CN109411030B - Method for predicting energy gap value of nano metal oxide - Google Patents
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- 229910044991 metal oxide Inorganic materials 0.000 title claims abstract description 74
- 150000004706 metal oxides Chemical class 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 24
- 239000002245 particle Substances 0.000 claims abstract description 20
- 239000000463 material Substances 0.000 claims abstract description 18
- 239000013078 crystal Substances 0.000 claims abstract description 16
- 229910052751 metal Inorganic materials 0.000 claims abstract description 14
- 239000002184 metal Substances 0.000 claims abstract description 14
- 239000000126 substance Substances 0.000 claims abstract description 9
- 125000004429 atom Chemical group 0.000 claims description 11
- GWEVSGVZZGPLCZ-UHFFFAOYSA-N Titan oxide Chemical compound O=[Ti]=O GWEVSGVZZGPLCZ-UHFFFAOYSA-N 0.000 claims description 8
- 230000000737 periodic effect Effects 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- BERDEBHAJNAUOM-UHFFFAOYSA-N copper(I) oxide Inorganic materials [Cu]O[Cu] BERDEBHAJNAUOM-UHFFFAOYSA-N 0.000 claims description 5
- KRFJLUBVMFXRPN-UHFFFAOYSA-N cuprous oxide Chemical compound [O-2].[Cu+].[Cu+] KRFJLUBVMFXRPN-UHFFFAOYSA-N 0.000 claims description 5
- 229940112669 cuprous oxide Drugs 0.000 claims description 5
- 229910000480 nickel oxide Inorganic materials 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- XLOMVQKBTHCTTD-UHFFFAOYSA-N Zinc monoxide Chemical compound [Zn]=O XLOMVQKBTHCTTD-UHFFFAOYSA-N 0.000 claims description 4
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- 238000012512 characterization method Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- AMWRITDGCCNYAT-UHFFFAOYSA-L hydroxy(oxo)manganese;manganese Chemical compound [Mn].O[Mn]=O.O[Mn]=O AMWRITDGCCNYAT-UHFFFAOYSA-L 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
- 125000004430 oxygen atom Chemical group O* 0.000 claims description 4
- XOLBLPGZBRYERU-UHFFFAOYSA-N tin dioxide Chemical compound O=[Sn]=O XOLBLPGZBRYERU-UHFFFAOYSA-N 0.000 claims description 4
- ZNOKGRXACCSDPY-UHFFFAOYSA-N tungsten trioxide Chemical compound O=[W](=O)=O ZNOKGRXACCSDPY-UHFFFAOYSA-N 0.000 claims description 4
- GNRSAWUEBMWBQH-UHFFFAOYSA-N oxonickel Chemical compound [Ni]=O GNRSAWUEBMWBQH-UHFFFAOYSA-N 0.000 claims description 3
- 229910001887 tin oxide Inorganic materials 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- WGLPBDUCMAPZCE-UHFFFAOYSA-N Trioxochromium Chemical compound O=[Cr](=O)=O WGLPBDUCMAPZCE-UHFFFAOYSA-N 0.000 claims description 2
- 238000002441 X-ray diffraction Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims description 2
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- 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
- 229910000423 chromium oxide Inorganic materials 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
- SZVJSHCCFOBDDC-UHFFFAOYSA-N ferrosoferric oxide Chemical compound O=[Fe]O[Fe]O[Fe]=O SZVJSHCCFOBDDC-UHFFFAOYSA-N 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
- UQSXHKLRYXJYBZ-UHFFFAOYSA-N iron oxide Inorganic materials [Fe]=O UQSXHKLRYXJYBZ-UHFFFAOYSA-N 0.000 claims description 2
- CPLXHLVBOLITMK-UHFFFAOYSA-N magnesium oxide Inorganic materials [Mg]=O CPLXHLVBOLITMK-UHFFFAOYSA-N 0.000 claims description 2
- 239000000395 magnesium oxide Substances 0.000 claims description 2
- AXZKOIWUVFPNLO-UHFFFAOYSA-N magnesium;oxygen(2-) Chemical compound [O-2].[Mg+2] AXZKOIWUVFPNLO-UHFFFAOYSA-N 0.000 claims description 2
- SYHGEUNFJIGTRX-UHFFFAOYSA-N methylenedioxypyrovalerone Chemical compound C=1C=C2OCOC2=CC=1C(=O)C(CCC)N1CCCC1 SYHGEUNFJIGTRX-UHFFFAOYSA-N 0.000 claims description 2
- NDLPOXTZKUMGOV-UHFFFAOYSA-N oxo(oxoferriooxy)iron hydrate Chemical compound O.O=[Fe]O[Fe]=O NDLPOXTZKUMGOV-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
- RVTZCBVAJQQJTK-UHFFFAOYSA-N oxygen(2-);zirconium(4+) Chemical compound [O-2].[O-2].[Zr+4] RVTZCBVAJQQJTK-UHFFFAOYSA-N 0.000 claims description 2
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- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 claims 1
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- TZCXTZWJZNENPQ-UHFFFAOYSA-L barium sulfate Chemical compound [Ba+2].[O-]S([O-])(=O)=O TZCXTZWJZNENPQ-UHFFFAOYSA-L 0.000 description 2
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- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention provides a method for predicting an energy gap value of a nano metal oxide, belonging to the field of nanotechnology. Firstly, the energy gap value of the nano metal oxide is obtained by a literature collection and experimental determination method. And secondly, acquiring the structural parameters of the nano metal oxide, including quantum chemical descriptors, collecting metal atom information, and determining experimental parameters such as the particle size and the crystal configuration of the metal oxide. And finally, constructing a relation model of the energy gap value and the structural parameter of the nano metal oxide by a partial least square method, and predicting the energy gap values of the nano metal oxides with different crystal forms and different particle sizes. The method established by the invention can quickly predict the energy gap values of the nano metal oxides with different crystal forms and different particle sizes; the method has low cost and high efficiency, can save manpower and material resources required by experimental tests, and can provide necessary basic data for ecological risk evaluation of the nano metal oxide and safety design of a novel catalyst and a novel nano metal oxide.
Description
Technical Field
The invention relates to a method for predicting an energy gap value of a nano metal oxide, belonging to the field of nanotechnology.
Background
The unique optical and antibacterial properties of the nano metal oxide enable the nano metal oxide to be widely applied to the industry as a new catalyst and a new bactericide. The nano metal oxide can generate active Oxygen Species (ROS) by light, and is a main reason of catalytic and bactericidal performance. In addition, ROS can also destroy normal lipid and protein in cellsAnd DNA molecules, which cause cell damage and cause cytotoxicity. The ability of nano-metal oxides to generate ROS is related to their band structure. The band structure includes an energy gap value (E)g) Conduction band floor (E)C) And top value of valence band (E)V) The conduction band bottom value and the valence band top value of the nano metal oxide can be calculated through the energy gap value, and the relation of 3 is shown in a formula.
EC＝-χoxide+0.50Eg(1)
EV＝-χoxide-0.50Eg (2)
In the formula, the electronegativity of the metal oxide xoxideThe calculation can be made 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)
χcation(P.u.) is metal cation electronegativity, P.u;
χcation(eV) is the electronegativity of the metal cation, eV;
χoxideis the electronegativity of the metal oxide, eV;
z is a metal ion charge;
r is the radius of the metal ion,
α is a coefficient, depending on the element ordinal number;
therefore, the acquisition of the energy gap value is the key for calculating the conduction band bottom value and the valence band top value of the nano metal oxide.
Currently, the band gap value is mainly determined by using an ultraviolet-visible diffuse reflection method. On one hand, the experimental process and the data processing process are more complicated, for example, pure barium sulfate needs to be analyzed to be used as a marking line, the absorbance of the material at different wavelengths is measured, the absorbance is converted through a Kubelka-Munk diffuse reflection equation, the hv is plotted, a tangent line is made at the position of the maximum derivative, and the like; on the other hand, the crystal forms of the nano metal oxides are various, and the energy gap values of the nano metal oxides with different crystal forms and different particle sizes are different. It is not practical to test the energy gap values of the nano metal oxides with different crystal forms and different particle sizes one by adopting an experimental method. Therefore, it is necessary to construct a method for predicting the band gap values of the nano metal oxides with different crystal forms and different particle sizes.
Disclosure of Invention
The invention provides a method for rapidly and efficiently predicting the energy gap value of a nano metal oxide. First, the energy gap value of the nano metal oxide is obtained through literature collection and experimental determination. And secondly, obtaining the structural parameters of the nano metal oxide, including a metal oxide quantitative descriptor, periodic table parameters and experimental characterization structural parameters. And finally, establishing a relation model of the energy gap value and the structural parameter of the nano metal oxide by using a partial least square method, and predicting the energy gap values of the nano metal oxides with different crystal forms and different particle sizes. The method can provide necessary basic data for ecological risk evaluation of the nano metal oxide, novel catalysts and safety design of the novel nano metal oxide.
The technical scheme of the invention is as follows:
a method for predicting the energy gap value of nano metal oxide comprises the following steps:
first, 91 band gap values of the nano metal oxides were obtained by literature collection and experimental determination, including different crystal configurations of 22 different nano metal oxides. The literature collects the rules of the energy gap value of the nano metal oxide: the size of the metal oxide must be nanometer size; the shape of the metal oxide particles is spherical or approximately spherical; the particles have a single chemical composition; the surface is not chemically modified; the characterization data of X-ray diffraction is required, and the determined crystal configuration is provided; the material has ultraviolet-visible spectrum analysis data. The experimental determination of the energy gap value of the nano metal oxide uses an ultraviolet-visible diffuse reflection method.
Quantum confinement effect is used as the theoretical basis for pretreatment of the energy gap value of the nano metal oxide. Two substances of nickel oxide (NiO) and tin oxide (SnO) with more grain size distribution are selected2) The energy gap value andrelationship of particle size (fig. 1). The ordinate axis of the graph represents the energy gap value of the material, the abscissa axis represents the square of the diameter of the material, and the abscissa axis of the dashed line parallel to the ordinate axis of the graph has a value of 100nm2I.e. the data points falling on the left side of the dotted line have a material diameter of less than 10nm and the data points falling on the right side of the dotted line have a material particle size of more than 10 nm. After fitting the curve, the energy gap values and the diameters of the two substances show inverse proportional function negative correlation, namely, the energy gap value of the material has a tendency of increasing gradually with the reduction of the particle size of the material. And compared with the data points of the material with the grain diameter larger than 10nm, the energy gap value of the data points of the material with the grain diameter smaller than 10nm is obviously increased overall. And the grain diameter is more than 10nm, which has no important effect on the energy gap value of the material. Therefore, in the collected energy gap values of the nano metal oxide, the energy gap value of the material with the size less than 10nm is directly used, and the energy gap value of the material with the size more than 10nm is taken as the average value of the particle size and the energy gap value. After preprocessing the energy gap value, 40 energy gap value data are obtained and are randomly divided into a verification set and a training set according to the proportion of 1: 3.
Secondly, constructing a primitive cell structure of the metal oxide, and carrying out geometric optimization by using a generalized gradient approximation functional (GGA) in VASP software; obtaining quantum chemical descriptors from a PM 7 method of MOPAC 2016 software and a POTCAR file and an OUTCAR file after VASP optimization, wherein the quantum chemical descriptors comprise generated heat, total energy, electron energy, nuclear-nuclear repulsive force, atomic energy, Fermi energy level, free energy of a system and the like; obtaining information of metal atoms from the periodic table of elements, wherein the information comprises the period number of the metal atoms, the electronegativity of the metal atoms, the valence electron number of the metal atoms, the ratio of the metal atoms to oxygen atoms of the formed metal oxide and the like, and the information is used as the periodic table parameters of the nano metal oxide; and (3) obtaining the material particle size of the nano metal oxide, the unit cell number contained in the nano metal oxide and the crystal configuration parameter of the nano metal oxide through experimental characterization.
And finally, establishing a relation model of the energy gap value of the nano metal oxide and the structural parameters by using a Partial Least Squares (PLS) method, and characterizing the model. The results are as follows:
wherein E isgRepresents the energy gap value, and HF represents the enthalpy of formation of the unit cell; BETA represents the unit cell BETA angle; d-2Represents the inverse of the square of the diameter of the material; v2 represents the unit cell vector length; eFermiRepresents the fermi level; TFW represents the thomas-fermi vector; r represents the ratio of metal atoms to oxygen atoms; eTRepresents the total energy; DENC stands for energy-1/2 Hartree; XCENC stands for electronic exchange correlation energy.
Figure 2 shows the fitting of the model and the results of the validation. Fitting correlation coefficient R of model2At 0.848, the root mean square error RMSE of the fit was 0.378eV, indicating that the model had a better linear fit (R is2>0.6); the model is subjected to internal cross validation by the first method, and the obtained RMSE is 0.478eV, which shows that the established model has better robustness; the external verification of the model comprises the energy gap value of 10 nano metal oxides and the Q of the external verification2 extAnd RMSE of 0.814 eV and 0.408eV, respectively, indicating that the model has better prediction capability (Q)2 ext>0.5)。
The nano metal oxide is a metal oxide with the particle size of 2.6nm to 70nm, and comprises cerium dioxide, cuprous oxide, gallium oxide, nickel oxide, tin oxide, chromium oxide, anatase, single crystal italics and rutile type titanium dioxide, aluminum oxide, ferric oxide, ferroferric oxide, hafnium oxide, indium oxide, lanthanum oxide, magnesium oxide, manganese oxide, antimony trioxide, tungsten trioxide, yttrium oxide, zirconium oxide and zinc oxide.
The invention has the beneficial effects that: the invention can quickly predict the energy gap values of the nano metal oxides with different crystal forms and different particle sizes; the method is low in cost, simple, convenient and quick, and can save manpower, cost and time required by experimental tests; the energy gap value prediction model established by the invention can provide necessary basic data for ecological risk evaluation of the nano metal oxide and safety design of a novel catalyst and a novel nano metal oxide.
Drawings
FIG. 1 shows NiO and SnO2The relationship between the energy gap value and the particle size.
FIG. 2 is a comparison graph of the calculated and measured values of the band gap of the nano metal oxide.
Detailed Description
The following further describes a specific embodiment of the present invention with reference to the drawings and technical solutions.
Example 1
Randomly giving 9nm cuprous oxide, and predicting energy gap value Eg。
Firstly, constructing an original cell structure of cuprous oxide, and carrying out geometric optimization by using a generalized gradient approximation functional (GGA) in VASP software; obtaining quantum chemical descriptors from POTCAR files and OUTCAR files after MOPAC 2016 software PM 7 method and VASP optimization are completed, and obtaining BETA of 0.40, HF of 1.07, V2 of 0.42 and E of 0.42Fermi＝0.00，TFW＝0.77，ET1.93, DENC 3.41, XCENC 1.48. And finding out that R is 0.8 by searching the periodic table of elements. Will D-2When the band parameters are substituted into equation (6) under the condition of 0.01, E is obtainedg2.29 eV. Looking up the literature, the energy gap value E of 9nm cuprous oxide is experimentally determinedgThe energy gap value calculated by the model is substantially consistent with the experimental value at 2.50 eV. The relationship between the nano metal oxide energy band parameter and the particle size established based on the invention can be used for predicting the energy band parameters of nano metal oxides with different particle sizes.
Claims (2)
1. A prediction method of a nanometer metal oxide energy gap value is characterized by comprising the following steps:
firstly, 91 energy gap values of the nano metal oxide are obtained through literature collection and experimental determination, wherein the energy gap values comprise different crystal configurations of 22 different metal oxides;
rule of energy gap value of nano metal oxide: (a) the size of the metal oxide must be nanometer size; (b) the shape of the metal oxide is spherical or nearly spherical; (c) has a single chemical composition; (d) the surface is not chemically modified; (e) the characterization data of X-ray diffraction is required, and the determined crystal configuration is provided; (f) ultraviolet-visible spectrum analysis data; the quantum confinement effect is used as the theoretical basis for pretreatment of the energy gap value of the collected nano metal oxide; after energy gap value preprocessing, obtaining 40 energy gap value data, and randomly dividing the data into a verification set and a training set according to the proportion of 1: 3;
secondly, constructing a primitive cell structure of the nano metal oxide, and carrying out geometric optimization on the primitive cell structure; obtaining the following structural parameters of the metal oxide: (1) obtaining a quantum chemical descriptor; (2) obtaining the information of the periodicity of the metal atoms, the electronegativity of the metal atoms, the valence electron number of the metal atoms and the ratio of the metal atoms to oxygen atoms of the formed metal oxide from the periodic table of elements as the periodic table parameters of the nano metal oxide; (3) obtaining the material grain diameter of the nano metal oxide, the unit cell number contained in the nano metal oxide and the crystal configuration parameters of the nano metal oxide through experimental representation;
finally, establishing a relation model of the energy gap value and the structural parameters of the nano metal oxide by using a partial least square method, and characterizing the model; the results are as follows:
wherein E isgRepresents the energy gap value, HFRepresents the enthalpy of formation of the unit cell; BETA represents the unit cell BETA angle; d-2Represents the inverse of the square of the diameter of the material; v2 represents the unit cell vector length; eFermiRepresents the fermi level; TFW represents the thomas-fermi vector; r represents the ratio of metal atoms to oxygen atoms; eTRepresents the total energy; DENC stands for energy-1/2 Hartree; XCENC stands for electronic exchange correlation energy;
coefficient of determination R of the model being built20.848, the root mean square error RMSE of the training set is 0.378eV, which indicates that the model has better linear fitting effect, R2>0.6; the model is subjected to internal cross validation by the first method, and the obtained RMSE is 0.478eV, which shows that the established model has better robustness; external verification of the model included 10 nanometal oxygensEnergy gap value of compound, Q of external verification2 extAnd RMSE of 0.814 and 0.408eV, respectively, indicating better predictive power of the model, Q2 ext>0.5。
2. The method of claim 1, wherein the nano metal oxide is a metal oxide with a particle size of 2.6nm to 70nm, and comprises ceria, cuprous oxide, gallium oxide, nickel oxide, tin oxide, chromium oxide, anatase, monoclinic and rutile forms of titania, alumina, ferric oxide, ferroferric oxide, hafnium oxide, indium oxide, lanthanum oxide, magnesium oxide, manganese oxide, antimony trioxide, tungsten trioxide, yttrium oxide, zirconium oxide and zinc oxide.
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| 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 |
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| JPH05215699A (en) * | 1992-02-05 | 1993-08-24 | Tdk Corp | Measuring method for optical band gap of hydrocarbon film using low energy electron spectroscopy |
| 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 |
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