CN114609220A - Method for solving ionic conductivity of three-phase interface - Google Patents
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Abstract
The invention belongs to the field of solving of ionic conductivity, and particularly relates to a method for solving the ionic conductivity of a three-phase interface, which is based on first-nature principle molecular dynamics and can be applied to a solid electrolyte gas sensor. The invention starts from the definition of the ion conductivity of the solid electrolyte, simulates the migration process of the moving ions on the basis of an atomic-scale model, and ensures that the microstructure evolution of a three-phase interface is truly visible. Firstly, a three-phase interface model is constructed, then the AIMD is utilized to simulate the microstructure evolution of the three-phase interface, and the microstructure evolution is further evolved into an ion conductivity value by combining the computer language Python. The method provided by the invention not only truly reduces the microstructure of the three-phase interface of the solid electrolyte gas sensor, but also truly simulates the reaction process of gas molecules in the three-phase interface, and solves the problems that the current solid electrolyte ion conductivity solving is difficult to model, cannot be suitable for the three-phase interface, and the solving result is difficult to analyze and process.
Description
Technical Field
The invention belongs to the field of solving of ionic conductivity, and particularly relates to a method for solving the ionic conductivity of a three-phase interface (solid electrolyte phase/electrode phase/gas phase).
Background
The working principle of the current type solid electrolyte gas sensor is that a constant bias voltage is applied to the outside of a solid electrolyte, and the related information of the gas to be measured is determined by measuring the current passing through the three-phase interface of the solid electrolyte under the constant bias voltage. The output signal of the sensor is usually a diffusion limit current determined by a diffusion barrier, and under a proper diffusion limit condition, the current output signal of the sensor and the concentration of the gas to be measured have a linear proportional relationship, and the linear proportional relationship generally spans over 3 orders of magnitude. Therefore, the amperometric solid electrolyte gas sensor has high sensitivity (measurement from ppb to ppm level) and good measurement accuracy. However, the sensor has an international bottleneck problem of poor stability in application, and one of the important reasons for causing the problem is as follows: when the sensor works at a high temperature for a long time, microstructure evolution can occur among heterogeneous materials of a three-phase interface, so that the ionic conductivity of the materials is degraded, a limit current signal is directly influenced, the output signal of the sensor is drifted, and the stability of the sensor is influenced. Therefore, the research on the ion conductivity change of the three-phase interface heterogeneous material in the temperature field is a key basic scientific problem for improving the stability and the reliability of the current type solid electrolyte gas sensor.
The gas phase reacts at the solid electrolyte phase/electrode phase interface to produce mobile ions in the solid electrolyte. The behavior of the mobile ions moving from one end of the solid electrolyte to the other is defined as the ionic conductivity of the solid electrolyte. In the current simulation method for describing the atomic-scale motion process, first-principle molecular dynamics (AIMD) is very consistent, but the difficulties of difficult modeling, single simulation material, difficult result analysis and processing and the like still exist.
Difficulty 1: the three-phase interface includes a yttria-stabilized solid electrolyte phase, an electrode phase and a gas phase. AIMD is based on a first principle and a density functional theory, so that the size of a microscopic three-phase interface model is at an atomic level, and the three-phase interface is difficult to be accurately embodied through the atomic-level model. Difficulty 2: in the ion conductivity solution of AIMD simulation, most of the solutions are simulated for a solid electrolyte, so the ion conductivity solution for a three-phase interface complex model of various atoms is also a difficult point. Difficulty 3: the result of the AIMD solution comprises a CONTCAR and an XDATCAR file, the CONTCAR comprises position information of each step length, and artificial analysis processing can be carried out. However, XDATCARR involves the calculation of average mean square displacement (average MSD), and the artificial analysis and the processing of effective data are difficult and serious.
Disclosure of Invention
Aiming at the problems or the defects, the invention provides a method for solving the ion conductivity of a three-phase interface (solid electrolyte phase/electrode phase/gas phase), which is suitable for solving the ion conductivity of the three-phase interface in a solid electrolyte gas sensor based on first-nature principle molecular dynamics, and aims to solve the problems that the current solid electrolyte ion conductivity modeling is difficult, the current solid electrolyte ion conductivity modeling cannot be suitable for the three-phase interface (the computing material is single), and the solution result analysis and processing are difficult.
A method for solving for three-phase interfacial ionic conductivity, comprising the steps of:
step 1: constructing a supercell model of a solid electrolyte phase and an electrode phase;
a cell model is constructed for the solid electrolyte phase (e.g., modeled using Materials Studio software), and then structural optimization is performed to place the constructed cell model in the energy minimum state, i.e., the steady state configuration of the solid electrolyte phase. And then amplifying the (110) surface of the steady-state configuration unit cell model of the established solid electrolyte phase to obtain a supercell model of the surface of the solid electrolyte phase (110).
And constructing a cell model for the electrode phase (for example, modeling by using Materials Studio software), taking the (111) surface of the cell model of the electrode phase, and amplifying the cell model to obtain a supercell model of the surface of the electrode phase (111).
Step 2: and (3) stacking the supercell model on the surface of the electrode phase (111) obtained in the step (1) on one side of the supercell model on the surface of the solid electrolyte phase (110), and taking the other side of the supercell model on the surface of the solid electrolyte phase (110) as a gas phase. And finishing the establishment of the three-phase interface model.
And step 3: and (3) carrying out structural optimization on the three-phase interface model obtained in the step (2) again by utilizing a computer program package (VASP) for atomic scale material simulation to obtain a stable-state configuration three-phase interface model for first-principle molecular dynamics AIMD. The target gas molecules are then placed at the gas phase location.
And 4, step 4: and performing first-principle molecular dynamics AIMD simulation by using VASP software to obtain a three-phase interface microstructure change result file and a data file for calculating the ionic conductivity, namely a CONTCAR file and an XDATCAR file.
The preset parameters of the VASP software for carrying out first-principle molecular dynamics AIMD simulation are as follows: the electronic optimization method ALGO is F, the initial wave function ICHARG is 2, the plane wave cutoff energy ENCUT is 350-400eV, the molecular dynamics calculation IBRION is 0, the calculation step NSW is 100-10000, the step length POTIM is 1-3, the energy convergence unit EDIFF is 1E-04eV and the force convergence unit EDIFFG is-1E-02 eV/A.
And 5: and 4, visually observing the atom structure information of each step by using the CONTCAR file obtained in the step 4.
And (4) importing the XDATCAR file obtained in the step (4) into a Pymatgen software package through a Python script, and respectively extracting and calculating data through the following formulas:
average MSD is the mean square displacement, ri(t) is the displacement of the ith moving ion at time t, where t is NSW, ri(t0) Is the displacement of the ith moving ion at time t ═ 0, and N is the number of moving ions.
DSFor self-diffusion coefficient, d is the diffusion dimension of the mobile ions in the solid electrolyte (generally, d is 3), and t is1Is the target gas diffusion time.
The gas phase reacts at the electrode phase position to generate mobile ions in the solid electrolyte, and the action of the mobile ions moving from one end of the solid electrolyte to the other end is defined as the ion conductivity of the solid electrolyte, so that the ion conductivity sigma of the three-phase interface has the following formula:
n is the material ion density of the solid electrolyte, e is the elementary charge, Z is the ionic valence state, kBIs the boltzmann constant and T is the temperature.
Further, the solid electrolyte phase is made of yttria-stabilized zirconia (YSZ), sodium ion conductor (NASICON), and sulfate (Na)2SO4,K2SO4) Or-00Aluminum oxide (` Harbin `)00-Al2O3)。
Further, the modeling of the step 1 adopts Materials Studio software.
Further, the method for solving the ionic conductivity of the three-phase interface is applied to solving the ionic conductivity of the three-phase interface in the current type solid electrolyte gas sensor.
The invention starts from the definition of the ion conductivity of the solid electrolyte, simulates the migration process of the moving ions on the basis of an atomic-scale model, and ensures that the microstructure evolution of a three-phase interface is truly visible. Firstly, a three-phase interface model is constructed, then the microstructure evolution of the three-phase interface is simulated by utilizing first-nature principle molecular dynamics AIMD, and the microstructure evolution is further evolved into an ion conductivity value by combining a computer language Python (powerful data processing language). The method not only truly reduces the microstructure of the three-phase interface of the solid electrolyte gas sensor, but also truly simulates the reaction process of gas molecules in the three-phase interface.
In conclusion, the method solves the problems that the current solid electrolyte ion conductivity modeling is difficult to solve, the method cannot be applied to a three-phase interface (the calculation material is single), and the analysis and the processing of the solution result are difficult.
Drawings
Fig. 1 is a three-phase boundary model of an embodiment.
FIG. 2 shows three-phase interface microstructures at steps 0, 200, 400, 600, 800 and 1000 after simulation of the AIMD of the examples.
FIG. 3 is a partial Python scripting language of an embodiment.
Fig. 4 shows the average mean square displacement (average MSD) results of the examples.
FIG. 5 is a flow chart of the solving process of the method of the present invention
Reference numerals: 1-zirconium atom, 2-yttrium atom, 3-platinum atom, 4-oxygen atom.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A method for solving the ionic conductivity of a three-phase interface is disclosed, in the embodiment, under a 623K temperature field, the solid electrolyte NO based on Yttria Stabilized Zirconia (YSZ) is solved through AIMDX/O2Ionic conductivity of the sensor three-phase interface.
The method comprises the following specific steps:
step 1: constructing a supercell model of a solid electrolyte phase (YSZ) and an electrode phase;
in Materials Studio software, the zirconium atom position was replaced with yttrium atoms so that the doping amount of yttrium oxide reached 8 mol%, and a zirconium oxide cell model was constructed. And then optimizing the structure to obtain a YSZ phase unit cell model with a steady-state configuration. Taking the (110) surface of the YSZ phase unit cell model, and amplifying the model surface to 3 x 3 to obtain the YSZ phase (110) surface supercell model.
And (3) constructing a platinum electrode phase cell model by using Materials Studio software, taking the (111) surface of the platinum phase cell model, and amplifying the model to 3 x 3 to obtain a supercell model of the platinum phase (111) surface.
Step 2: and (3) stacking the supercell model on the surface of the platinum phase (111) obtained in the step (1) on one side of the supercell model on the surface of the YSZ phase (110), and taking the other side as a gas phase. At this time, the three-phase interface model building is completed, see fig. 1. Wherein 1 is a zirconium atom, 2 is an yttrium atom, 3 is a platinum atom, and 4 is an oxygen atom.
And step 3: and (3) carrying out structural optimization on the three-phase interface model obtained in the step (2) again by using VASP to obtain a stable-state configuration three-phase interface model for carrying out first-principle molecular dynamics AIMD. Then placing an oxygen (O) gas at the gas phase position2) A molecule.
And 4, step 4: and performing first-principle molecular dynamics AIMD simulation by using VASP software to obtain a three-phase interface microstructure result file and a data file for solving the ionic conductivity, namely a CONTCAR file and an XDATCAR file.
The preset parameters of the VASP software for carrying out the first principle molecular dynamics simulation are as follows: the electronic optimization method ALGO is equal to F, the initial wave function ICHARG is equal to 2, the plane wave truncation energy ENCUT is equal to 400eV, the molecular dynamics calculation IBRION is equal to 0, the calculation step NSW is equal to 1000, the step length POTIM is equal to 2, the energy convergence unit EDIFF is equal to 1E-04eV, and the force convergence unit EDIFFG is equal to-1E-02 eV/A.
And 5: for the CONTCAR file in the VASP simulation result obtained in step 4, which contains the information of each atomic position structure in 1000 steps of molecular dynamics, microstructures in 0, 200, 400, 600, 800 and 1000 steps are respectively extracted, and the microstructure evolution of the three-phase interface in a 623K temperature field is visually observed, as shown in fig. 2 (the position of oxygen ions is in a circle).
Writing a Python script program (see partial program in figure 3), importing the XDATCAR file in the VASP simulation result obtained in the step 4 into a Pymatgen software package for calculation, and sequentially calculating to obtain average mean square displacement (average MSD) (see figure 4) and self-diffusion coefficient (D)S) And finally O2-Ionic conductivity sigma1223＝3.15S/cm。
The AIMD simulation solution is solved based on a 623K temperature fieldSolid electrolyte NO of YSZX/O2Ion conductivity of the sensor, for example. Changing the temperature of the temperature field to obtain ion conductivity change data under the influence of different temperature fields, which is the solid electrolyte NO based on YSZX/O2The research on the stability of the sensor provides a theoretical basis.
According to the embodiment, the migration process of the simulated moving ions is simulated on the basis of the atomic-scale model based on the definition of the ion conductivity of the solid electrolyte, so that the microstructure evolution of the three-phase interface is truly visible. Firstly, a three-phase interface model is constructed, then the microstructure evolution of the three-phase interface is simulated by utilizing first-nature principle molecular dynamics AIMD, and the microstructure evolution is further evolved into an ion conductivity value by combining a computer language Python (powerful data processing language). The method not only truly reduces the microstructure of the three-phase interface of the solid electrolyte gas sensor, but also truly simulates the reaction process of gas molecules in the three-phase interface. The method effectively solves the problems that the current solid electrolyte ion conductivity is difficult to model, cannot be applied to a three-phase interface (the calculation material is single), and the analysis and processing of the solution result are difficult.
The present invention has been described in detail and with reference to the following examples, which are intended to be illustrative only and are not limiting. The present invention is not limited to the above embodiments, and various changes may be made according to the purpose of the present invention without departing from the technical principles and inventive concept of the method for solving the ion conductivity of the three-phase interface of the amperometric solid electrolyte gas sensor by analog simulation in accordance with the first principle of the present invention.
Claims (6)
1. A method for solving the ionic conductivity of a three-phase interface is characterized by comprising the following steps:
step 1: constructing a supercell model of a solid electrolyte phase and an electrode phase;
constructing a cell model for the solid electrolyte phase, and then optimizing the structure to ensure that the constructed cell model is in the lowest energy state, namely the steady-state configuration cell model of the solid electrolyte phase; then amplifying the (110) surface of the steady-state configuration unit cell model of the established solid electrolyte phase to obtain a supercell model of the surface of the solid electrolyte phase (110);
constructing a cell model for the electrode phase, taking the (111) surface of the cell model of the electrode phase, and amplifying the cell model to obtain a supercell model of the surface of the electrode phase (111);
step 2: stacking the supercell model on the surface of the electrode phase (111) obtained in the step (1) on one side of the supercell model on the surface of the solid electrolyte phase (110), and taking the other side of the supercell model on the surface of the solid electrolyte phase (110) as a gas phase; at this point, the establishment of the three-phase interface model is completed;
and step 3: performing structural optimization on the three-phase interface model obtained in the step 2 again by using a computer program package VASP (virtual instance space phase) for atomic scale material simulation to obtain a stable-state configuration three-phase interface model for performing first-principle molecular dynamics AIMD (automated aided design); then placing target gas molecules at the gas phase position;
and 4, step 4: performing first-principle molecular dynamics AIMD simulation by using VASP software to obtain a three-phase interface microstructure change result file and a data file for calculating the ionic conductivity, namely a CONTCAR file and an XDATCA file;
and 5: the CONTCAR file obtained in the step 4 is visually used for observing the atomic structure information in each step;
and (4) importing the XDATCAR file obtained in the step (4) into a Pymatgen software package through a Python script, and respectively extracting and calculating data through the following formulas:
average MSD is the mean square displacement, ri(t) is the displacement of the ith moving ion at time t, where t is NSW, ri(t0) Is the displacement of the ith moving ion at time t ═ 0, and N is the number of moving ions;
DSd is the diffusion dimension of mobile ions in the solid electrolyte, t is the self-diffusion coefficient1Is the target gas ion diffusion time;
the gas phase reacts at the electrode phase position to generate mobile ions in the solid electrolyte, and the action of the mobile ions moving from one end of the solid electrolyte to the other end is defined as the ion conductivity of the solid electrolyte, so that the ion conductivity sigma of the three-phase interface has the following formula:
n is the material ion density of the solid electrolyte, e is the elementary charge, Z is the ionic valence state, kBIs the boltzmann constant and T is the temperature.
2. The method for solving for ionic conductivity of a three-phase interface as claimed in claim 1, wherein: the modeling of the step 1 adopts Materials Studio software.
3. The method for solving for ionic conductivity of a three-phase interface as claimed in claim 1, wherein: the solid electrolyte phase is prepared from Yttria Stabilized Zirconia (YSZ), sodium ion conductor (NASICON), and sulfate (Na)2SO4,K2SO4) Or beta '-alumina (beta' -Al)2O3)。
4. The method for solving for ionic conductivity of a three-phase interface as claimed in claim 1, wherein: the preset parameters of the VASP software for carrying out first-principle molecular dynamics AIMD simulation in the step 4 are as follows: the electronic optimization method ALGO is F, the initial wave function ICHARG is 2, the plane wave cutoff energy ENCUT is 350-400eV, the molecular dynamics calculation IBRION is 0, the calculation step NSW is 100-10000, the step length POTIM is 1-3, the energy convergence unit EDIFF is 1E-04eV and the force convergence unit EDIFFG is-1E-02 eV/A.
5. The method for solving for ionic conductivity of a three-phase interface as claimed in claim 1, wherein: the method is applied to solving the ionic conductivity of the three-phase interface in the current type solid electrolyte gas sensor.
6. The method for solving for ionic conductivity of a three-phase interface as claimed in claim 5, wherein: the solid electrolyte phase is Yttria Stabilized Zirconia (YSZ), the gas phase is oxygen, and the electrode phase is platinum.
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