A method for predicting the optimal dosage of additives in polyurethane materials

Abstract

The present invention discloses a method for predicting the optimal dosage of additives in polyurethane materials, comprising the following steps: S1: construction of a multi-component all-atom model; S2: construction of a plasticizer / polymer chain mixing model; S3: construction of a polyurethane network and additive mixing system; S4: calculation of the intermolecular interactions between the components of the plasticizer system; S5: calculation of the mechanical properties of crosslinked systems with different curing parameters, calculating the effects of the amount of plasticizer on the system performance and the effects of curing parameters on the mechanical properties of the material respectively, and clarifying the action mechanisms of plasticizer and curing agent on the material properties respectively. By constructing models with different plasticization ratios, the present invention predicts the changes in intermolecular interactions and the diffusion ability of small molecules, and discovers the crowding effect therein. At the same time, with the increase of curing parameters, there is a maximum value in the material performance. The invention can be used to predict the optimal addition dosage of polyurethane to guide experiments.

Description

Technical Field

[0001] The present invention relates to the technical field of polyurethane material property prediction in the chemical industry, and particularly relates to a method for predicting the optimal dosage of additives in polyurethane materials. Background Art

[0002] Polyurethane is a new type of organic polymer material. Due to its excellent properties, it is widely used in important fields such as aviation, aerospace, and medical treatment. In order for polyurethane materials to adapt to various changing environments, various additives are often added to endow the materials with some specific properties. However, due to the molecular characteristics of the additives, they interact with the substrate, resulting in changes in the microstructure of the materials and causing changes in the mechanical properties of the materials. Therefore, there is a balance value between the mechanical properties and the special properties of the materials, and there is an optimal value for the addition amount of the additives. For finding the optimal value, traditional experiments often use the long-term trial-and-error method and keep trying. However, due to the many contingencies in the experiments, it will increase the difficulty of the experiments. Molecular simulation technology can obtain the corresponding properties by constructing the corresponding models and given appropriate conditions, which can greatly guide the experiments to a large extent. Summary of the Invention

[0003] The purpose of the present invention is to solve the defects existing in the prior art, and to propose a method for predicting the optimal dosage of additives in polyurethane materials.

[0004] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A method for predicting the optimal dosage of additives in polyurethane materials, comprising the following steps:

[0006] S1: Construction of a multi-component all-atom model:

[0007] Use Materials Studios to construct an open-ring model of monomers 3,3-bis(azidomethyl)oxetane (BAMO) and tetrahydrofuran (THF);

[0008] Use the Build Polymers tool to establish a random copolymer with a degree of polymerization of 35-50, and perform energy minimization with a maximum number of iterations of 5000-10000 steps and energy comparison on 5-10 randomly constructed molecular chains, and screen out more suitable molecular chain conformations;

[0009] Materials Studios was also used to construct a small molecule additive, which included a chain extender (diethylene glycol), a cross-linker (trimethylolethane), a curing agent (2,4-toluene diisocyanate) and a plasticizer A3 (a mixture of bis(2,2-dinitropropanol formal) and bis(2,2-dinitropropanol acetal) in equal molar ratios), and the GeometryOptimization module was used to minimize its energy, after which all hydroxyl oxygen atoms were marked as "R1".

[0010] S2: Construction of hybrid model of plasticizer / polymer chain:

[0011] Use the Amorphous Cell module in Materials Studios to minimize the energy of the random copolymer and plasticizer A3 constructed above through Geometry Optimization, and select the structure with the lowest energy as the initial model for simulation;

[0012] The lowest energy configuration was selected as the initial model for annealing and relaxation treatment, and the temperature was cycled from 300 to 600 K for 3 times, with a total annealing time of 2 to 4 ns.

[0013] At a temperature of 300K, NVT dynamics simulations of 2 to 4 ns and NPT dynamics simulations of 2 to 4 ns were performed respectively;

[0014] When the system energy remains basically stable and the density is between 1.1 and 1.4 g / cm 3 The dynamic data can be collected by fluctuating within the range.

[0015] S3: Construction of polyurethane network and additive mixing system:

[0016] Use the Amorphous Cell module of Materials Studios to construct amorphous cells with a ratio of hydroxyl number to isocyanate number of 1:1 to 1:2, which is called the curing parameter; and mark the carbon atoms of isocyanate as "R2~R9" according to the curing parameter, and construct mixed cells with different additive contents while maintaining the initial configuration of the polymer as much as possible;

[0017] Constructing 5 to 10 unit cells of an amorphous structure, and screening the initial configuration through the screening step in the above step S2;

[0018] Then, the system structure is optimized by the annealing relaxation method in the above step S2;

[0019] Through the Perl cross-linking script program, the active atoms in the reactive functional groups are identified. By identifying the isocyanate carbon atom markers (R2-R9) with different labels, a random cross-linked network with the same initial configuration and different curing parameters is constructed, and the degree of reaction can reach 0-100%.

[0020] After outputting the cross-linked configuration, it will be annealed and relaxed in the same way as in step S2 above, and then the subsequent tensile dynamics simulation will be carried out to predict the mechanical properties of the material under different curing parameters.

[0021] S4: Calculation of the intermolecular interactions of each component in the plasticizer system:

[0022] Perform a dynamics simulation on the equilibrium configuration output in step S2 above to analyze the intermolecular interactions. Among them, in the simulation calculation at 300K, the total energy of the system and the energy of each component are output respectively. Subtracting the component energy from the system energy can analyze the change in the intermolecular interaction energy with the addition of the plasticizer. At the same time, calculate the diffusion rate of small molecules.

[0023] S5: Calculation of the mechanical properties of the cross-linked system with different curing parameters:

[0024] Select the random cross-linked networks constructed with different curing parameters in the S3 process. First, perform the relaxation step in step S2 above to make the model reach a relatively balanced state. Then, unidirectionally change the size of the box at a box shape change rate of 10 8 ~10 10 / s, output the stress and strain in the tensile direction, and obtain the uniaxial tensile curve. The slope of the straight line segment of the curve is the Young's modulus, the maximum stress is the tensile strength, and the strain at the rapid drop of the stress is the fracture elongation rate.

[0025] Furthermore, both the random copolymer and the plasticizer A3 are physical interactions, and there is no change in the components.

[0026] Furthermore, the dynamics simulation process is all carried out in LAMMPS, and the force field used is OPLS-AA.

[0027] Furthermore, the polymer chain is a polymer chain with a degree of polymerization of 35-50.

[0028] Furthermore, the amorphous unit cell is constructed in Materials Studios, the force field selected is COMPASSII, and for the dynamics simulation after obtaining the random cross-linked network, LAMMPS is used, and the force field selected is OPLS-AA.

[0029] Furthermore, the additive participates in the construction of the network, that is, the curing agent.

[0030] Furthermore, the randomly crosslinked network with different curing parameters is regulated by identifying isocyanate groups with specific markers, thereby minimizing the influence of the initial configuration.

[0031] Furthermore, the deformation rate is much greater than the actual experimental rate, but through the time-temperature equivalence principle, the reliability of the simulation data is obtained.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] (1) The present invention uses molecular simulation technology to predict the optimal dosage of additives in polyurethane materials to guide experiments.

[0034] (2) The present invention uses a computational program to construct a random crosslinked network / additive multi-component complex model.

[0035] (3) The present invention uses the idea of component classification to clarify the mechanism of action of plasticizers on polyurethane materials.

[0036] (4) The present invention uses the method of batch reaction of curing agents to control the initial conformation of molecular chains and maximize the control of other variables affecting mechanical properties. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0038] Figure 1 It is a schematic flow chart of a method for predicting the optimal dosage of additives in polyurethane materials proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0040] Refer to Figure 1 , a method for predicting the optimal dosage of additives in polyurethane materials, comprising the following steps:

[0041] S1: Construction of a multi-component all-atom model:

[0042] S101: Use Materials Studios to construct an open-ring model of monomers 3,3-bis(azidomethyl)oxetane (BAMO) and tetrahydrofuran (THF);

[0043] S102: Use the Build Polymers tool to build a random copolymer with a degree of polymerization of 35 - 50, and perform energy minimization with a maximum number of iterations of 5000 - 10000 steps and energy comparison on 5 - 10 randomly constructed molecular chains in sequence, and select a more suitable molecular chain conformation;

[0044] S103: Similarly, use Materials Studios to construct small molecule additives, and use the Geometry Optimization module to perform energy minimization on them. After that, mark all hydroxyl oxygen atoms as "R1";

[0045] Specifically, when using Materials Studios to construct small molecule additives, it includes chain extenders (diethylene glycol), crosslinking agents (trimethylolethane), curing agents (2,4-toluene diisocyanate), and plasticizer A3 (a mixture of bis(2,2-dinitropropyl formal) and bis(2,2-dinitropropyl acetal) in equimolar ratio), etc.

[0046] S2: Construction of a plasticizer / polymer chain hybrid model for calculating the effect of the amount of plasticizer on the system performance:

[0047] S201: Use the Amorphous Cell module in Materials Studios to screen out the structure with the lowest energy as the initial model for simulation after performing energy minimization on the above-mentioned constructed random copolymer and plasticizer A3 through Geometry Optimization;

[0048] S202: Use the configuration with the lowest energy screened out as the initial model for annealing relaxation treatment, perform 3 cycles at 300 - 600K, and undergo a total annealing of 2 - 4 ns;

[0049] S203: At a temperature of 300K, perform 2 - 4 ns of NVT kinetic simulation and 2 - 4 ns of NPT kinetic simulation respectively;

[0050] S204: When the system energy is basically stable and the density fluctuates within the range of 1.1 - 1.4 g / cm 3 Collect kinetic data within the range;

[0051] It should be noted that 5 - 10 amorphous structure unit cells are randomly stacked with a copolymer:plasticizer mass ratio of 1:1 - 2.

[0052] S3: Construction of a polyurethane network and additive hybrid system for calculating the effect of curing parameters on the mechanical properties of materials:

[0053] S301: Use the Amorphous Cell module of Materials Studios to construct amorphous cells with a ratio of hydroxyl number to isocyanate number of 1:1 to 1:2, which is called the curing parameter. And according to the curing parameter, the carbon atoms of isocyanate are marked as "R2~R9", and mixed cells with different additive contents are constructed while maintaining the initial configuration of the polymer as much as possible;

[0054] S302: constructing 5 to 10 unit cells of an amorphous structure, and screening the initial configuration through the screening step in the above step S2;

[0055] S303: Then, the system structure is optimized by the annealing relaxation method in the above step S2;

[0056] S304: Using the Perl cross-linking script program, the active atoms in the reaction functional groups are identified, and by identifying the differently labeled isocyanate carbon atom marks (R2~R9), the hydroxyl groups are prompted to react with the isocyanate groups to form carbamate groups, thereby constructing a random cross-linking network with the same initial configuration and different curing parameters;

[0057] S305: After the cross-linked configuration is output, the configuration is annealed and relaxed in the manner of step S2, and then a subsequent tensile dynamics simulation is performed to predict the mechanical properties of the material under different curing parameters;

[0058] S4: Calculation of molecular interactions among components of the plasticizer system:

[0059] S401: Performing dynamic simulation on the equilibrium configuration output in the above step S2 to analyze the interaction between components. In the simulation calculation at 300K, the total energy of the system and the energy of each component are output respectively. The change of the interaction energy between molecules with the addition of plasticizer can be analyzed, and the diffusion rate of small molecules can be calculated at the same time.

[0060] S5: Calculation of mechanical properties of cross-linked systems with different curing parameters:

[0061] S501: Select the random cross-linked network constructed under different curing parameters in the process of S3, first perform the relaxation step in the above step S2, and after the model reaches a relatively balanced state, perform 10 8 ~10 10 The box deformation rate of / s changes the size of the box in one direction, and the stress and strain in the tensile direction are output to obtain the uniaxial tensile curve. The slope of the straight line segment of the curve is Young's modulus, the maximum stress is the tensile strength, and the strain at the point where the stress drops rapidly is the breaking elongation.

[0062] In a specific embodiment of the present application, in the plasticizer / polymer chain mixing model of step S2, both the random copolymer and plasticizer A3 are physically interacting, and there is no change in the components.

[0063] In a specific embodiment of the present application, in step S203, the kinetic simulation process is carried out in LAMMPS, and the force field used is OPLS-AA.

[0064] In a specific embodiment of the present application, in the random copolymer model of step S201, the polymer chain is a macromolecular chain with a degree of polymerization of 35 to 50.

[0065] In a specific embodiment of the present application, in the mixing model of the polyurethane network and additives in step S3, the amorphous unit cell is constructed in Materials Studios, the force field selected is COMPASSII, and for the kinetic simulation after obtaining the random cross-linked network, LAMMPS is used, and the force field selected is OPLS-AA.

[0066] In a specific embodiment of the present application, in step S301, the additive participates in the construction of the network and is a curing agent.

[0067] In a specific embodiment of the present application, in step S304, the random cross-linked networks with different curing parameters are regulated by identifying isocyanate groups with specific markers, thereby minimizing the influence of the initial configuration.

[0068] In a specific embodiment of the present application, in step S305, the deformation rate is much greater than the rate of the actual experiment, and the reliability of the simulation data is obtained through the time-temperature equivalence principle.

[0069] In summary, through simulation calculations, it is found that with the addition of the plasticizer, the intermolecular interactions in the system gradually increase and tend to saturate, while the diffusion ability of the small molecules decreases after reaching a certain value due to the system crowding effect. With the increase in the curing agent, the mechanical properties of the material have a maximum value.

[0070] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the optimal dosage of additives in polyurethane materials, The following steps are involved: S1: Construction of multi-component all-atom model: Use Materials Studios to build a ring-opening model of monomers 3,3-bis(azidomethyl)oxetane (BAMO) and tetrahydrofuran (THF); Use the Build Polymers tool to build random copolymers with a degree of polymerization of 35-50, and perform energy minimization and energy comparison on 5-10 randomly constructed molecular chains with a maximum iteration number of 5000-10000 steps in Geometry Optimization to screen out more suitable molecular chain conformations; Also use Materials Studios to build small molecule additives and use the GeometryOptimization module to minimize their energy, after which all hydroxyl oxygen atoms are marked as "R1"; S2: Construction of hybrid model of plasticizer / polymer chain: Use the Amorphous Cell module in Materials Studios to minimize the energy of the random copolymer and plasticizer A3 constructed above through Geometry Optimization, and select the structure with the lowest energy as the initial model for simulation; Wherein, the plasticizer A3 is a mixture of 2,2-dinitropropanol formal and 2,2-dinitropropanol acetal in an equal molar ratio; The lowest energy configuration was selected as the initial model for annealing and relaxation treatment, and the temperature was cycled from 300 to 600 K for 3 times, with a total annealing time of 2 to 4 ns. At a temperature of 300K, NVT dynamics simulations of 2 to 4 ns and NPT dynamics simulations of 2 to 4 ns were performed respectively; When the system energy remains basically stable and the density fluctuates within the range of 1.1~1.4 g / cm 3 collect the kinetic data; S3: Construction of polyurethane network and additive mixing system: Use the Amorphous Cell module of Materials Studios to construct amorphous cells with a ratio of hydroxyl number to isocyanate number of 1:1 to 1:2, which is called the curing parameter; and mark the carbon atoms of isocyanate as "R2~R9" according to the curing parameter, and construct mixed cells with different additive contents while maintaining the initial configuration of the polymer as much as possible; Constructing 5 to 10 unit cells of an amorphous structure, and screening the initial configuration through the screening step in the above step S2; Then, the system structure is optimized by the annealing relaxation method in the above step S2; Through the Perl cross-linking script program, the active atoms in the reaction functional groups are identified, and by identifying the differently labeled isocyanate carbon atom marks (R2~R9), the hydroxyl groups are prompted to react with the isocyanate groups to form carbamate groups, thus constructing a random cross-linking network with the same initial configuration and different curing parameters; After the cross-linked configuration is output, the configuration is annealed and relaxed in the manner of step S2, and then a subsequent tensile dynamics simulation is performed to predict the mechanical properties of the material under different curing parameters; S4: Calculation of molecular interactions among components of the plasticizer system: Perform a kinetic simulation on the equilibrium configuration output in the above step S2 to analyze the interactions between components. Among them, in the simulation calculation at 300K, the total energy of the system and the energies of each component are output respectively. Subtracting the component energy from the system energy can analyze the change in the intermolecular interaction energy with the addition of the plasticizer, and at the same time calculate the diffusion rate of small molecules. S5: Calculation of the mechanical properties of the crosslinked system with different curing parameters: Select the randomly cross-linked networks constructed under different curing parameters in the process S3. First, perform the relaxation step in the above step S2 to make the model reach a relatively balanced state. After that, change the size of the box in a single direction at a box-shaped strain rate of 10 8 ~10 10 / s, output the stress and strain in the stretching direction, and obtain the uniaxial tensile curve. The slope of the straight line segment of the curve is the Young's modulus, the maximum stress is the tensile strength, and the strain at the rapid stress drop is the fracture elongation rate.

2. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 1, characterized in that in the construction of the plasticizer / polymer chain hybrid model in step S2, both the random copolymer and the plasticizer A3 are physically interacting, and there is no change in the components.

3. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 2, characterized in that in the construction of the plasticizer / polymer chain hybrid model in step S2, the kinetic simulation process is carried out in LAMMPS, and the force field used is OPLS-AA.

4. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 3, characterized in that in the random copolymer model in step S2, the polymer chain is a macromolecular chain with a degree of polymerization of 35 to 50.

5. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 4, characterized in that in step S3, the amorphous unit cell is constructed in Materials Studios, the force field selected is COMPASSII, and for the kinetic simulation after obtaining the random crosslinked network, LAMMPS is used, and the force field selected is OPLS-AA.

6. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 5, characterized in that in step S3, the additive participates in the construction of the network, which is a curing agent.

7. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 6, characterized in that in step S3, the random crosslinked networks with different curing parameters are regulated by identifying isocyanate groups with specific markers, thereby minimizing the influence of the initial configuration.

8. The method for predicting the optimal dosage of additives in a polyurethane material according to claim 7, characterized in that in step S5, the strain rate is much greater than the rate of the actual experiment, and the reliability of the simulation data is obtained through the time-temperature equivalence principle.

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