CN107798213B - Rapid simulation calculation method for palladium metal catalytic performance change - Google Patents
Rapid simulation calculation method for palladium metal catalytic performance change Download PDFInfo
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- CN107798213B CN107798213B CN201711000044.4A CN201711000044A CN107798213B CN 107798213 B CN107798213 B CN 107798213B CN 201711000044 A CN201711000044 A CN 201711000044A CN 107798213 B CN107798213 B CN 107798213B
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- KDLHZDBZIXYQEI-UHFFFAOYSA-N Palladium Chemical compound [Pd] KDLHZDBZIXYQEI-UHFFFAOYSA-N 0.000 title claims abstract description 117
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 60
- 239000002184 metal Substances 0.000 title claims abstract description 60
- 229910052763 palladium Inorganic materials 0.000 title claims abstract description 57
- 238000004364 calculation method Methods 0.000 title claims abstract description 17
- 230000003197 catalytic effect Effects 0.000 title claims abstract description 17
- 238000004088 simulation Methods 0.000 title claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 86
- QSNQXZYQEIKDPU-UHFFFAOYSA-N [Li].[Fe] Chemical compound [Li].[Fe] QSNQXZYQEIKDPU-UHFFFAOYSA-N 0.000 claims abstract description 36
- 230000003993 interaction Effects 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 14
- 230000008021 deposition Effects 0.000 claims abstract description 9
- GQYHUHYESMUTHG-UHFFFAOYSA-N lithium niobate Chemical compound [Li+].[O-][Nb](=O)=O GQYHUHYESMUTHG-UHFFFAOYSA-N 0.000 claims abstract description 7
- 238000003775 Density Functional Theory Methods 0.000 claims description 4
- 239000000758 substrate Substances 0.000 description 3
- 239000000969 carrier Substances 0.000 description 2
- 239000003054 catalyst Substances 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 150000002739 metals Chemical class 0.000 description 2
- 230000010287 polarization Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000002134 carbon nanofiber Substances 0.000 description 1
- CETPSERCERDGAM-UHFFFAOYSA-N ceric oxide Chemical compound O=[Ce]=O CETPSERCERDGAM-UHFFFAOYSA-N 0.000 description 1
- 229910000422 cerium(IV) oxide Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- -1 gamma-Al 2O3 Chemical compound 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical group [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000004215 lattice model Methods 0.000 description 1
- 229920002521 macromolecule Polymers 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical class C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 1
- 238000000329 molecular dynamics simulation Methods 0.000 description 1
- 239000002071 nanotube Substances 0.000 description 1
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- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
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- G—PHYSICS
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- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
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Abstract
The invention provides a rapid simulation calculation method for palladium metal catalytic performance change, which comprises the following steps: constructing a ball stick model of palladium metal atoms and lithium iron niobate electrons; calculating the total energy value of the ball stick model of palladium metal atoms and lithium iron niobate electrons; determining interaction force, interaction speed and interaction position among the particles by adopting a dissipative particle dynamics method; setting the number of each particle and the deposition temperature; performing two-dimensional treatment on lithium iron niobate electrons, namely constructing a two-dimensional grid of N x N, and distributing each particle of the lithium iron niobate electrons on the intersection points of the two-dimensional grid; determining the covering positions of palladium metal atoms and two-dimensional lithium niobate ferroelectrons at different deposition temperatures according to the interaction force among the particles; and calculating the coverage rate value of the palladium metal atoms covered on the lithium iron niobate electrons at different temperatures. The surface area change trend of palladium metal atoms on different ferroelectric surfaces at different temperatures can be effectively predicted, so that the change trend of the catalytic performance of the palladium metal atoms along with the temperature can be predicted.
Description
Technical Field
The invention belongs to the technical field of material science numerical simulation, and particularly relates to a rapid simulation calculation method for palladium metal catalytic performance change.
Background
Carrier research of supported metal catalysts is a hot research topic in the current scientific community, and some commonly used catalyst carriers researched at present comprise graphene, carbon nanofibers, gamma-Al 2O3, CeO2 nanotubes, high-molecular carriers and the like, and ferroelectrics are widely applied to the field of electronic devices, but the shape prediction of gold atom aggregation clusters formed on the surface of the ferroelectrics is few. Less research has been conducted on the use of ferroelectrics as supports, and we have conducted a simulation calculation study on the effect of using ferroelectrics as supports on the catalytic activity of catalytic metals.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rapid simulation calculation method for the catalytic performance change of palladium metal.
A rapid simulation calculation method for palladium metal catalytic performance change comprises the following steps:
step 1: constructing a ball stick model of palladium metal atoms and lithium iron niobate electrons;
step 2: calculating the total energy value of the ball stick model of palladium metal atoms and lithium iron niobate electrons;
and step 3: setting coordination number values of palladium metal atoms and lithium iron niobate electrons, and determining interaction force, interaction speed and interaction position among particles by adopting a dissipative particle dynamics method;
and 4, step 4: setting the number of each particle and the deposition temperature;
and 5: performing two-dimensional treatment on lithium iron niobate electrons, namely constructing a two-dimensional grid of N x N, and distributing each particle of the lithium iron niobate electrons on the intersection points of the two-dimensional grid;
step 6: determining the covering positions of palladium metal atoms and two-dimensional lithium niobate ferroelectrons at different deposition temperatures according to the interaction force among the particles;
and 7: and calculating the coverage rate value of the palladium metal atoms covered on the lithium iron niobate electrons at different temperatures.
The method for measuring the total energy value of the ball stick model of palladium metal atoms and lithium iron niobate electrons comprises the following steps: and (4) calculating the total energy value of the ball-rod model of the palladium metal atoms and the lithium iron niobate electrons by using a density functional theory calculation method.
The calculation formula for determining the interaction force, the interaction speed and the interaction position among the particles by using the dissipative particle dynamics method is as follows:
wherein,
aij(T)＝aii+1.451χijkBT；
aii＝15kBT；
Fijis the interaction force between particles i, j, vijIs the mutual velocity between particles i, j, rijIs the mutual position between the particles i, j,
is the dissipative force between the particles i, j,
is a random force between particles i, j, aijIs the maximum repulsive force between particles i, j, T is the temperature, kBR is a constant, ZijThe coordination number value of particle i relative to particle j, Eij(T) is the energy value of particle i relative to particle j, ZjiIs the coordination number value of particle j relative to particle i, Eji(T) is the energy value of particle j relative to particle i, ZiiIs the coordination number value of particle i, Eii(T) is the energy value of the particle i, ZjjIs the coordination number value of particle j, Ejj(T) is the energy value of particle j.
The invention has the beneficial effects that:
the invention provides a rapid simulation calculation method for the change of the catalytic performance of palladium metal, which can obviously observe that the coverage rate of palladium atoms increases along with the increase of temperature, and observe coverage rate curves obtained by each data, wherein the coverage rate curves obtained in two polarization directions have a tendency of folding along with the increase of temperature, so that the surface area change tendency of the palladium metal atoms at different temperatures can be effectively predicted, the surface area change tendency of the palladium metal atoms on different ferroelectric surfaces can be predicted, and the change tendency of the catalytic performance of the palladium metal atoms along with the temperature can be predicted.
Drawings
Fig. 1 is a flowchart of a rapid simulation calculation method for palladium metal catalytic performance change according to an embodiment of the present invention.
FIG. 2 is a two-dimensional view of palladium metal atoms at 500 and 600 ℃ with a two-dimensional lithium niobate ferroelectric coated location in accordance with an embodiment of the present invention;
FIG. 3 is a graph of palladium metal atoms and two-dimensional lithium iron niobate electron coverage at a temperature of 500 deg.C-1000 deg.C with 100 palladium metal atoms in accordance with an embodiment of the present invention;
FIG. 4 is a graph of palladium metal atoms and two-dimensional lithium iron niobate electron coverage at a temperature of 500 deg.C-1000 deg.C with 200 palladium metal atoms in accordance with an embodiment of the present invention;
FIG. 5 is a plot of palladium metal atoms at 500 deg.C-1000 deg.C and two-dimensional lithium iron niobate electron coverage for a palladium metal atom of 300 deg.C in accordance with an embodiment of the present invention;
FIG. 6 is a plot of palladium metal atoms at 400 deg.C and at 500 deg.C-1000 deg.C versus two-dimensional lithium iron niobate electron coverage for a specific embodiment of the present invention;
FIG. 7 is a graph of palladium metal atoms and bidimensionalized lithium iron niobate electron coverage at 500 deg.C-1000 deg.C with 500 palladium metal atoms in accordance with an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
A rapid simulation calculation method for palladium metal catalytic performance change is shown in figure 1, and comprises the following steps:
step 1: and constructing a ball stick model of palladium metal atoms and lithium iron niobate electrons.
In this embodiment, a polarized molecular structure of lithium iron niobate electrons (L innbo 3) is determined, a molecular structure diagram thereof is drawn by using Gauss software, and atoms are moved according to molecular polarization to obtain desired polarized molecules, thereby forming a ferroelectric substrate.
Step 2: and (4) calculating the total energy value of the ball stick model of the palladium metal atoms and the lithium iron niobate electrons.
In this embodiment, the total energy value of the ball-and-stick model of palladium metal atoms and lithium iron niobate electrons is calculated by using a study result that palladium metal atoms approach a ferroelectric substrate at a distance of 0.5 to 1000, calculating energy using Gauss energy calculation software, and calculating a Density Functional Theory (DFT) calculation method.
And step 3: and setting the coordination number value of palladium metal atoms and lithium iron niobate electrons, and determining the interaction force, the interaction speed and the interaction position among the particles by adopting a dissipative particle dynamics method.
In this embodiment, a Dissipative Particle Dynamics (DPD) is a molecular dynamics simulation algorithm for simulating the behavior of a complex fluid. The DPD method meets the condition of thermal equilibrium and is a non-lattice model method for simulating the movement of particles in continuous space and discontinuous time. The DPD method is one in which a single particle represents the entire molecule or a region of fluid containing multiple molecules, or a fragment of a macromolecule, rather than a single atom, and does not consider the behavioral details of an atom, which is considered process independent.
In this embodiment, the coordination number of the palladium metal atom and the lithium iron niobate electron is 1 to 10.
The calculation formula for determining the interaction force, the interaction speed and the interaction position among the particles by adopting a dissipative particle dynamics method is shown as formulas (1), (2) and (3):
wherein,
aij(T)＝aii+1.451χijkBT；
aii＝15kBT；
Fijis the interaction force between particles i, j, vijIs the mutual velocity between particles i, j, rijIs the mutual position between the particles i, j,
is the dissipative force between the particles i, j,
is a random force between particles i, j, aijIs the maximum repulsive force between particles i, j, T is the temperature, kBR is a constant, ZijThe coordination number value of particle i relative to particle j, Eij(T) is the energy value of particle i relative to particle j, ZjiIs the coordination number value of particle j relative to particle i, Eji(T) is the energy value of particle j relative to particle i, ZiiIs the coordination number value of the particle i,Eii(T) is the energy value of the particle i, ZjjIs the coordination number value of particle j, Ejj(T) is the energy value of particle j.
And 4, step 4: the number of particles and the deposition temperature were set.
In the present embodiment, the number of particles is graded from 100 to 1000 with 100 as a reference. Temperature selection: the span is 500-1000 deg.C, and the temperature gradient is 50 deg.C.
And 5: and (3) performing two-dimensional treatment on the lithium iron niobate electrons, namely constructing a two-dimensional grid of N x N, and distributing each particle of the lithium iron niobate electrons on the intersection points of the two-dimensional grid.
In this embodiment, a 100 × 100 two-dimensional grid is constructed, and each particle of lithium niobate electrons is distributed at an intersection of the two-dimensional grid.
Step 6: and determining the covering positions of palladium metal atoms and two-dimensional lithium niobate ferroelectrons at different deposition temperatures according to the interaction force among the particles.
In this embodiment, a Mathematica software is used to program and simulate a surface coverage view, a series of two-dimensional views are respectively obtained by taking the deposition temperature and the number of candidate particles as gradients, then the palladium metal atom coverage rate in the two-dimensional views is observed to represent the dispersion degree of palladium atoms in L iNbO3 polarized molecules as the substrate surface, and the catalytic activity of palladium atom catalytic metals is represented by the dispersion degree.
The two-dimensional view of the palladium metal atom and the two-dimensional lithium niobate ferroelectric electron coverage position at a temperature of 600 ℃ with 500 palladium metal atoms is shown in fig. 2.
And 7: and calculating the coverage rate value of the palladium metal atoms covered on the lithium iron niobate electrons at different temperatures.
In this embodiment, fig. 3-7 are graphs showing the coverage rate of palladium metal atoms and two-dimensional lithium iron niobate electrons at 500-1000 ℃ when the palladium metal atoms are 100-500-.
Claims (2)
1. A rapid simulation calculation method for palladium metal catalytic performance change is characterized by comprising the following steps:
step 1: constructing a ball stick model of palladium metal atoms and lithium iron niobate electrons;
step 2: calculating the total energy value of the ball stick model of palladium metal atoms and lithium iron niobate electrons;
and step 3: setting coordination number values of palladium metal atoms and lithium iron niobate electrons, and determining interaction force, interaction speed and interaction position among particles by adopting a dissipative particle dynamics method;
the calculation formula for determining the interaction force, the interaction speed and the interaction position among the particles by using the dissipative particle dynamics method is as follows:
wherein,
aij(T)＝aii+1.451χijkBT；
aii＝15kBT；
Fijis the interaction force between particles i, j, vijIs the mutual velocity between particles i, j, rijIs the mutual position between the particles i, j,
is the dissipative force between the particles i, j,
is a random force between particles i, j, aijIs the maximum repulsive force between particles i, j, T is the temperature, kBR is a constant, ZijThe coordination number value of particle i relative to particle j, Eij(T) is the energy value of particle i relative to particle j, ZjiIs the coordination number value of particle j relative to particle i, Eji(T) is the energy value of particle j relative to particle i, ZiiIs the coordination number value of particle i, Eii(T) is the energy value of the particle i, ZjjIs the coordination number value of particle j, Ejj(T) is the energy value of particle j; and 4, step 4: setting the number of each particle and the deposition temperature;
and 5: performing two-dimensional treatment on lithium iron niobate electrons, namely constructing a two-dimensional grid of N x N, and distributing each particle of the lithium iron niobate electrons on the intersection points of the two-dimensional grid;
step 6: determining the covering positions of palladium metal atoms and two-dimensional lithium niobate ferroelectrons at different deposition temperatures according to the interaction force among the particles;
and 7: and calculating the coverage rate value of the palladium metal atoms covered on the lithium iron niobate electrons at different temperatures.
2. The method for rapidly simulating and calculating the catalytic performance change of palladium metal according to claim 1, wherein the method for calculating the total energy value of the ball-and-stick model of palladium metal atoms and lithium iron niobate electrons specifically comprises the following steps:
and (4) calculating the total energy value of the ball-rod model of the palladium metal atoms and the lithium iron niobate electrons by using a density functional theory calculation method.
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| CN103678875A (en) * | 2013-11-04 | 2014-03-26 | 北京理工大学 | Prediction method of dispersion behaviors of flame retardant and flame retardant synergist in polymer matrix |
| CN104268405A (en) * | 2014-09-26 | 2015-01-07 | 安徽大学 | Monte carlo molecular simulation research method for kinetic process of polymerization reaction |
| CN105138744A (en) * | 2015-08-07 | 2015-12-09 | 长春理工大学 | Dissipative particle dynamics simulation method for grinding liquid particle characteristics |
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| CN103785390B (en) * | 2014-02-18 | 2016-03-02 | 北京工业大学 | A kind of ferroelectric-tourmaline plasma catalyst, preparations and applicatio |
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| CN103678875A (en) * | 2013-11-04 | 2014-03-26 | 北京理工大学 | Prediction method of dispersion behaviors of flame retardant and flame retardant synergist in polymer matrix |
| CN104268405A (en) * | 2014-09-26 | 2015-01-07 | 安徽大学 | Monte carlo molecular simulation research method for kinetic process of polymerization reaction |
| CN105138744A (en) * | 2015-08-07 | 2015-12-09 | 长春理工大学 | Dissipative particle dynamics simulation method for grinding liquid particle characteristics |
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| Molecular dynamic simulations of surface morphology and pulsed laser deposition growth of lithium niobate thin films on silicon substrate;Liu Yue et al;《Chin Phys B》;20150531;第24卷(第5期);第1-4页 * |
| 钯金属纳米颗粒催化剂在铁电表面的分散聚集模拟;刘悦 等;《第九届计算纳米科学与新能源材料国际研讨会》;20160630;第158页 * |
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