituted by different functional groups and the conductive polymer, determining the graphene oxide with the best adhesion performance, and laying a theoretical foundation for expanding the engineering application of the conductive polymer.

Preferably, the step 10) specifically includes:

constructing a compound monomer model, constructing a polymer single chain by using the compound monomer model, and constructing and forming a conductive polymer model by using the polymer single chain. Optimizing the conductive polymer model.

Taking the construction of a polythiophene conductive polymer model as an example, the following concrete steps:

constructing a thiophene monomer model in Materials studio software, then constructing a polythiophene single Chain in Build Polymer, selecting a thiophene monomer in a Repeat Unit, setting Chain length to be 10, Number of Chain to be 1, and Torssion to be Random, wherein the constructed polythiophene single Chain is shown in figure 4.

The polythiophene single chain obtained in the above way is copied, wound and crosslinked into a polythiophene box according to the principle of minimum energy by means of an Amorphous Cell module in Materials studio software. Task is set as Construction, sensitivity is set as 1.28g/cm3, the above polythiophene single chain is selected by the Molecule, and Loading is set as 30.

A polymeric force field (PCFF) was used to model the interaction between polythiophene substrate atoms. The energy calculation of the PCFF force field comprises healthy terms and non-healthy terms, and has been parameterized and verified for various polymers, and the reliability is high.

And performing energy minimization treatment on the constructed polythiophene model by using a steepest descent algorithm. The polythiophene model was then hot-bath equilibrated at a temperature of 700K for 5 nanoseconds under a canonical ensemble (NVT). The cyclic simulated annealing was performed under NVT. Specifically, within the temperature range of 100-500K, 100K is taken as the temperature step, the balance is simulated for 200 picoseconds at each temperature, and the reciprocating circulation treatment is carried out.

The stress relief annealing is performed under isothermal isobaric ensemble (NPT). Specifically, the model is thermodynamically balanced for 2 nanoseconds at 300K in NPT, the pressure is changed to 0.01GPa and is continuously balanced for 2 nanoseconds after the first annealing period, the pressure is set to 2GPa and is continuously balanced for 2 nanoseconds after the second cycle, the maximum pressure is 0.001GPa after the last cycle, and the final model configuration is finally thermodynamically balanced for 5 nanoseconds, wherein a Nose-Hoover thermostat and a Berendsen barostat are used for controllingThe temperature and pressure of the simulated system were made. The three-dimensional size of the balanced polythiophene box model isThe density was 1.29 g.cm^-3。

In the method provided by the embodiment of the invention, the constructed polymer single chains are crossed, wound and crosslinked to form a multi-chain polymer model with a stable configuration by using the energy minimization as a constraint condition, so that the constructed polymer model is closer to the actual polymer configuration compared with a model established by a traditional random method, and the accuracy of predicting the adhesion performance is further improved.

For the constructed conducting polymer model, a Forcite module in a molecular dynamics computing software Material studio is utilized, then a Calculation operation is selected, Task is changed from Energy to Geometry Optimization, and Quality is changed to Fine. Clicking the more.

Preferably, the step 20) specifically includes:

and constructing a single-layer graphene model, and randomly selecting carbon atoms on the surface of the single-layer graphene to connect with functional groups to obtain the graphene oxide model.

Specifically, a single-layer graphene model is constructed in Materials studio software, carbon atoms of the single-layer graphene model are numbered, a series of random numbers are generated by utilizing Matlab software, and the corresponding carbon atoms are determined to be carbon atoms connected with functional groups on the single-layer graphene model according to the random numbers. As shown in fig. 2, the single-layer graphene model is composed of 42 carbon atoms, and the carbon atoms are sequentially arranged as 1, 2, 41, 42. The random numbers generated are 11, 14, 15, 18, 30, 32, 34, corresponding to the carbon atoms on the monolayer graphene model, with the corresponding carbon atoms attached to an oxygenated functional group, such as Carboxyl (COOH), hydroxyl (OH), amino (NH)₂) Thereby obtaining the graphene oxide model. Oxidation of the resulting amino substitutionGraphene is shown in figure 3.

In the method provided by the embodiment of the invention, firstly, the oxidation sites on the surface of the graphene are determined by utilizing a random number generation principle, then, the attached functional groups are oxidized, and the potential energy of the graphene oxide model is calculated. Compared with the traditional method for calculating quantum from head calculation, the method can save a large amount of calculation resources and ensure higher reliability. The graphene oxide model is constructed in a mode of randomly attaching functional groups to the surface of graphene, so that various forms of graphene oxide models can be formed, and the graphene oxide model can be used for screening graphene oxide with high adhesion performance.

Preferably, the step 20) further comprises: and optimizing the graphene oxide model.

Specifically, the optimized graphene oxide model is obtained by thermodynamic equilibrium for 1 nanosecond at 300K in NPT, changing the pressure to 0.0001GPa after the first annealing period, continuously balancing for 1 nanosecond, setting the pressure to 1GPa after the second cycle, continuously balancing for 1 nanosecond, setting the maximum pressure to 0.0001GPa after the last cycle, and finally thermodynamic equilibrium for 2 nanoseconds.

For the constructed graphene oxide model, a Forcite module in a Material studio of molecular dynamics Calculation software is utilized, then a Calculation operation is selected, Task is changed from Energy to Geometry Optimization, and Quality is changed to Fine. Clicking a more.

Preferably, the step 30) specifically includes:

and connecting the graphene oxide model with the conductive polymer model, and contacting the graphene surface connected with the functional group with the side surface of the conductive polymer to form an adhesion system model.

Connecting graphene oxide and a polythiophene model together through a Build surface module, contacting the surface of the graphene connected with a functional group with the side surface of the polythiophene to form a contact model of an adhesion system, and adding a vacuum layer to the model in the z-axis direction to further expand the model toIn order to avoid that the whole adjacent system is disturbed by the periodic boundary conditions in the z-axis direction during the simulation. A simulation model of the adhesion system of polythiophene chains to hydroxyl-graphene oxide (0.48 hydroxyl groups per square nanometer) is shown in fig. 5.

Preferably, the step 40) specifically includes:

performing geometric configuration optimization on the adhesion system model by using a steepest gradient descent method, wherein the time step is set to be 1 femtosecond according to the size and the structure of the adhesion system model;

carrying out thermodynamic equilibrium relaxation on the adhesion system model under the NVT ensemble and the NPT ensemble, and regulating and controlling the adhesion system model in a reasonable and stable interval; performing simulated stress relief annealing at the temperature of 300K to 500K; under the NPT ensemble, the target temperature is set to 303K, the simulation duration is set to 1 nanosecond, the atmospheric pressure is set in the x-axis and y-direction, and the vacuum state is set in the z-axis direction. The parameter setting has two main reasons, one is that the atmospheric pressure is maintained under the periodic boundary condition on the plane to simulate the real external environment of the graphene oxide and the polythiophene, and the other is that the vacuum is set in the normal direction of the contact plane, namely the z-axis direction, to eliminate the influence of the external atmospheric pressure on the adhesion, so that the phenomenon and the mechanism of the interface adhesion can be analyzed more directly.

For the conductive polymer-graphene oxide adhesion system model obtained by construction, a Forcite module in a molecular dynamics Calculation software Material studio is utilized, then a Calculation operation is selected, Task is changed from Energy to Geometry Optimization, and Quality is changed to Fine. Clicking the more. And calculating for many times to obtain the total potential energy of the adhesion system in a stable state.

Preferably, the step 50) specifically includes:

the adhesion property between the conductive polymer and the graphene oxide was calculated using formula (1):

E_inter＝E_total-(E_polymer+E_FG) Formula (1)

In the formula, E_interIndicating the adhesion between the conductive polymer and graphene oxide, E_totalRepresents the total potential energy of the conductive polymer-graphene oxide system, E_polymerRepresents the potential energy of the conductive polymer, E_FGRepresents the potential energy of graphene oxide.

The method of the invention is used for respectively processing polythiophene and methyl (CH) with different surface densities₃) Carboxyl (COOH), hydroxyl (OH) and amino (NH)₂) The adhesion performance of graphene oxide is predicted, and the change of the interaction performance between the graphene oxide and polythiophene with different types of functional groups and functional groups with different surface densities is shown in fig. 6. All of the above functional groups are randomly distributed on the graphene surface. From the symbol of the interaction energy, it can be seen that polythiophene and graphene oxide are strongly attracted to each other and adhere to each other. In view of the general trend, for polar functional groups, for example, Carboxyl (COOH), hydroxyl (OH), amino (NH) groups₂) As the density of surface functional groups increases, the magnitude of the interaction energy of the entire analog system increases, indicating that these polar functional groups enhance the adhesion between graphene oxide and polythiophene. However, for the nonpolar group methyl (CH)₃) For substituted graphene oxide, the adhesion increases and then decreases with increasing surface density, where the maximum critical surface density is reached with two methyl groups per square nanometer (CH)₃) And the group, the adhesion between the graphene oxide and the polythiophene substrate is strongest. The reason behind this is that with methyl (CH)₃) When the density of the functional groups is increased and exceeds the critical density, the surface roughness of the contact surface is increased, and the higher surface roughness weakens the adhesion behavior of the graphene oxide and the polythiophene surface, so that the surface adhesion is reduced. In addition, the higher surface density of methyl functional groups does not further enhance the interfacial interactionsThere is a certain kinetic balance due to the influence of surface roughness and functional groups. On the other hand, for the polar functional group graphene oxide, although the increasing density of the surface functional group increases the roughness of the surface, the attractive force between the stronger polar group and the polythiophene matrix compensates the negative effect of the surface roughness, but the increase speed of the adhesion energy of the interaction is slowed down. Thus, for compounds having a carboxyl group (COOH), an amino group (NH)₂) And Hydroxyl (OH) functional groups, and an approximately monotonous trend in the change process of the interaction energy is found. As can be seen from the figure, the order of the enhanced interaction energy of the adhesion interface in the graphene oxide and polythiophene system is carboxyl-graphene oxide (FG-COOH)>Hydroxy-graphene oxide (FG-OH)>Amino-graphene oxide (FG-NH)₂)>Methyl-graphene oxide (FG-CH)₃). The difference between these graphene oxides with attached different kinds of functional groups is mainly the difference in electronegativity of the functional groups.

Table 1 shows the electronegativity of the different functional groups calculated by the Bratsch algorithm. It is obvious that the electronegativity of the polar functional group Carboxyl (COOH) is stronger than that of other functional groups, so that when graphene oxide with a more polar functional group is in contact with a polythiophene interface, the adhesion between the interfaces is enhanced more obviously by the more polar functional group.

TABLE 1 electronegativity of different kinds of functional groups

The method can determine the graphene oxide with the best adhesion performance by comparing the interaction potential energy of the graphene oxide substituted by different functional groups and the conductive polymer, and lays a theoretical foundation for the engineering application of the expanded conductive polymer.

The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the claims and their equivalents.