CN107341301A - The Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect - Google Patents

The Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect Download PDF

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CN107341301A
CN107341301A CN201710489127.8A CN201710489127A CN107341301A CN 107341301 A CN107341301 A CN 107341301A CN 201710489127 A CN201710489127 A CN 201710489127A CN 107341301 A CN107341301 A CN 107341301A
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graphene
defect
metal
cluster
wetability
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CN107341301B (en
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李多生
李锦锦
周贤良
洪跃
邹伟
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Nanchang Hangkong University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

Abstract

The Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect of the present invention, density functional theory based on first principle, the change of energy of adsorption, bond energy, structure, predicts the wetability between them between graphene and metal cluster by calculating introducing variety classes defect.The defects of graphene introduces is mainly point defect, doping Ni defects, absorption Ni defects.They and Ni metal are calculated respectively13Cluster or Al13Energy of adsorption, bond energy and structure change after cluster absorption, the wetability between graphene and metal can be improved by finding the introducing of graphene defect, and the wetability especially adulterated between the graphene and metal of Ni atoms is significantly improved.The present invention also can be according to the difference for introducing defect, or introduces the difference of defect object, and the wetability between object and metal to introducing different defects carries out effective forecast analysis.

Description

The Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect
Technical field
The present invention relates to a kind of Forecasting Methodology of wetability between graphene and metal for introducing defect, metal is particularly belonged to Based composites technical field.
Background technology
It is especially right in the adverse circumstances such as space field, ionising radiation with the development of automobile and aerospace field The performance requirements such as the specific strength of metal-base composites, specific modulus, corrosion resistance, conductive and heat-conductive are higher, traditional ceramic fibre and Reinforced particulate can not meet the requirement to material.It was considered as in recent years because of the excellent machinery of graphene and physical property It is optimal metal-base composites reinforcement.
Because the C atoms of graphene in itself are difficult to form stable chemical bond with metal, cause Metal Substrate graphene compound The graphene added in material is difficult to have preferable associativity with matrix, and it is considered as to be hopeful to solve that graphene, which is modified, Certainly wetability between graphene and metal, prepare the effective way of high-performance metal based composites.Between graphene and metal The quality of wetability determines the height of Metal Substrate graphene composite material performance, seeks simple and effective graphene and is modified raising Its wetability is always the focus studied.Experiment preparation research, the also Forecasting Methodology without correlation are had focused largely at present.
It is to improve the effective way of wetability between graphene and metal that defect is introduced to graphene and is modified wetability.Currently Also without specific method, the application is exactly to establish one for prediction or evaluation of the defects of for introducing to graphene wettability modification Plant fast and effectively to the evaluation for introducing the modified graphene wetability of defect and Forecasting Methodology.Graphene and gold after introducing defect Bond energy, bond angle and the electron density parameter of category change, and cause the wettability modification between metal, therefore from microcosmic angle The modified influence to graphene wetability of analysis is most direct, also most accurate.
The content of the invention
In order to solve the missing of prediction or evaluation method of the defects of introducing to graphene wettability modification, mesh of the invention The Forecasting Methodology for being to provide wetability between a kind of graphene and metal for introducing different defects.
The Forecasting Methodology of wetability is based on first principle between a kind of graphene and metal for introducing defect of the present invention Density functional theory, the graphene of defect and the energy of adsorption after metal cluster absorption and the change of bond energy and structure are introduced by calculating Change, predict the graphene of different defects and the wetability of metal, particular content is：
First, calculating process
First graphene model and metal Al of the structure containing different defect types13Cluster and Cu13Cluster models, respectively will be intact Fall into graphene and the graphene containing different defects is combined with metal cluster structure, then carry out structure optimization, obtain Most stable of structure, and carry out energy of adsorption, bond energy, Structure Calculation.
The defects of described, includes vacancy defect, adulterates Ni defects, adsorbs Ni defects.
All result of calculation analyses will compare with introducing the actual wetability effect after defect between graphene and metal Checking, finally determine to introduce the influence after defect to the wetability between graphene and metal, be high-performance metal base graphene The preparation of composite and modified offer Forecasting Methodology, while ensure the reliability of computational methods.
2nd, model construction
Zero defect graphene-structured, the graphene-structured and metal Al for introducing defect13Cluster or Cu13The model of cluster structure Construction method：
(1) it is carbon atom with SP according to graphene2The cellular flat film that hybrid form is formed, its planar structure parameter are： Bond distance a between 3 C atoms that each C atoms are connected with surrounding1=a2=a3=0.42nm, the angle β of three keys1=β2=β3=120 °, The two-dimensional structure with planar periodic is established using CASTEP computing modules, establishes 7 × 7 graphene super cell's model.
(2) point defect is established according to type the defects of introducing in actual experiment respectively, adulterates Ni, the two dimension for adsorbing Ni is put down Face graphene defect sturcture, its point defects graphene are that a C atom in super cell's structure by zero defect graphene is deleted The structure obtained except rear structure optimization；Doping Ni defects are that a C atom in super cell's structure by zero defect graphene is used The structure that structure optimization obtains after the substitution of Ni atoms；Absorption Ni defects are that super cell's structure of zero defect graphene is adsorbed into one The structure that structure optimization obtains after Ni atoms.
(3) metal Al13Cluster and Cu13Cluster structure is to be entered using 13 Al atoms or 13 Cu atoms as unit respectively The rock-steady structure obtained after row structure optimization, the bond distance between Cu-Cu, Al-Al are respectively 0.25nm and 0.27nm.
The lattice constant that described calculating process uses is the experiment value unanimously approved, the active force between electronics and electronics It is corrected using GGA (Generalized gradient approximations) functional, plane wave expansion is blocked 400eV can be taken, Brillouin zone gridding density takes 3 × 3 × 1, convergence (SCF) 1.0 × 10 in self-consistent field-6EV/atom, The stress acted in structure optimization on atom is less than 0.1eV/nm, and monatomic energy is less than 5.0 × 10-6EV/atom, atom position Move change and be less than 5.0 × 10-5nm。
Calculating process is included in the bond energy between the graphene and metal cluster of system, rock-steady structure and state under rock-steady structure Density, to obtain more stable precise results, first to zero defect, the graphene-structured for introducing defect, Al13Cluster and Cu13 Cluster carries out Geometrical optimization, after obtaining accurate parameter, then graphene and metal cluster articulated system is tied respectively Structure optimizes.
The change of the graphene of described introducing defect and energy of adsorption and bond energy and structure after metal cluster absorption includes： Graphene and metal cluster are introduced before and after defect into the change of key position, the change of bond energy, the change of element valence and system state The change of density, graphene and the change of metal cluster microstructure and property before and after introducing defect are finally determined, is specified microcosmic Influence of the change of structural behaviour to wetability, preparation and modification for high-performance metal base graphene composite material provide prediction Method.
Beneficial effects of the present invention：Prediction or evaluation method of the defects of based on to introducing to graphene wettability modification Missing, this method theoretically can fast and accurately determine the degree that different defects are lifted to graphene wetability, and then instruct Actual graphene defect is modified, and reduces modified blindness, saves time and cost.
Brief description of the drawings
Fig. 1：The prediction flow chart of wetability between defect graphene and metal of the present invention；
Fig. 2：Zero defect graphene model of the present invention；
Fig. 3：Point defect graphene model of the present invention；
Fig. 4：Present invention doping Ni atom graphene models；
Fig. 5：Present invention absorption Ni atom graphene models；
Fig. 6：Zero defect graphene of the present invention, vacancy defect graphene, doping Ni defects graphene, absorption Ni defects graphene point Not and Al13Structure after cluster combination；
Fig. 7：Zero defect graphene of the present invention, vacancy defect graphene, doping Ni defects graphene, absorption Ni defects graphene point Not and Cu13Structure after cluster combination；
Fig. 8：Zero defect graphene of the present invention, vacancy defect graphene, doping Ni defects graphene, absorption Ni defects graphene point Not and Al13Cluster Cu13The change that cluster combines rear bond energy counts.
Embodiment
Describe embodiments of the present invention in detail with reference to the accompanying drawings and examples.
The prediction process of wetability is as shown in Figure 1 between simulation calculating defect graphene and metal cluster in the present invention：
First, the defects of being determined to introduce according to theoretical experimental analysis type, then establishes the graphene mould containing defect respectively Type and metal cluster model, then carry out structure optimization respectively to above-mentioned model, secondly respectively by defect graphene and metal group Structure optimization is carried out again after cluster combination, is finally determined bond energy structure change rule, is specified influence of the Bond energy change to wetability, Wetability height between the graphene and metal of different defects carries out effective forecast analysis.
Embodiment 1
First, the defects of graphene model needs to establish type is determined according to type the defects of being introduced in experiment, according to phase Related parameter to zero defect graphene, vacancy defect graphene, doping Ni atom defects graphene, absorption Ni atoms graphene and Al13Cluster structure carries out model construction；Then, structure optimization is carried out to above-mentioned 5 kinds of structures respectively, obtains most rock-steady structure；Its It is secondary again by above-mentioned 4 kinds of graphene models and Al13Cluster, which combines and carries out structure optimization, obtains 4 groups of data, and stone after calculation optimization Bond energy between black alkene and metal cluster, structural parameters；Bond energy changing rule is finally determined, specifies Bond energy change to wetability Influence, the wetability height to introduce between the graphene and metal of different defects carries out effective forecast analysis.
Determine zero defect graphene, vacancy defect graphene, doping Ni atom defects graphene, absorption Ni atom defect stones Black 4 kinds of structural models of alkene, as shown in Fig. 2,3,4,5, establish Al13Cluster models simultaneously carry out structure optimization.
By 4 kinds of defect graphenes and metal Al13Cluster combines and obtains adsorptive behavior after carrying out structure optimization, such as Fig. 6 institutes Show.
The bond energy of the lower 4 groups of data of above-mentioned configuration is calculated, as shown in Figure 8.
Fig. 8 lists the change of bond energy for introducing that the defects of different is calculated, it can be seen in fig. 8 that defect is drawn Enter to improve bond energy between graphene and metal, when defect is the doping Ni atomic time, bond energy improves most obvious, now stone Wetability between black alkene and metal is best.
Embodiment 2
First, the defects of graphene model needs to establish type is determined according to type the defects of being introduced in experiment, according to phase Related parameter to zero defect graphene, vacancy defect graphene, doping Ni atom defects graphene, absorption Ni atoms graphene and Cu13Cluster structure carries out model construction；Then, structure optimization is carried out to above-mentioned 5 kinds of structures respectively, obtains most rock-steady structure；Its It is secondary again by above-mentioned 4 kinds of graphene models respectively with Cu13Cluster, which combines and carries out structure optimization, obtains 4 groups of data, and calculation optimization Bond energy between graphene and metal cluster afterwards, structural parameters；Bond energy changing rule is finally determined, specifies Bond energy change to wetting Property influence, the wetability height to introduce between the graphene and metal of different defects carries out effective forecast analysis.
Determine zero defect graphene, vacancy defect graphene, doping Ni atom defects graphene, absorption Ni atom defect stones Black 4 kinds of structural models of alkene, as shown in Fig. 2,3,4,5, establish Cu13Cluster models simultaneously carry out structure optimization.
By 4 kinds of defect graphenes and Ni metal13Cluster combines and obtains adsorptive behavior after carrying out structure optimization, such as Fig. 7 institutes Show.
The bond energy of the lower 4 groups of data of above-mentioned configuration is calculated, as shown in Figure 8.
The change for the bond energy that the difference that Fig. 8 lists introducing defect is calculated, it can be seen in fig. 8 that defect is drawn Enter to improve bond energy between graphene and metal, when defect is the doping Ni atomic time, bond energy improves most obvious, now stone Wetability between black alkene and metal is best.

Claims (3)

  1. A kind of 1. Forecasting Methodology of wetability between graphene and metal for introducing defect, it is characterised in that：Described prediction side Density functional theory of the method based on first principle, the graphene of defect and the absorption after metal cluster absorption are introduced by calculating The change of energy and bond energy and structure, predicts the graphene of different defects and the wetability of metal, particular content is：
    First, calculating process
    First graphene model and metal Al of the structure containing different defect types13Cluster and Cu13Cluster models, respectively by zero defect Graphene and graphene containing different defects are combined with metal cluster structure, then carry out structure optimization, are obtained most Stable structure, and carry out energy of adsorption, bond energy, Structure Calculation；
    The defects of described, includes vacancy defect, adulterates Ni defects, adsorbs Ni defects；
    All result of calculation analyses will compare checking with introducing the actual wetability effect after defect between graphene and metal, Finally determine to introduce the influence after defect to the wetability between graphene and metal, be high-performance metal base graphene composite wood The preparation of material and modified offer Forecasting Methodology, while ensure the reliability of computational methods；
    2nd, model construction
    Zero defect graphene-structured, the graphene-structured and metal Al for introducing defect13Cluster or Cu13The model structure of cluster structure Construction method：
    (1) it is carbon atom with SP according to graphene2The cellular flat film that hybrid form is formed, its planar structure parameter are： Bond distance a between 3 C atoms that each C atoms are connected with surrounding1=a2=a3=0.42nm, the angle β of three keys1=β2=β3=120 °, The two-dimensional structure with planar periodic is established using CASTEP computing modules, establishes 7 × 7 graphene super cell's model；
    (2) point defect is established according to type the defects of introducing in actual experiment respectively, adulterates Ni, adsorb Ni two dimensional surface stone Black alkene defect sturcture, its point defects graphene are after a C atom in super cell's structure by zero defect graphene is deleted The structure that structure optimization obtains；Doping Ni defects are that a C atoms Ni in super cell's structure by zero defect graphene is former The structure that structure optimization obtains after son substitution；Absorption Ni defects are that super cell's structure of zero defect graphene is adsorbed into a Ni original The structure that structure optimization obtains after son；
    (3) metal Al13Cluster and Cu13Cluster structure is to carry out structure as unit using 13 Al atoms or 13 Cu atoms respectively The rock-steady structure obtained after optimization, the bond distance between Cu-Cu, Al-Al are respectively 0.25nm and 0.27nm.
  2. 2. the Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect according to claim 1, it is special Sign is：The lattice constant that described calculating process uses is the experiment value unanimously approved, the active force between electronics and electronics It is corrected using GGA (Generalized gradient approximations) functional, plane wave expansion is blocked 400eV can be taken, Brillouin zone gridding density takes 3 × 3 × 1, convergence (SCF) 1.0 × 10 in self-consistent field-6EV/atom, The stress acted in structure optimization on atom is less than 0.1eV/nm, and monatomic energy is less than 5.0 × 10-6EV/atom, atom position Move change and be less than 5.0 × 10-5nm；
    It is close that calculating process is included in the bond energy between the graphene and metal cluster of system, rock-steady structure and state under rock-steady structure Degree, to obtain more stable precise results, first to zero defect, the graphene-structured for introducing defect, Al13Cluster and Cu13Group Cluster carries out Geometrical optimization, after obtaining accurate parameter, then carries out structure to graphene and metal cluster articulated system respectively Optimization.
  3. 3. the Forecasting Methodology of wetability between a kind of graphene and metal for introducing defect according to claim 1, it is special Sign is：The change of the graphene of described introducing defect and energy of adsorption and bond energy and structure after metal cluster absorption includes： Graphene and metal cluster are introduced before and after defect into the change of key position, the change of bond energy, the change of element valence and system state The change of density, graphene and the change of metal cluster microstructure and property before and after introducing defect are finally determined, is specified microcosmic Influence of the change of structural behaviour to wetability, preparation and modification for high-performance metal base graphene composite material provide prediction Method.
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CN108287982A (en) * 2018-01-16 2018-07-17 温州大学 A kind of modeling method of porous siloxicon ceramics
CN110647989A (en) * 2019-09-16 2020-01-03 长春师范大学 Graphene defect modification prediction method based on neural network
CN110824137A (en) * 2019-10-10 2020-02-21 中国建筑材料科学研究总院有限公司 Method and device for predicting crystallization order of silver film in low-emissivity glass on substrate
CN111863625A (en) * 2020-07-28 2020-10-30 哈尔滨工业大学 Single-material PN heterojunction and design method thereof
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CN108287982A (en) * 2018-01-16 2018-07-17 温州大学 A kind of modeling method of porous siloxicon ceramics
CN110647989A (en) * 2019-09-16 2020-01-03 长春师范大学 Graphene defect modification prediction method based on neural network
CN110647989B (en) * 2019-09-16 2023-04-18 长春师范大学 Graphene defect modification prediction method based on neural network
CN110824137A (en) * 2019-10-10 2020-02-21 中国建筑材料科学研究总院有限公司 Method and device for predicting crystallization order of silver film in low-emissivity glass on substrate
CN110824137B (en) * 2019-10-10 2022-03-11 中国建筑材料科学研究总院有限公司 Method and device for predicting crystallization order of silver film in low-emissivity glass on substrate
CN111863625A (en) * 2020-07-28 2020-10-30 哈尔滨工业大学 Single-material PN heterojunction and design method thereof
CN114368778A (en) * 2022-01-24 2022-04-19 哈尔滨工业大学 Method for limiting metal oxide agglomeration through low-valence metal ion doping based on first-nature principle calculation design

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