CN115148306A - Method for predicting optimal dosage of additive in polyurethane material - Google Patents
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Abstract
The invention discloses a method for predicting the optimal dosage of an additive in a polyurethane material, which comprises the following steps of S1: constructing a multi-component full-atom model; s2: constructing a plasticizer/polymer chain mixed model; s3: constructing a polyurethane network and additive mixing system; s4: calculating the intermolecular interaction of all the components of the plasticizer system; s5: and (3) calculating the mechanical properties of the crosslinking system with different curing parameters, respectively calculating the influence of the amount of the plasticizer on the system performance and the influence of the curing parameters on the mechanical properties of the material, and respectively clarifying the action mechanisms of the plasticizer and the curing agent on the material performance. The invention predicts the change of intermolecular interaction and small molecule diffusion capacity by constructing models with different plasticizing ratios, and discovers the crowding effect. At the same time, the maximum value of the performance of the material exists along with the improvement of the curing parameters. The invention can be used for predicting the optimal additive amount of polyurethane to guide the experiment.
Description
Technical Field
The invention relates to the technical field of performance prediction of polyurethane materials in chemical industry, in particular to a method for predicting the optimal dosage of an additive in a polyurethane material.
Background
Polyurethane is a new organic polymer material, and is widely applied to important fields of aviation, aerospace, medical treatment and the like due to excellent performance of the polyurethane. In order to adapt polyurethane materials to various environments, various additives are added to obtain specific properties of the materials. But due to the molecular characteristics of the additive, the additive interacts with the substrate, so that the microstructure of the material is changed, and the mechanical properties of the material are changed. Therefore, the mechanical property of the material has a balance value with the special property of the material, and the additive amount has an optimal value. For seeking the optimal value, the traditional experiment is always tried continuously by long-term trial and error, but the experiment is always difficult due to many contingencies. The molecular simulation technology can obtain corresponding performance by setting appropriate conditions through constructing corresponding models, and the experiment can be guided to a great extent.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for predicting the optimal dosage of an additive in a polyurethane material.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of predicting an optimal dosage of an additive in a polyurethane material, comprising the steps of:
s1: constructing a multi-component full-atom model:
materials Studios were used to construct open-loop models of the monomers 3, 3-bis (azidomethyl) oxetane (BAMO) and Tetrahydrofuran (THF);
building a random copolymer with 35-50 polymerization degrees by using Build Polymers, performing energy minimization and energy comparison with maximum iteration number of Geometry Optimization being 5000-10000 steps on 5-10 molecular chains built randomly, and screening out a more appropriate molecular chain conformation;
the construction of small molecule additives was also performed using Materials Studios, which included: chain extenders (diethylene glycol), crosslinkers (trimethylolethane), curatives (2, 4-toluene diisocyanate), and plasticizers A3 (an equimolar mixture of bis (2, 2-dinitropropanol formal) and bis (2, 2-dinitropropanol acetal), with a geometric Optimization for energy minimization, after which all hydroxyl oxygen atoms are labeled "R1".
S2: construction of a plasticizer/polymer chain mixture model:
the random copolymer constructed above was mixed with plasticizer A3 using the Amorphous Cell module in Materials students, where the ratio of copolymer: randomly stacking 5-10 crystal cells with amorphous structures according to the mass ratio of the plasticizer to 1-2, and screening out the crystal cell with the lowest energy as a simulated configuration after minimizing the energy through Geometry Optimization;
annealing the initial model, and carrying out 3 times of circulation at 300-600K, wherein the total time of annealing is 2-4 ns;
respectively carrying out NVT dynamic simulation of 2-4 ns and NPT dynamic simulation of 2-4 ns at the temperature of 300K;
when the system energy is basically kept stable and the density fluctuates within the range of 1.1-1.4 g/cm < 3 >, the collection of the kinetic data can be carried out.
S3: construction of polyurethane network and additive mixing System:
an Amorphous Cell is constructed by using an Amorphous Cell module of Materials students according to the proportion of hydroxyl number to isocyanate number (curing parameter) 1-2, and carbon atoms of isocyanate are marked as R2-R9 according to the curing parameter, and on the premise of keeping the initial configuration of a macromolecule as much as possible, mixed cells with different additive contents are constructed;
constructing 5-10 unit cells with amorphous structures, and screening initial configurations through the screening step in the step S2;
then, optimizing the system structure by the annealing relaxation means in the step S2;
through a Perl cross-linking script program, active atoms in reaction functional groups are identified, through identifying isocyanate carbon atom marks (R2-R9) with different marks, a random cross-linking network with the same initial configuration and different curing parameters is constructed, and the reactivity can reach 0-100%. After the cross-linking configuration is output, subsequent tensile dynamics simulation is carried out after relaxation treatment in the step S2, and the mechanical properties of the material under different curing parameters are predicted.
S4: calculating the intermolecular interaction of all the components of the plasticizer system:
and (3) performing dynamic simulation on the equilibrium configuration output in the step (S2) and analyzing the interaction among the components. In the simulation calculation of 300K, the total energy of the system and the energy of each component are respectively output, and the change of the interaction energy between molecules along with the addition of the plasticizer can be analyzed by subtracting the energy of the components from the system energy. And simultaneously calculating the diffusion rate of the small molecules.
S5: calculating the mechanical properties of the cross-linking system with different curing parameters:
selecting a 60% -90% reactivity crosslinking model of 1-2 curing parameters, firstly carrying out a relaxation step in the step S2, after the model reaches a relatively balanced state, carrying out box deformation rate of 108-1010/S on an amorphous unit cell to change the size of a box in a single direction, outputting stress and strain in a stretching direction, obtaining a uniaxial stretching curve, wherein the slope of a straight-line segment of the curve is Young modulus, the maximum stress is tensile strength, and the strain at a stress rapid decline part is fracture stretching rate.
Further, the molecules are all physical interactions, and there is no change in composition.
Furthermore, the dynamic simulation process is carried out in LAMMPS, and the force fields used are all OPLS-AA.
Further, the polymer chain has a polymerization degree of 35 to 50.
Further, the model is constructed in Materials students, the selected force field is COMPASSII, the dynamic simulation after the cross-linked structure model is obtained is carried out, LAMMPS is used, and OPLS-AA is selected as the force field.
Further, the additive is involved in the construction of the network, i.e. the curing agent.
Further, the model of the different curing parameters is manipulated by identifying the isocyanate groups of the specific mark, thereby minimizing the effect of the initial configuration.
Furthermore, the simulated stretching speed is far greater than the speed of an actual experiment, but the reliability of the simulated data is obtained through a time-temperature equivalent principle.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention adopts a molecular simulation technology to predict the optimal additive amount of the polyurethane material to guide the experiment.
(2) The invention adopts a computer program to construct a random cross-linked network/additive multi-component complex model.
(3) The invention adopts the idea of component classification to clarify the action mechanism of the plasticizer on the polyurethane material.
(4) The invention adopts a batch reaction method of the curing agent to control the initial conformation of a molecular chain and maximally control other variables influencing the mechanical property.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a method for predicting the optimal dosage of an additive in a polyurethane material according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a method for predicting an optimal dosage of an additive in a polyurethane material includes the steps of:
s1: constructing a multi-component full-atom model:
s101: materials Studios were used to construct ring-opening models of monomer 3, 3-bis (azidomethyl) oxetane (BAMO) and Tetrahydrofuran (THF);
s102: building a random copolymer with the polymerization degree of 35-50 by using Build Polymers, sequentially performing energy minimization and energy comparison with the maximum iteration number of geometrics Optimization 5000-10000 steps on 5-10 randomly built molecular chains, and screening out a more appropriate molecular chain conformation;
s103: construction of small molecule additives was also performed using Materials Studios, and Geometry Optimization for energy minimization, after which all hydroxyl oxygen atoms were labeled "R1";
specifically, materials Studios are also used for constructing the small-molecule additive, and the small-molecule additive comprises a chain extender (diethylene glycol), a cross-linking agent (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 ratio.
S2: and (3) constructing a plasticizer/polymer chain mixed model for calculating the influence of the amount of the plasticizer on the system performance:
s201: using an Amorphous Cell module in Materials students to minimize the energy of the constructed random copolymer and the plasticizer A3 through Geometry Optimization, and screening out the configuration with the lowest energy as a simulation;
s202: annealing the initial model, and carrying out 3 times of circulation at 300-600K, wherein the total time of annealing is 2-4 ns;
s203: respectively carrying out NVT dynamic simulation of 2-4 ns and NPT dynamic simulation of 2-4 ns at the temperature of 300K;
s204: when the system energy is basically kept stable and the density fluctuates within the range of 1.1-1.4 g/cm < 3 >, collecting the kinetic data;
in addition, the copolymer: and randomly stacking 5-10 crystal cells with amorphous structures according to the mass ratio of the plasticizer to 1-2.
S3: the construction of a polyurethane network and additive mixing system for calculating the effect of curing parameters on the mechanical properties of the material:
s301: using an Amorphous Cell module of Materials students to construct an Amorphous Cell according to the proportion of hydroxyl number to isocyanate number (curing parameter) 1-2, marking carbon atoms of isocyanate as R2-R9 according to the curing parameter, and constructing mixed cells of additives with different contents on the premise of keeping the initial configuration of a macromolecule as much as possible;
s302: constructing 5-10 unit cells with amorphous structures, and screening initial configurations through the screening step in the step S2;
s303: then, optimizing the system structure by the annealing relaxation means in the step S2;
s304: identifying active atoms in the reaction functional groups through a Perl cross-linking script program, and constructing a random cross-linking network with the same initial configuration and different curing parameters through identifying isocyanate carbon atom marks (R2-R9) with different marks;
s305: after the cross-linking configuration is output, subsequent tensile dynamics simulation is carried out after relaxation treatment in the step S2, and the mechanical properties of the material under different curing parameters are predicted;
s4: calculating the intermolecular interaction of all the components of the plasticizer system:
s401: performing dynamic simulation on the equilibrium configuration output in the step S2, and analyzing interaction among the components, wherein in the simulation calculation of 300K, total energy of the system and energy of each component are output respectively, and the change of the interaction energy among molecules along with the addition of the plasticizer can be analyzed by subtracting the energy of the components from the system energy, and meanwhile, the diffusion rate of the micromolecules is calculated;
s5: calculating the mechanical properties of the cross-linking system with different curing parameters:
s501: selecting a 60% -90% reactivity crosslinking model of 1-2 curing parameters, firstly carrying out a relaxation step in the step S2, after the model reaches a relatively balanced state, carrying out box deformation rate of 108-1010/S on an amorphous unit cell to change the size of a box in a single direction, outputting stress and strain in a stretching direction, obtaining a uniaxial stretching curve, wherein the slope of a straight-line segment of the curve is Young modulus, the maximum stress is tensile strength, and the strain at a stress rapid decline part is fracture stretching rate.
In the specific example of the present application, in the plasticizer/polymer chain mixed model of step S2, the molecules are all physical interactions, and there is no change in composition.
In the specific embodiment of the present application, in step S203, the dynamic simulation process is performed in LAMMPS, and the force fields used are all OPLS-AA.
In the specific example of the present application, in the random copolymer model of step S201, the polymer chain is a polymer chain having a degree of polymerization of 35 to 50.
In the embodiment of the present application, in the mixed model of the polyurethane network and the additive in step S3, the model is constructed in Materials students, the selected force field is compossii, the dynamic simulation after the cross-linked structure model is obtained is performed, LAMMPS is used, and OPLS-AA is used as the force field.
In the embodiment of the present application, in step S301, the additive participates in the network construction, i.e., is a curing agent.
In the specific embodiment of the present application, the model of the different curing parameters is manipulated by identifying the isocyanate groups of the specific mark in step S304, thereby minimizing the influence of the initial configuration.
In the embodiment of the present application, in step S305, the simulated stretching rate is far greater than the actual experiment rate, and the reliability of the simulated data is obtained through the time-temperature equivalence principle.
In conclusion, through simulation calculation, the intermolecular interaction in the system is gradually increased and tends to be saturated along with the addition of the plasticizer, the small molecule diffusion capacity in the system is reduced after reaching a certain value due to the system crowding effect, and the mechanical property of the material has the maximum along with the increase of the curing agent.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. A method of predicting the optimal dosage of an additive in a polyurethane material, comprising the steps of:
s1: constructing a multi-component full-atom model:
materials Studios were used to construct ring-opening models of monomer 3, 3-bis (azidomethyl) oxetane (BAMO) and Tetrahydrofuran (THF);
building a random copolymer with the polymerization degree of 35-50 by using Build Polymers, sequentially performing energy minimization and energy comparison with the maximum iteration number of geometrics Optimization 5000-10000 steps on 5-10 randomly built molecular chains, and screening out a more appropriate molecular chain conformation;
construction of small molecule additives was also performed using Materials Studios, and Geometry Optimization for energy minimization, after which all hydroxyl oxygen atoms were labeled "R1";
s2: construction of a plasticizer/polymer chain mixture model:
using an Amorphous Cell module in Materials students to minimize the energy of the constructed random copolymer and the plasticizer A3 through Geometry Optimization, and screening out the configuration with the lowest energy as a simulation;
annealing the initial model, and carrying out 3 times of circulation at 300-600K, wherein the total time of annealing is 2-4 ns;
respectively carrying out NVT dynamic simulation of 2-4 ns and NPT dynamic simulation of 2-4 ns at the temperature of 300K;
when the system energy is basically kept stable and the density fluctuates within the range of 1.1-1.4 g/cm < 3 >, collecting the kinetic data;
s3: construction of polyurethane network and additive mixing System:
using an Amorphous Cell module of Materials students to construct an Amorphous Cell according to the proportion of hydroxyl number to isocyanate number (curing parameter) 1-2, marking carbon atoms of isocyanate as R2-R9 according to the curing parameter, and constructing mixed cells of additives with different contents on the premise of keeping the initial configuration of a macromolecule as much as possible;
constructing 5-10 unit cells with amorphous structures, and screening initial configurations through the screening step in the step S2;
then, optimizing the system structure by the annealing relaxation means in the step S2;
identifying active atoms in the reaction functional groups through a Perl cross-linking script program, and constructing a random cross-linking network with the same initial configuration and different curing parameters through identifying isocyanate carbon atom marks (R2-R9) with different marks;
after the cross-linking configuration is output, performing subsequent tensile dynamics simulation after relaxation treatment in the step S2, and predicting the mechanical properties of the material under different curing parameters;
s4: calculating the intermolecular interaction of all the components of the plasticizer system:
performing dynamic simulation on the equilibrium configuration output in the step S2, and analyzing interaction among the components, wherein in the simulation calculation of 300K, total energy of the system and energy of each component are output respectively, and the change of the interaction energy among molecules along with the addition of the plasticizer can be analyzed by subtracting the energy of the components from the system energy, and meanwhile, the diffusion rate of the micromolecules is calculated;
s5: calculating the mechanical properties of the cross-linking system with different curing parameters:
selecting a 60% -90% reactivity crosslinking model of 1-2 curing parameters, firstly carrying out a relaxation step in the step S2, after the model reaches a relatively balanced state, carrying out box deformation rate of 108-1010/S on an amorphous unit cell to change the size of a box in a single direction, outputting stress and strain in a stretching direction, obtaining a uniaxial stretching curve, wherein the slope of a straight-line segment of the curve is Young modulus, the maximum stress is tensile strength, and the strain at a stress rapid decline part is fracture stretching rate.
2. The method for predicting the optimal dosage of the additive in the polyurethane material according to claim 1, wherein the molecules are physically interacted without a change in composition in the step S2 of constructing the plasticizer/polymer chain mixture model.
3. The method of claim 2, wherein the dynamic simulation process is performed in LAMMPS and the force field is OPLS-AA during the construction of the plasticizer/polymer chain mixture model in step S2.
4. The method for predicting the optimal dosage of the additive in the polyurethane material as claimed in claim 3, wherein in the random copolymer model of step S2, the polymer chain is a polymer chain with a degree of polymerization of 35-50.
5. The method of claim 4, wherein in step S3, the model is constructed in Materials students, the force field is COMPASSII, and the dynamic simulation after obtaining the cross-linked structure model is performed by using LAMMPS and the force field is OPLS-AA.
6. The method for predicting the optimal dosage of the additive in the polyurethane material as claimed in claim 5, wherein in step S3, the additive participates in the network construction, i.e. is the curing agent.
7. The method for predicting the optimum dosage of additives in polyurethane according to claim 6, wherein the model of different curing parameters is adjusted by recognizing the isocyanate group of a specific mark in step S3, thereby minimizing the influence of initial configuration.
8. The method for predicting the optimal dosage of the additive in the polyurethane material as claimed in claim 7, wherein in the step S3, the simulated stretching speed is much higher than the actual experiment speed, and the reliability of the simulated data is obtained by the time-temperature equivalence principle.
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| CN116223224A (en) * | 2023-05-08 | 2023-06-06 | 山东清洋新材料有限公司 | Method for detecting influence of curing agent on mechanical properties of product based on image processing |
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| CN116223224A (en) * | 2023-05-08 | 2023-06-06 | 山东清洋新材料有限公司 | Method for detecting influence of curing agent on mechanical properties of product based on image processing |
| CN116223224B (en) * | 2023-05-08 | 2023-08-11 | 山东清洋新材料有限公司 | Method for detecting influence of curing agent on mechanical properties of product based on image processing |
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