A method for predicting ice inhibition performance of nanosilica based on molecular dynamics simulation

Abstract

The application provides a method for predicting the ice inhibition performance of nanometer silicon dioxide based on molecular dynamics simulation. The application comprises the following steps: establishing a functionalized nanometer silicon dioxide model, and establishing an ice-containing water box model according to the size of the functionalized nanometer silicon dioxide model; assembling the nanometer silicon dioxide model and the water box model; selecting a suitable force field for the constructed model and performing energy minimization optimization. Molecular dynamics simulation calculation is performed on the silicon dioxide-ice-water model after energy minimization, and analysis is performed on the model after calculation. The root mean square deviation, root mean square fluctuation and binding energy are analyzed, and the number of hydrogen bonds is counted. Based on the analysis data, the feasibility and effect of the material for inhibiting ice are judged. The application constructs different functionalized materials and evaluates their behavior in the ice-water system, thereby greatly reducing the application cost, saving experimental resources, and guiding the development of a freezing protectant based on nanometer materials.

Description

Technical Field

[0001] This invention relates to the field of computer simulation technology for nanomaterials, and in particular to a method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation. Background Technology

[0002] Spherical nano-silica, as a three-dimensional nanomaterial, has the advantages of simple synthesis methods and high surface hydroxyl content, easy modification of functional groups, and high biocompatibility. Therefore, it is often used as a targeted drug delivery agent or sustained-release agent in biomedicine. As a low-toxicity biomaterial, spherical nano-silica has attracted increasing attention. However, due to limitations in time, space, and technology, it is difficult to determine the interaction mechanisms between molecules and atoms experimentally, resulting in a long research cycle for the design and development of novel cryoprotectants containing it. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for predicting the ice-suppressing performance of nano-silica based on molecular dynamics simulations. This invention uses molecular dynamics simulations to study the interaction between spherical functionalized nano-silica and the ice-water system, revealing the interaction mechanism between the material and the surrounding aquatic environment, and thus guiding the development of novel protective agents for nanomaterials. The technical means employed in this invention are as follows:

[0004] A method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulations includes the following steps:

[0005] Step 1: Establish a functionalized nano-silica model, and build a water box model containing ice according to the size of the functionalized nano-silica model. Assemble the nano-silica model and the water box model to construct a silica-ice-water system model. Select a suitable force field for the constructed model and perform energy minimization optimization.

[0006] Step 2: Molecular dynamics simulations were performed on the energy-minimized silica-ice-water model, followed by model analysis. This included analyzing the root mean square deviation, root mean square fluctuation, and binding energy, and statistically analyzing the number of hydrogen bonds formed between the material and the system. Based on the analysis data, the feasibility and effectiveness of using the material for ice suppression were determined. This invention reveals the mechanism of interaction between nanomaterials and the ice-water interface at low temperatures, providing guidance for the development of cryoprotectants based on nanomaterials.

[0007] Furthermore, the functionalized nano-silica model includes establishing silica nanomaterial models with different surface functional groups, including hydroxyl, amino, and carboxyl groups.

[0008] Furthermore, the particle size of the silica nanomaterial is 1–2 nm.

[0009] Furthermore, the total number of water and ice molecules in the water box is between 8,000 and 15,000.

[0010] Furthermore, in step 1, the energy minimization process is simulated using Gromacs software, and the force field type is charmm27 force field.

[0011] Furthermore, in step 1, the Steep algorithm is used for the energy minimization process, with a maximum number of iterations of 50,000.

[0012] Furthermore, in step 2, the molecular dynamics simulation calculation is performed using Gromacs software, and the force field type is charmm27 force field.

[0013] Furthermore, in step 2 of the simulation, the ensemble is selected as NPT; the temperature control method is selected as V-rescale, and the temperature is selected as 200-300K; the pressure control method is selected as Parrinello-Rahman, and the pressure is selected as 1-400bar.

[0014] Furthermore, in step 2, the molecular dynamics simulation adopts equilibrium molecular dynamics simulation, and focuses on analyzing its non-equilibrium processes;

[0015] The specific process of molecular dynamics simulation includes:

[0016] Simulations were performed in the range of 10–150 ns with a step size of 1 fs, and parameters such as energy and temperature of the system were recorded. The molecular dynamics simulations, performed in the range of 10–150 ns with a step size of 1 fs, were used for subsequent analysis.

[0017] This invention starts with the practicality of nano-silica, constructs and evaluates the behavior of different functionalized materials in ice-water systems, thereby greatly reducing application costs, saving experimental resources, and guiding the design and development of novel cryoprotectants, reducing the time from research to industrialization. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of molecular dynamics simulation provided for a preferred embodiment of the present invention.

[0020] Figure 2A model diagram of hydroxyl-functionalized spherical nano-silica provided for a preferred embodiment of the present invention.

[0021] Figure 3 This is an evolution diagram of the silica-ice-water model at different times provided for a preferred embodiment of the present invention. At t = 0 ns, the initial structure is a simulated water box with a size of 5.4 nm * 10.5 nm * 5.9 nm; at t = 100 ns, a clear unfrozen water interface is observed around the material, forming a curved structure with the surrounding frozen ice layer; at t = 150 ns, the material significantly slows down the diffusion of the ice layer, showing a certain ability to inhibit ice growth.

[0022] Figure 4 The root mean square deviation (RMSD) plot of the model provided for a preferred embodiment of the present invention.

[0023] Figure 5 The root mean square fluctuation (RMSF) plot of the model provided for a preferred embodiment of the present invention.

[0024] Figure 6 The model provided for a preferred embodiment of the present invention shows the hydrogen bond diagram between the material and water molecules. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] like Figure 1 As shown in the figure, this embodiment discloses a method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation, including the following steps:

[0027] Step 1: Establish a functionalized nano-silica model, and build a water box model containing ice according to the size of the functionalized nano-silica model. Assemble the nano-silica model and the water box model to construct a silica-ice-water system model. Select a suitable force field for the constructed model and perform energy minimization optimization.

[0028] Step 2: Molecular dynamics simulations were performed on the energy-minimized silica-ice-water model, followed by model analysis. This included analyzing the root mean square deviation, root mean square fluctuation, and binding energy, and statistically analyzing the number of hydrogen bonds formed between the material and the system. Based on the analysis data, the feasibility and effectiveness of using the material for ice suppression were determined. This invention reveals the mechanism of interaction between nanomaterials and the ice-water interface at low temperatures, providing guidance for the development of cryoprotectants based on nanomaterials.

[0029] Furthermore, the functionalized nano-silica model includes establishing silica nanomaterial models with different surface functional groups, including hydroxyl, amino, and carboxyl groups.

[0030] Furthermore, the particle size of the silica nanomaterial is 1–2 nm.

[0031] Furthermore, the total number of water and ice molecules in the water box is between 8,000 and 15,000.

[0032] Furthermore, in step 1, the energy minimization process is simulated using Gromacs software, and the force field type is charmm27 force field.

[0033] Furthermore, in step 1, the Steep algorithm is used for the energy minimization process, with a maximum number of iterations of 50,000.

[0034] Furthermore, in step 2, the molecular dynamics simulation calculation is performed using Gromacs software, and the force field type is charmm27 force field.

[0035] Furthermore, in step 2 of the simulation, the ensemble is selected as NPT; the temperature control method is selected as V-rescale, and the temperature is selected as 200-300K; the pressure control method is selected as Parrinello-Rahman, and the pressure is selected as 1-400bar.

[0036] Furthermore, in step 2, the molecular dynamics simulation adopts equilibrium molecular dynamics simulation, and focuses on analyzing its non-equilibrium processes;

[0037] The specific process of molecular dynamics simulation includes:

[0038] Simulations were performed in the range of 10–150 ns with a step size of 1 fs, and parameters such as energy and temperature of the system were recorded. The molecular dynamics simulations, performed in the range of 10–150 ns with a step size of 1 fs, were used for subsequent analysis.

[0039] Example 1

[0040] This embodiment provides specific technical solution steps:

[0041] (1) As Figure 2 As shown, the spherical silica model was constructed using the Nanocluster module of Materials Studio software. Specifically, SiO2_21A_3d.msi from the Materials Studio database was used as the initial unit cell for silica. The Build-BuildNanostructure-Nanocluster module was used, Sphere was selected, the initial radius was set, and Build was clicked to complete the initial model construction. The constructed model was then modified according to the actual simulation requirements, such as manually constructing functional group information, saturated / unsaturated chemical bonds, adjusting unreasonable structures, etc., and geometric optimization was performed to obtain a more reasonable spherical silica model.

[0042] In this embodiment, the initial radius is set to A 2 nm hydroxyl-functionalized spherical silica model was constructed. Inappropriate structures in the model were modified, and H atoms were added to the surface to obtain a silica model suitable for simulation. Its surface has 88 hydroxyl functional groups, with a group density of approximately 7 groups / nm. 2 The obtained model is exported and saved as a pdb file for subsequent simulations.

[0043] (2) Construction of the silica-ice-water model: The ice-water model was constructed using Packmol modeling software and then optimized before being assembled with the silica nanomaterials from step (1). Specifically, the initial ice crystal file provided by Hayward JA, Reimers J R. Unit cells for the simulation of hexagonal ice[J].Journal of Chemical Physics,1997,106(4):1518-1529 was expanded to the size required for simulation using the Build-Symmetry-Supercell module of MaterialsStudio software and exported as a pdb file. In addition, a water molecule model was created in MaterialsStudio and exported as a pdb file. Then, write the PDB files of ice and water into the input file of Packmol, where the number of water molecules is 10,000. Use Packmol software to obtain a water box with an initial size of 5.4nm*10.5nm*5.9nm. (In this embodiment, the obtained SPC three-point water model is also converted into a tip4p four-point water model using a script file.) After minimizing the energy using Gromacs simulation software, use Packmol software to assemble the water box with the nano silica model in step (1), and export the PDB format and convert it into Gro format using VMD software for subsequent simulation.

[0044] In this embodiment, the cell expansion yielded an ice layer with dimensions of 5.4nm*1.5nm*5.9nm and a total of 1536 water molecules;

[0045] (3) System Energy Minimization Optimization: The energy of the water box obtained in step (2) is minimized using Gromacs. Specifically, the process force field type is charmm27 force field, the water model is tip4p water model, the algorithm is steep, and the maximum number of iterations is 50,000 steps. In this example, the system energy is reduced to 10.0 kJ / mol in approximately 500-1000 finite iteration steps.

[0046] (4) Molecular dynamics simulation of the optimized model: Molecular dynamics simulation of the energy-minimizing silica-ice-water model was performed using Gromacs molecular dynamics simulation software. Specifically, the molecular dynamics simulation used a charmm27 force field, a tip4p water model, and an NPT ensemble. The evolution diagram of this example at different times is shown below. Figure 3As shown. In the NPT simulation, the system pressure was controlled by Parrinello-Rahman, the system temperature was thermally coupled using the V-rescale method, the electrostatic interaction was performed using the PME method, the van der Waals forces were calculated using the cut-off method with a cutoff value of 0.1 nm, the calculation step size was selected as 1 fs, and a molecular dynamics simulation was performed for a period of time, with the last 10 ns of data used for subsequent analysis.

[0047] In this embodiment, the LJ parameters and other data of the silica model can be obtained from Emami FS, Puddu V, Berry RJ, et al. Force Field and a Surface Model Database for Silica to Simulate Interfacial Properties in Atomic Resolution[J]. Chemistry of Materials, 2014, 26(8).

[0048] (5) Analyze the simulation results, that is, use simulation prediction software such as Gromacs and VMD to perform root mean square deviation and root mean square displacement analysis on different functionalized silica nanomaterials, and count the number of hydrogen bonds and related binding energies between the material and the surrounding system to evaluate and analyze the excellent performance of various materials.

[0049] In this embodiment, the root mean square deviation and root mean square displacement of the hydroxylated silica nanomaterials are measured as follows: Figure 4 , Figure 5 As shown, the number of hydrogen bonds formed with water molecules is as follows Figure 6 As shown, the root mean square deviation fluctuates within a very small range of 0.025–0.035 nm, indicating that the material maintains good stability in the system. However, the root mean square displacement of the outer atoms fluctuates more significantly, indicating that the outer atoms interact more intensely with the system. Furthermore, the material can form a stable hydrogen bond structure with water molecules within a time of 9–10 ns, approximately 155 ± 4.6 bonds. This structure effectively disrupts the original hydrogen bond network. Based on the above evaluation and analysis, this material exhibits excellent anti-icing performance.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation, characterized in that, Includes the following steps: Step 1: Establish a functionalized nano-silica model, and build a water box model containing ice according to the size of the functionalized nano-silica model. Assemble the nano-silica model and the water box model, select a suitable force field for the constructed model and optimize it by minimizing energy. Step 2: Perform molecular dynamics simulation calculations on the silica-ice-water model after minimizing energy, and analyze the model after the calculation. Specifically, this includes analyzing the root mean square deviation, root mean square fluctuation and binding energy, and statistically analyzing the number of hydrogen bonds. Based on the analysis data, determine the feasibility and effectiveness of the material for ice suppression. The functionalized nano silica model includes establishing silica nanomaterial models with different surface functional groups, including hydroxyl, amino, and carboxyl groups.

2. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, The particle size of silica nanomaterials is 1–2 nm.

3. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, The total number of water and ice molecules in the water box is between 8,000 and 15,000.

4. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, In step 1, the energy minimization process is simulated using Gromacs software, and the force field type is charmm27 force field.

5. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, In step 1, the Steep algorithm is used for the energy minimization process, with a maximum number of iterations of 50,000.

6. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, In step 2, the molecular dynamics simulation calculation is performed using Gromacs software, and the force field type is charmm27 force field.

7. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, In step 2 of the simulation, the ensemble was selected as NPT; the temperature control method was selected as V-rescale, and the temperature was selected as 200–300 K; the pressure control method was selected as Parrinello-Rahman, and the pressure was selected as 1–400 bar.

8. The method for predicting the anti-icing performance of nano-silica based on molecular dynamics simulation according to claim 1, characterized in that, In step 2, the molecular dynamics simulation adopts equilibrium molecular dynamics simulation, and focuses on analyzing its non-equilibrium processes; The specific process of molecular dynamics simulation includes: Simulations were performed in the range of 10–150 ns with a step size of 1 fs, and the energy and temperature parameters of the system were recorded. The molecular dynamics simulation process was performed in the range of 10–150 ns with a step size of 1 fs for subsequent analysis.

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