Simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization

Abstract

The present invention provides a simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization, and relates to the field of sensor simulation technology. The method includes microscale modeling, mesoscale modeling and macroscale modeling. In microscale modeling, under the joint action of adsorption energy and charge transfer amount, the target gas with the highest theoretical response of the gas-sensitive material is selected; in mesoscale modeling, the lattice Boltzmann method is used to simulate the diffusion of the gas to be measured; in macroscale modeling, a multi-layer structure sensor model is established, the substrate material is silicon, the supporting layer is SiO2, Si3N4, and the heating electrode and test electrode materials are platinum. The present invention performs simulation modeling and analysis in three dimensions: micro, meso, and macro, and provides a micro-meso-macro cross-scale, gas sensor gas-sensitive mechanism, gas mass transfer, and sensor thermal optimization full-process solution.

Description

Technical Field

[0001] The present invention relates to the field of sensor simulation technology, and in particular to a simulation method for cross-scale gas sensor sensitivity mechanism analysis and structure optimization. Background Art

[0002] Most of the analysis of the sensitivity mechanism of traditional gas sensor materials is based on macroscopic electron transfer theory, lacking quantitative calculation of the transfer relationship between electrons and atomic nuclei of materials at the microscale. At the mesoscopic scale, there are few simulation modeling theories for existing gas sensors. At the macroscopic scale of gas sensors, existing modeling methods mostly focus on the analysis of the thermal characteristics of sensors, lacking mechanical analysis of sensors. Traditional gas sensor designs lack cross-scale theoretical simulation modeling, and are mostly simulations in a certain dimension, with cumbersome calculations and poor interpretability. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a cross-scale simulation method for gas sensor sensitivity mechanism analysis and structural optimization in response to the above-mentioned deficiencies in the prior art. Simulation modeling and analysis are performed through three dimensions: microscopic, mesoscopic, and macroscopic, to provide a micro-mesoscopic-macroscopic cross-scale, full-process solution for gas sensor gas-sensitive mechanism, gas mass transfer, and sensor thermal optimization.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization, including microscale modeling, mesoscale modeling and macroscale modeling.

[0006] In micro-scale modeling, under the combined effect of adsorption energy and charge transfer amount, the target gas with the highest theoretical response of the gas-sensitive material is selected to explain the gas-sensitive mechanism of the material at the micro-scale.

[0007] In mesoscopic scale modeling, the lattice Boltzmann method is used to simulate the diffusion of the gas to be measured;

[0008] In macro-scale modeling, a multi-layer sensor model is established, including a substrate, a supporting layer, a heating electrode, an insulating layer, a test electrode and a gas-sensitive material layer. The substrate material is silicon, the supporting layer is SiO2, Si3N4, and the heating electrode and the test electrode are made of platinum.

[0009] The beneficial effect of adopting the above technical scheme is that the simulation method for cross-scale gas sensor sensitive mechanism analysis and structural optimization provided by the present invention performs simulation modeling analysis in three dimensions: microscopic, mesoscopic and macroscopic, and provides a microscopic-mesoscopic-macroscopic cross-scale, gas sensor gas-sensitive mechanism, gas mass transfer, and sensor thermal optimization full-process solution. At the microscopic scale, by analyzing the combined effects of adsorption energy and charge transfer amount, the target gas with the highest theoretical response of the gas-sensitive material is selected, the gas-sensitive mechanism can be explained, and the experimental efficiency is improved. At the mesoscopic scale, the lattice Boltzmann method is used to simulate the diffusion of the gas to be measured, the grain boundary barrier width is calculated, and the potential difference at both ends of the sensor sensitive layer is obtained, and then the change in material resistance after the gas-sensitive film material contacts the gas to be measured is simulated, thereby realizing the simulation of the gas-sensitive response of the sensor sensitive material to the gas to be measured. At the macroscopic scale, through the thermoelectric multi-physical field coupling analysis, the thermal uniformity and mechanical stability of the sensor are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the micro-meso-macro overall modeling method provided in the first embodiment of the present invention;

[0011] Figure 2 A flow chart of the microscopic modeling and simulation method provided in the first embodiment of the present invention;

[0012] Figure 3 A flow chart of the mesoscopic modeling and simulation method provided in the first embodiment of the present invention;

[0013] Figure 4 This is a flow chart of the macro modeling and simulation method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0014] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] The present invention provides a simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization. Through simulation modeling and analysis in three dimensions, namely microscopic, mesoscopic and macroscopic, a micro-mesoscopic-macroscopic cross-scale, full-process solution for gas sensor gas-sensitive mechanism, gas mass transfer and sensor thermal optimization is provided.

[0016] The first principle can solve the changes of element electrons by calculating the transfer relationship between material electrons and atomic nuclei through basic physical quantity constants. The lattice Boltzmann method is a mesoscopic simulation method that can accurately describe the interaction between different phases and is easy to implement in dealing with boundary conditions. It has broad prospects for multiphase flow in porous media. Finite element multi-physics field coupling simulation divides the solution area into grids, solves each grid unit, and obtains a suitable approximate solution.

[0017] The method of the present invention is described in detail below through specific examples.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the method of this embodiment is described as follows.

[0020] A simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization, including microscale modeling, mesoscale modeling and macroscale modeling.

[0021] Take zinc stannate gas sensitive material as an example. Figure 2 As shown, the method of micro-scale modeling is:

[0022] Step 1.1: Determine the type of gas-sensitive material. Based on the principle of lowest crystal plane energy and best crystal plane stability, select the (111) crystal plane of zinc stannate for calculation.

[0023] Step 1.2: Establish a ball-and-stick model of zinc stannate gas-sensitive material, conduct an oxygen molecule adsorption test on oxygen vacancies, and use the adsorption energy calculation formula to complete the calculation of the oxygen adsorption defect model.

[0024] The adsorption energy calculation formula is: ΔE ads =E gas+surface -E gas -E surface .

[0025] Among them, ΔE ads represents the adsorption energy, E gas+surface It represents the energy calculated after the gas is adsorbed on the surface of the material, E gas Represents the energy of a gas molecule, E surface represents the energy of the adsorption surface.

[0026] Step 1.3: If the calculated result shows that the adsorption energy of an oxygen molecule adsorbed on the set oxygen vacancy is negative, it means that the adsorption of oxygen on the surface of the material is an exothermic process, the system releases energy outward, and the adsorption process can occur autonomously. It also proves that the basic oxygen vacancy adsorption can be applied to the gas-sensitive material initially set during model calculation, then continue to step 1.4; if the calculated adsorption energy is a positive value, indicating that the adsorption process is an endothermic process, then it means that the adsorption is unstable and the material crystal plane selection is wrong, and it is necessary to return to step 1.1 and recalculate.

[0027] Step 1.4: Establish a ball-and-stick model of the gas molecules to be tested (ethanol, methanol, and n-propanol), and calculate the adsorption energy and charge transfer amount of the gas molecules respectively. The greater the adsorption energy, the better the adsorption effect of the gas molecules. The more the transferred charge, the more electrons will be attracted to transfer after the gas is adsorbed on the surface of the material.

[0028] Step 1.5: Under the combined effect of adsorption energy and charge transfer amount, select the target gas with the highest theoretical response of the gas-sensitive material, that is, select the material with large adsorption energy and large charge transfer amount. Through calculation, it can be seen that the response value of zinc stannate to n-propanol is higher than that of ethanol and methanol.

[0029] like Figure 3 As shown, the method of mesoscopic scale modeling is:

[0030] Step 2.1: Establish a multi-pore zinc stannate gas-sensitive film model.

[0031] Step 2.2: Use the lattice Boltzmann method to simulate the diffusion of the n-propanol gas to be measured.

[0032] Step 2.3: Obtain the potential difference across the sensitive layer of the sensor by calculating the width of the grain boundary barrier.

[0033] Step 2.4: Calculate the change in resistance of the zinc stannate gas-sensitive material after the zinc stannate gas-sensitive film material is exposed to n-propanol gas, and obtain the response curve of the sensor.

[0034] like Figure 4 As shown, the method of macro-scale modeling is:

[0035] Step 3.1: Establish a macroscopic simulation model of the sensor. The sensor model is a multi-layer structure, including a substrate, a support layer, a heating electrode, an insulating layer, a test electrode and a gas-sensitive material layer.

[0036] Step 3.2: Set the material properties of different layers of the sensor; the substrate material is silicon, with a thickness of 400 μm; the supporting layer is SiO2 and Si3N4; the heating electrode and test electrode materials are platinum, with a thickness of 0.1 μm.

[0037] Step 3.3: Add physics fields, including electric fields, thermal expansion, and solid mechanics.

[0038] Step 3.4: Set the heating voltage, resistivity, thermal convection coefficient, thermal conductivity coefficient and thermal radiation coefficient of the model heating electrode, and set the fixed constraints of the mechanical module.

[0039] Step 3.5: Divide the model physical field grid for calculation and obtain the sensor temperature distribution and stress distribution.

[0040] Step 3.6: By reducing the distance between the heating electrodes and increasing the area of ​​the heating electrodes and the test electrodes, the goal of uniform distribution of the sensor surface temperature can be achieved; by reducing the thickness of the heating electrodes and the test electrodes and increasing the thickness of the insulation layer, the goal of reducing the thermal stress of the sensor can be achieved.

[0041] Embodiment 2

[0042] The difference between this embodiment and the first embodiment is that in step 3.2, the substrate material is silicon with a thickness of 450 μm; the heating electrode and the test electrode material are platinum with a thickness of 0.1 μm.

[0043] The thickness of the substrate material and the heating electrode and test electrode materials will affect the temperature and thermal stress of the sensor surface. When the thickness of the sensor's test electrode and heating electrode remains unchanged, as the thickness of the silicon substrate increases, the sensor surface temperature decreases and the thermal stress on the sensor surface decreases.

[0044] Embodiment 3

[0045] The difference between this embodiment and the previous embodiment is that in step 3.2, the substrate material is silicon with a thickness of 450 μm; the heating electrode and the test electrode material are platinum with a thickness of 0.3 μm.

[0046] When the thickness of the silicon substrate of the sensor remains unchanged, as the thickness of the test electrode and the heating electrode increases, the surface temperature of the sensor rises and the thermal stress on the sensor surface increases.

[0047] Embodiment 4

[0048] The difference between this embodiment and the previous embodiment is that in step 3.2, the substrate material is silicon with a thickness of 500 μm; the heating electrode and the test electrode material are platinum with a thickness of 0.5 μm.

[0049] When the thickness of the silicon substrate of the sensor increases, and the thickness of the heating electrode and the test electrode increases, the sensor can achieve a balance between temperature and thermal stress, that is, while the sensor surface obtains a higher temperature, the thermal stress on the sensor surface is smaller.

[0050] Therefore, during design, the thickness of the silicon substrate and the thickness of the heating electrode and the test electrode should be adjusted to appropriate values ​​at the same time to obtain a more ideal sensor model.

[0051] Finally, 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization, characterized by: The approach includes microscale modeling, mesoscale modeling, and macroscale modeling; In micro-scale modeling, under the combined effect of adsorption energy and charge transfer amount, the target gas with the highest theoretical response of the gas-sensitive material is selected to achieve the explanation of the gas-sensitive mechanism of the material at the micro-scale; the specific method of the micro-scale modeling is: Step 1.1: Determine the type of gas-sensitive material and select the material crystal plane for calculation based on the principle of lowest crystal plane energy and best crystal plane stability; Step 1.2: Establish a ball-and-stick model of gas-sensitive materials, conduct oxygen molecule adsorption tests on oxygen vacancies, and use the adsorption energy calculation formula to complete the calculation of the oxygen adsorption defect model; Step 1.3: If the calculated result shows that the adsorption energy of an oxygen molecule adsorbed on the set oxygen vacancy is negative, it means that the adsorption of oxygen on the surface of the material is an exothermic process, the system releases energy outward, and the adsorption process can occur autonomously. It also proves that the basic oxygen vacancy adsorption can be applied to the gas-sensitive material initially set during the model calculation, then continue to step 1.4; if the calculated adsorption energy is a positive value, indicating that the adsorption process is an endothermic process, then it means that the adsorption is unstable and the material crystal plane selection is wrong, and it is necessary to return to step 1.1 and recalculate; Step 1.4: Establish a ball-and-stick model of the gas molecules to be measured and calculate the adsorption energy and charge transfer amount of the gas molecules respectively Step 1.5: Under the combined effect of adsorption energy and charge transfer amount, select the target gas with the highest theoretical response of the gas-sensitive material, that is, select the material with large adsorption energy and large charge transfer amount; In mesoscopic scale modeling, the lattice Boltzmann method is used to simulate the diffusion of the gas to be measured; the specific method of the mesoscopic scale modeling is: Step 2.1: Establish a multi-aperture gas-sensitive film model; Step 2.2: Use the lattice Boltzmann method to simulate the diffusion of the gas to be measured; Step 2.3: Obtain the potential difference across the sensitive layer of the sensor by calculating the width of the grain boundary barrier; Step 2.4: Calculate the resistance change of the gas-sensitive material after it contacts the gas to be tested, and obtain the response curve of the sensor; In the macro-scale modeling, a sensor model of a multi-layer structure is established, including a substrate, a support layer, a heating electrode, an insulating layer, a test electrode and a gas-sensitive material layer; the specific method of the macro-scale modeling is: Step 3.1: Establish a macroscopic simulation model of the sensor. The sensor model is a multi-layer structure, including a substrate, a support layer, a heating electrode, an insulating layer, a test electrode and a gas-sensitive material layer; Step 3.2: Set the material properties of different layers of the sensor; the substrate material is silicon, the support layer is SiO2, Si3N4; the heating electrode and test electrode materials are platinum; Step 3.3: Add physical fields, including electric field, thermal expansion and solid mechanics; Step 3.4: Set the heating voltage, resistivity, thermal convection coefficient, thermal conductivity coefficient and thermal radiation coefficient of the model heating electrode, and set the fixed constraints of the mechanical module; Step 3.5: Divide the model physical field grid for calculation and obtain the sensor temperature distribution and stress distribution; Step 3.6: By reducing the distance between the heating electrodes and increasing the area of ​​the heating electrodes and the test electrodes, the goal of uniform distribution of the sensor surface temperature can be achieved; by reducing the thickness of the heating electrodes and the test electrodes and increasing the thickness of the insulation layer, the goal of reducing the thermal stress of the sensor can be achieved.

2. The simulation method for cross-scale gas sensor sensitivity mechanism analysis and structural optimization according to claim 1 is characterized by: The adsorption energy calculation formula in step 1.2 is: Δ E ads = E gas+surface − E gas − E surface ; Among them, Δ E ads represents the adsorption energy, E gas+surfac It represents the energy calculated after the gas is adsorbed on the surface of the material. E gas represents the energy of a gas molecule, E surface represents the energy of the adsorption surface.

3. The simulation method for cross-scale gas sensor sensitivity mechanism analysis and structure optimization according to claim 1 is characterized by: In step 3.2, the thickness of the substrate material silicon is set to 400-500 μm.

4. The simulation method for cross-scale gas sensor sensitivity mechanism analysis and structure optimization according to claim 1 is characterized by: In the step 3.2, the thickness of the platinum material of the heating electrode and the test electrode is set to 0.1-0.5 μm.

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