A quantitative characterization method for the extraction capability of transport layers in perovskite photovoltaic devices

Abstract

This invention belongs to the field of perovskite photovoltaic devices and provides a quantitative characterization method for the extraction capability of the transport layer in perovskite photovoltaic devices, which can accurately quantify the influence of the transport layer on the transport and recombination behavior of charge carriers in perovskite photovoltaic devices. First, this invention proposes a two-group diffusion recombination model to accurately reflect the dynamic behavior of charge carriers in perovskite photovoltaic devices. Second, based on the two-group diffusion recombination model, it proposes quantitative characterization parameters for the transport layer extraction capability: surface recombination velocity and diffusion coefficient. Finally, combined with transient fluorescence decay tests, the quantitative characterization parameters for the transport layer extraction capability are fitted using the fluorescence decay test curves of the characterized device and the control device. The fitting results are used to quantitatively analyze the charge carrier extraction capability of the transport layer in perovskite photovoltaic devices. In summary, this invention not only provides a new theoretical tool for the research of perovskite photovoltaic devices but also provides a feasible quantitative method for device optimization in practical applications.

Description

Technical Field

[0001] This invention belongs to the field of perovskite photovoltaic devices, and specifically provides a quantitative characterization method for the extraction capability of the transport layer (CTL) of perovskite photovoltaic devices based on transient fluorescence testing and a diffusion recombination model. Background Technology

[0002] Perovskite materials possess excellent light absorption properties and long carrier diffusion lengths, making them promising candidates for applications in solar cells. In recent years, perovskite photovoltaic devices have received widespread attention and research in the photovoltaic field, exhibiting high photoelectric conversion efficiency and relatively simple and low-cost fabrication processes. However, despite significant progress in efficiency, perovskite photovoltaic devices still face challenges such as carrier dynamics, device stability, and efficiency improvement. Among these, carrier dynamics has a crucial impact on photovoltaic device performance, especially in perovskite photovoltaic devices where carrier transport and recombination processes directly affect photoelectric conversion efficiency.

[0003] Perovskite photovoltaic (PV) devices typically consist of multiple layers, including light-absorbing layers and transport layers. The transport layer plays a crucial role in carrier separation and extraction; therefore, accurately characterizing its extraction capability is essential for optimizing the performance of perovskite PV devices. Existing research mainly uses the traditional single-group diffusion-recombination model to describe the carrier dynamics within perovskite materials. This model primarily considers carrier diffusion and recombination processes and is suitable for some simple thin-film structures. However, it has limitations when dealing with complex PV device structures. For example, the single-group model cannot fully account for the different dynamic behaviors experienced by different carriers (such as electrons and holes) in the PV device, nor can it effectively characterize the influence of the built-in electric field on carrier separation. Therefore, the single-group diffusion-recombination model may not provide sufficient accuracy in describing the complex carrier behavior in perovskite PV devices.

[0004] Furthermore, existing technologies have certain gaps in the quantitative characterization of transport layer extraction capability. Although some studies have explored carrier recombination behavior through transient fluorescence testing, there is still a lack of effective analytical tools for quantitatively analyzing the specific impact of the transport layer on carrier extraction capability. Solving this problem is of great theoretical and practical significance for further improving the efficiency and stability of perovskite photovoltaic devices. Therefore, this invention proposes a quantitative characterization method for the transport layer extraction capability of perovskite photovoltaic devices to accurately quantify the carrier extraction capability of the transport layer, provide a scientific basis for the optimization of perovskite photovoltaic devices, and promote the further development of perovskite photovoltaic technology. Summary of the Invention

[0005] The purpose of this invention is to propose a quantitative characterization method for the transport layer extraction capability of perovskite photovoltaic devices, so as to accurately quantify the influence of the transport layer on the transport and recombination behavior of charge carriers in perovskite photovoltaic devices. This invention proposes a two-group diffusion recombination model, and extracts key parameters (such as surface recombination rate and diffusion coefficient) as indicators by constructing and fitting transient fluorescence test curves to quantify the extraction capability of CTL. This invention is not only applicable to the study of transport layers in perovskite photovoltaic devices, but can also be extended to other photovoltaic devices with similar structures and characteristics.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A quantitative characterization method for the extraction capability of the transport layer in a perovskite photovoltaic device, characterized by comprising the following steps:

[0008] Step 1. Construct a two-group diffusion recombination model for carrier dynamics analysis in perovskite photovoltaic devices;

[0009] Step 2. Construct the characterization device and the control device, and perform transient fluorescence tests on the characterization device and the control device respectively to obtain fluorescence decay test curves; the only difference between the control device and the characterization device is that the characterization device contains the transport layer to be characterized.

[0010] Step 3. Set the fitting range for all model parameters in the dual-group diffusion recombination model. Perform initial data fitting on the dual-group diffusion recombination model based on the fluorescence decay test curve of the control device. After successful fitting, obtain the model parameter values ​​in the dual-group diffusion recombination model, including: carrier diffusion coefficient, recombination probability and surface recombination velocity.

[0011] Step 4. Select the characterization parameters of the characterization device according to the type of transport layer to be characterized, including: a first characterization parameter and a second characterization parameter, wherein the first characterization parameter is the surface recombination velocity and the second characterization parameter is the carrier diffusion coefficient;

[0012] Step 5. Perform a second data fitting on the dual-group diffusion recombination model based on the fluorescence decay test curve of the characterization device. If the fitting is successful, obtain the fitting result of the first characterization parameter; otherwise, perform a third data fitting. If the fitting is successful, obtain the fitting result of the second characterization parameter.

[0013] Step 6. Quantitatively characterize the transport layer extraction capability of the perovskite photovoltaic device based on the fitting results of the first characterization parameter or the fitting results of the second characterization parameter, calculate the difference between the fitting results of the characterization parameter and its corresponding initial value, and use the difference as the quantitative characterization value of the transport layer extraction capability of the perovskite photovoltaic device.

[0014] Furthermore, in step 1, the equation expression for the two-group diffusion recombination model is:

[0015]

[0016] n(x,0)=N0e -αx

[0017] p(x,0)=N0e -αx

[0018] Where n(x,t) and p(x,t) represent the spatiotemporal distributions of electrons and holes, respectively, x represents the spatial variable, and t represents the time variable; D e With D h Let N and k represent the electron diffusion coefficient and hole diffusion coefficient, respectively; k1 represents the first-order recombination probability (defect recombination); k2 represents the second-order recombination probability (radiative recombination); N0 represents the initial carrier concentration; and α represents the absorption coefficient of the medium for monochromatic light.

[0019] The corresponding boundary conditions are:

[0020]

[0021] Where l represents the thickness of the perovskite absorber layer, S0 represents the surface recombination velocity at x = 0, and S l This represents the surface recombination velocity at x = l.

[0022] Furthermore, in step 3, the specific model parameter values ​​in the two-group diffusion recombination model include: the electron diffusion coefficient D. e With hole diffusion coefficient D h The first-order recombination probability k1 and the second-order recombination probability k2, as well as the surface recombination velocities S0 and S... l .

[0023] Furthermore, in step 4, the selection process for the characterization parameter is as follows: if the transport layer to be characterized is at x = 0, then the surface recombination velocity S0 is selected as the first characterization parameter; if the transport layer to be characterized is at x = 1, then the surface recombination velocity S... l The hole diffusion coefficient D is selected as the first characterization parameter; if the transport layer to be characterized is a hole transport layer (HTL), then the hole diffusion coefficient D is selected. h As the second characterization parameter, if the transport layer to be characterized is an electron transport layer (ETL), then the electron diffusion coefficient D is chosen. e As a second characterization parameter.

[0024] Furthermore, in step 5, the specific process of the second data fitting is as follows:

[0025] The model parameter values ​​obtained from the first data fitting are used as initial values. The first characterization parameter is set as a free parameter, and the other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the first characterization parameter is obtained; otherwise, the third data fitting is performed.

[0026] Furthermore, in step 5, the specific process of the third data fitting is as follows:

[0027] The model parameters obtained from the initial data fitting are used as initial values. The second characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the second characterization parameter is obtained. Otherwise, the process jumps to step 3 to adjust the fitting range of the model parameters and performs the initial data fitting again.

[0028] Based on the above technical solution, the beneficial effects of the present invention are as follows:

[0029] This invention provides a quantitative characterization method for the transport layer extraction capability of perovskite photovoltaic devices. First, a two-group diffusion-recombination model is employed. Compared to the traditional single-group model, the two-group diffusion-recombination model can separately describe the diffusion processes of electrons and holes, thus more accurately reflecting the dynamic behavior of charge carriers in perovskite photovoltaic devices. Second, based on the two-group diffusion-recombination model, quantitative characterization parameters for the transport layer extraction capability are proposed: surface recombination velocity and diffusion coefficient. The surface recombination velocity characterizes the recombination rate of charge carriers at the perovskite layer boundary, directly affecting the migration capability of charge carriers from the perovskite absorber layer to the transport layer. The diffusion coefficient characterizes the diffusion capability of charge carriers within the perovskite material, determining the velocity of charge carriers within the material. Finally, combined with transient fluorescence decay testing, the quantitative characterization parameters for the transport layer extraction capability are fitted using the fluorescence decay test curves of the characterized device and the control device. The fitting results are used to quantitatively analyze the charge carrier extraction capability of the transport layer in the perovskite photovoltaic device.

[0030] In summary, this invention not only provides new theoretical tools for the research of perovskite photovoltaic devices, but also provides feasible quantitative methods for the optimization of devices in practical applications. It is not only applicable to perovskite photovoltaic devices, but can also be extended to the research of other types of photovoltaic devices and related materials. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of a perovskite photovoltaic device (characterization device) containing a transport layer to be characterized in Embodiment 1 of the present invention.

[0032] Figure 2 This is a schematic diagram of the structure of a perovskite photovoltaic device (control device) in Embodiment 1 of the present invention that does not include the transport layer to be characterized.

[0033] Figure 3 This is a comparison chart of the transient fluorescence test curve of the control device and the fitted curve after the initial data fitting in Embodiment 1 of the present invention.

[0034] Figure 4 This is a comparison chart of the transient fluorescence test curve and the fitted curve of the device after the second data fitting in Embodiment 1 of the present invention.

[0035] Figure 5 This is a comparison chart of the transient fluorescence test curve and the fitted curve of the device after the third data fitting in Embodiment 1 of the present invention.

[0036] Figure 6 This is a comparison chart of the transient fluorescence test curve of the control device and the fitted curve after the initial data fitting in Embodiment 2 of the present invention.

[0037] Figure 7 This is a comparison chart of the transient fluorescence test curve and the fitted curve of the device after the second data fitting in Embodiment 2 of the present invention.

[0038] Figure 8 This is a comparison chart of the transient fluorescence test curve and the fitted curve of the device after the third data fitting in Embodiment 2 of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0040] Example 1

[0041] This embodiment provides a quantitative characterization method for the extraction capability of the transport layer in perovskite photovoltaic devices, including the following steps:

[0042] Step 1. Construct a two-group diffusion recombination model for carrier dynamics analysis in perovskite photovoltaic devices to describe the influence of the transport layer on carrier dynamics behavior;

[0043] In the prior art, the basic equation expression of the single-group diffusion recombination model is:

[0044]

[0045] n(x,0)=N0e -αx

[0046] Where n(x,t) represents the spatiotemporal distribution of electrons and holes, x represents the spatial variable, t represents the time variable; D represents the diffusion coefficient, k1 represents the first-order recombination probability (defect recombination), k2 represents the second-order recombination probability (radiative recombination); N0 represents the initial concentration of charge carriers, and α represents the absorption coefficient of the medium for monochromatic light.

[0047] Its boundary conditions are:

[0048]

[0049] Where l represents the thickness of the perovskite absorber layer, S0 represents the surface recombination velocity at x = 0, and S l This represents the surface recombination velocity at x = l;

[0050] The single-group diffusion recombination model is very effective in describing perovskite thin films with balanced electron and hole diffusion coefficients because electrons and holes behave identically. However, in devices with added transport layer structures (CTLs), due to the selective extraction effect of the transport layer and the fact that electrons and holes in the perovskite absorber layer typically have different diffusion coefficients, the electron and hole behaviors become dissimilar, rendering the single-group diffusion recombination model inapplicable. It is necessary to consider the diffusion of electrons and holes independently. Therefore, this invention constructs a two-group diffusion recombination model, whose equation is:

[0051]

[0052] n(x,0)=N0e -αx

[0053] p(x,0)=N0e -αx

[0054] Where n(x,t) and p(x,t) represent the spatiotemporal distributions of electrons and holes, respectively, x represents the spatial variable, and t represents the time variable; D e With D h Let N and k represent the electron diffusion coefficient and hole diffusion coefficient, respectively; k1 represents the first-order recombination probability (defect recombination); k2 represents the second-order recombination probability (radiative recombination); N0 represents the initial carrier concentration; and α represents the absorption coefficient of the medium for monochromatic light.

[0055] The corresponding boundary conditions are:

[0056]

[0057] Where l represents the thickness of the perovskite absorber layer, S0 represents the surface recombination velocity at x = 0, and S l This represents the surface recombination velocity at x = l;

[0058] In the above two-group diffusion recombination model, the spatial variable x and the time variable t are the independent variables, the spatiotemporal distribution of electrons n(x,t) and the spatiotemporal distribution of holes p(x,t) are the results to be solved, and the carrier diffusion coefficient D is... e With D h Recombination probabilities k1 and k2, surface recombination velocities S0 and S l These are the model parameters; where the surface recombination velocities (S0 and S) are... l The diffusion coefficient (D) reflects the rate at which charge carriers "escape" from the perovskite layer boundary. e With D h The surface recombination velocity (S0) characterizes the carrier transport capability within the perovskite layer. The upper limit of the surface recombination velocity is determined by the velocity of carriers moving from the perovskite mass to the boundary. Therefore, based on the aforementioned two-group diffuse recombination model, this invention proposes that the extraction capability of the transport layer can be determined by the surface recombination velocity (S0 and S2). l ) and diffusion coefficient (D e With D h Quantify;

[0059] Step 2. Prepare the characterization device and the control device. The only difference between the characterization device and the control device is that the characterization device contains the transport layer to be characterized (while the control device does not contain the transport layer to be characterized). Perform transient fluorescence tests on the characterization device and the control device respectively to obtain fluorescence decay test curves.

[0060] Step 3. Set the fitting range for all model parameters in the two-group diffusion recombination model. Perform initial data fitting on the two-group diffusion recombination model based on the fluorescence decay test curve of the control device. After successful fitting, obtain the model parameter values ​​in the two-group diffusion recombination model: carrier diffusion coefficient D. e With D h Recombination probabilities k1 and k2, surface recombination velocities S0 and S l ;

[0061] Step 4. Select the characterization parameters of the characterization device according to the type of the transport layer to be characterized, including: a first characterization parameter and a second characterization parameter; if the transport layer to be characterized is at x=0, then the surface recombination velocity S0 is selected as the first characterization parameter; if the transport layer to be characterized is at x=1, then the surface recombination velocity S... l The hole diffusion coefficient D is selected as the first characterization parameter; if the transport layer to be characterized is a hole transport layer (HTL), then the hole diffusion coefficient D is selected. h As the second characterization parameter, if the transport layer to be characterized is an electron transport layer (ETL), then the electron diffusion coefficient D is chosen. e As a second characterization parameter;

[0062] Step 5. Perform a second data fitting on the dual-group diffusion recombination model based on the fluorescence decay test curves of the characterized device. The specific process is as follows:

[0063] The model parameter values ​​obtained from the first data fitting are used as initial values. The first characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the first characterization parameter is obtained; otherwise, the third data fitting is performed.

[0064] The specific process of the third data fitting is as follows:

[0065] The model parameters obtained from the initial data fitting are used as initial values. The second characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the second characterization parameter is obtained. Otherwise, the process jumps to step 3 to adjust the fitting range of the model parameters and performs the initial data fitting again.

[0066] Step 6. Quantitatively characterize the transport layer extraction capability of the perovskite photovoltaic device based on the fitting results of the first or second characterization parameter. Calculate the difference between the fitting result of the characterization parameter and its corresponding initial value (the model parameter value obtained from the initial data fitting). Use this difference as the quantitative characterization value of the transport layer extraction capability of the perovskite photovoltaic device. In other words, when the fitting result of the first characterization parameter is obtained from the second data fitting, the quantitative characterization value of the transport layer extraction capability of the perovskite photovoltaic device is the surface recombination velocity (S0 or S). l The difference between the value and its corresponding initial value; when the fitting result of the second characterization parameter is obtained by the third data fitting, the quantitative characterization value of the transport layer extraction capability of the perovskite photovoltaic device is the carrier diffusion coefficient (D). e Or D h The difference between the initial value and the corresponding initial value; thus, the optimization effect of the transport layer can be reflected by the change in the surface recombination velocity or diffusion coefficient.

[0067] Specifically, in this embodiment, the perovskite photovoltaic device used as a characterization device is as follows: Figure 1 As shown, it includes: a transparent conductive glass substrate, a perovskite absorber layer (PER), a hole transport layer (HTL), and electrodes; a perovskite photovoltaic device as a control device is shown below. Figure 2As shown, it includes: a transparent conductive glass substrate, a perovskite absorber layer (PER), and electrodes. The only difference from the characterization device is that it does not include a hole transport layer (HTL). In the perovskite photovoltaic device, the absorber layer is made of the perovskite material MAPbI3, the hole transport layer uses Spiro, and the transparent conductive substrate is ITO. The data fitting program includes the following modules: a particle swarm optimization (PSO) module, which optimizes parameters based on the relative residual between the measured fluorescence decay curve and the simulated fluorescence decay curve; a partial differential equation calculation module, which calculates the spatiotemporal distribution of carrier concentration based on the input parameters; a fluorescence curve simulation module, which generates the fitting curve and calculates the residual; and a plotting module, which is used for data visualization and result display. The transient fluorescence test curve of the control device and the fitting curve after the initial data fitting in step 3 are shown below. Figure 3 As shown in the figure, the initial data fitting is complete; in step 4, the surface recombination velocity S is selected according to the type of transport layer to be characterized. l As the primary characterization parameter, the hole diffusion coefficient D h As the second characterization parameter; the transient fluorescence test curve of the characterization device after the second data fitting in step 5 is as follows: Figure 4 As shown in the figure, the second data fitting failed, and the transient fluorescence test curve of the characterization device after the third data fitting is as follows: Figure 5 As shown in the figure, the third data fitting is complete; therefore, the final quantitative characterization value of the extraction capability of the transport layer is the hole diffusion coefficient D. h The difference between its corresponding initial value and the initial value.

[0068] Example 2

[0069] This embodiment uses the same device structure and steps as Embodiment 1. The only difference is that the perovskite absorber layer in this embodiment is FAPbI3; the transient fluorescence test curve of the control device after the initial data fitting in step 3 is as follows: Figure 6 As shown in the figure, the initial data fitting is complete; in step 4, the surface recombination velocity S is selected according to the type of transport layer to be characterized. l As the primary characterization parameter, the hole diffusion coefficient D h As the second characterization parameter; the transient fluorescence test curve of the characterization device after the second data fitting in step 5 is as follows: Figure 7 As shown in the figure, the second data fitting failed, and the transient fluorescence test curve of the characterization device after the third data fitting is as follows: Figure 8 As shown in the figure, the third data fitting is complete; therefore, the final quantitative characterization value of the extraction capability of the transport layer is the hole diffusion coefficient D. h The difference between its corresponding initial value and the initial value.

[0070] The above embodiments represent only some embodiments of the present invention. In actual use, as long as the prerequisite requirements for the use of the present invention are met, the strategy of the present invention can be used to quantify CTL extraction capabilities.

[0071] The above description is merely a specific embodiment of the present invention. Any feature disclosed in this specification may be replaced by other equivalent or similar features unless otherwise specified. All disclosed features, or steps in all methods or processes, may be combined in any way except for mutually exclusive features and / or steps.

Claims

1. A quantitative characterization method for the extraction capability of the transport layer in a perovskite photovoltaic device, characterized in that, Includes the following steps: Step 1. Construct a two-group diffusion-recombination model for carrier dynamics analysis in perovskite photovoltaic devices; the equation expression of the two-group diffusion-recombination model is: n(x,0)=N0e -αx p(x,0)=N0e -αx Where n(x,t) and p(x,t) represent the spatiotemporal distributions of electrons and holes, respectively, x represents the spatial variable, and t represents the time variable; D e With D h Let N and k represent the electron diffusion coefficient and hole diffusion coefficient, respectively; k1 represents the first-order recombination probability (defect recombination); k2 represents the second-order recombination probability (radiative recombination); N0 represents the initial carrier concentration; and α represents the absorption coefficient. The corresponding boundary conditions are: Where l represents the thickness of the perovskite absorber layer, S0 represents the surface recombination velocity at x = 0, and S l This represents the surface recombination velocity at x = l; Step 2. Construct the characterization device and the control device, and perform transient fluorescence tests on the characterization device and the control device respectively to obtain fluorescence decay test curves; the only difference between the control device and the characterization device is that the characterization device contains the transport layer to be characterized. Step 3. Set the fitting range for all model parameters in the dual-group diffusion recombination model. Perform initial data fitting on the dual-group diffusion recombination model based on the fluorescence decay test curve of the control device. After successful fitting, obtain the model parameter values ​​in the dual-group diffusion recombination model, including: carrier diffusion coefficient, recombination probability and surface recombination velocity. Step 4. Select the characterization parameters of the characterization device according to the type of transport layer to be characterized, including: a first characterization parameter and a second characterization parameter, wherein the first characterization parameter is the surface recombination velocity and the second characterization parameter is the carrier diffusion coefficient; Step 5. Perform a second data fitting on the dual-group diffusion recombination model based on the fluorescence decay test curve of the characterization device. If the fitting is successful, obtain the fitting result of the first characterization parameter; otherwise, perform a third data fitting. If the fitting is successful, obtain the fitting result of the second characterization parameter. Step 6. Quantitatively characterize the transport layer extraction capability of the perovskite photovoltaic device based on the fitting results of the first characterization parameter or the fitting results of the second characterization parameter, calculate the difference between the fitting results of the characterization parameter and its corresponding initial value, and use the difference as the quantitative characterization value of the transport layer extraction capability of the perovskite photovoltaic device.

2. The quantitative characterization method for the extraction capability of the transport layer in perovskite photovoltaic devices according to claim 1, characterized in that, In step 3, the specific model parameter values ​​in the two-group diffusion recombination model include: the electron diffusion coefficient D. e With hole diffusion coefficient D h The first-order recombination probability k1 and the second-order recombination probability k2, as well as the surface recombination velocities S0 and S... l .

3. The quantitative characterization method for extracting the transport layer capability of perovskite photovoltaic devices according to claim 1, characterized in that, In step 4, the selection process for the characterization parameter is as follows: if the transport layer to be characterized is at x = 0, then the surface recombination velocity S0 is selected as the first characterization parameter; if the transport layer to be characterized is at x = 1, then the surface recombination velocity S... l The hole diffusion coefficient D is selected as the first characterization parameter; if the transport layer to be characterized is a hole transport layer (HTL), then the hole diffusion coefficient D is selected. h As the second characterization parameter, if the transport layer to be characterized is an electron transport layer (ETL), then the electron diffusion coefficient D is chosen. e As a second characterization parameter.

4. The quantitative characterization method for extracting the transport layer capability of perovskite photovoltaic devices according to claim 1, characterized in that, In step 5, the specific process of the second data fitting is as follows: The model parameter values ​​obtained from the first data fitting are used as initial values. The first characterization parameter is set as a free parameter, and the other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the first characterization parameter is obtained; otherwise, the third data fitting is performed.

5. The quantitative characterization method for the extraction capability of the transport layer in perovskite photovoltaic devices according to claim 1, characterized in that, In step 5, the specific process of the third data fitting is as follows: The model parameters obtained from the initial data fitting are used as initial values. The second characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The dual-group diffusion recombination model is fitted according to the fluorescence decay test curve of the characterization device. If the fitting is successful, the fitting result of the second characterization parameter is obtained. Otherwise, the process jumps to step 3 to adjust the fitting range of the model parameters and performs the initial data fitting again.

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