A novel method for optimizing dye-sensitized solar cells

Abstract

The application discloses a novel dye-sensitized solar cell optimization method, and relates to the technical field of semiconductor devices, and comprises the following steps: on the basis of a dye-sensitized solar cell model, first, the material of a hole transport layer of the dye-sensitized solar cell model is changed, a plurality of first cell models are obtained correspondingly, and performance simulation is performed; based on the performance simulation result, an intermediate cell model is selected from the plurality of first cell models; then, the electrode material of the intermediate cell model is changed, a plurality of second cell models are obtained correspondingly, and performance simulation is performed; based on the performance simulation result, a to-be-adjusted cell model is selected from the plurality of second cell models; finally, a plurality of adjustable parameters of the to-be-adjusted cell model are optimized, and a target cell model is obtained. Through optimization of the material of the hole transport layer, the material of the electrode and the numerical value of part of the adjustable parameters of the dye-sensitized solar cell model, the performance of a solar cell corresponding to the dye-sensitized solar cell model can be improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor device technology, and to, but is not limited to, a novel method for optimizing dye-sensitized solar cells. Background Technology

[0002] Solid-state dye-sensitized solar cells (SS-DSSCs) have become a research hotspot in recent years due to their advantages such as being environmentally friendly, low-cost, high-performance, simple in structure, and mature in fabrication process, and have effectively overcome the problems of easy volatilization and leakage of traditional liquid electrolytes.

[0003] In SS-DSSCs, the hole transport layer (HTL) and electrodes are key components that determine battery performance, carrier transport efficiency, and ultimately, cost. Traditionally, high-performance SS-DSSCs have relied on specific hole transport materials (such as certain small organic molecules or polymers) and electrode materials with high work functions (such as noble metals). The cost and environmental impact of these materials have, to some extent, limited the large-scale application of the batteries.

[0004] Therefore, while searching for new dyes, optimizing the material selection of the hole transport layer and electrodes to improve the overall performance of the battery, reduce production costs, and enhance its environmental friendliness has become one of the key paths to promote the development of solid-state dye-sensitized solar cell technology. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this application provides a novel optimization method for dye-sensitized solar cells. The method aims to improve the performance of the solar cell corresponding to the dye-sensitized solar cell model by optimizing the material of the hole transport layer, the material of the electrodes, and the values ​​of some adjustable parameters in the model.

[0006] The technical solution of this application embodiment is implemented as follows:

[0007] This application provides a novel method for optimizing dye-sensitized solar cells, the method comprising:

[0008] A dye-sensitized solar cell model to be optimized is obtained. Using solar cell simulation software, performance simulations are performed on multiple first-cell models to obtain performance simulation curves for each model. Each first-cell model is generated by concentrating a preset hole transport material into each material, which is then used as the material for the hole transport layer in the dye-sensitized solar cell model. Based on the analysis results of the performance simulation curves, an intermediate cell model is selected from the multiple first-cell models. Using solar cell simulation software, performance simulations are performed on multiple second-cell models to obtain performance simulation parameters for each model. Each second-cell model is generated by concentrating a preset hole transport material into each material, which is then used as the material for the hole transport layer in the dye-sensitized solar cell model. Each electrode material in the electrode material set corresponds to the electrode generated in the intermediate battery model. Based on the analysis results of performance simulation parameters, a battery model to be adjusted is selected from multiple second battery models. For each adjustable parameter in the battery model to be adjusted, while keeping other adjustable parameters unchanged, multiple values ​​within the possible range of the adjustable parameter are traversed to obtain the set of models to be simulated corresponding to the adjustable parameter. Using solar cell simulation software, the models to be simulated in the set of models to be simulated corresponding to each adjustable parameter are simulated to obtain a set of simulation results. Based on the set of simulation results, multiple adjustable parameters of the battery model to be adjusted are optimized to obtain the target battery model.

[0009] The beneficial effects of the technical solutions provided in this application include at least the following:

[0010] This application provides a novel optimization method for dye-sensitized solar cells. In the execution of this method, firstly, a dye-sensitized solar cell model to be optimized is obtained; secondly, solar cell simulation software is used to perform performance simulations on multiple first cell models, obtaining performance simulation curves for each first cell model; wherein each first cell model is generated by concentrating a preset hole transport material into each material, corresponding to the material used as the hole transport layer in the dye-sensitized solar cell model; based on the analysis results of the performance simulation curves, an intermediate cell model is selected from the multiple first cell models; then, solar cell simulation software is used to perform performance simulations on multiple second cell models, obtaining performance simulation parameters for each second cell model; wherein... Each of the multiple second battery models is generated by concentrating electrode materials into each electrode material, corresponding to the electrodes in the intermediate battery model. Based on the analysis results of performance simulation parameters, a battery model to be adjusted is selected from the multiple second battery models. Finally, for each adjustable parameter in the battery model to be adjusted, while keeping other adjustable parameters unchanged, multiple values ​​within the possible range of the adjustable parameter are traversed to obtain the set of models to be simulated corresponding to the adjustable parameter. Solar cell simulation software is used to simulate the models to be simulated in the set of models to be simulated corresponding to each adjustable parameter, and a set of simulation results is obtained. Based on the set of simulation results, the multiple adjustable parameters of the battery model to be adjusted are optimized to obtain the target battery model. Thus, firstly, by using a pre-defined hole transport material to concentrate each material as the material for the hole transport layer in the dye-sensitized solar cell model, multiple first cell models are generated. Performance simulations are then performed on these first cell models to obtain their performance simulation curves. The first cell model with the optimal performance simulation curve is selected as the intermediate cell model. Next, by using an electrode material to concentrate each electrode material as the electrode in the intermediate cell model, multiple second cell models are generated. Performance simulations are then performed on these second cell models to obtain their performance simulation parameters. The second cell model with the optimal performance simulation parameters is selected as the cell model to be adjusted. Finally, the values ​​of each adjustable parameter in the cell model to be adjusted are adjusted to obtain the corresponding model to be simulated. Based on the simulation data obtained from the model to be simulated, the multiple adjustable parameters of the cell model to be adjusted are optimized to obtain the target cell model. In this way, by optimizing the hole transport layer material, electrode material, and some adjustable parameter values ​​of the dye-sensitized solar cell model, a target battery model corresponding to improved battery performance can be obtained, thereby providing parameter support for further reducing battery costs and improving battery environmental performance while maintaining battery performance. Attached Figure Description

[0011] Figure 1 A flowchart illustrating a novel dye-sensitized solar cell optimization method provided in this application embodiment;

[0012] Figure 2 The figures provided in this application are schematic diagrams of the experimental and simulated JV curves of the initial battery model under 0.5 sunlight exposure and under 1 sunlight exposure, respectively. Part (a) of the figure is a schematic diagram of the experimental and simulated JV curves of the initial battery model under 0.5 sunlight exposure, and part (b) of the figure is a schematic diagram of the experimental and simulated JV curves of the initial battery model under 1 sunlight exposure.

[0013] Figure 3 This is the energy level arrangement diagram for the first battery model;

[0014] Figure 4 The figures provided in this application are schematic diagrams of the JV curves and QE curves of multiple first cell models generated by using different materials as the hole transport layer in the dye-sensitized solar cell model. Part (a) in the figure is a schematic diagram of the JV curve, and part (b) in the figure is a schematic diagram of the QE curve.

[0015] Figure 5 The figures provided in this application are schematic diagrams of the battery performance parameter variation trends and JV curves of multiple second battery models under different metal work functions. Part (a) in the figure is a schematic diagram of the battery performance parameter variation trends, and part (b) in the figure is a schematic diagram of the JV curves.

[0016] Figure 6 The figures provided in this application are schematic diagrams of the battery performance parameter variation trends and JV curves for each model in the simulation model set 1 with different dye absorption layer thicknesses. Part (a) in the figure is a schematic diagram of the battery performance parameter variation trends, and part (b) in the figure is a schematic diagram of the JV curves.

[0017] Figure 7 The figures show schematic diagrams of the battery performance parameter variation trends, JV curves, carrier lifetime variation trends, and recombination rate variation trends for each model in the simulation model set 2 under different dye absorption layer defect densities provided in this application embodiment. Part (a) of the figure shows the battery performance parameter variation trends, part (b) shows the JV curves, part (c) shows the carrier lifetime variation trends, and part (d) shows the recombination rate variation trends.

[0018] Figure 8The diagram shows the trend of battery performance parameters of the simulation model set 3 under different electron transport layer thicknesses and hole transport layer thicknesses provided in the embodiments of this application. Part (a) in the figure shows the trend of photoelectric conversion efficiency PCE, part (b) shows the trend of open circuit voltage Voc, part (c) shows the trend of short circuit current density Jsc, and part (d) shows the trend of battery fill factor FF.

[0019] Figure 9 The diagram shows the trend of battery performance parameters of the simulation model set 4 under different electron transport layer doping concentrations and hole transport layer doping concentrations provided in the embodiments of this application. Part (a) in the figure shows the trend of photoelectric conversion efficiency PCE, part (b) shows the trend of open circuit voltage Voc, part (c) shows the trend of short circuit current density Jsc, and part (d) shows the trend of battery fill factor FF.

[0020] Figure 10 The figures show schematic diagrams of the JV curves and QE curves of the battery model before and after optimization in the embodiments of this application. Part (a) of the figure shows the JV curve, and part (b) shows the QE curve. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0023] It should be noted that the terms "first, second, and third" used in the embodiments of this application are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0024] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0025] Example 1

[0026] Please see Figure 1 , Figure 1 This is a flowchart illustrating a novel dye-sensitized solar cell optimization method provided in an embodiment of this application. The novel dye-sensitized solar cell optimization method includes:

[0027] Step 101: Obtain the dye-sensitized solar cell model to be optimized.

[0028] In some embodiments, dye-sensitized solar cells (DSSCs) are “sandwich” structure solar cells that mimic plant photosynthesis. They utilize dye molecules adsorbed on the semiconductor surface to absorb sunlight, then inject electrons into the semiconductor to generate an electric current. The energy conversion process of a DSSC can be divided into four key steps: light absorption and excitation, electron injection, charge transport and collection, and charge recombination.

[0029] In some embodiments, step 101 above can be implemented by steps 1011 to 1014 (not shown in the figure):

[0030] Step 1011: Obtain an initial battery model from bottom to top, including a conductive glass substrate, an electron transport layer, a dye absorption layer, a hole transport layer, and electrodes.

[0031] In some embodiments, firstly, fluorine-doped tin oxide (FTO) is used as a conductive glass substrate, and a certain thickness of titanium dioxide (TiO2) is deposited on the conductive glass substrate by spray pyrolysis or spin coating to form an electron transport layer; secondly, a certain thickness of specific dye molecules (S5) is infiltrated into the electron transport layer by adsorption or spin coating combined with a photoanode to form a dye absorption layer; then, a certain thickness of Spiro-OMeTAD is deposited on the dye absorption layer by spin coating, sputtering, or electrochemical precipitation to form an HTL; finally, a certain thickness of metal (e.g., Ag) is deposited on the HTL as an electrode by physical vapor deposition, electrochemical precipitation, or solution deposition to form the initial battery model. Here, the materials and structural parameters of each layer of the initial battery model are shown in Table 1.

[0032] Table 1. Material and structural parameters of each layer in the initial battery model.

[0033] ;

[0034] Meanwhile, during the fabrication of the actual battery (corresponding to the initial battery model in this application), defects exist at the interface due to differences in the fabrication process. Therefore, an interface defect layer (IL) 1 is usually introduced between the dye absorption layer and the electron transport layer in the initial battery model, and an IL 2 is introduced between the dye absorption layer and the HTL, in order to better fit the initial battery model. The corresponding parameters are shown in Table 2.

[0035] Table 2. Parameter descriptions of IL1 and IL2

[0036] ;

[0037] Step 1012: Conduct battery performance tests on the initial battery model to obtain the performance test parameters of the initial battery model.

[0038] In some embodiments, under morning illumination conditions, the illuminance is 100 mW / cm². 2 Battery performance was tested on the physical model corresponding to the initial battery model at a temperature of 25℃. To further illustrate the accuracy of the initial battery model, battery performance was also tested on the physical model corresponding to the initial battery model under 0.5 days of sunlight. The performance parameters of the physical model corresponding to the initial battery model under 0.5 days of sunlight and the performance parameters of the physical model corresponding to the initial battery model under 1 day of sunlight were obtained.

[0039] Step 1013: Use solar cell simulation software to perform performance simulation on the initial battery model to obtain the initial simulation parameters of the initial battery model.

[0040] In some embodiments, a one-dimensional solar cell capacitor simulator (Solar Cell Capacitance Simulator-1D, SCAPS-1D) software is used as the simulation platform. This software has the characteristics of simple operation and clean interface, and has become an important software for simulating solar cells in recent years. This software couples the Poisson equation (i.e., Equation (1)), the continuity equation (i.e., Equations (2) and (5)), the carrier drift-diffusion equation (i.e., Equations (3) and (4)), the Shockley-Read-Hall recombination model (RSH) (i.e., Equation (6)), and the Newton-Raphson iterative solution, thereby enabling the exploration of various device parameters and the optimization of its function in a virtual environment, wherein:

[0041] Formula (1);

[0042] Formula (2);

[0043] Formula (3);

[0044] Formula (4);

[0045] Formula (5);

[0046] Formula (6);

[0047] in, Spatial location The electric potential; Electric field strength; It is the elementary charge; The dielectric constant of the material; Hole concentration; Electron concentration; Spatial location donor doping concentration; Spatial location The acceptor doping concentration; Spatial location The defect concentration; Electron current density; Carrier generation rate; Electron mobility; Hole mobility; The electron diffusion coefficient; is the diffusion coefficient of the hole; Hole current density; SRH composite rate; For the lifetime of electrons; The lifespan of a hole; The intrinsic carrier concentration of electrons; The intrinsic carrier concentration of holes; This refers to the effective electric field or potential energy term related to electron transport; This refers to the effective electric field or potential energy term related to hole transport.

[0048] In some embodiments, under 0.5 days of sunlight and under 1 day of sunlight, respectively, and with a light intensity of 100 mW / cm², the conditions are as follows. 2 At a temperature of 25℃, the initial battery model was simulated using SCAPS-1D to obtain the initial simulation parameters of the initial battery model under 0.5 days of sunlight and under 1 day of sunlight.

[0049] Step 1014: If the error between the performance experimental parameters and the initial simulation parameters is less than the preset error value, the initial cell model is determined as the dye-sensitized solar cell model to be optimized.

[0050] In some embodiments, the performance experimental parameters and initial simulation parameters of the initial battery model under 0.5 days of sunlight, and the performance experimental parameters and initial simulation parameters of the initial battery model under 1 day of sunlight are respectively fitted to obtain the experimental current density-voltage characteristic curve (JV curve) and the simulated JV curve of the initial battery model under 0.5 days of sunlight, as shown below. Figure 2 As shown in section (a); the experimental and simulated JV curves of the initial battery model under one sun exposure are as follows. Figure 2 As shown in section (b); where the vertical axis represents current density, with units of mA / cm². 2 The horizontal axis represents voltage (V), with the unit being V. Specifically, the red curve represents the simulated JV curve, and the gray curve represents the experimental JV curve.

[0051] In some embodiments, the experimental JV curve and simulated JV curve of the initial battery model under 0.5 days of sunlight are compared to obtain error 1. The experimental JV curve and simulated JV curve of the initial battery model under 1 day of sunlight are compared to obtain error 2. If both error 1 and error 2 are less than a preset error value, the initial battery model is determined as the dye-sensitized solar cell model to be optimized. Here, the preset error value can be determined according to actual needs or based on specific experimental data, and this application does not impose any limitations on it.

[0052] Step 102: Using solar cell simulation software, perform performance simulations on multiple first battery models to obtain the performance simulation curves for each first battery model.

[0053] In this context, each of the multiple first battery models is generated by concentrating a preset hole transport material into each material, which is then used as the material for the hole transport layer in the dye-sensitized solar cell model.

[0054] In some embodiments, the preset hole transport material set includes: PEDOT, P3HT, CuSCN, Spiro-OMeTAD, NiO, Cu2O, CNTS, and Zn3P2. PEDOT, P3HT, and Spiro-OMeTAD are organic materials; CuSCN, NiO, Cu2O, CNTS, and Zn3P2 are inorganic materials.

[0055] In some embodiments, each of the plurality of first cell models is generated by concentrating a preset hole transport material into each material, corresponding to the material used as the hole transport layer in the dye-sensitized solar cell model. The energy level arrangement diagram of the first cell model is shown below. Figure 3 As shown. Figure 3 The vertical axis represents energy (eV), with smaller values ​​(downwards) indicating lower energy levels and larger values ​​(upwards) indicating higher energy levels. The horizontal axis, from left to right, shows the materials of the conductive glass substrate (FTO), electron transport layer (TiO2), dye absorption layer (S5), preset hole transport material set for the hole transport layer, and electrode material (Ag) in the corresponding first battery model. The structural parameters of the HTL in multiple first battery models are shown in Table 3.

[0056] Table 3 Structural parameters of HTL in multiple first-cell models

[0057] ;

[0058] It should be noted that the parameters in the first row of Table 3 are multiple materials from the preset hole transport material set that can be used as the hole transport layer in the dye-sensitized solar cell model. Here, when Spiro-OMeTAD is used as the material for the hole transport layer in the dye-sensitized solar cell model, the corresponding structural parameters can be referred to in Table 2, and will not be repeated in Table 3.

[0059] In some embodiments, SCAPS-1D is used to perform performance simulations on multiple first battery models to obtain the performance simulation curve for each first battery model. Here, the eigenvalues ​​or smoothing trends on the performance simulation curves of any two first battery models may be different, and the performance simulation curve may include: a JV curve and a quantum efficiency (QE) curve.

[0060] Step 103: Based on the analysis results of the performance simulation curves, select an intermediate battery model from multiple first battery models.

[0061] In some embodiments, the performance simulation curves include the JV curve and the QE curve, such as Figure 4 As shown, the JV and QE curves of several first-cell models simulated using different materials as the hole transport layer in a dye-sensitized solar cell model are presented. Figure 4 Part (a) shows the JV curve, with the vertical axis representing current density and the horizontal axis representing voltage. Different colored curves represent the JV curves corresponding to different materials. Figure 4 Part (b) shows the QE curve, with the vertical axis representing quantum efficiency and the horizontal axis representing wavelength. Different colored curves represent the QE curves corresponding to different materials.

[0062] In some embodiments, before performing step 103 above, the following step A1 also needs to be performed:

[0063] Step A1: Analyze the JV curve and QE curve in the performance simulation curve of each first battery model to infer the valence band offset between the hole transport layer and the adjacent layer of each first battery model.

[0064] In some embodiments, the impact of various preset hole transport materials (including organic and inorganic materials) as HTL materials on battery performance is discussed. For example, the energy level matching relationship between the HTL and the dye absorber layer is crucial for hole transport and various aspects of battery performance. The performance differences of batteries using different materials as HTLs are related to the valence band offset (VBO) between the valence band of the dye absorber layer and the valence band of the HTL; whereby VBO is calculated by the following formula:

[0065] Formula (7);

[0066] in, VBO This is the price band offset. The valence band energy of the dye absorption layer; VBO represents the valence band energy of the HTL. Here, when VBO is negative, meaning the HTL valence band is higher than the dye absorption layer valence band, a cliff-structured interfacial barrier (i.e., a cliff barrier) will form at the interface between the dye absorption layer and the HTL. Conversely, a spike barrier will form. The carrier activation energies differ under different barrier structures, as shown in the following equation:

[0067] , VBO >0, spike barrier formula (8);

[0068] , VBO <0, Cliff barrier formula (9);

[0069] in, The activation energy of charge carriers in HTL; denoted as the band gap of the dye absorption layer.

[0070] Here, when a spike barrier is formed, it somewhat hinders hole transport, but the recombination activation energy of charge carriers is relatively large, which can reduce interfacial recombination; when a cliff barrier is formed, it promotes hole transport, but the recombination activation energy is smaller, and the recombination probability is greater. Different materials used as HTL materials result in different battery performance parameters and VBO, as shown in Table 4.

[0071] It should be noted that the first column in Table 4 represents each material in the preset hole transport material set; the second column represents the open circuit voltage (Voc); the third column represents the short-circuit current density (Jsc); the fourth column represents the fill factor (FF) of the cell; the fifth column represents the photovoltaic conversion efficiency (PCE); and the sixth column represents the valence band offset.

[0072] Table 4. Battery performance parameters and VBO when different materials are used as HTL.

[0073] ;

[0074] Correspondingly, step 103 above can be implemented through the following step 1031 (not shown in the figure):

[0075] Step 1031: Among multiple first battery models, select the first battery model with the lowest absolute value of valence band offset as the intermediate battery model.

[0076] In some embodiments, as shown in Table 4, it can be seen that when NiO is used as the material of HTL, VBO is -0.03, the absolute value of VBO is the smallest (i.e. the lowest), close to 0, the recombination activation energy of charge carriers is very low, which can reduce the recombination probability and ensure the effective transport of holes. Therefore, the first cell model corresponding to NiO is selected as the intermediate cell model. Correspondingly, the materials of each layer of the intermediate cell model from bottom to top can be: FTO, TiO2, S5, NiO, Ag.

[0077] Step 104: Use solar cell simulation software to perform performance simulation on multiple second battery models to obtain the performance simulation parameters of each second battery model.

[0078] In this context, each of the multiple second battery models is generated by concentrating the electrode materials into each electrode material, which corresponds to the electrode generated in the intermediate battery model.

[0079] In some embodiments, the electrode material set includes Zn, Fe, Cu, C, Ni, and Pt.

[0080] In some embodiments, the intermediate battery model initially uses Ag as the electrode, with a work function of 4.26 eV. To study the battery performance of the second battery models under different metals, this application selects several low-cost metals as research objects. The work functions corresponding to the electrode material set are: Zn (4.33 eV), Fe (4.5 eV), Cu (4.65 eV), C (5.0 eV), Ni (5.15 eV), and the noble metal Pt (5.65 eV). Each electrode material in the electrode material set is used as an electrode in the intermediate battery model to generate multiple second battery models for simulation. The obtained performance simulation parameters may include: the battery performance parameters and JV curves corresponding to each second battery model.

[0081] Step 105: Based on the analysis results of the performance simulation parameters, select the battery model to be adjusted from multiple second battery models.

[0082] In some embodiments, the performance simulation parameters include at least: battery performance parameters and JV curves; correspondingly, before performing step 105 above, the following steps B1 and B2 also need to be performed:

[0083] Step B1: Use the battery performance parameters from the performance simulation parameters of multiple second battery models as the vertical axis and the work function value of the electrode material set as the horizontal axis to construct a performance simulation change curve.

[0084] In some embodiments, the battery performance parameters include at least: photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor; correspondingly, step B1 above can be achieved by step B11:

[0085] Step B11: Using the photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor from the performance simulation parameters of multiple second battery models as the ordinate and the work function value of the electrode material set as the abscissa, construct the performance simulation change curves of photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor.

[0086] In some embodiments, such as Figure 5 As shown in section (a), from bottom to top, are: the simulated performance curves of photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and fill factor of the battery. The vertical axis of each curve represents PCE, Voc, Jsc, and FF from bottom to top, while the horizontal axis represents the work function. In other words, Figure 5 Section (a) shows a schematic diagram illustrating the changing trends of battery performance parameters in simulations of multiple second battery models under different metal work functions. Here, Figure 5 Part (b) also shows the JV curves of the second battery model when different metals (Zn, Fe, Cu, C, Ni and Pt) are used as electrodes, with the vertical axis representing current density and the horizontal axis representing voltage.

[0087] Step B2: Determine the target value of the work function when the performance simulation curve in the performance simulation change curve graph tends to be stable.

[0088] As described above, step B2 can be achieved through the following steps B21 to B25:

[0089] Step B21: Determine the first work function sub-target value corresponding to when the photoelectric conversion efficiency performance variation curve in the simulation curve of photoelectric conversion efficiency performance tends to be stable.

[0090] Step B22: Determine the second work function sub-objective value corresponding to the time when the open-circuit voltage change curve in the open-circuit voltage performance simulation change curve graph tends to be stable.

[0091] Step B23: Determine the third work function sub-target value when the short-circuit current density change curve in the short-circuit current density performance simulation change curve graph tends to be stable.

[0092] Step B24: Determine the fourth work function sub-objective value corresponding to when the battery's fill factor change curve in the battery's fill factor performance simulation change curve graph tends to be stable.

[0093] In some embodiments, from Figure 5 In Part (a), it can be concluded that: the first work function sub-target value corresponding to the photoelectric conversion efficiency change curve tending to be stable is 5.0 eV; the second work function sub-target value corresponding to the open-circuit voltage performance simulation change curve tending to be stable is 4.65 eV; the third work function sub-target value corresponding to the short-circuit current density performance simulation change curve tending to be stable is 5.0 eV; and the fourth work function sub-target value corresponding to the battery fill factor performance simulation change curve tending to be stable is 5.0 eV.

[0094] Step B25 involves a comprehensive analysis of the first, second, third, and fourth work function sub-objective values ​​to obtain the work function objective value.

[0095] In some embodiments, a comprehensive analysis is performed on the first work function sub-target value of 5.0 eV, the second work function sub-target value of 4.65 eV, the third work function sub-target value of 5.0 eV, and the fourth work function sub-target value of 5.0 eV, such as selecting the average value or removing outliers, to obtain the work function target value of 5.0 eV. Simultaneously, according to... Figure 5 As shown in section (a), it can be concluded that the performance parameters of various second battery models increase with the increase of metal work function, and the trend of change tends to be stable after reaching 5.0 eV.

[0096] Correspondingly, step 105 above can be achieved through steps 1051 and 1052 (not shown in the figure):

[0097] Step 1051: Select the second battery model corresponding to the objective value of the work function from multiple second battery models as the third battery model.

[0098] In some embodiments, from a plurality of second battery models, the second battery model corresponding to the work function target value of 5.0 eV (as mentioned above: the work function of C is 5.0 eV) is selected as the third battery model, that is, the materials of each layer corresponding to the third battery model from bottom to top can be: FTO, TiO2, S5, NiO, C.

[0099] Step 1052: If the error between the JV curve in the performance simulation parameters of the third battery model and the JV curve in the initial simulation parameters of the initial battery model is less than the first error value, the third battery model is taken as the battery model to be adjusted.

[0100] In some embodiments, according to Figure 5The JV curves of the multiple second battery models shown in section (b) show that the JV curves basically overlap after the metal work function reaches 5.0 eV, and the optimal performance is achieved. The third battery model is used as the battery model to be adjusted, that is, the materials of each layer of the battery model to be adjusted from bottom to top can be: FTO, TiO2, S5, NiO, C.

[0101] As those skilled in the art should know, in solar cells, increasing the work function has a greater impact on the open-circuit (FF) and Voc (Volume Oc), but a smaller impact on the power spectral density (Jsc). Therefore, the metal back electrode primarily affects the photoelectric conversion efficiency of the solar cell through FF and Voc. Specifically, increasing the work function optimizes the band structure between the back electrode (HTL) and the counter electrode. The band structure changes before and after the metal-semiconductor contact; with increasing work function, the band exhibits different bending trends, forming different contact types, as shown in the following equation:

[0102] Formula (10);

[0103] in, Schottky barrier at the HTL / metal back electrode interface; Let be the work function of the back contact metal; For HTL's electron affinity; This is the bandgap width of HTL. Let be the elementary charge. Here, when When this occurs, it forms a Schottky barrier, which hinders hole transport; as... As the interface barrier increases, the interface barrier gradually decreases. The formation of ohmic contacts allows for faster and more efficient carrier transport, reduces recombination and accumulation at the interface, and optimizes the band structure. Therefore, this application selects a metal with a high work function as the electrode, which is beneficial to improving the performance of the solar cell. Considering cost, carbon C, a more economical material, is chosen as the electrode.

[0104] Step 106: For each adjustable parameter in the battery model to be adjusted, while keeping other adjustable parameters unchanged, iterate through multiple values ​​within the possible range of values ​​of the adjustable parameter to obtain the simulation model set corresponding to the adjustable parameter.

[0105] In some embodiments, the materials of each layer of the battery model to be adjusted from bottom to top are: FTO, TiO2, S5, NiO, and C. Correspondingly, the adjustable parameters of the battery model to be adjusted include at least: the thickness of S5, the defect density of S5, the thickness of the electron transport layer (ETL), the thickness of the HTL, the doping concentration of the ETL, and the doping concentration of the HTL.

[0106] Following the description above, only the thickness of S5 in the battery model to be adjusted is adjusted, while the values ​​of other adjustable parameters remain unchanged, to obtain the subset 1 of the simulation model corresponding to different S5 thicknesses.

[0107] In some embodiments, S5 is a critical position in the battery model to be adjusted, where light absorption and carrier generation occur. It significantly impacts light absorption efficiency, battery internal resistance, carrier transport process, and recombination rate. Therefore, optimizing S5 plays a decisive role in the performance of the battery model to be adjusted. In this application, the thickness of S5 is adjusted to 0.1 μm, 0.27 μm, 0.6 μm, 1 μm, 1.5 μm, 2 μm, 2.5 μm, and 3 μm, resulting in simulation models with corresponding thicknesses, forming the subset 1 of simulation models.

[0108] Meanwhile, the defect density of S5 in the battery model to be adjusted can be adjusted while the values ​​of other adjustable parameters remain unchanged, thus obtaining the subset 2 of the simulation model corresponding to different S5 defect densities.

[0109] In some embodiments, defect density is a key indicator for evaluating the quality of the dye absorber layer, and it has a significant impact on cell efficiency, carrier lifetime, diffusion length, and recombination rate, as shown in the following formula:

[0110] Formula (11);

[0111] Formula (12);

[0112] Formula (13);

[0113] in, Carrier lifetime; This is the carrier capture section; Electron concentration; Hole concentration; The intrinsic carrier concentration of holes; Total defect density; The thermal rate of charge carriers; The intrinsic carrier concentration of electrons; The diffusion length of charge carriers; The mobility of charge carriers; It is the elementary charge; SRH composite rate; For the lifetime of electrons; This refers to the lifespan of a hole.

[0114] Based on the above formulas (11), (12) and (13), it can be found that the defect density is inversely proportional to the carrier lifetime and diffusion length, and directly proportional to the recombination rate.

[0115] In some embodiments, the defect density of S5 is adjusted to 1×102 11 cm -3 1×10 12 cm -3 1×10 13 cm -3 1×10 14 cm -3 1×10 15 cm -3 1×10 16 cm -3 and 1×10 17 cm -3 The corresponding defect density is obtained to form the simulation model, which is a subset 2 of the simulation models.

[0116] Furthermore, the thicknesses of the ETL and HTL in the battery model to be adjusted can be modified separately, while the values ​​of other adjustable parameters remain unchanged, resulting in a subset 3 of the simulation models corresponding to the adjusted ETL and HTL thicknesses. Specifically, the ETL thickness is adjusted from 0.5 μm, increasing in fixed steps of 0.5 μm to 3 μm, and the HTL thickness is adjusted from 0.1 μm, increasing in fixed steps of 0.1 μm to 0.8 μm, thus obtaining simulation models with different ETL thicknesses and different HTL thicknesses, forming the simulation model subset 3.

[0117] Furthermore, the doping concentrations of ETL and HTL in the battery model to be adjusted are adjusted respectively, while the values ​​of other adjustable parameters remain unchanged, to obtain the simulation models corresponding to different ETL doping concentrations and the simulation models corresponding to different HTL doping concentrations, thus forming a subset 4 of the simulation models.

[0118] In some embodiments, the doping concentration of the ETL is adjusted from 10 15 cm -3 Initially, the number of units was increased gradually, starting with orders of magnitude and increasing to 10. 21 cm -3 And adjust the doping concentration of HTL from 10 15 cm -3 Initially, the number of orders of magnitude was gradually increased to 10. 21 cm -3 We obtained simulation models corresponding to different ETL doping concentrations and different HTL doping concentrations, forming a subset 4 of simulation models.

[0119] In other words, the set of models to be simulated includes: subset 1, subset 2, subset 3, and subset 4.

[0120] Step 107: Using solar cell simulation software, simulate the model to be simulated in the model set corresponding to each of the multiple adjustable parameters to obtain the simulation result set.

[0121] In some embodiments, SCAPS-1D can be used to simulate each model in the subset 1 of simulation models, resulting in the simulation result subset 1, such as... Figure 6 As shown. This application embodiment presents the battery performance parameter variation trends and JV curves for each simulation model in subset 1 under different dye absorption layer thicknesses. Among them, Figure 6 Part (a) is a schematic diagram representing the changing trend of battery performance parameters. The vertical axis from bottom to top represents PCE, Voc, Jsc and FF, and the horizontal axis represents the thickness of S5. Figure 6 Part (b) is the JV curve, with the vertical axis representing current density and the horizontal axis representing voltage.

[0122] In some embodiments, SCAPS-1D is used to simulate each model in the subset 2 of simulation models, resulting in a subset 2 of simulation results. Figure 7 As shown, the battery performance parameters, JV curves, carrier lifetime, and recombination rate of each simulation model in subset 2 of the simulation models under different dye absorption layer defect densities provided in this application embodiment exhibit trends. Among them, Figure 7 Part (a) is a schematic diagram of the changing trends of battery performance parameters. The vertical axis from bottom to top represents PCE, Voc, Jsc and FF, and the horizontal axis represents the defect density of S5. Figure 7 Part (b) is the JV curve, with the vertical axis representing current density and the horizontal axis representing voltage; Figure 7 Part (c) is a schematic diagram illustrating the trend of carrier lifetime variation; in which, Figure 7 In section (c), the vertical axis on one side represents carrier lifetime, the vertical axis on the other side represents diffusion length, and the horizontal axis represents defect density at S5; and Figure 7 The middle (d) section shows the trend of the recombination rate, where the vertical axis represents the recombination rate at different locations in the battery, and the horizontal axis represents the location.

[0123] It should be noted that, Figure 7 The numbers 1E10, 1E11, 1E12, 1E13, 1E14, 1E15, 1E16, and 1E17 shown represent different defect density orders (e.g., 1E17 corresponds to 1×10⁻⁶). 17 cm -3 ).

[0124] In some embodiments, SCAPS-1D is used to simulate each model in the subset 3 of simulation models, resulting in a subset 3 of simulation results, such as... Figure 8 As shown in the figure. This application embodiment illustrates the variation trends of battery performance parameters in subset 3 of the simulation model under different electron transport layer thicknesses and hole transport layer thicknesses. Among them, Figure 8 Part (a) is a schematic diagram showing the PCE variation trend, that is, the PCE of the simulation model corresponding to TiO2 at different thicknesses when TiO2 is used as the ETL material, and the PCE of the simulation model corresponding to NiO at different thicknesses when NiO is used as the HTL material; correspondingly, Figure 8 Part (b) shows a schematic diagram illustrating the trend of Voc changes; Figure 8 Part (c) is a schematic diagram illustrating the changing trend of Jsc; Figure 8 The middle (d) section is a schematic diagram of the changing trend of FF.

[0125] In some embodiments, SCAPS-1D is used to simulate each model in the subset 4 of simulation models, resulting in a subset 4 of simulation results, such as... Figure 9 As shown in the figure. This application embodiment presents the battery performance parameter variation trends of subset 4 of the simulation model under different electron transport layer doping concentrations and hole transport layer doping concentrations. Among them, Figure 9 Part (a) is a schematic diagram showing the PCE variation trend, that is, the PCE of the simulation model corresponding to TiO2 with different doping concentrations when TiO2 is used as the ETL material, and the PCE of the simulation model corresponding to NiO with different doping concentrations when NiO is used as the HTL material; correspondingly, Figure 9 Part (b) shows a schematic diagram illustrating the trend of Voc changes; Figure 9 Part (c) is a schematic diagram illustrating the changing trend of Jsc; Figure 9 The middle (d) section is a schematic diagram of the changing trend of FF.

[0126] Here, the simulation result set includes: simulation result subset 1, simulation result subset 2, simulation result subset 3, and simulation result subset 4.

[0127] Step 108: Based on the simulation result set, optimize multiple adjustable parameters of the battery model to be adjusted to obtain the target battery model.

[0128] In some embodiments, according to the above Figure 6As shown, it can be concluded that with the increase of the dye absorption layer thickness, PCE increases from the initial 7.54% to a maximum of 13.99% at 1.5 μm, and then decreases to 13.66% at 3 μm, exhibiting a parabolic trend of first increasing and then decreasing. Meanwhile, Voc and FF decrease by approximately 0.17V and 20% respectively compared to the initial values, while Jsc shows an increasing trend, which slows down after the thickness reaches 2 μm. This is because organic S5 has a high absorption coefficient (…). According to Beer-Lambert's law, as the thickness of S5 increases, the absorbance also increases, leading to the absorption of more photons and causing Jsc to increase from 7.41 mA / cm². 2 Increased to 20.77 mA / cm 2 When the thickness of S5 is thinner, the carrier transport path is shorter, the recombination probability is lower, and the internal resistance is smaller, resulting in higher initial Voc and FF values. However, as the thickness of S5 increases, the battery's internal resistance and recombination probability also increase, correspondingly reducing FF and Voc. PCE is affected by all three factors; when the performance improvement obtained from the effective photon absorption site is less than the adverse effects of recombination, the battery performance begins to decline. Considering all these factors, the optimal thickness of S5 is selected as 1.5 μm.

[0129] At the same time, according to the above Figure 7 As shown, FF, Jsc, Voc, and PCE all decrease with increasing defect density in S5. The defect density is 1×10⁻⁶. 11 cm -3 At that time, the PCE was approximately 12.16%, while the defect density reached 1×10⁻⁶. 17 cm -3 At that time, efficiency dropped to 8.87%, and FF, Jsc, and Voc decreased from 78.10% to 66.00%, 13.01 mA / cm², respectively. 2 Up to 12.60 mA / cm 2 The voltage ranged from 1.197V to 1.068V. The carrier diffusion length decreased from 50.8μm to 0.1608μm, which is less than the thickness of S5, making it difficult for the electrode to effectively collect carriers. Based on the above... Figure 7 As shown in section (d), it can be noted that as the defect density of S5 increases, the recombination rate of charge carriers inside the solar cell increases sharply, which also verifies the results of the above analysis.

[0130] Simulations revealed that reducing the defect density of S5 is an effective way to improve battery performance. However, due to limitations in manufacturing processes, it is difficult to achieve excessively low defect densities. Therefore, the defect density of S5 can be set to 1×10⁻⁶. 13 cm -3 .

[0131] In some embodiments, according to the above Figure 8 As shown, when the HTL thickness is fixed at 0.4 μm, increasing the ETL thickness from 0.5 μm to 3 μm reduces PCE and Jsc, decreasing from 15.89% to 15.78% and 19.15 mA / cm, respectively. 2 It decreased to 19.02 mA / cm 2 The FF and Voc remained essentially constant at 73.8% and 1.124V, respectively. When the ETL thickness was fixed, changes in the HTL thickness had virtually no impact on battery performance. This indicates that changes in the HTL thickness do not affect its energy level matching and interface properties with the S5 and counter electrodes. Furthermore, NiO material itself has high hole mobility, so even with increased thickness, its resistance change is minimal. Since the HTL is located on the back side of the photosensitive layer, it absorbs less visible light, thus having little impact on battery performance. Simultaneously, increasing the HTL thickness leads to the absorption of some visible light, reducing the number of photons reaching the photosensitive layer. The electron transport path lengthens, increasing the probability of electron recombination, thereby reducing Jsc and PCE, while energy level matching remains unaffected by thickness, and Voc and FF remain essentially unchanged. Considering all factors, the optimal thickness for the ETL is 1 μm, and the optimal thickness for the HTL is 0.4 μm.

[0132] In some embodiments, according to the above Figure 9 As shown, with the increase of doping concentration, the performance parameters of the battery were improved to varying degrees. PCE and FF increased from the initial 13.83% to 16.70% and 65.00% to 77.54%, respectively; Jsc increased from 19.07 mA / cm². 2 Rising to 19.15 mA / cm 2 The increase is relatively small. This is because a suitable doping concentration increases light absorption, improves light absorption performance, and increases Jsc. However, since the parameters of the dye absorption layer itself have a significant impact on photocurrent, as the parameters of the dye absorption layer reach their optimal levels, subsequent changes in these parameters have a less significant impact on Jsc. Furthermore, the conductivity of semiconductors is greatly affected by doping concentration. Doping increases the conductivity of semiconductors, enhances charge extraction, enables faster and more efficient charge transfer, reduces recombination probability, and also reduces the internal resistance of the battery. Voc remains essentially unchanged because when NiO is used as an HTL, the energy level matching between cells is already close to optimal, and changes in its doping concentration have a negligible impact on energy level matching and interlayer potential. When the doping concentrations of both reach 10... 20 cm -3 At this point, the performance parameters tend to stabilize, indicating that charge transport has reached an optimized state at a higher doping concentration, and further increasing the doping concentration has no significant effect on improving battery performance. Therefore, the optimal doping concentration for ETL is 10. 20 cm -3The optimal doping concentration for HTL is 10. 20 cm -3 .

[0133] In some embodiments, based on the above, the thickness of S5 in the battery model to be adjusted (the corresponding materials from bottom to top are: FTO, TiO2, S5, NiO, C) is adjusted to 1.5 μm, and the defect density of S5 is set to 1×10⁻⁶. 13 cm -3 The ETL thickness was adjusted to 1 μm, the HTL thickness to 0.4 μm, and the ETL doping concentration to 10. 20 cm -3 The doping concentration of HTL was adjusted to 10. 20 cm -3 The remaining parameters are consistent with the initial battery model parameters and can be used as the final target battery model.

[0134] In some embodiments, the comparison of the battery model before and after optimization includes:

[0135] By optimizing parameters such as the HTL material, S5 thickness, S5 defects, ETL and HTL thicknesses, ETL and HTL doping concentrations, and metal work function of the battery model, the optimal parameters for each layer were obtained. Table 5 shows a comparison of the battery performance parameters before and after optimization, and the JV and QE curves are shown below. Figure 10 As shown. The JV curves and QE curves of the battery model before and after optimization provided in this application embodiment are shown. Figure 10 Part (a) shows the JV curve, with the vertical axis representing current density and the horizontal axis representing voltage. Figure 10 Part (b) shows the QE curve, with the vertical axis representing quantum efficiency and the horizontal axis representing wavelength. Table 5 shows that the optimized PCE, Voc, Jsc, and FF improved by 7.7%, 0.181V, and 6.13mA / cm², respectively, compared to the unoptimized values. 2 The 4.39% indicates that battery performance has been well optimized. From... Figure 10 As shown in section (b), the QE curves reveal that the light absorption efficiency of the battery is significantly improved in the visible light wavelength range before and after optimization, with the absorption coefficient approaching 1 in the 300-600nm range. Before optimization, the quantum efficiency was close to 0 in the near-infrared region, i.e., after the wavelength reaches 680nm, indicating virtually no absorption of visible light; however, after optimization, the quantum efficiency in this band was significantly improved, and the Jsc was also greatly increased before and after optimization.

[0136] Table 5 Comparison of battery performance parameters before and after optimization

[0137] ;

[0138] In summary, this application uses the organic dye S5 as the object and establishes an initial cell model of FTO / TiO2 / S5 / Spiro-OMeTAD / Ag based on SCAPS-1D software. The performance of the cells under various high-level chromatic lattice (HTL) conditions was studied, and NiO was determined as the optimal HTL material. Further optimization was performed based on the FTO / TiO2 / S5 / NiO / Ag model. The effects of S5 thickness and defect density, ETL and HTL thickness and doping concentration, and the work function of the electrode material on the photoelectric performance of the dye-sensitized solar cell were systematically investigated. The results show that C can replace noble metals as electrodes, and its higher work function ensures both cell performance and reduces electrode cost. With the increase of S5 thickness, the PCE (Power Capacitor Equivalent) shows a trend of first increasing and then decreasing, with the optimal value selected as 1.5 μm. Defect density has a significant impact on the photoelectric characteristics, carrier lifetime and diffusion length, and recombination rate of the cell. 10 Up to 10 14 cm -3 Within a certain range, the battery performs better, while when the defect density exceeds 10... 14 cm -3 At this point, the photoelectric properties decrease significantly. The thickness of ETL and HTL has little impact on battery performance, especially the thickness of HTL, which has virtually no effect; however, as the doping concentration of both increases, battery performance improves. The optimal doping concentration for both ETL and HTL is 10. 20 cm -3 Comparing the battery performance parameters before and after optimization reveals that PCE, Voc, Jsc, and FF increased from 8.96% to 16.66%, Jsc from 0.942V to 1.123V, and FF increased by 13.02mA / cm². 2 Increased to 19.15 mA / cm 2The absorption coefficient increased from 73.06% to 77.45%. The QE curve also improved significantly, with the absorption coefficient of the battery approaching 1 in the 300nm to 600nm range. Furthermore, it can absorb some near-infrared photon energy up to 680nm, broadening the spectral absorption range. These results demonstrate that the embodiments of this application, by optimizing the hole transport layer and electrode materials of the battery model, and based on this, by optimizing some parameters of the dye-sensitized solar cell model whose battery performance has been improved (i.e., the parameters to be adjusted in the battery model), such as the thickness of S5, the defect density of S5, the thickness of ETL, the thickness of HTL, the doping concentration of ETL, and the doping concentration of HTL, can achieve both improved battery performance and reduced battery cost while improving battery environmental performance. In other words, the work done in the embodiments of this application successfully optimized battery performance. This work highlights the great potential of novel dyes (including organic and inorganic dyes) in the application of dye-sensitized solar cells, providing more possibilities for the further development and application of novel dyes, and providing a theoretical basis and optimization ideas for future research on novel dyes.

[0139] The novel dye-sensitized solar cell optimization method provided in this application involves the following steps: First, by using a preset hole transport material to concentrate each material as the material for the hole transport layer in the dye-sensitized solar cell model, multiple first cell models are generated. Performance simulations are then performed on these multiple first cell models to obtain their performance simulation curves. The first cell model with the optimal performance simulation curve is selected as the intermediate cell model. Next, by using an electrode material to concentrate each electrode material as the electrode in the intermediate cell model, multiple second cell models are generated. Performance simulations are then performed on these multiple second cell models to obtain their performance simulation parameters. The second cell model with the optimal performance simulation parameters is selected as the cell model to be adjusted. Finally, the values ​​of each adjustable parameter in the cell model to be adjusted are adjusted to obtain the corresponding simulation model. Based on the simulation data obtained from the simulation model, the multiple adjustable parameters of the cell model to be adjusted are optimized to obtain the target cell model. In this way, by optimizing the hole transport layer material, electrode material, and some adjustable parameter values ​​of the dye-sensitized solar cell model, a target battery model corresponding to improved battery performance can be obtained, thereby providing parameter support for further reducing battery costs and improving battery environmental performance while maintaining battery performance.

[0140] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0141] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways.

[0143] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0144] The features disclosed in the methods provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0145] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A novel method for optimizing dye-sensitized solar cells, characterized in that, The method includes: Obtain the dye-sensitized solar cell model to be optimized; Using solar cell simulation software, performance simulations were performed on multiple first cell models to obtain the performance simulation curves of each first cell model. Each first cell model was generated by concentrating a preset hole transport material into each material, which is then used as the material for the hole transport layer in the dye-sensitized solar cell model. By analyzing the JV and QE curves in the performance simulation curves of each first battery model, the valence band offset between the hole transport layer and the adjacent layer of each first battery model is inferred. Among multiple first-cell models, the first-cell model with the lowest absolute value of valence band offset is selected as the intermediate-cell model. Solar cell simulation software was used to perform performance simulations on multiple second cell models to obtain the performance simulation parameters of each second cell model. Each second cell model was generated by concentrating the electrode materials in each electrode material, which corresponded to the electrodes in the intermediate cell model. The battery performance parameters in the performance simulation parameters of multiple second battery models are used as the vertical axis, and the work function value of the electrode material set is used as the horizontal axis to construct a performance simulation change curve. Determine the target value of the work function when the performance simulation curve in the performance simulation change curve graph tends to be stable; From multiple second battery models, the second battery model corresponding to the objective value of the work function is selected as the third battery model; If the error between the JV curve in the performance simulation parameters of the third battery model and the JV curve in the initial simulation parameters of the initial battery model is less than the first error value, the third battery model is used as the battery model to be adjusted; wherein, the initial battery model is a model that includes a conductive glass substrate, an electron transport layer, a dye absorption layer, a hole transport layer and electrodes from bottom to top. For each adjustable parameter in the battery model to be adjusted, while keeping other adjustable parameters unchanged, iterate through multiple values ​​within the possible range of values ​​of the adjustable parameter to obtain the set of simulation models corresponding to the adjustable parameter. Using solar cell simulation software, the simulation results are obtained by simulating the models in the simulation model set corresponding to each of the multiple adjustable parameters. Based on the simulation results set, multiple adjustable parameters of the battery model to be adjusted are optimized to obtain the target battery model.

2. The novel dye-sensitized solar cell optimization method according to claim 1, characterized in that, Obtain the dye-sensitized solar cell model to be optimized, including: Obtain an initial battery model from bottom to top, including a conductive glass substrate, an electron transport layer, a dye absorption layer, a hole transport layer, and electrodes; The battery performance of the initial battery model was tested to obtain the performance parameters of the initial battery model. Solar cell simulation software was used to perform performance simulation on the initial battery model, and the initial simulation parameters of the initial battery model were obtained. If the error between the performance experimental parameters and the initial simulation parameters is less than the preset error value, the initial cell model is determined as the dye-sensitized solar cell model to be optimized.

3. The novel dye-sensitized solar cell optimization method according to claim 1, characterized in that, The preset hole transport material set includes: PEDOT, P3HT, CuSCN, Spiro-OMeTAD, NiO, Cu2O, CNTS, and Zn3P2.

4. The novel dye-sensitized solar cell optimization method according to claim 1, characterized in that, The electrode materials include: Zn, Fe, Cu, C, Ni, and Pt.

5. The novel dye-sensitized solar cell optimization method according to claim 1, characterized in that, Battery performance parameters include: photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor; By using the battery performance parameters from multiple second battery models as the ordinate and the work function values ​​of the electrode material set as the abscissa, a performance simulation variation curve is constructed, including: Using the photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor from the performance simulation parameters of multiple second battery models as the ordinate, and the work function of the electrode material set as the abscissa, we constructed the performance simulation curves of photoelectric conversion efficiency, open-circuit voltage, short-circuit current density, and battery fill factor. The determination of the target value of the work function corresponding to the point where the performance simulation change curve in the performance simulation change curve graph tends to be stable includes: Determine the first sub-target value of the work function when the photoelectric conversion efficiency performance simulation curve tends to stabilize. Determine the second sub-objective value of the work function when the open-circuit voltage variation curve in the open-circuit voltage performance simulation curve graph tends to be stable; Determine the third work function sub-target value corresponding to when the short-circuit current density change curve in the short-circuit current density performance simulation curve graph tends to be stable; The fourth work function sub-objective value is determined when the battery's fill factor performance variation curve in the simulation curve tends to be stable. By comprehensively analyzing the sub-objective values ​​of the first, second, third, and fourth work functions, the objective value of the work function is obtained.

6. The novel dye-sensitized solar cell optimization method according to claim 1, characterized in that, Several adjustable parameters in the battery model to be adjusted include: the thickness of the dye absorption layer, the defect density of the dye absorption layer, the thickness of the electron transport layer, the thickness of the hole transport layer, the doping concentration of the electron transport layer, and the doping concentration of the hole transport layer.

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