A multi-scale design method for doped perovskite manganese oxide intelligent thermal control coating

Abstract

The present invention discloses a multi-scale design method for a doped perovskite manganese oxide intelligent thermal control coating, which belongs to the technical field of functional material design. The present invention aims to solve the technical problems of long development cycle and high cost of existing doped perovskite manganese oxide. The method is as follows: determine the LaMnO 3 Unit cell model of crystal materials; use A atoms to replace La atoms at different positions to establish a supercell model of doped materials; perform self-consistent calculations on the supercell model after structural optimization; calculate optical constants; construct a perovskite intelligent thermal control coating with microstructure from the micro-nano scale, and use the time-domain finite difference method to calculate the emissivity of the doped perovskite manganese oxide intelligent thermal control coating based on the calculated optical constants. The method of the present invention can significantly reduce the selection range of some parameters in the experimental process, reduce the human labor loss caused by erroneous experiments, and improve the efficiency of the development of intelligent thermal control coatings.

Description

Technical Field

[0001] The present invention belongs to the technical field of functional material design, and specifically relates to a multi-scale design method for a doped perovskite manganese oxide intelligent thermal control coating. Background Art

[0002] Thermal control coating is one of the most commonly used means of temperature control on the surface of spacecraft. In the vacuum environment of space, the heat transfer between the spacecraft and its surrounding space is only controlled by thermal radiation, and the emissivity of the coating has an important influence on its thermal control performance. During the on-orbit operation of the spacecraft, the temperature fluctuates with the space environment and the heat generated by the payload. When the spacecraft is in a cold environment, the thermal control coating needs to have a low reflectivity to reduce heat leakage; when the spacecraft is in a hot environment, the thermal control coating needs to have a high reflectivity to enhance heat removal. As spacecraft develop towards miniaturization and intelligence, the demand for intelligent thermal control coatings with adaptively adjustable emissivity is becoming more and more urgent. Perovskite manganese oxide is a thermochromic variable emissivity material. It is in a metallic state at low temperatures and has low emissivity characteristics. It is in an insulating state at high temperatures and has high emissivity characteristics. It is one of the most promising intelligent thermal control coating materials.

[0003] Existing perovskite manganese oxide (LaMnO3) mainly enhances the variable emissivity characteristics of the coating by element doping. Under suitable element types and doping concentrations, perovskite manganese oxide exhibits unique thermal radiation characteristics. Common doping elements include divalent or monovalent alkali metal elements such as Sr, Ca, K, Ba, Be (La (Sr, Ca, K, Ba, Be, etc.) MnO3), and doping methods include single element doping, double element doping, etc. The goal of doping is to obtain intelligent thermal control coating materials with more significant variable emissivity performance. Although some doped perovskite manganese oxides have been developed and show certain variable emissivity characteristics, they are basically found through a large number of experiments to find the optimal material, which is a method with high cost and low efficiency. Summary of the invention

[0004] The development of doped perovskite manganese oxides mainly relies on a large number of experiments. The types of doped elements and concentration ratios are limited by experimental conditions and costs. The number of synthesized products is small, high-throughput experiments are difficult to conduct, and the material development cycle is long.

[0005] Since doped perovskite manganese oxide is a new type of material, there is no database of relevant physical property parameters, which leads to a lack of accurate input conditions for the analysis of the thermal radiation characteristics of its coating structure, and there is no comprehensive design method for doped perovskite manganese oxide intelligent thermal control coatings from atomic scale to micro-nano scale.

[0006] Based on the above problems existing in the prior art, the present invention provides a multi-scale design method for doped perovskite manganese oxide intelligent thermal control coating. The performance of doped perovskite manganese oxide intelligent thermal control coating mainly depends on the intrinsic optical properties of the material at the atomic scale and the micro-nano scale coating structure effect. Therefore, the multi-scale doped perovskite manganese oxide coating design method developed by the present invention is of great value to the development of high-performance intelligent thermal control coating.

[0007] In order to develop a new type of doped perovskite manganese oxide intelligent thermal control coating, the variable emissivity performance of the intelligent thermal control coating under different doping elements, doping ratios and coating structures is analyzed. The present invention adopts the following technical solutions:

[0008] The object of the present invention is to provide a multi-scale design method for a doped perovskite manganese oxide intelligent thermal control coating, comprising the following steps:

[0009] Step 1: Determine the unit cell model of LaMnO3 crystal material at high and low temperature states;

[0010] Step 2: Use A atoms to replace La atoms at different positions in LaMnO3 under different unit cell models to establish doped La 1-x (A) x Supercell model of MnO3 material;

[0011] Step 3: La in step 2 1-x (A) x The MnO3 supercell model was structurally optimized and then self-consistently calculated;

[0012] Step 4: Calculate La for different supercell models at the atomic scale based on density functional theory 1-x (A) x Optical constants of MnO3 materials;

[0013] Step 5: Construct a perovskite intelligent thermal control coating with a microstructure from the micro-nano scale. Based on the optical constants calculated in step 4, the emissivity of the doped perovskite manganese oxide intelligent thermal control coating is calculated using the time-domain finite difference method.

[0014] It is further defined that the step 1 is specifically:

[0015] Step 1.1: Determine the unit cell type of LaMnO3 material at high temperature and the unit cell model at low temperature;

[0016] Step 1.2: Select the unit cell model at different temperatures in the Material Project crystal library, export it as a CIF file, open it in an editor to extract the POSCAR information required by VASP, and convert it into a POSCAR file.

[0017] Further defined, the step 2 is specifically as follows:

[0018] Step 2.1: Import the CIF file into Materials Studio, expand the original LaMnO3 cell according to the doping ratio of A atoms, and construct a×b×nc (n=2, 4, 6, 8...) supercell structure model;

[0019] Step 2.2: In the supercell structure model, use A atoms to replace the La element in the unit cell structure model and design a La doping ratio 1-x (A) x MnO3 supercell model;

[0020] Step 2.3: Export the material constructed in step 2.2 as a CIF file, open it with an editor, extract the POSCAR information required by VASP, and convert it into a POSCAR file.

[0021] Further defined, the structural optimization of step 3 is specifically as follows:

[0022] Step 3.1: According to La 1-x (A) x The element composition in the MnO3 supercell model, select the corresponding functional and basis set, construct the POTCAR file, select the ENCUT, EDIFF and EDIFFG points, and construct the INCAR and KPOINTS files;

[0023] Step 3.2: Submit POSCAR in step 2.3 and POTCAR, INCAR, and KPOINTS in step 3.1 to the VASP application for structural optimization to obtain the CONTCAR file.

[0024] Further defined, the self-consistent calculation of step 3 is specifically:

[0025] Copy the CONTCAR obtained from the structural optimization in step 3.2 as POSCAR, and then submit it to the VASP application for self-consistent calculation to obtain the CHGCAR file.

[0026] Further defined, the step 4 is specifically:

[0027] Step 4.1: Add LOPTICS=.TURE. to the INCAR file in step 3.1 to calculate the frequency-dependent dielectric matrix;

[0028] Step 4.2: Add CSHIFT=0.1 to the INCAR file in step 4.1 and turn on the linear response calculation.

[0029] Step 4.3: Submit the INCAR file in step 4.2, the CHGCAR file obtained by the self-consistent calculation in step 3.3, and the POSCAR, POTCAR, and KPOINTS files in step 3.2 to the VASP application for optical property calculation;

[0030] Step 4.4: Calculate La 1-x (A) x The real part ε1(ω), imaginary part ε2(ω) and refractive index n(ω) of the dielectric function of MnO3 material are calculated as follows:

[0031]

[0032]

[0033]

[0034] In the formula, V and C represent the conduction band and valence band respectively; E C (K) represents the intrinsic energy level on the angular conduction band; E V (K) represents the intrinsic energy level on the valence band; K represents the reciprocal lattice vector; represents Planck's constant; e represents the electron charge; ω represents the angular frequency; BZ represents the first Brillouin zone; |e·M CV (K) 2 Represents the pillar transition matrix.

[0035] Further defined, the step 5 is specifically:

[0036] Step 5.1: Place the designed La 1-x (A) x The optical constants of MnO3 material are imported into the material library of FDTDSolutions program through txt file;

[0037] Step 5.2: Construct a physical model with a micron-scale structure in the FDTD Solutions program and simulate the infrared spectral reflectivity of the physical model;

[0038] Step 5.3: Simulate and obtain the infrared light reflectance curve that varies with wavelength, export the curve in the form of a picture, and export the recorded data points in the form of text;

[0039] Step 5.4: Based on the derived spectral reflectance data, calculate the infrared spectral emissivity ε(λ) of the coating using the following formula:

[0040] ε(λ)=α(λ)=1-τ(λ)-ρ(λ);

[0041] Where ε(λ) is the spectral emissivity; α(λ) is the spectral absorptivity; τ(λ) is the spectral transmittance; ρ(λ) is the spectral reflectivity; and λ is the wavelength.

[0042] Step 5.5: Calculate the emissivity of the intelligent thermal control coating according to the emissivity calculation formula;

[0043] The emissivity calculation formula is: Where ε(T) is the emissivity; E λ,b (λ,T) is the spectral emissivity of the black body at temperature T; T is the temperature.

[0044] Further defined, the step 5.2 is specifically:

[0045] The wavelength range of the light source (Source) is set to 2.5μm~25μm, the incident direction is downward along the z axis, and the coating has macroscopic periodicity in the x and y directions. The periodic boundary conditions (Periodic) are set on the xz and yz surfaces in the model, and only the perfectly matched layer (PML) is selected on the xy surface;

[0046] The coating should have sufficient thickness, and the surface structure should have clear characteristic physical parameters that can be controlled. The spectral reflectance monitor (PowerMonitor) is placed behind the light source to monitor and record the reflectance of the coating, recording one data point for every 0.1μm wavelength;

[0047] Adaptive grids are used within the simulation range, and meshes are set as needed at the coating structure. The mesh density is not greater than 1 / 3 of the feature size.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention couples the first-principles calculation method at the atomic scale with the time-domain finite-difference calculation method at the micro-nano scale, and comprehensively designs multi-scale factors that have a significant impact on the emissivity of the doped perovskite manganese oxide intelligent thermal control coating, such as the type of doping elements, doping ratio and coating surface microstructure, to effectively guide the development of new intelligent thermal control coatings.

[0050] The multi-scale design of the doped perovskite manganese oxide intelligent thermal control coating of the present invention can significantly reduce the selection range of some parameters in the experimental process, reduce the human labor loss caused by erroneous experiments, and improve the development efficiency of the intelligent thermal control coating.

[0051] In order to further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the attached drawings are only provided for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the multi-scale design method of doped perovskite manganese oxide intelligent thermal control coating;

[0053] Figure 2 It is the unit cell model of LaMnO3 material at high and low temperature states. (a) It is determined to be a trigonal system at low temperature, and (b) it is determined to be a cubic system at high temperature.

[0054] Figure 3 It is the supercell model of LaMnO3 material;

[0055] Figure 4 It's La 5 / 6 Mn 1 / 6 Supercell model of O3 material. DETAILED DESCRIPTION

[0056] The present invention will be described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, and do not limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0057] Embodiment 1: A multi-scale design method for a doped perovskite manganese oxide intelligent thermal control coating of the present invention comprises the following steps:

[0058] Step 1: Determine the unit cell model of LaMnO3 crystal material at high and low temperature states; specifically:

[0059] Step 1.1: Determine the unit cell type of LaMnO3 material at high temperature and the unit cell model at low temperature; it is determined to be a trigonal system at low temperature and a cubic system at high temperature. The specific unit cell model is as follows: Figure 2 shown.

[0060] Step 1.2: Select the unit cell model at different temperatures in the Material Project crystal library, export it as a CIF file, open it in an editor to extract the POSCAR information required by VASP, and convert it into a POSCAR file.

[0061] Step 2: Use Sr atoms to replace La atoms at different positions in LaMnO3 under different unit cell models to establish doped La 1-x (Sr) x The supercell model of MnO3 material is as follows:

[0062] Step 2.1: Import the CIF file into Materials Studio, expand the original LaMnO3 cell according to the doping ratio of Sr atoms, and construct an a×b×nc (n=6) supercell structure model. The specific cell model is as follows: Figure 3 shown.

[0063] Step 2.2: In the supercell structure model, use Sr atoms to replace the La element in the cell structure model to design a La doping ratio of 1 / 6. 5 / 6 Sr 1 / 6 MnO3 supercell model, the specific cell model is as follows Figure 4 shown.

[0064] Step 2.3: Export the material constructed in step 2.2 as a CIF file, open it with an editor, extract the POSCAR information required by VASP, and convert it into a POSCAR file.

[0065] Step 3: La in step 2 5 / 6 Sr 1 / 6 The MnO3 supercell model is self-consistently calculated after structural optimization; specifically:

[0066] Step 3.1: According to La 5 / 6 Sr 1 / 6 The element composition in the MnO3 supercell model, select the corresponding functional and basis set, construct the POTCAR file, select the ENCUT, EDIFF and EDIFFG points, and construct the INCAR and KPOINTS files;

[0067] Step 3.2: Submit POSCAR in step 2.3 and POTCAR, INCAR and KPOINTS in step 3.1 to the VASP application for structural optimization to obtain the CONTCAR file;

[0068] Step 3.3: Copy the CONTCAR obtained from the structural optimization in step 3.2 as POSCAR, and then submit it to the VASP application for self-consistent calculation to obtain the CHGCAR file.

[0069] Step 4: Calculate La for different supercell models at the atomic scale based on density functional theory 5 / 6 Sr 1 / 6 The optical constants of MnO3 materials are:

[0070] Step 4.1: Add LOPTICS=.TURE. to the INCAR file in step 3.1 to calculate the frequency-dependent dielectric matrix;

[0071] Step 4.2: Add CSHIFT=0.1 to the INCAR file in step 4.1 and turn on the linear response calculation.

[0072] Step 4.3: Submit the INCAR file in step 4.2, the CHGCAR file obtained by the self-consistent calculation in step 3.3, and the POSCAR, POTCAR, and KPOINTS files in step 3.2 to the VASP application for optical property calculation;

[0073] Step 4.4: Calculate La 5 / 6 Sr 1 / 6 The real part ε1(ω), imaginary part ε2(ω) and refractive index n(ω) of the dielectric function of MnO3 material are calculated as follows:

[0074]

[0075]

[0076]

[0077] In the formula, V and C represent the conduction band and valence band respectively; E C (K) represents the intrinsic energy level on the angular conduction band; E V (K) represents the intrinsic energy level on the valence band; K represents the reciprocal lattice vector; represents Planck's constant; e represents the electron charge; ω represents the angular frequency; BZ represents the first Brillouin zone; |e·M CV (K) 2 Represents the pillar transition matrix.

[0078] Step 5: Construct a perovskite intelligent thermal control coating with a microstructure from the micro-nano scale. Based on the optical constants calculated in step 4, the emissivity of the doped perovskite manganese oxide intelligent thermal control coating is calculated using the finite difference time domain method; specifically:

[0079] Step 5.1: Place the designed La 5 / 6 Sr 1 / 6 The optical constants of MnO3 material are imported into the material library of FDTDSolutions program through txt file;

[0080] Step 5.2: Construct a physical model with a micrometer-scale structure in the FDTD Solutions program and simulate the spectral reflectance of the physical model;

[0081] The wavelength range of the light source (Source) is set to 2.5μm~25μm, the incident direction is downward along the z-axis, and the coating has macroscopic periodicity in the x and y directions. Then the periodic boundary conditions (Periodic) are set on the xz and yz surfaces in the model, and only the perfectly matched layer (PML) is selected on the xy surface.

[0082] The coating should have sufficient thickness, and the surface structure should have clear characteristic physical parameters that can be controlled. The spectral emissivity monitor (PowerMonitor) is placed behind the light source to detect and record the reflectivity of the coating, recording one data point for every 0.1μm wavelength;

[0083] Adaptive mesh is used within the simulation range, and mesh is set as needed at the coating structure, with mesh density not exceeding 1 / 3 of the feature size;

[0084] Step 5.3: Simulate and obtain the infrared light reflectance curve that varies with wavelength, export the curve in the form of a picture, and export the recorded data points in the form of text;

[0085] Step 5.4: Based on the derived spectral reflectance data, calculate the infrared spectral emissivity ε(λ) of the coating using the following formula:

[0086] ε(λ)=α(λ)=1-τ(λ)-ρ(λ);

[0087] Where ε(λ) is the spectral emissivity; α(λ) is the spectral absorptivity; τ(λ) is the spectral transmittance; ρ(λ) is the spectral reflectivity; and λ is the wavelength.

[0088] Step 5.5: Calculate the emissivity of the intelligent thermal control coating according to the emissivity calculation formula;

[0089] The emissivity calculation formula is: Where ε(T) is the emissivity; E λ,b (λ,T) is the spectral emissivity of the black body at temperature T; T is the temperature.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the present invention.

Claims

1. A multi-scale design method for doped perovskite manganese oxide intelligent thermal control coating, characterized in that: The following steps are involved: Step 1: Determine the unit cell model of LaMnO3 crystal material at high and low temperature states; Step 2: Use A atoms to replace La atoms at different positions in LaMnO3 under different unit cell models to establish doped La 1-x (A) x Supercell model of MnO3 material; Step 3: La in step 2 1-x (A) x The MnO3 supercell model was structurally optimized and then self-consistently calculated; Step 4: Calculate La for different supercell models at the atomic scale based on density functional theory 1-x (A) x Optical constants of MnO3 materials; Step 5: Construct a perovskite smart thermal control coating with a microstructure from the micro-nano scale, and calculate the emissivity of the doped perovskite manganese oxide smart thermal control coating using the finite difference time domain method based on the optical constants calculated in step 4; The step 1 is specifically as follows: Step 1.1: Determine the unit cell type of LaMnO3 material at high temperature and the unit cell model at low temperature; Step 1.2: Select the unit cell model at different temperatures in the Material Project crystal library, export it as a CIF file, open it in an editor to extract the POSCAR information required by VASP, and convert it into a POSCAR file; The step 2 is specifically as follows: Step 2.1: Import the CIF file into Materials Studio, expand the original LaMnO3 cell according to the doping ratio of A atoms, and construct a×b×nc (n=2, 4, 6, 8...) supercell structure model; Step 2.2: In the supercell structure model, use A atoms to replace the La element in the unit cell structure model and design a La doping ratio 1-x (A) x MnO3 supercell model; Step 2.3: Export the material constructed in step 2.2 as a CIF file, open it with an editor to extract the POSCAR information required by VASP, and convert it into a POSCAR file; The structural optimization of step 3 is specifically as follows: Step 3.1: According to La 1-x (A) x The element composition in the MnO3 supercell model, select the corresponding functional and basis set, construct the POTCAR file, select the ENCUT, EDIFF and EDIFFG points, and construct the INCAR and KPOINTS files; Step 3.2: Submit POSCAR in step 2.3 and POTCAR, INCAR and KPOINTS in step 3.1 to the VASP application for structural optimization to obtain the CONTCAR file; The self-consistent calculation of step 3 is specifically as follows: Copy the CONTCAR obtained from the structural optimization in step 3.2 as POSCAR, and then submit it to the VASP application for self-consistent calculation to obtain the CHGCAR file; The step 4 is specifically as follows: Step 4.1: Add LOPTICS=.TURE. to the INCAR file in step 3.1 to calculate the frequency-dependent dielectric matrix; Step 4.2: Add CSHIFT=0.1 to the INCAR file in step 4.1 and turn on the linear response calculation. Step 4.3: Submit the INCAR file in step 4.2, the CHGCAR file obtained by the self-consistent calculation in step 3.3, and the POSCAR, POTCAR, and KPOINTS files in step 3.2 to the VASP application for optical property calculation; Step 4.4: Calculate La 1-x (A) x The real part ε1(ω), imaginary part ε2(ω) and refractive index n(ω) of the dielectric function of MnO3 material are calculated as follows: In the formula, V and C represent the conduction band and valence band respectively; E C (K) represents the intrinsic energy level on the angular conduction band; E V (K) represents the intrinsic energy level on the valence band; K represents the reciprocal lattice vector; represents Planck's constant; e represents the electron charge; ω represents the angular frequency; BZ represents the first Brillouin zone; e·M CV (K) 2 represents the pillar transition matrix; The step 5 is specifically as follows: Step 5.1: Place the designed La 1-x (A) x The optical constants of MnO3 material are imported into the material library of FDTDSolutions program through txt file; Step 5.2: Construct a physical model with a micrometer-scale structure in the FDTDSolutions program, and simulate the infrared spectral reflectivity of the physical model; Step 5.3: Simulate and obtain the infrared light reflectance curve that varies with wavelength, export the curve in the form of a picture, and export the recorded data points in the form of text; Step 5.4: Based on the derived spectral reflectance data, calculate the infrared spectral emissivity ε(λ) of the coating using the following formula: ε(λ)=α(λ)=1-τ(λ)-ρ(λ); Where, ε(λ) is the spectral emissivity; α(λ) is the spectral absorptivity; τ(λ) is the spectral transmittance; ρ(λ) is the spectral reflectivity; λ is the wavelength; Step 5.5: Calculate the emissivity of the intelligent thermal control coating according to the emissivity calculation formula; The emissivity calculation formula is: Where ε(T) is the emissivity; E λ,b (λ,T) is the spectral emissivity of the black body at temperature T; T is the temperature.

2. The method according to claim 1, characterized in that: The step 5.2 is specifically as follows: The wavelength range of the light source is set to 2.5μm to 25μm, the incident direction is downward along the z axis, and the coating has macroscopic periodicity in the x and y directions. The xz and yz planes in the model are set as periodic boundary conditions, and only the xy plane uses a perfect matching layer; To set the coating thickness, the surface structure should have clear characteristic physical parameters that can be controlled; the spectral reflectance monitor is placed behind the light source to monitor and record the coating reflectance, recording one data point for every 0.1μm wavelength; Adaptive grids are used within the simulation range, and grids are set as needed at the coating structure. The grid density is not greater than 1 / 3 of the feature size.

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