CN109411030A - The prediction technique of nano-metal-oxide edge energy - Google Patents
The prediction technique of nano-metal-oxide edge energy Download PDFInfo
- Publication number
- CN109411030A CN109411030A CN201811298480.9A CN201811298480A CN109411030A CN 109411030 A CN109411030 A CN 109411030A CN 201811298480 A CN201811298480 A CN 201811298480A CN 109411030 A CN109411030 A CN 109411030A
- Authority
- CN
- China
- Prior art keywords
- oxide
- metal
- nano
- edge energy
- energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 229910044991 metal oxide Inorganic materials 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 21
- 239000000463 material Substances 0.000 claims abstract description 18
- 150000004706 metal oxides Chemical class 0.000 claims abstract description 16
- 239000013078 crystal Substances 0.000 claims abstract description 15
- 239000000126 substance Substances 0.000 claims abstract description 7
- 125000004429 atom Chemical group 0.000 claims description 11
- 229910052751 metal Inorganic materials 0.000 claims description 10
- 239000002184 metal Substances 0.000 claims description 10
- 239000002245 particle Substances 0.000 claims description 9
- GWEVSGVZZGPLCZ-UHFFFAOYSA-N Titan oxide Chemical compound O=[Ti]=O GWEVSGVZZGPLCZ-UHFFFAOYSA-N 0.000 claims description 6
- 230000000737 periodic effect Effects 0.000 claims description 6
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
- 239000001301 oxygen Substances 0.000 claims description 5
- XOLBLPGZBRYERU-UHFFFAOYSA-N tin dioxide Chemical compound O=[Sn]=O XOLBLPGZBRYERU-UHFFFAOYSA-N 0.000 claims description 5
- CPLXHLVBOLITMK-UHFFFAOYSA-N Magnesium oxide Chemical compound [Mg]=O CPLXHLVBOLITMK-UHFFFAOYSA-N 0.000 claims description 4
- XLOMVQKBTHCTTD-UHFFFAOYSA-N Zinc monoxide Chemical compound [Zn]=O XLOMVQKBTHCTTD-UHFFFAOYSA-N 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- BERDEBHAJNAUOM-UHFFFAOYSA-N copper(I) oxide Inorganic materials [Cu]O[Cu] BERDEBHAJNAUOM-UHFFFAOYSA-N 0.000 claims description 4
- KRFJLUBVMFXRPN-UHFFFAOYSA-N cuprous oxide Chemical compound [O-2].[Cu+].[Cu+] KRFJLUBVMFXRPN-UHFFFAOYSA-N 0.000 claims description 4
- 229940112669 cuprous oxide Drugs 0.000 claims description 4
- QDOXWKRWXJOMAK-UHFFFAOYSA-N dichromium trioxide Chemical compound O=[Cr]O[Cr]=O QDOXWKRWXJOMAK-UHFFFAOYSA-N 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- SZVJSHCCFOBDDC-UHFFFAOYSA-N ferrosoferric oxide Chemical compound O=[Fe]O[Fe]O[Fe]=O SZVJSHCCFOBDDC-UHFFFAOYSA-N 0.000 claims description 4
- MRELNEQAGSRDBK-UHFFFAOYSA-N lanthanum(3+);oxygen(2-) Chemical compound [O-2].[O-2].[O-2].[La+3].[La+3] MRELNEQAGSRDBK-UHFFFAOYSA-N 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 125000004430 oxygen atom Chemical group O* 0.000 claims description 4
- 238000012512 characterization method Methods 0.000 claims description 3
- 229910001887 tin oxide Inorganic materials 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 229910000410 antimony oxide Inorganic materials 0.000 claims description 2
- CETPSERCERDGAM-UHFFFAOYSA-N ceric oxide Chemical compound O=[Ce]=O CETPSERCERDGAM-UHFFFAOYSA-N 0.000 claims description 2
- 229910000422 cerium(IV) oxide Inorganic materials 0.000 claims description 2
- 238000007385 chemical modification Methods 0.000 claims description 2
- 150000001875 compounds Chemical class 0.000 claims description 2
- 238000002790 cross-validation Methods 0.000 claims description 2
- AJNVQOSZGJRYEI-UHFFFAOYSA-N digallium;oxygen(2-) Chemical compound [O-2].[O-2].[O-2].[Ga+3].[Ga+3] AJNVQOSZGJRYEI-UHFFFAOYSA-N 0.000 claims description 2
- 229940056319 ferrosoferric oxide Drugs 0.000 claims description 2
- 229910001195 gallium oxide Inorganic materials 0.000 claims description 2
- 229910000449 hafnium oxide Inorganic materials 0.000 claims description 2
- WIHZLLGSGQNAGK-UHFFFAOYSA-N hafnium(4+);oxygen(2-) Chemical compound [O-2].[O-2].[Hf+4] WIHZLLGSGQNAGK-UHFFFAOYSA-N 0.000 claims description 2
- 229910003437 indium oxide Inorganic materials 0.000 claims description 2
- PJXISJQVUVHSOJ-UHFFFAOYSA-N indium(iii) oxide Chemical compound [O-2].[O-2].[O-2].[In+3].[In+3] PJXISJQVUVHSOJ-UHFFFAOYSA-N 0.000 claims description 2
- 239000000395 magnesium oxide Substances 0.000 claims description 2
- TYTHZVVGVFAQHF-UHFFFAOYSA-N manganese(3+);oxygen(2-) Chemical compound [O-2].[O-2].[O-2].[Mn+3].[Mn+3] TYTHZVVGVFAQHF-UHFFFAOYSA-N 0.000 claims description 2
- GEYXPJBPASPPLI-UHFFFAOYSA-N manganese(III) oxide Inorganic materials O=[Mn]O[Mn]=O GEYXPJBPASPPLI-UHFFFAOYSA-N 0.000 claims description 2
- 230000003647 oxidation Effects 0.000 claims description 2
- 238000007254 oxidation reaction Methods 0.000 claims description 2
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 claims description 2
- SIWVEOZUMHYXCS-UHFFFAOYSA-N oxo(oxoyttriooxy)yttrium Chemical compound O=[Y]O[Y]=O SIWVEOZUMHYXCS-UHFFFAOYSA-N 0.000 claims description 2
- VTRUBDSFZJNXHI-UHFFFAOYSA-N oxoantimony Chemical compound [Sb]=O VTRUBDSFZJNXHI-UHFFFAOYSA-N 0.000 claims description 2
- RVTZCBVAJQQJTK-UHFFFAOYSA-N oxygen(2-);zirconium(4+) Chemical compound [O-2].[O-2].[Zr+4] RVTZCBVAJQQJTK-UHFFFAOYSA-N 0.000 claims description 2
- 238000010183 spectrum analysis Methods 0.000 claims description 2
- 239000004408 titanium dioxide Substances 0.000 claims description 2
- 239000011787 zinc oxide Substances 0.000 claims description 2
- 229910001928 zirconium oxide Inorganic materials 0.000 claims description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims 2
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 claims 2
- 241001269238 Data Species 0.000 claims 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims 1
- 229910052737 gold Inorganic materials 0.000 claims 1
- 239000010931 gold Substances 0.000 claims 1
- 229910052742 iron Inorganic materials 0.000 claims 1
- 229910052759 nickel Inorganic materials 0.000 claims 1
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 claims 1
- 229910052721 tungsten Inorganic materials 0.000 claims 1
- 239000010937 tungsten Substances 0.000 claims 1
- 238000002474 experimental method Methods 0.000 abstract description 5
- 239000003054 catalyst Substances 0.000 abstract description 4
- 238000013461 design Methods 0.000 abstract description 3
- 238000005259 measurement Methods 0.000 abstract description 3
- 238000012502 risk assessment Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 abstract description 2
- 102100021164 Vasodilator-stimulated phosphoprotein Human genes 0.000 description 4
- 229910000480 nickel oxide Inorganic materials 0.000 description 4
- 230000000844 anti-bacterial effect Effects 0.000 description 2
- 150000001768 cations Chemical class 0.000 description 2
- 229910021645 metal ion Inorganic materials 0.000 description 2
- GNRSAWUEBMWBQH-UHFFFAOYSA-N oxonickel Chemical compound [Ni]=O GNRSAWUEBMWBQH-UHFFFAOYSA-N 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 108010054220 vasodilator-stimulated phosphoprotein Proteins 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- DQMUQFUTDWISTM-UHFFFAOYSA-N O.[O-2].[Fe+2].[Fe+2].[O-2] Chemical compound O.[O-2].[Fe+2].[Fe+2].[O-2] DQMUQFUTDWISTM-UHFFFAOYSA-N 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 1
- 238000002441 X-ray diffraction Methods 0.000 description 1
- XUBKPYAWPSXPDZ-UHFFFAOYSA-N [Ba].OS(O)(=O)=O Chemical compound [Ba].OS(O)(=O)=O XUBKPYAWPSXPDZ-UHFFFAOYSA-N 0.000 description 1
- 238000002835 absorbance Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006555 catalytic reaction Methods 0.000 description 1
- 230000005779 cell damage Effects 0.000 description 1
- 230000003013 cytotoxicity Effects 0.000 description 1
- 231100000135 cytotoxicity Toxicity 0.000 description 1
- -1 energy Chemical class 0.000 description 1
- 230000000855 fungicidal effect Effects 0.000 description 1
- 239000000417 fungicide Substances 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000003834 intracellular effect Effects 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 150000002927 oxygen compounds Chemical class 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000003642 reactive oxygen metabolite Substances 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 239000010936 titanium Substances 0.000 description 1
- 229910052719 titanium Inorganic materials 0.000 description 1
- CMPGARWFYBADJI-UHFFFAOYSA-L tungstic acid Chemical compound O[W](O)(=O)=O CMPGARWFYBADJI-UHFFFAOYSA-L 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Catalysts (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The present invention provides the prediction techniques of nano-metal-oxide edge energy, belong to field of nanometer technology.Firstly, obtaining the edge energy of nano-metal-oxide by literature's store and the method for measuring.Secondly, obtain nano-metal-oxide structural parameters, including quantum chemical descriptor, collect metallic atom information and measurement metal oxide partial size, the experiment parameters such as crystal configuration.Finally, the relational model of nano-metal-oxide edge energy and structural parameters is constructed by Partial Least Squares, the edge energy of prediction different crystal forms, different-grain diameter nano-metal-oxide.The method that the present invention establishes, can be with quick predict different crystal forms, the edge energy of different-grain diameter nano-metal-oxide；This method is at low cost, high-efficient, man power and material needed for experiment test can be saved, and necessary basic data can be provided for the safe design of the ecological risk assessment of nano-metal-oxide, new catalyst and novel nano metal oxide for this method.
Description
Technical field
The present invention relates to a kind of prediction techniques of nano-metal-oxide edge energy, belong to field of nanometer technology.
Background technique
The unique optics of nano-metal-oxide and antibacterial properties make it as new catalyst and fungicide, in industry
It is used widely on boundary.Nano-metal-oxide can photic generation active oxygen species (ReactiveOxygen Species,
ROS), the main reason for being catalysis, bactericidal property.Divide in addition, ROS can also destroy intracellular normal lipid, protein and DNA
Son generates cellular damage, causes cytotoxicity.The ability that nano-metal-oxide generates ROS is related to its band structure.Energy band
Structure includes edge energy (Eg), conduction band floors (EC) and valence band top value (EV), nano-metal-oxide can be calculated by edge energy
Conduction band floors and valence band top value, 3 relationship is as shown by the equation.
EC=-χoxide+0.50Eg (1)
EV=-χoxide-0.50Eg (2)
In formula, metal oxide electronegativity χoxideIt can be calculated by the following formula.
χcation(P.u.)≈0.274Z-0.15Zr-0.01r+1+α(3)
χcation(eV)≈(χcation(P.u.)+0.2061)/0.336(4)
χoxide≈0.45χcation+3.36(5)
χcationIt (P.u.) is metal cation electronegativity, P.u；
χcationIt (eV) is metal cation electronegativity, eV；
χoxideFor metal oxide electronegativity, eV；
Z is metal ion charge；
R is metal ion radius,
α is coefficient, depends on element ordinal；
Therefore the acquisition of edge energy is the key that calculate nano-metal-oxide conduction band floors and valence band top value.
Currently, the measurement of edge energy mainly uses UV-visible reflection spectrum.On the one hand, experimentation and data processing
Process is relatively complicated, for example analytical pure sulfuric acid barium is needed to make graticule, and measurement material passes through in the absorbance of the light of different wave length
Kubelka-Munk diffusing reflection is equations turned, maps to hv, makees tangent line etc. at maximum derivative；On the other hand, nano metal oxygen
Compound crystal form is many kinds of, and for the nano-metal-oxide of different crystal forms, different-grain diameter, edge energy also can be poor
It is different.Using experimental method, different crystal forms are tested one by one, the edge energy of different-grain diameter nano-metal-oxide is unpractical.Cause
This, it is necessary to construct a kind of method that can predict different crystal forms, different-grain diameter nano-metal-oxide edge energy.
Summary of the invention
The present invention provides a kind of methods for quickly, efficiently predicting nano-metal-oxide edge energy.Firstly, passing through document
Collect the edge energy that nano-metal-oxide is obtained with measuring.Secondly, the structural parameters of nano-metal-oxide are obtained, packet
Include metal oxide quantificational description symbol, periodic table parameter and Experimental Characterization structural parameters.Finally, being built using Partial Least Squares
The relational model of vertical nano-metal-oxide edge energy and structural parameters, predicts different crystal forms, different-grain diameter nano metal oxide
The edge energy of object.This method can be ecological risk assessment, new catalyst and the novel nano metal of nano-metal-oxide
The safe design of oxide provides necessary basic data.
Technical scheme is as follows:
A kind of prediction technique of nano-metal-oxide edge energy, steps are as follows:
It include 22 kinds firstly, obtaining edge energy 91 of nano-metal-oxide by literature's store and measuring
The different crystal configuration of different nano-metal-oxides.The rule of literature's store nano-metal-oxide edge energy: metal oxidation
The size of object must be nano-scale；The shape of metal oxide particle is ball-type or approximate ball-type；Particle has single chemistry
Ingredient；Surface is without chemical modification；There must be the characterize data of X-ray diffraction, there is determining crystal configuration；Material have it is ultraviolet-can
See spectral analysis data.Measuring nano-metal-oxide edge energy uses UV-visible reflection spectrum.
Using quantum limitation effect as the pretreated theoretical foundation of nano-metal-oxide edge energy.Have chosen particle diameter distribution
More two kinds of substance nickel oxide (NiO) and tin oxide (SnO2), analyze the relationship (Fig. 1) of its edge energy and partial size.It is indulged in figure
Axis indicates that the edge energy of material, horizontal axis indicate square of material diameter, the horizontal seat of the blue dotted line parallel with axis of ordinates in figure
Scale value is 100nm2, that is, the material diameter of the data point in blue left side of dotted line region is fallen in less than 10nm, and it is right to fall in blue dotted line
The material particle size of the data point of side is greater than 10nm.By being fitted to curve, the edge energy and diameter of both substances are found
Square show inverse proportion function negative correlativing relation, i.e., with the reduction of material particle size, the edge energy of material, which has, gradually to be increased
Big trend.And it is greater than the data point of 10nm compared to material particle size, the edge energy of data point of the material particle size less than 10nm
Generally it is significantly increased.And partial size is greater than 10nm and does not play an important role to the edge energy of material.Therefore, collection is received
In rice metal oxide edge energy, the edge energy of material of the size less than 10nm is used directly, and size is greater than the material of 10nm
Edge energy, take the average value of its partial size and edge energy.After pre-processing to edge energy, 40 edge energy numbers are obtained
According to, according to the ratio of 1:3 be randomly divided into verifying collection and training set.
Secondly, the primitive cell structure of building metal oxide, is carried out with the generalized gradient approximation functional (GGA) in VASP software
Geometry optimization；In POTCAR file and OUTCAR file after the completion of 7 method of PM and the VASP optimization of 2016 software of MOPAC
Quantum chemical descriptor is obtained, including generates heat, gross energy, electronics energy, core-nuclear repulsion power, nuclear energy, fermi level, body
The free energy etc. of system；The information that metallic atom is obtained from the periodic table of elements, periodicity, metallic atom including metallic atom
Metallic atom and the ratio of oxygen atom of the metal oxide of electronegativity, the valence electron number of metallic atom and formation etc. are made
For the periodic table parameter of nano-metal-oxide；Material particle size, the nanogold of nano-metal-oxide are obtained by Experimental Characterization
Belong to the crystal configuration parameter of structure cell number and nano-metal-oxide that oxide is included.
Finally, establishing the relationship mould of nano-metal-oxide edge energy and structural parameters using Partial Least Squares (PLS)
Type, and model is characterized.As a result as follows:
Wherein, EgEdge energy is represented, HF represents the structure cell enthalpy of formation；BETA represents the angle structure cell β；D-2Represent material diameter square
Inverse；V2 represents structure cell vector length；EFermiRepresent fermi level；TFW represents Thomas-Fermi's vector；R represents metal original
Son and oxygen atom ratio；ETRepresent gross energy；DENC representative -1/2Hartree energy；XCENC represents electron exchange correlation energy.
Fig. 2 illustrates fitting and the verification result of model.The fitting correlation coefficient R of model2Be 0.848, fitting it is equal
Square error RMSE is 0.378eV, indicates that model has preferable linear fit effect (R2>0.6)；Model is through one method of past
Cross-validation, obtained RMSE are 0.478eV, show that established model has preferable robustness；The outside of model
Verifying includes the edge energy of 10 nano-metal-oxides, the Q of external certificate2 extIt is respectively 0.814 and 0.408eV with RMSE,
Show model has preferable predictive ability (Q2 ext>0.5)。
The nano-metal-oxide refers to the metal oxide of partial size covering 2.6nm to 70nm, includes ceria,
Cuprous oxide, gallium oxide, nickel oxide, tin oxide, chrome green, the titanium dioxide of anatase, monocrystalline italic and rutile-type
Titanium, aluminum oxide, di-iron trioxide, ferroso-ferric oxide, hafnium oxide, indium oxide, lanthana, magnesia, manganese sesquioxide managnic oxide,
Antimony oxide, tungstic acid, yttrium oxide, zirconium oxide and zinc oxide.
Beneficial effects of the present invention: the present invention can be with quick predict different crystal forms, different-grain diameter nano-metal-oxide energy
Gap value；This method is at low cost, simple and efficient, manpower, expense and the time needed for capable of saving experiment test；The present invention is established
Energy gap value prediction model, can be nano-metal-oxide ecological risk assessment, new catalyst and novel nano metal oxygen
The safe design of compound provides necessary basic data.
Detailed description of the invention
Fig. 1 is NiO and SnO2The relational graph of edge energy and partial size.
Fig. 2 is the calculated value of nano-metal-oxide energy gap and the comparison chart of measured value.
Specific embodiment
Below in conjunction with attached drawing and technical solution, a specific embodiment of the invention is further illustrated.
Embodiment 1
Given 9nm cuprous oxide at random, predicts its edge energy Eg。
The primitive cell structure for constructing cuprous oxide first, is carried out several with the generalized gradient approximation functional (GGA) in VASP software
What optimizes；It is obtained in POTCAR file and OUTCAR file after the completion of 7 method of PM and the VASP optimization of 2016 software of MOPAC
Quantum chemical descriptor is taken, BETA=0.40, HF=1.07, V2=0.42, E are obtainedFermi=0.00, TFW=0.77, ET=
1.93, DENC=3.41, XCENC=1.48.R=0.8 is obtained by searching for the periodic table of elements.By D-2=0.01 and above-mentioned energy band
Parameter substitutes into formula (6), obtains Eg=2.29eV.Consulting literatures, the edge energy E of measuring 9nm cuprous oxidegFor 2.50eV,
The edge energy and experiment value calculated by model is almost the same.Illustrate the nano-metal-oxide energy band ginseng established based on the present invention
Relationship between several and partial size, can be used for predicting the band parameter of different-grain diameter nano-metal-oxide.
Claims (2)
1. a kind of prediction technique of nano-metal-oxide edge energy, which is characterized in that steps are as follows:
Firstly, obtaining edge energy 91 of nano-metal-oxide, including 22 kinds of different gold by literature's store and measuring
Belong to the different crystal configuration of oxide；
The rule of nano-metal-oxide edge energy: (a) size of metal oxide must be nano-scale；(b) metal aoxidizes
The shape of object is ball-type or nearly ball-type；(c) there is single chemical component；(d) surface is without chemical modification；(e) X-ray must spreads out
The characterize data penetrated has determining crystal configuration；(f) there are ultraviolet-visible light spectrum analysis data；Using quantum limitation effect as receipts
The pretreated theoretical foundation of nano-metal-oxide edge energy of collection；After edge energy pretreatment, 40 energy gap Value Datas are obtained, are pressed
Verifying collection and training set are randomly divided into according to the ratio of 1:3；
Secondly, the primitive cell structure of building nano-metal-oxide, carries out geometry optimization to primitive cell structure；Obtain metal oxide
Following structural parameters: (1) quantum chemical descriptor is obtained；(2) periodicity, the metal of metallic atom are obtained from the periodic table of elements
The metallic atom of the metal oxide of the electronegativity of atom, the valence electron number of metallic atom and formation and the ratio of oxygen atom
The information of equal metallic atoms, the periodic table parameter as nano-metal-oxide；(3) nano metal oxygen is obtained by Experimental Characterization
The crystal configuration for the structure cell number and nano-metal-oxide that material particle size, the nano-metal-oxide of compound are included is joined
Number；
Finally, establishing the relational model of nano-metal-oxide edge energy and structural parameters using Partial Least Squares, and to mould
Type is characterized；As a result as follows:
Wherein, EgRepresent edge energy, HFRepresent the structure cell enthalpy of formation；BETA represents the angle structure cell β；D-2Represent falling for material diameter square
Number；V2 represents structure cell vector length；EFermiRepresent fermi level；TFW represents Thomas-Fermi's vector；R represent metallic atom with
Oxygen atom ratio；ETRepresent gross energy；DENC representative -1/2Hartree energy；XCENC represents electron exchange correlation energy；
The coefficient of determination R of model built2Be 0.848, the root-mean-square error RMSE of training set is 0.378eV, indicate model have compared with
Good linear fit effect, R2>0.6；Cross-validation of the model through one method of past, obtained RMSE are 0.478eV, are shown
The model established has preferable robustness；The external certificate of model includes the edge energy of 10 nano-metal-oxides, outside
The Q of portion's verifying2 extIt is respectively 0.814 and 0.408eV with RMSE, show model has preferable predictive ability, Q2 ext>0.5。
2. the prediction technique of nano-metal-oxide edge energy according to claim 1, which is characterized in that the nanometer
Metal oxide refers to the metal oxide of partial size covering 2.6nm to 70nm, includes ceria, cuprous oxide, gallium oxide, oxygen
Change nickel, tin oxide, chrome green, the titanium dioxide of anatase, monocrystalline italic and rutile-type, aluminum oxide, three oxygen
Change two iron, ferroso-ferric oxide, hafnium oxide, indium oxide, lanthana, magnesia, manganese sesquioxide managnic oxide, antimony oxide, three oxidations
Tungsten, yttrium oxide, zirconium oxide and zinc oxide.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811298480.9A CN109411030B (en) | 2018-11-02 | 2018-11-02 | Method for predicting energy gap value of nano metal oxide |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811298480.9A CN109411030B (en) | 2018-11-02 | 2018-11-02 | Method for predicting energy gap value of nano metal oxide |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109411030A true CN109411030A (en) | 2019-03-01 |
| CN109411030B CN109411030B (en) | 2021-04-20 |
Family
ID=65471504
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811298480.9A Active CN109411030B (en) | 2018-11-02 | 2018-11-02 | Method for predicting energy gap value of nano metal oxide |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109411030B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112651108A (en) * | 2020-12-07 | 2021-04-13 | 中国水利水电科学研究院 | Method for decoupling influences of meteorological elements and vegetation dynamics on hydrological elements |
| CN114944202A (en) * | 2022-04-24 | 2022-08-26 | 江西理工大学 | A screening method for a rare earth oxide cluster-supported carbon-based model for an efficient catalytic oxygen electrode reaction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05215699A (en) * | 1992-02-05 | 1993-08-24 | Tdk Corp | Measuring method for optical band gap of hydrocarbon film using low energy electron spectroscopy |
| US20020116162A1 (en) * | 2000-09-29 | 2002-08-22 | Kabushiki Kaisha Toshiba | Device simulation method, device simulation system and device simulation program |
| CN106446583A (en) * | 2016-10-19 | 2017-02-22 | 南京工程学院 | Predicting method for high-pressure behavior of high-energy ionic salt |
| CN108021784A (en) * | 2017-12-01 | 2018-05-11 | 大连理工大学 | The photic Forecasting Methodology for producing active oxygen species of nano-metal-oxide |
-
2018
- 2018-11-02 CN CN201811298480.9A patent/CN109411030B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05215699A (en) * | 1992-02-05 | 1993-08-24 | Tdk Corp | Measuring method for optical band gap of hydrocarbon film using low energy electron spectroscopy |
| US20020116162A1 (en) * | 2000-09-29 | 2002-08-22 | Kabushiki Kaisha Toshiba | Device simulation method, device simulation system and device simulation program |
| CN106446583A (en) * | 2016-10-19 | 2017-02-22 | 南京工程学院 | Predicting method for high-pressure behavior of high-energy ionic salt |
| CN108021784A (en) * | 2017-12-01 | 2018-05-11 | 大连理工大学 | The photic Forecasting Methodology for producing active oxygen species of nano-metal-oxide |
Non-Patent Citations (2)
| Title |
|---|
| ZHANG H，ET AL.: "《Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation》", 《ACS NANO》 * |
| 吴雪炜，等: "《钛酸锶纳米颗粒界面层特性的光谱学研究》", 《物理学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112651108A (en) * | 2020-12-07 | 2021-04-13 | 中国水利水电科学研究院 | Method for decoupling influences of meteorological elements and vegetation dynamics on hydrological elements |
| CN112651108B (en) * | 2020-12-07 | 2024-04-19 | 中国水利水电科学研究院 | Method for decoupling influence of meteorological elements and vegetation dynamics on hydrologic elements |
| CN114944202A (en) * | 2022-04-24 | 2022-08-26 | 江西理工大学 | A screening method for a rare earth oxide cluster-supported carbon-based model for an efficient catalytic oxygen electrode reaction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109411030B (en) | 2021-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Finegan et al. | Spatially resolving lithiation in silicon–graphite composite electrodes via in situ high-energy x-ray diffraction computed tomography | |
| Holder et al. | Tutorial on powder X-ray diffraction for characterizing nanoscale materials | |
| Hall et al. | Applications of in situ Raman spectroscopy for identifying nickel hydroxide materials and surface layers during chemical aging | |
| Derevyannikova et al. | Structural insight into strong Pt–CeO2 interaction: from single Pt atoms to PtO x clusters | |
| Hafiz et al. | Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials | |
| Kullgren et al. | Supercharged low-temperature oxygen storage capacity of ceria at the nanoscale | |
| Taniguchi et al. | Identifying defects in ceria-based nanocrystals by UV resonance Raman spectroscopy | |
| Shanmugasundaram et al. | Hierarchical In (OH) 3 as a precursor to mesoporous In2O3 nanocubes: a facile synthesis route, mechanism of self-assembly, and enhanced sensing response toward hydrogen | |
| Hirose et al. | Oxygen-diffusion-driven oxidation behavior and tracking areas visualized by X-ray spectro-ptychography with unsupervised learning | |
| Nelson et al. | Three-dimensional microstructural changes in the Ni–YSZ solid oxide fuel cell anode during operation | |
| Belliere et al. | Phase segregation in cerium− lanthanum solid solutions | |
| Jhong et al. | Combining structural and electrochemical analysis of electrodes using micro-computed tomography and a microfluidic fuel cell | |
| CN109411030A (en) | The prediction technique of nano-metal-oxide edge energy | |
| Arabac 횤 | Effect of the calcination temperature on the properties of Sm-doped CeO2 | |
| Douillard et al. | Local electronic structure of Ce-doped Y 2 O 3: An XPS and XAS study | |
| Amy et al. | The effect of morphology on the optical and electrical properties of sodium titanate nanostructures | |
| Aritani et al. | Characterization of Li-doped MgO catalysts for oxidative coupling of methane by means of Mg K-edge XANES | |
| Yu et al. | Tracking nanoparticle degradation across fuel cell electrodes by automated analytical electron microscopy | |
| Dudorova et al. | Hydrothermal Synthesis of Nickel Oxide and Its Application in the Additive Manufacturing of Planar Nanostructures | |
| Ueno et al. | Measurement informatics in synchrotron radiation x-ray spectroscopy | |
| Du et al. | Catalyst deactivation during water electrolysis: Understanding and mitigation | |
| Wang et al. | Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles | |
| Jabeen et al. | Electrochemical characterization and structural analysis of (In2O3)/(Fe2O3) nanocomposites for high-performance supercapacitors | |
| Wang et al. | Application of Machine Learning and Laser-Induced Breakdown Spectroscopy to Flame Spray Pyrolysis for the Prediction of Catalyst Properties | |
| Junaid et al. | Hydrothermal preparation of iron doped nickel oxide for electrochemical sensing of urea |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |