Quantitative characterization method for extraction capability of transport layer of perovskite photovoltaic device

Abstract

The invention belongs to the field of perovskite photovoltaic devices, and provides a quantitative characterization method for the extraction capability of a transport layer of a perovskite photovoltaic device, which is used for accurately and quantitatively characterizing the influence of the transport layer on the transport and recombination behaviors of carriers in the perovskite photovoltaic device. The method comprises the following steps: firstly, proposing a double-group diffusion composite model to accurately reflect dynamic behaviors of carriers in the perovskite photovoltaic device; secondly, on the basis of the double-group diffusion recombination model, providing quantitative characterization parameters of the transport layer extraction capability: surface recombination velocity and diffusion coefficient; and finally, in combination with a transient fluorescence decay test, fitting quantitative characterization parameters of the transport layer extraction capability through fluorescence decay test curves of the characterization device and the control device, and quantitatively analyzing the carrier extraction capability of the transport layer in the perovskite photovoltaic device through a fitting result. In conclusion, the method not only provides a new theoretical tool for the research of the perovskite photovoltaic device, but also provides a feasible quantification method for the optimization of the device in practical application.

Description

Technical Field

[0001] The present invention belongs to the field of perovskite photovoltaic devices, and specifically provides a method for quantitatively characterizing the extraction capability of a transport layer (CTL) of a perovskite photovoltaic device based on transient fluorescence testing and a diffusion composite model. Background Art

[0002] Perovskite materials have excellent light absorption properties and long carrier diffusion length, which makes them have broad application prospects in solar cells. In recent years, perovskite photovoltaic devices have received extensive attention and research in the photovoltaic field. Perovskite photovoltaic devices have high photoelectric conversion efficiency and relatively simple and low-cost preparation process. However, although perovskite photovoltaic devices have made significant progress in efficiency, they still face challenges such as carrier dynamics, device stability and efficiency improvement. Among them, carrier dynamics behavior has an important influence on the performance of photovoltaic devices, especially in perovskite photovoltaic devices, the carrier transmission and recombination process directly affects the photoelectric conversion efficiency.

[0003] Perovskite photovoltaic devices are usually composed of multiple layers, including light absorption layer, transport layer and other structures. The transport layer plays a vital role in the carrier separation and extraction process. Therefore, accurately characterizing the extraction capacity of the transport layer is crucial to optimizing the performance of perovskite photovoltaic devices. Existing studies mainly use the traditional single-group diffusion recombination model to describe the carrier dynamics inside perovskite materials. This model mainly considers the diffusion and recombination process of carriers and is suitable for some simple thin film structures, but has certain limitations when dealing with complex photovoltaic device structures; for example, the single-group model cannot fully consider the different kinetic behaviors experienced by different carriers (such as electrons and holes) in photovoltaic devices, nor can it effectively characterize the effect of the built-in electric field in photovoltaic devices on carrier separation; therefore, the single-group diffusion recombination model may not provide sufficient accuracy when describing the complex carrier behavior in perovskite photovoltaic devices.

[0004] In addition, there are certain gaps in the prior art in the quantitative characterization of the extraction capacity of the transport layer. Although some studies have explored the recombination behavior of carriers through transient fluorescence testing methods, there is still a lack of effective analytical tools for quantitatively analyzing the specific impact of the transport layer on the carrier extraction capacity. The solution to this problem is of great theoretical and practical significance for further improving the efficiency and stability of perovskite photovoltaic devices. Therefore, the present invention proposes a quantitative characterization method for the extraction capacity of the transport layer of a perovskite photovoltaic device, which is used to accurately quantify the extraction capacity of the transport layer for carriers, 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 the present invention is to propose a quantitative characterization method for the extraction capacity of the transport layer of a perovskite photovoltaic device, so as to accurately quantify the influence of the transport layer on the transport and recombination behavior of carriers in the perovskite photovoltaic device; the present invention proposes a double-group diffusion recombination model, which extracts key parameters (such as surface recombination velocity and diffusion coefficient) as indicators by constructing and fitting transient fluorescence test curves to quantify the extraction capacity of CTL; the present invention is not only suitable for the study of the transport layer in perovskite photovoltaic devices, but can also be expanded to other photovoltaic devices with similar structures and characteristics.

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

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

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

[0009] Step 2. construct a characterization device and a control device, and perform transient fluorescence tests on the characterization device and the control device respectively to obtain a fluorescence decay test curve; wherein the only difference between the control device and the characterization device is that the characterization device includes a transmission layer to be characterized;

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

[0011] Step 4. Selecting 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, the first characterization parameter being the surface recombination velocity, and the second characterization parameter being the carrier diffusion coefficient;

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

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

[0014] Furthermore, in step 1, the equation expression of the dual-group diffusion composite model is:

[0015]

[0016] n(x,0)=N 0 e -αx

[0017] p(x,0)=N 0 e -αx

[0018] Where n(x, t) and p(x, t) represent the spatiotemporal distribution of electrons and holes, respectively, x represents the spatial variable, and t represents the temporal variable; D e With D h represent the electron diffusion coefficient and hole diffusion coefficient, respectively, k 1 represents the first-order compound probability (defect compound), k 2 is the second-order recombination probability (radiative recombination); N 0 represents the initial concentration of carriers, and α represents the absorption coefficient of the medium to monochromatic light;

[0019] The corresponding boundary conditions are:

[0020]

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

[0022] Furthermore, in step 3, the model parameter values ​​in the dual-group diffusion composite model specifically include: the electron diffusion coefficient D e and the hole diffusion coefficient D h , first-order compound probability k 1 and the second-order compound probability k 2 and the surface recombination velocity S 0 With S l .

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

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

[0025] The model parameter values ​​obtained from the initial data fitting are used as the initial values, the first characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The double-group diffusion composite 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:

[0027] The model parameters obtained from the initial data fitting are used as the initial values, the second characterization parameters are set as free parameters, and the other parameters are fixed as initial values. The double-group diffusion composite 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, jump to step 3 to adjust the fitting range of the model parameters and re-fit the initial data.

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

[0029] The present invention provides a quantitative characterization method for the extraction capacity of a transport layer of a perovskite photovoltaic device. Firstly, a double-group diffusion composite model is adopted. Compared with a traditional single-group model, the double-group diffusion composite model can respectively describe the diffusion process of electrons and holes, thereby more accurately reflecting the kinetic behavior of carriers in the perovskite photovoltaic device. Secondly, based on the double-group diffusion composite model, quantitative characterization parameters of the extraction capacity of the transport layer are proposed: surface recombination velocity and diffusion coefficient. The surface recombination velocity is used to characterize the recombination rate of carriers at the boundary of the perovskite layer, which directly affects the migration capacity of carriers from the perovskite absorption layer to the transport layer. The diffusion coefficient is used to characterize the diffusion capacity of carriers inside the perovskite material, which determines the movement speed of carriers inside the material. Finally, in combination with a transient fluorescence decay test, the quantitative characterization parameters of the extraction capacity of the transport layer are fitted through the fluorescence decay test curves of the characterization device and the control device, and the carrier extraction capacity of the transport layer in the perovskite photovoltaic device is quantitatively analyzed through the fitting results.

[0030] In summary, the present invention not only provides a new theoretical tool for the study of perovskite photovoltaic devices, but also provides a feasible quantitative method for the optimization of devices in practical applications. It is not only applicable to perovskite photovoltaic devices, but can also be extended to the study of other types of photovoltaic devices and related materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the structure of the perovskite photovoltaic device (characterization device) including the transport layer to be characterized in Example 1 of the present invention.

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

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

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

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

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

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

[0038] Figure 8 This is a comparison diagram of the transient fluorescence test curve and the fitting curve of the device after the third data fitting in Example 2 of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0040] Example 1

[0041] This embodiment provides a method for quantitatively characterizing the extraction capability of a transport layer of a perovskite photovoltaic device, comprising the following steps:

[0042] Step 1. Construct a dual-group diffusion composite 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 equation expression of the basic single-group diffusion composite model is:

[0044]

[0045] n(x,0)=N 0 e -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, k 1 represents the first-order compound probability (defect compound), k 2 is the second-order recombination probability (radiative recombination); N 0 represents the initial concentration of carriers, and α represents the absorption coefficient of the medium to monochromatic light;

[0047] Its boundary conditions are:

[0048]

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

[0050] The single-group diffusion composite model is very effective in describing the perovskite film with balanced electron and hole diffusion coefficients, because the electrons and holes behave in the same way; however, in the device with added transport layer structure (CTL), due to the selective extraction effect of the transport layer, the electrons and holes in the perovskite absorption layer usually have different diffusion coefficients, which makes the electron and hole behaviors alienated. The single-group diffusion composite model is no longer applicable, and it is necessary to consider the diffusion of electrons and holes independently. Therefore, the present invention constructs a double-group diffusion composite model, and its equation expression is:

[0051]

[0052] n(x,0)=N 0 e -αx

[0053] p(x,0)=N 0 e -αx

[0054] Where n(x, t) and p(x, t) represent the spatiotemporal distribution of electrons and holes, respectively, x represents the spatial variable, and t represents the temporal variable; D e With D h represent the electron diffusion coefficient and hole diffusion coefficient, respectively, k 1 represents the first-order compound probability (defect compound), k 2 is the second-order recombination probability (radiative recombination); N 0 represents the initial concentration of carriers, and α represents the absorption coefficient of the medium to monochromatic light;

[0055] The corresponding boundary conditions are:

[0056]

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

[0058] In the above two-group diffusion composite model, the spatial variable x and the time variable t are 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 e With D h , compound probability k 1 With k 2 , surface recombination velocity S 0 With S l are model parameters; among them, the surface recombination velocity (S 0 With S l ) reflects the “outflow” rate of carriers at the boundary of the perovskite layer, and the diffusion coefficient (D e With D h ) characterizes the transport capacity of carriers in the perovskite layer, and the upper limit of the surface recombination velocity is determined by the speed at which carriers move from the perovskite body to the boundary; therefore, based on the above-mentioned double-group diffusion recombination model, the present invention proposes that the extraction capacity of the transport layer can be measured by the surface recombination velocity (S 0 With S l ) and the diffusion coefficient (D e With D h ) for quantification;

[0059] Step 2. Prepare a characterization device and a control device, wherein the only difference between the control device and the characterization device is that the characterization device includes a transmission layer to be characterized (while the control device does not include the transmission 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 of all model parameters in the dual-group diffusion composite model, perform the initial data fitting of the dual-group diffusion composite model according to the fluorescence decay test curve of the control device, and obtain the model parameter values ​​in the dual-group diffusion composite model after successful fitting: carrier diffusion coefficient D e With D h , compound probability k 1 With k 2 , surface recombination velocity S 0 With S l ;

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

[0062] Step 5. Perform a second data fitting on the dual-group diffusion composite model based on the fluorescence decay test curve of the characterization 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 double-group diffusion composite 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:

[0065] The model parameters obtained from the initial data fitting are used as initial values, the second characterization parameters are set as free parameters, and the other parameters are fixed as initial values. The double-group diffusion composite 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, jump to step 3 to adjust the fitting range of the model parameters and re-fit the initial data;

[0066] Step 6. Quantitatively characterize the extraction capacity of the transport layer of the perovskite photovoltaic device according to the fitting result of the first characterization parameter or the fitting result of the second characterization parameter, calculate the difference between the fitting result of the characterization parameter and its corresponding initial value (the model parameter value obtained by the initial data fitting), and use the difference as the quantitative characterization value of the extraction capacity of the transport layer of the perovskite photovoltaic device; in other words, when the fitting result of the first characterization parameter is obtained by the second data fitting, the quantitative characterization value of the extraction capacity of the transport layer of the perovskite photovoltaic device is the surface recombination velocity (S 0 or S l ) and its corresponding initial value; when the third data fitting obtains the fitting result of the second characterization parameter, the quantitative characterization value of the extraction capacity of the transport layer of the perovskite photovoltaic device is the carrier diffusion coefficient (D e or D h ) and its corresponding initial value; thereby reflecting the optimization effect of the transport layer through the change of the surface recombination velocity or diffusion coefficient.

[0067] Specifically, in this embodiment, the perovskite photovoltaic device as a characterization device is as follows Figure 1As shown, it includes: a transparent conductive glass substrate, a perovskite absorption layer (PER), a hole transport layer (HTL) and an electrode; a perovskite photovoltaic device as a control device is shown Figure 2 As shown, it includes: a transparent conductive glass substrate, a perovskite absorber layer (PER) and an electrode. The only difference from the characterization device is that it does not contain a hole transport layer (HTL); in the perovskite photovoltaic device, the absorber layer is the perovskite material MAPbI 3 , Spiro is used for the hole transport layer, and ITO is used for the transparent conductive ratio substrate. The data fitting program includes the following modules: a particle swarm optimization algorithm (PSO) module, which optimizes the parameters according to the relative residual between the fluorescence decay measured curve and the fluorescence decay simulation curve; a partial differential equation calculation module, which calculates the spatiotemporal distribution of the carrier concentration based on the input parameters; a fluorescence curve simulation module, which generates a fitting curve and calculates the residual; a drawing module, which is used for data visualization and result display. After the initial data fitting in step 3, the transient fluorescence test curve of the control device is compared with the fitting curve. Figure 3 As shown in the figure, the initial data fitting is completed; in step 4, the surface composite velocity S is selected according to the type of the transmission layer to be characterized. l As the first characterization parameter, the hole diffusion coefficient D h As the second characterization parameter; after the second data fitting in step 5, the transient fluorescence test curve of the characterization device and the fitting curve are as follows Figure 4 As shown in the figure, the second data fitting failed. After the third data fitting, the transient fluorescence test curve of the device is characterized by the fitting curve as shown in Figure 5 As shown in the figure, the third data fitting is completed; it can be seen that the final quantitative characterization value of the extraction ability of the transport layer to be characterized is the hole diffusion coefficient D h The difference from its corresponding 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 absorption layer in this embodiment is FAPbI 3 ; After the initial data fitting in step 3, the transient fluorescence test curve of the control device is compared with the fitting curve as shown in Figure 6 As shown in the figure, the initial data fitting is completed; in step 4, the surface composite velocity S is selected according to the type of the transmission layer to be characterized. l As the first characterization parameter, the hole diffusion coefficient D h As the second characterization parameter; after the second data fitting in step 5, the transient fluorescence test curve of the characterization device and the fitting curve are as follows Figure 7 As shown in the figure, the second data fitting failed. After the third data fitting, the transient fluorescence test curve of the device is characterized by the fitting curve as shown in Figure 8As shown in the figure, the third data fitting is completed; it can be seen that the final quantitative characterization value of the extraction ability of the transport layer to be characterized is the hole diffusion coefficient D h The difference from its corresponding initial value.

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

[0071] The above description is only a specific implementation mode of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other alternative features that are equivalent or have similar purposes; all the disclosed features, or all the steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A quantitative characterization method for the extraction capacity of the transport layer of a perovskite photovoltaic device, characterized in that: The following steps are involved: Step 1. Construct a dual-group diffusion recombination model for carrier dynamics analysis in perovskite photovoltaic devices; Step 2. construct a characterization device and a control device, and perform transient fluorescence tests on the characterization device and the control device respectively to obtain a fluorescence decay test curve; wherein the only difference between the control device and the characterization device is that the characterization device includes a transmission layer to be characterized; Step 3. Set the fitting range of all model parameters in the dual-group diffusion composite model, perform initial data fitting on the dual-group diffusion composite model according to the fluorescence decay test curve of the control device, and obtain the model parameter values ​​in the dual-group diffusion composite model after successful fitting, including: carrier diffusion coefficient, recombination probability and surface recombination velocity; Step 4. Selecting 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, the first characterization parameter being the surface recombination velocity, and the second characterization parameter being the carrier diffusion coefficient; Step 5. Perform a second data fitting on the dual-group diffusion composite model according to the fluorescence decay test curve of the characterization device, and obtain the fitting result of the first characterization parameter after the fitting is successful; otherwise, perform a third data fitting, and obtain the fitting result of the second characterization parameter after the fitting is successful; Step 6. Quantitatively characterize the extraction capability of the transport layer of the perovskite photovoltaic device according to the fitting result of the first characterization parameter or the fitting result of the second characterization parameter, calculate the difference between the fitting result of the characterization parameter and its corresponding initial value, and use the difference as the quantitative characterization value of the extraction capability of the transport layer of the perovskite photovoltaic device.

2. The quantitative characterization method of the extraction capacity of the transport layer of the perovskite photovoltaic device according to claim 1, characterized in that: In step 1, the equation expression of the dual-group diffusion composite model is: n(x,0)=N0e -αx p(x,0)=N0e -αx Where n(x, t) and p(x, t) represent the temporal and spatial distribution of electrons and holes, respectively, x represents the spatial variable, and t represents the temporal variable; D e With D h Respectively represent the electron diffusion coefficient and the hole 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 carriers, and α represents the absorption coefficient; The corresponding boundary conditions are: Where l represents the thickness of the perovskite absorption layer, S0 represents the surface recombination velocity at x = 0, and S l represents the surface recombination velocity at x=l.

3. The quantitative characterization method of the extraction capacity of the transport layer of the perovskite photovoltaic device according to claim 1, characterized in that: In step 3, the model parameter values ​​in the dual-group diffusion composite model specifically include: electron diffusion coefficient D e and the hole diffusion coefficient D h , the first-order recombination probability k1 and the second-order recombination probability k2, and the surface recombination velocity S0 and S l .

4. The quantitative characterization method of the extraction capacity of the transport layer of the perovskite photovoltaic device according to claim 1, characterized in that: In step 4, the selection process of the characterization parameters is as follows: if the transmission layer to be characterized is at x=0, the surface recombination velocity S0 is selected as the first characterization parameter; if the transmission layer to be characterized is at x=1, the surface recombination velocity S l As the first characterization parameter; if the transport layer to be characterized is a hole transport layer (HTL), 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), the electron diffusion coefficient D is selected e as the second characterization parameter.

5. The quantitative characterization method of the extraction capacity of the transport layer of the perovskite photovoltaic device according to claim 1, characterized in that: In step 5, the specific process of the second data fitting is: The model parameter values ​​obtained from the initial data fitting are used as the initial values, the first characterization parameter is set as a free parameter, and other parameters are fixed as initial values. The double-group diffusion composite 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.

6. The quantitative characterization method for the extraction capacity of the transport layer of a perovskite photovoltaic device according to claim 1, characterized in that: In step 5, the specific process of the third data fitting is: The model parameters obtained from the initial data fitting are used as the initial values, the second characterization parameters are set as free parameters, and the other parameters are fixed as initial values. The double-group diffusion composite 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, jump to step 3 to adjust the fitting range of the model parameters and re-fit the initial data.

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