A method for predicting the diffusion coefficient of zinc ions in a system of contaminated soil solidified with hydrated calcium silicate

Abstract

The application provides a method for predicting the diffusion coefficient of zinc ions in a hydrated calcium silicate solidified contaminated soil system, comprising the following steps: (1) constructing a pore model of hydrated calcium silicate gel; (2) constructing a solution model of zinc salt; (3) assembling the pore model and the solution model to form a stable molecular structure after relaxation; (4) applying force to each atom in the molecular structure, giving a preset condition and a boundary condition, and calculating the atomic motion coordinate trajectory; (5) according to the atomic motion coordinate trajectory, the diffusion coefficient of zinc ions in the pore model is calculated, which is the predicted value of the diffusion coefficient of zinc ions in the hydrated calcium silicate solidified contaminated soil system. The prediction method provided by the application provides a fast access way for the calculation of the migration parameters of the contaminated site, shortens the test and in-situ contaminated soil system maintenance and test period, improves the prediction accuracy, and avoids secondary environmental pollution.

Description

Technical Field

[0001] This invention belongs to the field of simulation and calculation technology, and relates to a method for predicting the diffusion coefficient of metal ions, and more particularly to a method for predicting the diffusion coefficient of zinc ions in a hydrated calcium silicate solidified contaminated soil system. Background Technology

[0002] Heavy metal pollution in soil is characterized by its long-term nature, insidiousness, irreversibility, and incomplete decomposition, making remediation difficult and posing significant environmental risks. Furthermore, heavy metals can undergo metamorphic transformations within the soil environment through physicochemical reactions, remaining there for extended periods and proving difficult to remove. Cement-based materials are considered the most commonly used solidification materials for controlling the migration of heavy metal ions. During cement hydration, the pH of the solution changes, affecting the migration of metal ions. Therefore, pH changes are closely related to the diffusion performance of metal ions. Calcium silicate hydrate gel is the main product of Portland cement hydration. As the main component of the cement hydration gel phase, it exists as 1-100 nm gel particles, with pores between these particles providing channels for metal ion transport. Since the diffusion performance of heavy metal ions within these gel channels is a crucial parameter for evaluating the effectiveness of solidification materials in controlling pollutants, calculating and predicting the diffusion coefficient of metal ions is of significant research importance.

[0003] Currently, the diffusion coefficient of heavy metal ions is mainly determined by soil column tests, which are widely used because they can represent the migration characteristics of pollutants under diffusion-dominant conditions. However, soil column tests have disadvantages such as inconvenience, long testing cycles, cumbersome operation, susceptibility to secondary pollution, and susceptibility to human error.

[0004] Therefore, it is evident that providing a method for predicting the diffusion coefficient of heavy metal ions in hydrated calcium silicate solidified contaminated soil systems, simplifying the operation process, shortening the testing cycle, improving prediction accuracy, and avoiding secondary pollution has become an urgent problem for those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the diffusion coefficient of zinc ions in a hydrated calcium silicate solidified contaminated soil system. The prediction method simplifies the operation process, shortens the testing cycle, improves the prediction accuracy, and avoids secondary pollution.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] This invention provides a method for predicting the diffusion coefficient of zinc ions in a hydrated calcium silicate solidified contaminated soil system. The prediction method includes the following steps:

[0008] (1) Construct a pore model of hydrated calcium silicate gel;

[0009] (2) Construct a solution model of zinc salt;

[0010] (3) Assemble the pore model obtained in step (1) and the solution model obtained in step (2), and after relaxation, form a stable molecular structure;

[0011] (4) Apply force to each atom in the molecular structure obtained in step (3), and calculate the atomic motion coordinate trajectory by giving preset conditions and boundary conditions;

[0012] (5) Based on the atomic motion coordinate trajectory obtained in step (4), the diffusion coefficient of zinc ions in the pore model is calculated, which is the predicted value of the diffusion coefficient of zinc ions in the calcium silicate hydrate solidified contaminated soil system.

[0013] Steps (1) and (2) are not in any particular order.

[0014] This invention predicts the diffusion coefficient of zinc ions in a calcium silicate-based solidified contaminated soil system using molecular dynamics simulation. This not only effectively solves the problems of inconvenient testing, long testing cycle, cumbersome operation, and easy secondary pollution in the prediction of diffusion coefficient of heavy metal ions in existing technologies, but also realizes the visualization of the migration process at the molecular scale. The obtained data provides a reliable basis for the performance of cement-based materials in solidifying heavy metal ions.

[0015] Preferably, the method for constructing the pore model in step (1) includes: obtaining the space group number and lattice parameters of the tobermorite crystal structure, constructing a monoclinic unit cell; orthogonalizing the obtained monoclinic unit cell, and then sequentially expanding and cutting the cell to obtain a pore size of [missing information]. The channel model, for example, could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0016] Preferably, the interlayer spacing of the tobermorite crystal structure is [missing information]. For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0017] Preferably, the axial angle of the monoclinic cell is α = β = 90°, γ = 120-130°, for example, γ can be 120°, 121°, 122°, 123°, 124°, 125°, 126°, 127°, 128°, 129° or 130°, but is not limited to the listed values, other unlisted values ​​within this range are also applicable.

[0018] Preferably, the axial lengths a, b, and c of the supercell obtained after cell expansion are each independently [missing information]. For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0019] Preferably, the cell division is performed along the 001 plane.

[0020] Preferably, the size of the simulation box of the channel model is [size missing].

[0021] In this invention, the size of the simulation box of the channel model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0022] In this invention, the size of the simulation box of the channel model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0023] In this invention, the size of the simulation box of the channel model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0024] Preferably, the zinc salt in step (2) includes zinc chloride.

[0025] Preferably, the solution pH of the solution model in step (2) is 1-13, for example, it can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0026] In this invention, when constructing a solution model with a pH of 1-7, 0-10 hydrochloric acid molecules need to be added to the solution model, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 molecules; when constructing a solution model with a pH of 7-13, 0-10 sodium hydroxide molecules need to be added to the solution model, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 molecules.

[0027] Preferably, the concentration of the solution model in step (2) is 17000-18000 mg / L, for example, it can be 17000 mg / L, 17100 mg / L, 17200 mg / L, 17300 mg / L, 17400 mg / L, 17500 mg / L, 17600 mg / L, 17700 mg / L, 17800 mg / L, 17900 mg / L or 18000 mg / L, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0028] Preferably, the size of the simulation box of the solution model in step (2) is [size missing].

[0029] In this invention, the simulation box size of the solution model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0030] In this invention, the simulation box size of the solution model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0031] In this invention, the simulation box size of the solution model is... For example, it could be or However, this does not apply to all values ​​listed; other unlisted values ​​within the same range also apply.

[0032] Preferably, step (3) further includes setting a first graphene plate on the side of the solution model away from the pore model, setting a second graphene plate in the middle of the solution model and the pore model, and deleting the second graphene plate after the relaxation is completed, so that the solution can diffuse into the pore.

[0033] Preferably, the relaxation in step (3) is performed under the NVT ensemble.

[0034] Preferably, the relaxation time in step (3) is 100-500ps, for example, it can be 100ps, 150ps, 200ps, 250ps, 300ps, 350ps, 400ps, 450ps or 500ps, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0035] Preferably, the force field applied to each atom in step (4) is an empirical force field, the basic form of which is expressed by the formula:

[0036] E total =E valence +E crossterm +E non-bond ;

[0037] E valence =E bond +E angle +E torsion ;

[0038] E non-bond =E vdw +E coulomb +E hbond ;

[0039] In the formula, E total E represents the total potential energy. valence E represents the bonding energy. crossterm E represents the energy of the cross term. non-bond E represents the nonbonded energy. bond E represents the bond stretching potential energy. angle E represents the potential energy of bond angle bending. torsion E represents the dihedral torsional potential energy. vdw E represents the van der Waals force. coulomb E represents the Coulomb electrostatic potential energy. hbond This indicates the hydrogen bond energy.

[0040] Preferably, the preset conditions in step (4) include system temperature, system pressure and total time step.

[0041] Preferably, the system temperature is 298-300K, for example, it can be 298K, 298.2K, 298.4K, 298.6K, 298.8K, 299K, 299.2K, 299.4K, 299.6K, 299.8K or 300K, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0042] Preferably, the system pressure is 0.8-1.2 atm, for example, it can be 0.8 atm, 0.85 atm, 0.9 atm, 0.95 atm, 1 atm, 1.05 atm, 1.1 atm, 1.15 atm or 1.2 atm, but it is not limited to the listed values, and other unlisted values ​​within this range are also applicable.

[0043] Preferably, the total time step is 600-1000ps, for example, it can be 600ps, 650ps, 700ps, 750ps, 800ps, 850ps, 900ps, 950ps or 1000ps, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0044] Preferably, the boundary conditions in step (4) are given based on the outer edge of the molecular structure.

[0045] Preferably, the algorithms involved in the calculation process of the atomic motion coordinate trajectory in step (4) include the Nose-Hoover isothermal calculation method and the Verlet algorithm.

[0046] In this invention, the Nose-Hoover isothermal calculation method is used to ensure the stability of the system during the simulation process.

[0047] Preferably, the calculation process of the diffusion coefficient in step (5) includes: firstly calculating the mean square displacement of zinc ions transported in the pore model based on the atomic motion coordinate trajectory, and then calculating the diffusion coefficient of zinc ions in the pore model based on Einstein's diffusion law.

[0048] Preferably, the formula for calculating the mean square displacement is:

[0049] MSD(t) = <|r i (t)-r i (0)| 2 >;

[0050] In the formula, r i (t) represents the position of atom i in the molecular model at time t, r i (0) represents the initial time i and the position of atom i, t represents time, and <> represents the average value.

[0051] Preferably, the diffusion coefficient is calculated using the following formula:

[0052]

[0053] In the formula, n represents the number of model dimensions, and n = 1-3.

[0054] As a preferred technical solution of the present invention, the prediction method includes the following steps:

[0055] (1) Constructing a pore model of hydrated calcium silicate gel, specifically: obtaining... Using the space group number and lattice parameters of the tobermorite crystal structure, a monoclinic unit cell was constructed with α = β = 90° and γ = 120-130°. After orthogonalizing the obtained monoclinic unit cell, further cell expansion was performed to obtain… The supercell was then cut along the 001 plane to obtain a pore size of... The channel model, and the size of the simulation box of the channel model is...

[0056] (2) Construct a solution model of zinc chloride, wherein the solution pH of the model is 1-13, the solution concentration is 17000-18000 mg / L, and the simulation box size is [missing information].

[0057] (3) Assemble the pore model obtained in step (1) and the solution model obtained in step (2). Place the first graphene plate on the side of the solution model away from the pore model and place the second graphene plate in the middle of the solution model and the pore model. Relax for 100-500ps under the NVT ensemble to form a stable molecular structure. After the relaxation is completed, delete the second graphene plate.

[0058] (4) Apply a force to each atom in the molecular structure obtained in step (3). The force field can be in the form of an empirical force field, and its basic form is expressed by the formula:

[0059] E total =E valence +E crossterm +E non-bond ;

[0060] E valence =E bond +E angle +E torsion ;

[0061] E non-bond =E vdw +E coulomb +E hbond ;

[0062] In the formula, E total E represents the total potential energy. valence E represents the bonding energy. crossterm E represents the energy of the cross term. non-bond E represents the nonbonded energy. bond E represents the bond stretching potential energy. angle E represents the potential energy of bond angle bending. torsion E represents the dihedral torsional potential energy. vdw E represents the van der Waals force. coulomb E represents the Coulomb electrostatic potential energy. hbond Indicates hydrogen bond energy;

[0063] Then, given preset conditions and boundary conditions, the Nose-Hoover isothermal calculation method is used to ensure the stability of the system during the simulation, and the Verlet algorithm is used to calculate the atomic motion coordinate trajectory. The preset conditions include system temperature of 298-300K, system pressure of 0.8-1.2atm, and total time step of 600-1000ps. The boundary conditions are given based on the outer edge of the molecular structure.

[0064] (5) First, calculate the mean square displacement of zinc ions transported in the pore model based on the atomic motion coordinate trajectory obtained in step (4), and then calculate the diffusion coefficient of zinc ions in the pore model based on Einstein's diffusion law, which is used as the predicted value of the diffusion coefficient of zinc ions in the calcium silicate hydrate solidified contaminated soil system.

[0065] The formula for calculating the mean square displacement is:

[0066] MSD(t) = <|r i (t)-r i (0)| 2 >;

[0067] In the formula, r i (t) represents the position of atom i in the molecular model at time t, r i (0) represents the initial position of atom i at time i, t represents time, and <> represents the average value;

[0068] Preferably, the diffusion coefficient is calculated using the following formula:

[0069]

[0070] In the formula, n represents the number of model dimensions, and n = 1 - 3;

[0071] Steps (1) and (2) are not in any particular order.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] This invention predicts the diffusion coefficient of zinc ions in a calcium silicate-based solidified contaminated soil system using molecular dynamics simulation. This not only effectively solves the problems of inconvenient testing, long testing cycle, cumbersome operation, and easy secondary pollution in the prediction of diffusion coefficient of heavy metal ions in existing technologies, but also realizes the visualization of the migration process at the molecular scale. The obtained data provides a reliable basis for the performance of cement-based materials in solidifying heavy metal ions. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the pore model of calcium silicate hydrate gel in the prediction method provided in Example 1;

[0075] Figure 2 This is a schematic diagram of the solution model with pH=1 in the prediction method provided in Example 1;

[0076] Figure 3 This is a schematic diagram of the solution model with pH=7 in the prediction method provided in Example 1;

[0077] Figure 4 This is a schematic diagram of the solution model with pH=13 in the prediction method provided in Example 1;

[0078] Figure 5 This is a schematic diagram of the overall molecular dynamics simulation in the prediction method provided in Example 1;

[0079] Figure 6 This is a schematic diagram of the mean square displacement changing over time in the prediction method provided in Example 1;

[0080] Figure 7 This is a schematic diagram showing the change of diffusion coefficient over time in the prediction method provided in Example 1.

[0081] Wherein: 1-Tobermorite; 2-First graphene plate. Detailed Implementation

[0082] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0083] Example 1

[0084] This embodiment provides a method for predicting the diffusion coefficient of zinc ions in a hydrated calcium silicate solidified contaminated soil system. The prediction method includes the following steps:

[0085] (1) Constructing a pore model of hydrated calcium silicate gel, specifically: obtaining... Based on the space group number and lattice parameters of the tobermorite crystal structure, a monoclinic unit cell with α = β = 90° and γ = 123.25° was constructed. After orthogonalizing the obtained monoclinic unit cell, it was then enlarged by 2, 3, and 1 times along the X, Y, and Z axes, respectively, to obtain... The supercell was then dissected along the 001 plane to obtain the following result: Figure 1 The aperture shown is The channel model, and the size of the simulation box of the channel model is...

[0086] (2) Construct zinc chloride solution models with three different pH values. Specifically, first construct a model with a size of... The simulation box was then randomly filled with 24 zinc ions, 57 chloride ions, 9 hydrogen ions, and 5000 water molecules to form a zinc chloride solution model with pH=1 and a concentration of 17010 mg / L (see [link to simulation box]). Figure 2 ); 24 zinc ions, 48 ​​chloride ions, and 5000 water molecules were randomly added to a simulation box of the same size to form a zinc chloride solution model with pH=7 and a concentration of 17010 mg / L (see Figure 3 ); In a simulation box of the same size as described above, 24 zinc ions, 48 ​​chloride ions, 9 sodium ions, 9 hydroxide ions, and 5000 water molecules were randomly added to form a zinc chloride solution model with pH=13 and a concentration of 17010 mg / L (see Figure 4 Since the above three solution models are completely identical except for pH, this embodiment only performs subsequent calculations for the solution model with pH=7; then, charges are assigned to zinc ions, chloride ions, calcium ions, sodium ions, hydrogen ions, and silicon atoms and oxygen atoms in the silicon-oxygen tetrahedron to maintain charge balance, and the total charge of the solution is 0 when the charge is balanced.

[0087] (3) Figure 5 As shown, the pore model obtained in step (1) and the solution model obtained in step (2) are assembled. A first graphene plate 2 is placed on the side of the solution model away from the tobermorite 1, and a second graphene plate (not shown in the figure) is placed in the middle of the solution model and the pore model. The solution model is relaxed for 500 ps under the NVT ensemble to form a stable molecular structure. After the relaxation is completed, the second graphene plate is removed to facilitate the diffusion of the solution into the pore.

[0088] Calcium silicate hydrate (CSH) gel is the main product of Portland cement hydration. As the main component of the cement hydration gel phase, it exists in the form of 1-100 nm gel particles. Experiments have shown that the pore size distribution of CSH gel is 0.5-10 nm. The pores between gel particles provide channels for the transport of ion solutions, and the hydrophilicity of the CSH gel surface provides the driving force for capillary transport. In this embodiment, the CSH gel pore model is formed by placing a CSH gel molecular model as a substrate in parallel, leaving transport channels in the middle. The pore diameter and length can be adjusted by translating and extending the CSH substrate.

[0089] (4) Apply a force to each atom in the molecular structure obtained in step (3). The force field describes the potential energy driving the atomic motion in the system. It is converted into the acceleration of each atom's motion through Newton's second law. Given the initial position and velocity, the coordinates of each atom at each time point can be calculated. The force field adopts an empirical force field, the basic form of which is shown in the following formula, including the bond energy, cross term energy and non-bond energy between atoms:

[0090] E total =E valence +E crossterm +E non-bond ;

[0091] E valence =E bond +E angle +E torsion ;

[0092] E non-bond =E vdw +E coulomb +E hbond ;

[0093] In the formula, E total E represents the total potential energy. valence E represents the bonding energy. crossterm E represents the energy of the cross term. non-bond E represents the nonbonded energy. bond E represents the bond stretching potential energy. angle E represents the potential energy of bond angle bending. torsion E represents the dihedral torsional potential energy. vdw E represents the van der Waals force. coulomb E represents the Coulomb electrostatic potential energy. hbond This indicates the hydrogen bond energy.

[0094] Then, given preset and boundary conditions, the Nose-Hoover isothermal calculation method is used to ensure the stability of the system during the simulation. The Verlet algorithm is used to calculate the atomic motion coordinate trajectories, specifically: establishing... Figure 5 The molecular dynamics model shown was used, and after applying force fields to each atom, simulations were performed in Materials Studio. The system temperature was set to 300 K, the system pressure to 1 atm, and the total time step to 1 fs. Periodic boundaries were set in the X, Y, and Z directions. The initial velocity of each atom was randomly generated based on the initial temperature. The Verlet algorithm was used to calculate the position of the atom at the next time step, with a long-range force cutoff radius of [missing value]. First, the structure was energy optimized by minimizing energy. Then, a barrier (i.e., a second graphene plate) was placed between the solution and the CSH substrate to temporarily prevent the solution from entering the pores. Simultaneously, under a canonical ensemble (NVT), the entire system was allowed to move for 500 ps to achieve equilibrium in each part of the structure. Finally, under the canonical ensemble, the barrier (i.e., the second graphene plate) was removed, and a 5 MPa thrust was applied to the graphene plate to allow the solution to enter the pores, moving for 600 ps. During the simulation, thermodynamic parameters such as temperature and pressure, as well as the dimensions of the simulation chamber, were output every 4 ps, along with the coordinates of all atoms, for a total of 151 frames of atomic coordinate output.

[0095] (5) First, calculate the mean square displacement of zinc ions transported in the pore model based on the atomic motion coordinate trajectories obtained in step (4) (see Figure 6 Then, the diffusion coefficient of zinc ions in the pore model was calculated according to Einstein's diffusion law (see...). Figure 7 ), which is the predicted value of the diffusion coefficient of zinc ions in the calcium silicate hydrate solidified contaminated soil system.

[0096] The formula for calculating the mean square displacement is:

[0097] MSD(t) = <|r i (t)-r i (0)| 2 >;

[0098] In the formula, r i (t) represents the position of atom i in the molecular model at time t, r i (0) represents the initial time i and the position of atom i, t represents time, and <> represents the average value.

[0099] The formula for calculating the diffusion coefficient is:

[0100]

[0101] In the formula, n represents the number of model dimensions, and n = 3.

[0102] To further investigate the specific effects of solution pH on metal ions in cement-based materials, this example measured the changes in mean square displacement and diffusion coefficient over time in solution environments with pH=1, pH=7, and pH=13. Figure 6 and 7 As shown, it can be seen that in the first 100 ps, ​​before the solution enters the pore, the diffusion coefficient decreases as the solution pH increases. After entering the pore, the diffusion coefficient of metal ions in solution pH=7 is higher than that in the other two cases.

[0103] Therefore, this invention predicts the diffusion coefficient of zinc ions in the calcium silicate hydrate solidified contaminated soil system through molecular dynamics simulation. This not only effectively solves the problems of inconvenient testing, long testing cycle, cumbersome operation, and easy secondary pollution in the prediction of the diffusion coefficient of heavy metal ions in the existing technology, but also realizes the visualization of the migration process at the molecular scale. The obtained data provides a reliable basis for the performance of cement-based materials in solidifying heavy metal ions.

[0104] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for predicting the diffusion coefficient of zinc ions in a calcium silicate hydrate solidified contaminated soil system, characterized in that, The prediction method includes the following steps: (1) Construct a pore model of hydrated calcium silicate gel; The method for constructing the pore model includes: obtaining the space group number and lattice parameters of the tobermorite crystal structure, and constructing a monoclinic unit cell; orthogonalizing the obtained monoclinic unit cell, and then performing cell expansion and cell cutting in sequence to obtain a pore model with a pore size of 34-35 Å; the interlayer spacing of the tobermorite crystal structure is 10-12 Å. The axial angle of the monoclinic unit cell is , The axial lengths a, b, and c of the supercell obtained after cell expansion are independently 22-23 Å; the cell cutting is performed along the 001 plane; the simulated box size of the pore model is a=(22-23) Å, b=(60-80) Å, c=(80-90) Å; (2) Construct a solution model of zinc salt; The zinc salt includes zinc chloride; the solution model has a solution pH of 1-13, a solution concentration of 17000-18000 mg / L, and simulation box dimensions of a=(22-23) Å, b=(60-80) Å, c=(90-100) Å; (3) Assemble the pore model obtained in step (1) and the solution model obtained in step (2), and after relaxation, form a stable molecular structure; The assembly and relaxation process also includes setting a first graphene plate on the side of the solution model away from the pore model, setting a second graphene plate in the middle of the solution model and the pore model, and deleting the second graphene plate after the relaxation is completed. (4) Apply force to each atom in the molecular structure obtained in step (3), and calculate the atomic motion coordinate trajectory given preset conditions and boundary conditions; The force field that applies force to each atom can be categorized into empirical force fields, the basic form of which is expressed by the formula: ； ； ； In the formula, Represents the total potential energy. Indicates bonding energy, Indicates the cross-term energy. Indicates nonbonded energy. This represents the bond stretching potential energy. This represents the potential energy at the bond angle during bending. This represents the dihedral torsional potential energy. This represents the van der Waals force. This represents the Coulomb electrostatic potential energy. Indicates hydrogen bond energy; (5) Based on the atomic motion coordinate trajectory obtained in step (4), the diffusion coefficient of zinc ions in the pore model is calculated, which is used as the predicted value of the diffusion coefficient of zinc ions in the calcium silicate hydrate solidified contaminated soil system. Steps (1) and (2) are not in any particular order.

2. The prediction method according to claim 1, characterized in that, The relaxation described in step (3) is performed under the NVT ensemble.

3. The prediction method according to claim 1, characterized in that, The relaxation time in step (3) is 100-500 ps.

4. The prediction method according to claim 1, characterized in that, The preset conditions in step (4) include system temperature, system pressure and total time step.

5. The prediction method according to claim 4, characterized in that, The system temperature is 298-300K.

6. The prediction method according to claim 4, characterized in that, The system pressure is 0.8-1.2 atm.

7. The prediction method according to claim 4, characterized in that, The total time step is 600-1000ps.

8. The prediction method according to claim 1, characterized in that, The boundary conditions in step (4) are given based on the outer edge of the molecular structure.

9. The prediction method according to claim 1, characterized in that, The algorithms involved in the calculation process of the atomic motion coordinate trajectory in step (4) include the Nose-Hoover isothermal calculation method and the Verlet algorithm.

10. The prediction method according to claim 1, characterized in that, The calculation process of the diffusion coefficient in step (5) includes: first, calculating the mean square displacement of zinc ions transported in the pore model based on the atomic motion coordinate trajectory, and then calculating the diffusion coefficient of zinc ions in the pore model based on Einstein's diffusion law.

11. The prediction method according to claim 10, characterized in that, The formula for calculating the mean square displacement is: ； In the formula, This represents the position of atom i in the molecular model at time t. This indicates the position of atom i at the initial time. Indicates time, This represents the average value.

12. The prediction method according to claim 10, characterized in that, The formula for calculating the diffusion coefficient is: ； In the formula, n represents the number of model dimensions, and n = 1-3.

Images