CN114609220B - Method for solving ionic conductivity of three-phase interface - Google Patents

Method for solving ionic conductivity of three-phase interface Download PDF

Info

Publication number
CN114609220B
CN114609220B CN202210166235.2A CN202210166235A CN114609220B CN 114609220 B CN114609220 B CN 114609220B CN 202210166235 A CN202210166235 A CN 202210166235A CN 114609220 B CN114609220 B CN 114609220B
Authority
CN
China
Prior art keywords
phase
solid electrolyte
model
phase interface
ion
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.)
Active
Application number
CN202210166235.2A
Other languages
Chinese (zh)
Other versions
CN114609220A (en
Inventor
杜晓松
廖睿
王洋
黄文君
龙吟
谢光忠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202210166235.2A priority Critical patent/CN114609220B/en
Publication of CN114609220A publication Critical patent/CN114609220A/en
Application granted granted Critical
Publication of CN114609220B publication Critical patent/CN114609220B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/403—Cells and electrode assemblies
    • G01N27/406—Cells and probes with solid electrolytes
    • G01N27/407—Cells and probes with solid electrolytes for investigating or analysing gases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Abstract

The invention belongs to the field of solution of ionic conductivity, in particular to a method for solving ionic conductivity of a three-phase interface, which is based on first principle molecular dynamics and can be applied to a solid electrolyte gas sensor. The invention is developed from the definition of the ion conductivity of the solid electrolyte, and on the basis of an atomic-level model, the migration process of the simulated moving ions is simulated, so that the microstructure evolution of the three-phase interface is truly visible. Firstly, constructing a three-phase interface model, then simulating microstructure evolution of the three-phase interface by using AIMD, and simultaneously combining a computer language Python to further evolve the microstructure evolution into an ion conductivity value. The invention truly restores the microstructure of the three-phase interface of the solid electrolyte gas sensor, truly simulates the reaction process of gas molecules at the three-phase interface, and solves the problems that the current solution of the solid electrolyte ion conductivity is difficult to model, can not be applied to the three-phase interface and is difficult to analyze and process the solution result.

Description

Method for solving ionic conductivity of three-phase interface
Technical Field
The invention belongs to the field of solution of ionic conductivity, in particular to a method for solving ionic conductivity of a three-phase interface (solid electrolyte phase/electrode phase/gas phase), which is based on first principle molecular dynamics and can be applied to a solid electrolyte gas sensor.
Background
The working principle of the current type solid electrolyte gas sensor is that a constant bias voltage is applied to the outside of the solid electrolyte, and the related information of the gas to be detected 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 typically the diffusion-limited current determined by the diffusion barrier, and under the proper diffusion-limited conditions, the current output signal of the sensor is in a linear proportional relationship with the concentration of the gas to be measured, which typically spans more than 3 orders of magnitude. Therefore, the amperometric solid electrolyte gas sensor has higher sensitivity (measurement from ppb to ppm level) and better measurement accuracy. However, the sensor has an international bottleneck problem of poor stability in application, and one of the important reasons for the problem is that: the sensor works under the high-temperature condition 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, limit current signals are directly influenced, the output signals of the sensor drift, and the stability of the sensor is influenced. Therefore, the research on the ion conductivity change of the material three-phase interface heterogeneous material under a temperature field is a key basic scientific problem for improving the stability and reliability of the current type solid electrolyte gas sensor.
The gas phase reacts at the solid electrolyte phase/electrode phase interface to generate mobile ions in the solid electrolyte. The behavior 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. In the current simulation method described for atomic-level motion process, first principle molecular dynamics (AIMD) is very consistent, but the problems 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 the first principle and density functional theory, so that the size of a microscopic three-phase interface model is atomic, and the three-phase interface is difficult to accurately embody through an atomic model. Difficulty 2: in AIMD simulation ion conductivity solution, most of the simulation is performed on a solid electrolyte, so that ion conductivity solution on a three-phase interface complex model of multiple kinds of atoms is also a great difficulty. Difficulty 3: the AIMD solving result comprises a CONTAR file and an XDATCAR file, wherein the CONTAR file comprises position information of each step length, and can be subjected to artificial analysis and processing. However, XDATCAR involves the calculation of average mean square displacement (average MSD), and it is difficult to process effective data by human analysis.
Disclosure of Invention
Aiming at the problems or the defects, the invention provides a method for solving the ionic conductivity of a three-phase interface (solid electrolyte phase/electrode phase/gas phase), which is suitable for solving the ionic conductivity of the three-phase interface in a solid electrolyte gas sensor based on first principle molecular dynamics, in order to solve the problems that the current solid electrolyte ionic conductivity is difficult to model, can not be suitable for the three-phase interface (single calculation material) and is difficult to analyze and process the solving result.
A method for solving ionic conductivity of a three-phase interface, comprising the steps of:
step 1: constructing a supercell model of a solid electrolyte phase and an electrode phase;
the unit cell model is built for the solid electrolyte phase (e.g., using Materials Studio software modeling), and then the structure is optimized to place the built unit cell model in the lowest energy state, i.e., the steady state configuration unit cell model 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 (110) surface of the solid electrolyte phase.
And constructing a unit cell model for the electrode phase (for example, modeling by using Materials Studio software), taking the (111) surface of the unit cell model of the electrode phase, and amplifying the (111) surface of the unit cell model of the electrode phase to obtain a supercell model of the (111) surface of the electrode phase.
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), wherein the other side of the supercell model on the surface of the solid electrolyte phase (110) is used as a gas phase. Thus, the three-phase interface model is built.
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 simulating atomic scale materials to obtain a steady-state configuration three-phase interface model for carrying out first-principle molecular dynamics AIMD. The target gas molecules are then placed at the gas phase location.
Step 4: and carrying out 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 ion conductivity, namely a CONTCAR file and an XDATCAR file.
The preset parameters of the VASP software for carrying out the first principle molecular dynamics AIMD simulation are as follows: the electron optimization method algo=f, the initial wave function icharg=2, the plane wave cutoff energy encut=350-400 eV, the molecular dynamics calculation ibrion=0, the calculation step nsw=100-10000, the step size pots=1-3, the energy convergence unit ediff=1e-04 eV and the force convergence unit ediffg= -1E-02eV/a.
Step 5: and (4) intuitively observing the atomic structure information of each step by using the CONTCAR file obtained in the step 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:
Figure BDA0003516105610000021
average MSD is the average mean square displacement, r i (t) is the displacement of the ith moving ion at the moment t, and t is taken as NSW, r i (t 0 ) Is the displacement of the ith mobile ion at time t=0, N is the number of mobile ions.
Figure BDA0003516105610000022
D S For self-diffusion coefficient, d is the diffusion dimension of the mobile ion in the solid electrolyte (generally taking d=3), t 1 Is the target gas diffusion time.
The gas phase reacts at the electrode phase position to generate moving ions in the solid electrolyte, and the behavior of the moving ions moving from one end of the solid electrolyte to the other end is defined as the ion conductivity of the solid electrolyte, so the ion conductivity sigma of the three-phase interface is:
Figure BDA0003516105610000031
n is the material ion density of the solid electrolyte, e is the meta-charge, Z is the ionic valence state, k B Is BoltzMannheim constant, T is temperature.
Further, the solid electrolyte phase is made of Yttria Stabilized Zirconia (YSZ), sodium ion conductor (NASICON), sulfate (Na) 2 SO 4 ,K 2 SO 4 ) Or- 00 -alumina (+) 00 -Al 2 O 3 )。
Further, the modeling in the step 1 adopts Materials Studio software.
Furthermore, 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 is developed from the definition of the ion conductivity of the solid electrolyte, and on the basis of an atomic-level model, the migration process of the simulated moving ions is simulated, 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-principle molecular dynamics AIMD, and meanwhile, the microstructure evolution is further evolved into an ionic conductivity value by combining a computer language Python (powerful data processing language). The method truly restores the microstructure of the three-phase interface of the solid electrolyte gas sensor, and truly simulates the reaction process of gas molecules at the three-phase interface.
In conclusion, the method solves the problems that the current solution of the solid electrolyte is difficult in ionic conductivity modeling, cannot be applied to a three-phase interface (single calculation material) and is difficult to analyze and process.
Drawings
Fig. 1 is a three-phase interface model of an embodiment.
FIG. 2 shows three-phase interfacial microstructures of steps 0, 200, 400, 600, 800 and 1000 after AIMD simulation in accordance with an embodiment.
FIG. 3 is a portion of a Python scripting language for embodiments.
Fig. 4 is an average mean square shift (average MSD) result of an embodiment.
FIG. 5 is a diagram showing a solution flow of the method of the present invention
Reference numerals: 1-zirconium atom, 2-yttrium atom, 3-platinum atom, 4-oxygen atom.
Detailed Description
The invention will now be described in further detail with reference to the drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In this embodiment, at 623K Wen Changxia, a solid electrolyte NO based on Yttria Stabilized Zirconia (YSZ) is solved by AIMD X /O 2 Ion 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, yttrium atoms were substituted for zirconium atom positions to achieve an yttrium oxide doping level of 8mol% and a zirconium oxide unit cell model was constructed. And then carrying out structural optimization to obtain a YSZ phase unit cell model with a steady-state configuration. Taking the (110) surface of the YSZ phase cell model, and amplifying the model surface to 3 x 3 to obtain the YSZ phase (110) surface supercell model.
The platinum electrode phase cell model is constructed by using Materials Studio software, the (111) surface of the platinum phase cell model is taken, and the model is amplified to 3 x 3, so that a supercell model of the platinum phase (111) surface is obtained.
Step 2: stacking the supercell model of the platinum phase (111) surface obtained in the step 1 on one side of the YSZ phase (110) surface supercell model, and taking the other side as a gas phase. At this time, the three-phase interface model is built up, 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.
Step 3: and (3) carrying out structural optimization on the three-phase interface model obtained in the step (2) again by using the VASP to obtain a steady-state configuration three-phase interface model for carrying out first-principle molecular dynamics AIMD. Then placing an oxygen (O) at the gas phase position 2 ) A molecule.
Step 4: and carrying out 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 first principle molecular dynamics simulation are as follows: the electronic optimization method algo=f, the initial wave function icharg=2, the plane wave cutoff energy encut=400 eV, the molecular dynamics calculation ibrion=0, the calculation step nsw=1000, the step size pots=2, the energy convergence unit ediff=1e-04 eV, the force convergence unit ediffg= -1E-02eV/a.
Step 5: for the CONTCAR file in the VASP simulation result obtained in step 4, which contains the information of the atomic position structure of each step 1000 of molecular dynamics, the microstructures of steps 0, 200, 400, 600, 800 and 1000 are respectively extracted, and the microstructure evolution of the three-phase interface at 623K Wen Changxia is intuitively observed, as shown in fig. 2 (the positions of oxygen ions in the circles).
Writing Python script program (part of program see figure 3), importing XDATCAR file in VASP simulation result obtained in step 4 into Pymatgen software package for calculation, sequentially calculating to obtain average mean square displacement (average MSD) (see figure 4), self-diffusion coefficient (D) S ) And finally O 2- Ion conductivity sigma 1223 ＝3.15S/cm。
In this example, the simulation of AIMD was used to solve the YSZ-based solid electrolyte NO at 623K Wen Changxia X /O 2 The ion conductivity of the sensor is taken as an example. Changing the temperature of the temperature field to obtain ion conductivity change data under the influence of different temperature fields, wherein the change data is YSZ-based solid electrolyte NO X /O 2 The sensor stability research provides a theoretical basis.
According to the embodiment, the method is developed from the definition of the ion conductivity of the solid electrolyte, and on the basis of an atomic model, the migration process of the motion ions is simulated, 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-principle molecular dynamics AIMD, and meanwhile, the microstructure evolution is further evolved into an ionic conductivity value by combining a computer language Python (powerful data processing language). The method truly restores the microstructure of the three-phase interface of the solid electrolyte gas sensor, and truly simulates the reaction process of gas molecules at the three-phase interface. The method effectively solves the problems that the current solution of the solid electrolyte is difficult in ionic conductivity modeling, cannot be applied to a three-phase interface (single calculation material) and is difficult in analysis and processing of the solution result.
The foregoing embodiments have been described in some detail by way of illustration of the invention, and are shown in the accompanying drawings. However, the invention is not limited to the above embodiment, and various changes can be made according to the inventive object of the present invention, so long as the technical principle and the inventive concept of the method for solving the ionic conductivity of the three-phase interface of the molecular dynamics calculation current type solid electrolyte gas sensor according to the first principle of the present invention are not deviated, and all the technical principles and the inventive concepts belong to the protection scope of the present invention.

Claims (3)

1. A method for solving ionic conductivity of a three-phase interface, comprising the steps of:
step 1: constructing a supercell model of a solid electrolyte phase and an electrode phase;
constructing a unit cell model for the solid electrolyte phase, and then optimizing the structure to enable the constructed unit cell model to be in the lowest energy state, namely a steady-state configuration unit 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 (110) surface of the solid electrolyte phase;
constructing a unit cell model for the electrode phase, taking the (111) surface of the unit cell model of the electrode phase, and amplifying the unit cell model to obtain a supercell model of the (111) surface of the electrode phase;
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), wherein the other side of the supercell model on the surface of the solid electrolyte phase (110) is used as a gas phase; thus, the three-phase interface model is built;
step 3: performing structural optimization on the three-phase interface model obtained in the step 2 again by utilizing a computer program package VASP of atomic scale material simulation to obtain a steady-state configuration three-phase interface model for performing first-principle molecular dynamics AIMD; then placing target gas molecules at the gas phase position;
step 4: carrying out 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 ion conductivity, namely a CONTCAR file and an XDATCAR file;
step 5: the CONTCAR file obtained in the step 4 is intuitively used for observing the atomic structure information of each step;
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:
Figure FDA0004180123140000011
average MSD is the average mean square displacement, r i (t+t 0 ) Is the displacement of the ith moving ion at the moment t, and the value of t is NSW and r i (t 0 ) Is the displacement of the ith moving ion at time t=0, N is the number of moving ions;
Figure FDA0004180123140000012
D S for self-diffusion coefficient, d is the diffusion dimension of the mobile ion in the solid electrolyte, t 1 Is the target gas ion diffusion time;
the gas phase reacts at the electrode phase position to generate moving ions in the solid electrolyte, and the behavior of the moving ions moving from one end of the solid electrolyte to the other end is defined as the ion conductivity of the solid electrolyte, so the ion conductivity sigma of the three-phase interface is:
Figure FDA0004180123140000013
n is the material ion density of the solid electrolyte, e is the meta-charge, Z is the ionic valence state, k B Is the boltzmann constant, T is the temperature;
the solid electrolyte phase is yttria stabilized zirconia YSZ, the gas phase is oxygen, and the electrode phase is platinum; the method is applied to the ion conductivity solving of the three-phase interface in the current type solid electrolyte gas sensor.
2. The method for solving for ionic conductivity of a three-phase interface according to claim 1, wherein: the modeling in the step 1 adopts Materials Studio software.
3. The method for solving for ionic conductivity of a three-phase interface according to claim 1, wherein:
the preset parameters of the VASP software in the step 4 for carrying out the first principle molecular dynamics AIMD simulation are as follows: the electronic optimization method algo=f, the initial wave function icharg=2, the plane wave cutoff energy encut=400 eV, the molecular dynamics calculation ibrion=0, the calculation step nsw=1000, the step size pots=2, the energy convergence unit ediff=1e-04 eV and the force convergence unit ediffg= -1E-02eV/a.
CN202210166235.2A 2022-02-23 2022-02-23 Method for solving ionic conductivity of three-phase interface Active CN114609220B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210166235.2A CN114609220B (en) 2022-02-23 2022-02-23 Method for solving ionic conductivity of three-phase interface

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210166235.2A CN114609220B (en) 2022-02-23 2022-02-23 Method for solving ionic conductivity of three-phase interface

Publications (2)

Publication Number Publication Date
CN114609220A CN114609220A (en) 2022-06-10
CN114609220B true CN114609220B (en) 2023-05-26

Family

ID=81858225

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210166235.2A Active CN114609220B (en) 2022-02-23 2022-02-23 Method for solving ionic conductivity of three-phase interface

Country Status (1)

Country Link
CN (1) CN114609220B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5905000A (en) * 1996-09-03 1999-05-18 Nanomaterials Research Corporation Nanostructured ion conducting solid electrolytes
JP2011232805A (en) * 2010-04-23 2011-11-17 Toyota Motor Corp Simulation method of ion conductivity
CN106383977A (en) * 2016-11-21 2017-02-08 中博源仪征新能源科技有限公司 Rectangular electrode/electrolyte interface based SOFC (solid oxide fuel cell) simulation method
KR20190030631A (en) * 2017-09-14 2019-03-22 주식회사 엘지화학 A method for predicting ion conductivity of an electrode for all solid type battery electrolyte and selecting the same
CN113420483A (en) * 2021-06-30 2021-09-21 哈尔滨工业大学（深圳） Method for establishing SOFC/SOEC electrode microstructure electrochemical model

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3574439B2 (en) * 2002-05-28 2004-10-06 コリア インスティテュート オブ サイエンス アンド テクノロジー Microstructured electrode with extended three-phase interface by porous ion-conductive ceria membrane coating and method of manufacturing the same
JP5371041B2 (en) * 2009-04-15 2013-12-18 国立大学法人埼玉大学 Solid oxide fuel cell
EP2680357A1 (en) * 2012-06-27 2014-01-01 Forschungszentrum Jülich GmbH Layered electrolyte with high ionic conductivity
WO2018081808A1 (en) * 2016-10-31 2018-05-03 The Regents Of The University Of California Lithium and sodium superionic conductors
CN109086564B (en) * 2018-06-21 2021-07-20 太原理工大学 Method for improving catalytic hydrogen evolution performance of molybdenum disulfide
CN109061304B (en) * 2018-07-09 2020-08-18 兰州空间技术物理研究所 Method for calculating conductivity variation of palladium in extremely dilute hydrogen environment
CN109542968B (en) * 2018-11-21 2022-02-08 成都材智科技有限公司 VASP software-based computing data processing method and device
CN111161808B (en) * 2020-01-15 2023-03-21 长安大学 Asphalt mixture water damage resistance evaluation method based on molecular dynamics

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5905000A (en) * 1996-09-03 1999-05-18 Nanomaterials Research Corporation Nanostructured ion conducting solid electrolytes
JP2011232805A (en) * 2010-04-23 2011-11-17 Toyota Motor Corp Simulation method of ion conductivity
CN106383977A (en) * 2016-11-21 2017-02-08 中博源仪征新能源科技有限公司 Rectangular electrode/electrolyte interface based SOFC (solid oxide fuel cell) simulation method
KR20190030631A (en) * 2017-09-14 2019-03-22 주식회사 엘지화학 A method for predicting ion conductivity of an electrode for all solid type battery electrolyte and selecting the same
CN113420483A (en) * 2021-06-30 2021-09-21 哈尔滨工业大学（深圳） Method for establishing SOFC/SOEC electrode microstructure electrochemical model

Also Published As

Publication number Publication date
CN114609220A (en) 2022-06-10

Similar Documents

Publication Publication Date Title
Doyle et al. Analysis of transference number measurements based on the potentiostatic polarization of solid polymer electrolytes
Kallay et al. Reversible charging of the ice–water interface: I. Measurement of the surface potential
Chan et al. Enhancing oxygen iron conductivity of 8YSZ electrolytes in SOFC by doping with Fe2O3
Seo et al. Mechanism of improved electrochemical performance of anode-supported solid oxide fuel cells by mesostructural modification of electrode–electrolyte interface
CN113420483B (en) Method for establishing electrochemical model of SOFC/SOEC electrode microstructure
CN114609220B (en) Method for solving ionic conductivity of three-phase interface
Fang et al. Cyclic voltammetric responses for inlaid microdisks with shields of thickness comparable to the electrode radius: a simulation of reversible electrode kinetics
Lee et al. Open data, models, and codes for vanadium redox batch cell systems: a systems approach using zero-dimensional models
CN102768529B (en) Volume flow correction method for mass flow controller
Hettiarachchi et al. The viability of pH measurements in supercritical aqueous systems
Raikova et al. Inductance correction in impedance studies of solid oxide fuel cells
Zhang et al. New approaches for the determination of electrochemical parameters in the model of proton-conducting solid oxide fuel cell
Kolev et al. Mathematical modelling of the chronoamperometric response of an array of rectangular microelectrodes
Chang et al. Measurement experiment and mathematical model of nitrate ion selective electrode
Brunello et al. Quantitative interpretation of impedance spectroscopy data on porous LSM electrodes using X-ray computed tomography and Bayesian model-based analysis
Hossain et al. Structural dependence of effective mass transport properties in lithium battery electrodes
CN117542431B (en) Electrochemical simulation parameter determination method and device for lithium ion battery electrolyte
Li et al. Fabrication of thin film potentiometric CO2 sensors on differentiate substrate surfaces and their characteristics
Andrei et al. Design of the Catalyst Layers in PEMFCs Using an Adjoint Sensitivity Analysis Approach
Gong Design of data fusion algorithm in electrochemical gas sensor
Häffelin et al. Three-dimensional performance simulation of SOFC anodes using FIB-tomography reconstructions
Vacek et al. Transient diffusion of oxygen through the three spherical layers of the Clark oxygen sensor
CN114018326B (en) Low-voltage transformer area environment multi-parameter detection method based on microsystem sensor array
Slepushkin et al. Devices for local electrochemical analysis
Tang et al. A micro room temperature oxygen sensor based on LaF3 solid electrolyte thin film

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