CN107341301B - Method for predicting wettability between defect-introduced graphene and metal - Google Patents
Method for predicting wettability between defect-introduced graphene and metal Download PDFInfo
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Abstract
The invention relates to a lead-in notchThe method for predicting the wettability between the trapped graphene and the metal is based on the density functional theory of the first sexual principle, and the wettability between the trapped graphene and the metal cluster is predicted by calculating the changes of adsorption energy, bond energy and structure between the trapped graphene and the metal cluster, wherein different types of defects are introduced. Defects introduced by the graphene are mainly point defects, Ni-doped defects and Ni-adsorbed defects. Calculate them separately from the metal Cu13Clusters or Al13The adsorption energy, bond energy and structure change after cluster adsorption, and the introduction of graphene defects is found to improve the wettability between graphene and metal, and particularly the wettability between graphene doped with Ni atoms and metal is remarkably improved. The invention can also carry out effective prediction analysis on the wettability between the objects with different introduced defects and the metal according to the difference of the introduced defects or the difference of the introduced defect objects.
Description
Technical Field
The invention relates to a method for predicting wettability between defect-introduced graphene and metal, and particularly belongs to the technical field of metal-based composite materials.
Background
With the development of the fields of automobiles and aerospace, particularly in the field of space, the requirements on the properties of metal matrix composite materials such as specific strength, specific modulus, corrosion resistance, electric conduction and heat conduction in severe environments such as ionizing radiation and the like are higher, and the requirements on the materials cannot be met by the traditional ceramic fibers and particle reinforcements. Graphene has been considered the most desirable metal matrix composite reinforcement in recent years because of its excellent mechanical and physical properties.
Since the C atom of the graphene is difficult to form a stable chemical bond with the metal, the graphene added in the metal-based graphene composite material is difficult to have good bonding property with a matrix, and the modification of the graphene is considered to be an effective way for hopefully solving the problem of wettability between the graphene and the metal and preparing the high-performance metal-based composite material. The wettability between graphene and metal determines the performance of the metal-based graphene composite material, and it has been a research hotspot to search for simple and effective modification of graphene to improve the wettability. At present, most of the research is focused on experimental preparation, and no related prediction method exists.
The defect introduced into the graphene to modify the wettability is an effective way for improving the wettability between the graphene and the metal. At present, no specific method exists for predicting or evaluating the wettability change of the introduced defects on the graphene, and the method for rapidly and effectively evaluating and predicting the wettability of the modified graphene with the introduced defects is established. After the defects are introduced, the bond energy, bond angle and electron density parameters of the graphene and the metal are changed, so that the wettability of the graphene and the metal is changed, and therefore, the influence of the modification on the wettability of the graphene is analyzed from a microscopic angle most directly and accurately.
Disclosure of Invention
In order to solve the defect of the method for predicting or evaluating the change of the wettability of the graphene caused by the introduced defects, the invention aims to provide a method for predicting the wettability between the graphene and the metal caused by the introduced different defects.
The invention relates to a method for predicting wettability between defect-introduced graphene and metal, which is based on a density functional theory of a first principle, and predicts wettability of graphene and metal with different defects by calculating adsorption energy, bond energy and structural change after the defect-introduced graphene and metal clusters are adsorbed, and specifically comprises the following steps:
a calculation process
Firstly, constructing graphene models containing different defect types and metal Al13Cluster and Cu13And the cluster model is used for respectively combining the defect-free graphene and the graphene with different defects with the metal cluster structure, then carrying out structural optimization to obtain the most stable structure, and carrying out adsorption energy, bond energy and structure calculation.
The defects comprise vacancy defects, doped Ni defects and adsorbed Ni defects.
And comparing and verifying the analysis of all calculation results with the actual wettability effect between the graphene and the metal after the defect is introduced, finally determining the influence on the wettability between the graphene and the metal after the defect is introduced, providing a prediction method for the preparation and modification of the high-performance metal-based graphene composite material, and simultaneously ensuring the reliability of the calculation method.
Second, model construction
Defect-free graphene structure, defect-introduced graphene structure, and metallic Al13Clusters or Cu13The method for constructing the cluster structure model comprises the following steps:
(1) according to the fact that graphene is a carbon atom and SP2The honeycomb-shaped planar film formed by the hybridization mode has the following planar structure parameters: bond length a between each C atom and the 3C atoms attached to the periphery1=a2=a3=0.42nm, angle of three bonds β1=β2=β3=120 °, a two-dimensional structure with planar periodicity was established using a casep calculation module, and a 7 × 7 graphene super-cell model was established.
(2) Respectively establishing a point defect, Ni-doped and Ni-adsorbed two-dimensional planar graphene defect structure according to defect types introduced in an actual experiment, wherein the point defect graphene is a structure obtained by structure optimization after a C atom in a super-cell structure of defect-free graphene is deleted; the Ni-doped defect is a structure obtained by structural optimization after a C atom in a super-unit cell structure of the defect-free graphene is replaced by a Ni atom; the Ni defect adsorption is a structure obtained by structure optimization after a super-unit cell structure of the defect-free graphene adsorbs a Ni atom.
(3) Metallic Al13Cluster and Cu13The cluster structure is a stable structure obtained by structural optimization with 13 Al atoms or 13 Cu atoms as units, and the bond lengths between Cu-Cu and Al-Al are respectively 0.25nm and 0.27 nm.
The lattice constant adopted in the calculation process is a consistent approved experimental value, the acting force between electrons is corrected by GGA (generalized gradient approximations) general functions, the truncation energy of plane wave expansion is 400eV, the gridding density of a Brillouin area is 3 multiplied by 1, and the convergence Standard (SCF) in a self-consistent field is 1.0 multiplied by 10-6eV/atom, stress acting on atoms in structural optimization is less than 0.1eV/nm, and single atom energy is less than 5.0 multiplied by 10-6eV/atom, atomic shift variation less than 5.0×10-5nm。
The calculation process comprises the bond energy between the graphene and the metal cluster of the system under a stable structure, the stable structure and the state density, and in order to obtain a relatively stable accurate result, the defect-free graphene structure and the defect-introduced Al are firstly subjected to13Cluster and Cu13And (3) carrying out geometric structure optimization on the clusters, and respectively carrying out structure optimization on the graphene and metal cluster combined system after obtaining accurate parameters.
The change of the adsorption energy, bond energy and structure after the defect-introduced graphene and the metal cluster are adsorbed comprises the following steps: the method comprises the steps of introducing the change of bonding positions of graphene and metal clusters before and after defects, the change of bonding energy, the change of element valence and the change of system state density, finally determining the change of microstructure and properties of the graphene and the metal clusters before and after the defects are introduced, determining the influence of the change of microstructure properties on wettability, and providing a prediction method for the preparation and modification of the high-performance metal-based graphene composite material.
The invention has the beneficial effects that: based on the defect of the method for predicting or evaluating the change of the wettability of the introduced defects to the graphene, the method can theoretically, quickly and accurately determine the improvement degree of the wettability of the graphene by different defects, further guide the actual defect modification of the graphene, reduce the blindness of the modification and save time and cost.
Drawings
FIG. 1: the invention discloses a flow chart for predicting wettability between defective graphene and metal;
FIG. 2: the invention has no defect graphene model;
FIG. 3: the invention relates to a point defect graphene model;
FIG. 4: doping a Ni atom graphene model;
FIG. 5: the method adsorbs a Ni atom graphene model;
FIG. 6: the defect-free graphene, the vacancy-defect graphene, the Ni-defect-doped graphene and the Ni-defect-adsorbed graphene are respectively mixed with Al13(ii) a structure after cluster bonding;
FIG. 7: the invention is flawless graphene and vacancy defect graphiteThe alkene, the Ni-doped defective graphene and the Ni-absorbed defective graphene are respectively connected with Cu13(ii) a structure after cluster bonding;
FIG. 8: the defect-free graphene, the vacancy-defect graphene, the Ni-defect-doped graphene and the Ni-defect-adsorbed graphene are respectively mixed with Al13Cluster Cu13And (5) counting the change of the bond energy after cluster combination.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
The prediction process for simulating and calculating the wettability between the defective graphene and the metal cluster in the invention is shown in fig. 1:
firstly, determining the types of defects capable of being introduced according to theoretical experimental analysis, then respectively establishing a graphene model containing the defects and a metal cluster model, respectively carrying out structure optimization on the models, respectively combining the defective graphene and the metal cluster, then carrying out structure optimization again, finally determining the change rule of the bond energy structure, determining the influence of the change of the bond energy on wettability, and carrying out effective prediction analysis on the wettability between the graphene with different defects and metal.
Example 1
Firstly, determining the defect type required to be established by a graphene model according to the defect type capable of being introduced in the experiment, and carrying out defect-free graphene, vacancy defect graphene, Ni atom-doped defect graphene, Ni atom-adsorbed graphene and Al according to related parameters13Carrying out model construction on the cluster structure; then, respectively carrying out structure optimization on the 5 structures to obtain the most stable structure; secondly, the 4 graphene models and Al are mixed13Cluster combination and structural optimization are carried out to obtain 4 groups of data, and bond energy and structural parameters between the optimized graphene and the metal clusters are calculated; and finally, determining a bond energy change rule, determining the influence of the bond energy change on the wettability, and performing effective prediction analysis on the wettability between the graphene and the metal with different defects.
Determining 4 structural modes of defect-free graphene, vacancy-defect graphene, Ni atom-defect-doped graphene and Ni atom-defect-adsorbed grapheneType, as shown in FIGS. 2, 3, 4, 5, establishes Al13Clustering the model and carrying out structural optimization.
4 kinds of defective graphene and metal Al13The cluster combination and the structure optimization are carried out to obtain an adsorption configuration, as shown in fig. 6.
The key energy of 4 sets of data in the above configuration was calculated as shown in fig. 8.
Fig. 8 lists the calculated changes of bond energies by introducing different defects, and it can be seen in fig. 8 that the introduction of defects can improve the bond energy between graphene and metal, and when the defects are doped with Ni atoms, the bond energy is improved most obviously, and the wettability between graphene and metal is the best.
Example 2
Firstly, determining the defect type required to be established by a graphene model according to the defect type capable of being introduced in the experiment, and carrying out defect-free graphene, vacancy defect graphene, Ni atom defect doped graphene, Ni atom adsorption graphene and Cu according to related parameters13Carrying out model construction on the cluster structure; then, respectively carrying out structure optimization on the 5 structures to obtain the most stable structure; secondly, the 4 graphene models are respectively matched with Cu13Cluster combination and structural optimization are carried out to obtain 4 groups of data, and bond energy and structural parameters between the optimized graphene and the metal clusters are calculated; and finally, determining a bond energy change rule, determining the influence of the bond energy change on the wettability, and performing effective prediction analysis on the wettability between the graphene and the metal with different defects.
Determining 4 structural models of defect-free graphene, vacancy defect graphene, Ni atom defect doped graphene and Ni atom defect adsorbed graphene, and establishing Cu atom defect model as shown in figures 2, 3, 4 and 513Clustering the model and carrying out structural optimization.
4 kinds of defective graphene and metal Cu13The cluster combination and the structure optimization result in an adsorption configuration, as shown in fig. 7.
The key energy of 4 sets of data in the above configuration was calculated as shown in fig. 8.
Fig. 8 lists the variation of the bond energies obtained by different calculations for introducing defects, and it can be seen in fig. 8 that the introduction of defects can increase the bond energy between graphene and metal, and when the defects are doped with Ni atoms, the bond energy is increased most remarkably, and the wettability between graphene and metal is the best.
Claims (3)
1. A method for predicting wettability between defect-introduced graphene and metal is characterized by comprising the following steps: the prediction method is based on a density functional theory of a first sexual principle, and predicts the wettability of graphene and metal with different defects by calculating the adsorption energy and bond energy and structural change after the graphene and metal clusters with the introduced defects are adsorbed, and the specific contents are as follows:
a calculation process
Firstly, constructing graphene models containing different defect types and metal Al13Cluster and Cu13A cluster model, which combines the structures of the defect-free graphene and the graphene containing different defects with the metal cluster respectively, then carries out structural optimization to obtain the most stable structure, and carries out adsorption energy, bond energy and structure calculation;
the defects comprise vacancy defects, doped Ni defects and adsorbed Ni defects;
comparing and verifying the analysis of all calculation results with the actual wettability effect between the graphene and the metal after the defect is introduced, finally determining the influence on the wettability between the graphene and the metal after the defect is introduced, providing a prediction method for the preparation and modification of the high-performance metal-based graphene composite material, and simultaneously ensuring the reliability of the calculation method;
second, model construction
Defect-free graphene structure, defect-introduced graphene structure, and metallic Al13Clusters or Cu13The method for constructing the cluster structure model comprises the following steps:
⑴ carbon atom by SP according to graphene2The honeycomb-shaped planar film formed by the hybridization mode has the following planar structure parameters: bond length a between each C atom and the 3C atoms attached to the periphery1＝a2＝a30.42nm, three bond angle β1＝β2＝β3Using CASEP calculation at 120 °The module establishes a two-dimensional structure with planar periodicity, and establishes a 7 x 7 graphene super-cell model;
⑵, respectively establishing a point defect, Ni-doped and Ni-adsorbed two-dimensional planar graphene defect structure according to defect types introduced in practical experiments, wherein the point defect graphene is a structure obtained by structure optimization after a C atom in a super-cell structure of the defect-free graphene is deleted;
⑶ metallic Al13Cluster and Cu13The cluster structure is a stable structure obtained by structural optimization with 13 Al atoms or 13 Cu atoms as units, and the bond lengths between Cu-Cu and Al-Al are respectively 0.25nm and 0.27 nm.
2. The method for predicting the wettability between the defect-introduced graphene and the metal according to claim 1, wherein: the lattice constant adopted in the calculation process is a consistent approved experimental value, the acting force between electrons is corrected by adopting a GGA (general Gaussian mixture ratio) general function, the truncation energy of the plane wave expansion is 400eV, the gridding density of the Brillouin area is 3 multiplied by 1, and the convergence standard in a self-consistent field is 1.0 multiplied by 10-6eV/atom, stress acting on atoms in structural optimization is less than 0.1eV/nm, and single atom energy is less than 5.0 multiplied by 10-6eV/atom, atomic shift variation less than 5.0X 10-5nm；
The calculation process comprises the bond energy between the graphene and the metal cluster of the system under a stable structure, the stable structure and the state density, and in order to obtain a relatively stable accurate result, the defect-free graphene structure and the defect-introduced Al are firstly subjected to13Cluster and Cu13And (3) carrying out geometric structure optimization on the clusters, and respectively carrying out structure optimization on the graphene and metal cluster combined system after obtaining accurate parameters.
3. The method for predicting the wettability between the defect-introduced graphene and the metal according to claim 1, wherein: the change of the adsorption energy, bond energy and structure after the defect-introduced graphene and the metal cluster are adsorbed comprises the following steps: the method comprises the steps of introducing the change of bonding positions of graphene and metal clusters before and after defects, the change of bonding energy, the change of element valence and the change of system state density, finally determining the change of microstructure and properties of the graphene and the metal clusters before and after the defects are introduced, determining the influence of the change of microstructure properties on wettability, and providing a prediction method for the preparation and modification of the high-performance metal-based graphene composite material.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007105822A (en) * | 2005-10-12 | 2007-04-26 | National Institute For Materials Science | Atomic scale metal wire or metal nanocluster, and method for manufacturing same |
| CN101517125A (en) * | 2006-09-20 | 2009-08-26 | 英国贝尔法斯特女王大学 | Method of coating a metallic article with a surface of tailored wettability |
| CN103253663A (en) * | 2013-06-05 | 2013-08-21 | 武汉大学 | Method for directly preparing graphene on SiO2/Si substrate |
| CN104759268A (en) * | 2015-04-15 | 2015-07-08 | 长沙学院 | Preparation method of ACF/GO/PEI composite material capable of efficiently adsorbing heavy metal |
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| JP5599356B2 (en) * | 2011-03-31 | 2014-10-01 | 富士フイルム株式会社 | A simulation method, a program, a recording medium on which the program is recorded, a method of creating a droplet arrangement pattern using the program, a nanoimprint method, a method of manufacturing a patterned substrate, and an inkjet apparatus. |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007105822A (en) * | 2005-10-12 | 2007-04-26 | National Institute For Materials Science | Atomic scale metal wire or metal nanocluster, and method for manufacturing same |
| CN101517125A (en) * | 2006-09-20 | 2009-08-26 | 英国贝尔法斯特女王大学 | Method of coating a metallic article with a surface of tailored wettability |
| CN103253663A (en) * | 2013-06-05 | 2013-08-21 | 武汉大学 | Method for directly preparing graphene on SiO2/Si substrate |
| CN104759268A (en) * | 2015-04-15 | 2015-07-08 | 长沙学院 | Preparation method of ACF/GO/PEI composite material capable of efficiently adsorbing heavy metal |
Non-Patent Citations (1)
| Title |
|---|
| 石墨烯/Al 复合材料的微观结构及力学性能;李多生;《中国有色金属学报》;20150630;第1498-1504页 * |
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