A method and system for photovoltaic material energy loss analysis

Abstract

The application discloses a photovoltaic material energy loss analysis method and system, and belongs to the technical field of new material detection and photoelectric material analysis. The method comprises the following steps: determining intrinsic radiation loss, charge transfer state radiation loss and non-radiation recombination loss of a photovoltaic device based on a basic parameter data set; establishing a correlation between microstructure parameters and the charge transfer state radiation loss and the non-radiation recombination loss; establishing a correlation between Urbach energy and the non-radiation recombination loss; establishing a mapping relationship between carrier dynamics parameters and the charge transfer state radiation loss and the non-radiation recombination loss; and constructing a multi-dimensional energy loss evaluation model based on the basic parameter data set, the microstructure parameters, the Urbach energy and the carrier dynamics parameters and the correlation or mapping relationship between the parameters and corresponding energy losses.

Description

Technical Field

[0001] This application relates to the field of new material testing and optoelectronic material analysis technology, and in particular to a method and system for analyzing energy loss of photovoltaic materials, applicable to the performance evaluation of silicon-based composite photovoltaic materials and related optoelectronic devices. Background Technology

[0002] As the core carrier for solar energy conversion and utilization, the performance of photovoltaic materials is mainly constrained by energy loss processes. Organic photovoltaic materials, in particular, have been extensively studied due to their advantages such as strong designability and solution processing capabilities, but they still generally suffer from low photoelectric conversion efficiency. The fundamental reason for this lies in the complex and diverse energy loss mechanisms within the devices.

[0003] During the operation of photovoltaic devices, the main energy losses include intrinsic radiation loss, charge transfer state-related loss, and nonradiative recombination loss. These losses are closely related to factors such as material bandgap characteristics, interface energy level matching, and defect state-carrier recombination dynamics. Existing technologies typically analyze energy losses independently based on single spectral, electrical, or morphological characterization methods, lacking the ability to fuse multi-source information and unify modeling capabilities. This makes it difficult to achieve accurate decomposition of various energy losses and identification of dominant factors, thus limiting the guiding effect on material and device optimization.

[0004] In summary, how to achieve unified characterization, quantitative decomposition, and identification of dominant limiting factors for various energy loss mechanisms in photovoltaic materials and devices, in order to improve device performance analysis and optimization design capabilities, has become an urgent technical problem to be solved. Summary of the Invention

[0005] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a method for energy loss analysis of photovoltaic materials, which includes the following steps: The absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device fabricated based on the target photovoltaic material, are obtained to construct a basic parameter dataset; Based on the basic parameter dataset, the intrinsic radiation loss, charge transfer state radiation loss, and nonradiative recombination loss of photovoltaic devices are determined. The microstructure parameters of the target photovoltaic material were obtained, and the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state was established. Based on the exponential absorption tail state at the low energy end of the external quantum efficiency curve, the Urbach energy characterizing the energy disorder of the active layer of the device is obtained, and the correlation between the Urbach energy and the nonradiative recombination loss is established. Obtain the carrier dynamics parameters of the photovoltaic device and establish the mapping relationship between the carrier dynamics parameters and the radiative loss and nonradiative recombination loss of the charge transfer state; A multidimensional energy loss evaluation model is constructed based on the basic parameter dataset, microstructure parameters, Urbach energy and carrier dynamic parameters, and their correlation or mapping relationship with the corresponding energy loss.

[0006] In some embodiments, the intrinsic radiation loss, charge-transfer state radiation loss, and nonradiative recombination loss are determined as follows: Determining the optical band gap based on the absorption spectrum of the target photovoltaic material And calculate the radiation-limiting open-circuit voltage under preset solar spectrum conditions. ; Obtain the actual open-circuit voltage of the photovoltaic device And based on the radiation limit open circuit voltage Compared with the actual open circuit voltage The difference between them determines the total voltage drop. ; Based on optical band gap and radiation limit open circuit voltage The intrinsic radiation loss is obtained. ; Fitting analysis was performed on the low-energy end of the electroluminescence spectrum of photovoltaic devices to obtain the charge transfer state energy levels. And based on optical bandgap With charge transfer state energy level The difference is used to determine the radiation loss of the charge-transferred state. ; Calculation of nonradiative recombination loss based on electroluminescence quantum efficiency and satisfy the relation + + = .

[0007] In some embodiments, the correlation between microstructure parameters and charge-transfer state radiation loss is established as follows: The correlation between microstructure parameters and charge-transfer state radiation loss was analyzed, and candidate structural parameters affecting charge-transfer state radiation loss were screened. The candidate structural parameters are parameterized to construct a unified microstructural parameter characterization system; Based on the microstructure parameter characterization system, an initial mathematical model is constructed between charge transfer state radiation loss and multiple microstructure parameters; Characteristic importance analysis of microstructural parameters was performed to screen out key structural parameters that significantly affect the radiation loss of charge-transfer states; The initial mathematical model was optimized and verified based on key structural parameters to obtain the optimized mathematical model. Based on the optimized mathematical model, the correlation between microstructure parameters and charge transfer state radiation loss was determined.

[0008] In some embodiments, the method for constructing the correlation between microstructure parameters and nonradiative recombination loss is as follows: Defect state tests were performed on the target photovoltaic material to obtain the trap state density and its energy level parameters; By correlating the microstructure parameters of the target photovoltaic material with the trap state density, the correspondence between defect states and microstructure parameters is obtained. Based on the density of trapped states and its energy level parameters, the carrier capture and release process is derived, and the equivalent carrier migration behavior parameters and recombination center trapping characteristic parameters are determined. A functional model was constructed to connect the nonradiative recombination loss with the trap state density, the equivalent carrier migration behavior parameters, and the recombination center trapping characteristic parameters, and the parameters were fitted. By introducing phase separation scale and molecular packing parameters, the function model is modified to obtain a modified model that characterizes the regulatory effect of microstructure on composite pathways. The modified model was verified and optimized based on numerical simulation, and the correlation between microstructure parameters and nonradiative recombination loss was obtained.

[0009] In some embodiments, the method for extracting the Urbach energy to characterize the energy disorder of the active layer of a device is as follows: Data from the low-response range in the external quantum efficiency curve, located on the low-energy side of the absorption edge and corresponding to the optical bandgap of 50 to 200 millielectron volts, were selected. By performing a natural logarithmic transformation on the external quantum efficiency curve in the low response range, the relationship curve between the natural logarithm of the external quantum efficiency and the photon energy is obtained. The natural logarithm of the external quantum efficiency is used to filter out data points affected by measurement noise. Linear fitting was performed on the natural logarithm of the selected external quantum efficiency to obtain the slope of the linear region of the relationship curve; Based on the slope of the linear region, the Urbach energy, which characterizes the energy disorder of the material, is obtained.

[0010] In some embodiments, the method for establishing the correlation between Urbach energy and nonradiative recombination loss is as follows: Temperature dependence analysis was performed on the Urbach energy under different temperature conditions, and the static Urbach energy was obtained by extrapolation. A comparative analysis was conducted on the Urbach energy of photovoltaic devices prepared under different process conditions to determine the influence of process parameters on energy disorder. Establish a functional relationship model between Urbach energy and the corresponding nonradiative recombination loss, and perform fitting analysis; Based on a functional relationship model, the quantitative correlation between Urbach energy and nonradiative recombination loss was determined.

[0011] In some embodiments, the method for establishing the mapping relationship between carrier dynamic parameters and charge-transfer state radiation loss and non-radiative recombination loss is as follows: A first initial theoretical model is established between the carrier dynamic parameters and the nonradiative recombination loss, and a second initial theoretical model is established between the carrier dynamic parameters and the charge-transfer state radiation loss. Based on the carrier dynamics parameters and the corresponding nonradiative recombination loss and charge-transfer state radiation loss, the first initial theoretical model and the second initial theoretical model are fitted and corrected to obtain the mapping relationship between the carrier dynamics parameters and the nonradiative recombination loss, as well as the mapping relationship between the carrier dynamics parameters and the charge-transfer state radiation loss.

[0012] In some embodiments, the multidimensional energy loss evaluation model is constructed using a hierarchical architecture, including: The basic parameter layer is used to acquire raw measurement data, which includes absorption spectrum, photoluminescence quantum efficiency, electroluminescence spectrum and external quantum efficiency curve, and introduces donor-acceptor energy level matching degree, phase separation scale and molecular packing orientation information for the target photovoltaic material. The intermediate parameter layer is used to extract key parameters characterizing material properties based on the original measurement data. These key parameters include microstructure parameters, Urbach energy, and carrier dynamics parameters. The energy loss calculation layer is used to calculate intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss based on detailed equilibrium theory and the correlation between key parameters, and to constrain and correct the determination of charge-transfer state radiation loss and the measurement process of electroluminescence external quantum efficiency. The comprehensive evaluation layer is used to integrate intrinsic radiation loss, charge-transfer state radiation loss, non-radiative recombination loss and their dependence on material parameters, and to construct the energy loss decomposition results and their characterization spectra.

[0013] In some embodiments, the photovoltaic material energy loss analysis method further includes: A multidimensional energy loss evaluation model was used to quantitatively decompose each energy loss, and sensitivity analysis was used to determine the contribution of each parameter to each energy loss. If the contribution of any energy loss exceeds a preset threshold, the energy loss is marked as the dominant limiting factor, and material optimization suggestions for the dominant limiting factor are output. If the contribution of each energy loss does not exceed the preset threshold, the energy loss distribution is determined to be balanced, and the energy loss distribution and the ranking of key limiting factors obtained based on parameter sensitivity analysis are output.

[0014] The purpose of this application is also to provide a photovoltaic material energy loss analysis system for implementing the above-mentioned photovoltaic material energy loss analysis method, including: The spectral acquisition module is used to acquire the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device prepared based on the target photovoltaic material; The electrical testing module is used to obtain the carrier dynamics parameters and Urbach energy of photovoltaic devices, establish the mapping relationship between carrier dynamics parameters and charge transfer state radiation loss and non-radiative recombination loss, and the correlation relationship between Urbach energy and non-radiative recombination loss; The microstructure characterization module is used to obtain the microstructure parameters of the target photovoltaic material and establish the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state. The model building module is used to construct a multidimensional energy loss evaluation model, perform quantitative decomposition and sensitivity analysis on each energy loss, identify the dominant limiting factors, and output material optimization suggestions or a ranking of key limiting factors.

[0015] Compared with the prior art, this application has the following beneficial effects: This application constructs a multidimensional unified evaluation model for energy loss by integrating multi-source information from spectral, electrical, and microstructure sources, thereby enabling quantitative decomposition of various energy losses in photovoltaic devices and accurate identification and optimization guidance of dominant limiting factors. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a photovoltaic material energy loss analysis method disclosed in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the Sobol sensitivity analysis results in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram showing the performance comparison of the device before and after optimization in this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0020] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0022] like Figure 1 As shown, a method for analyzing energy loss in photovoltaic materials includes the following steps: The absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device fabricated based on the target photovoltaic material, are obtained to construct a basic parameter dataset; Based on the basic parameter dataset, the intrinsic radiation loss, charge transfer state radiation loss, and nonradiative recombination loss of photovoltaic devices are determined. The microstructure parameters of the target photovoltaic material were obtained, and the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state was established. Based on the exponential absorption tail state at the low energy end of the external quantum efficiency curve, the Urbach energy, which characterizes the energy disorder of the active layer of the device, is obtained, and the correlation between the Urbach energy and the nonradiative recombination loss is established. Obtain the carrier dynamics parameters of the photovoltaic device and establish the mapping relationship between the carrier dynamics parameters and the radiative loss and nonradiative recombination loss of the charge transfer state; Based on the basic parameter dataset, microstructure parameters, Urbach energy and carrier dynamics parameters and their correlation or mapping relationship with the corresponding energy loss, a multidimensional energy loss evaluation model is constructed. A multidimensional energy loss evaluation model was used to quantitatively decompose each energy loss, and sensitivity analysis was used to determine the contribution of each parameter to each energy loss. If the contribution of any energy loss exceeds a preset threshold, the energy loss is marked as the dominant limiting factor, and material optimization suggestions for the dominant limiting factor are output. If the contribution of each energy loss does not exceed the preset threshold, the energy loss distribution is determined to be balanced, and the energy loss distribution and the ranking of key limiting factors obtained based on parameter sensitivity analysis are output.

[0023] This application describes a method for analyzing energy loss in photovoltaic materials. By acquiring the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, and combining this with the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device, a basic parameter dataset is constructed, thereby achieving a unified characterization of the device's multidimensional photoelectric response information. Through analysis of the basic parameter dataset, intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss are quantitatively decomposed, achieving a mechanistic breakdown and identification of photovoltaic device energy loss. By introducing microstructure parameters and establishing their correlation with charge-transfer state radiation loss and non-radiative recombination loss, a quantitative characterization of the influence of material microstructure on the energy loss mechanism is achieved. Furthermore, by extracting the Urbach energy based on the low-energy exponential absorption tail state of the external quantum efficiency curve and establishing its correlation with non-radiative recombination loss, the coupling relationship between the energy disorder of the active layer and non-radiative loss is realized. The model dynamically describes the impact of carrier transport processes on energy loss by introducing carrier dynamics parameters and constructing a mapping relationship between them and radiative and non-radiative recombination losses in charge-transfer states. Based on this, a multi-dimensional energy loss evaluation model is constructed by integrating fundamental parameter datasets, microstructural parameters, Urbach energy, and carrier dynamics parameters and their corresponding energy loss relationships. This enables collaborative modeling and quantitative assessment of energy losses across multiple mechanisms and scales. Furthermore, sensitivity analysis is used to quantitatively decompose each energy loss and determine the contribution of each parameter, thus identifying and ranking the dominant loss mechanisms. When the contribution of any energy loss exceeds a preset threshold, it is identified as a dominant limiting factor, and targeted material optimization suggestions are output. When the contribution of each energy loss does not exceed the preset threshold, the energy loss distribution is considered balanced, and the distribution of each energy loss and the ranking of key limiting factors based on sensitivity analysis are output, thereby achieving a full-chain analysis and optimization guidance for photovoltaic material energy loss.

[0024] In some embodiments, the method for obtaining the absorption spectrum and photoluminescence quantum efficiency is as follows: The target photovoltaic material is prepared as a thin film sample of a predetermined thickness; Absorption spectroscopy was performed on the thin film sample to obtain the absorption spectrum curve and characteristic absorption peak parameters; Based on the absorption spectrum curve and characteristic absorption peak parameters, the excitation wavelength and excitation intensity parameters of the photoluminescence test are determined. Under preset environmental conditions, the thin film sample was subjected to photoluminescence testing to obtain the number of emitted photons and the number of absorbed photons; The photoluminescence quantum efficiency of the target photovoltaic material is obtained based on the number of emitted photons and the number of absorbed photons.

[0025] For example, the preset thickness of the thin film sample is 80-150 nm, wherein: the polymer donor material is prepared by spin coating, and the spin coating speed is controlled at 1000-3000 r / min; the small molecule acceptor material is formed by thermal evaporation or solution method.

[0026] Specifically, the absorption spectrum was measured using a UV-Vis-NIR spectrophotometer, scanning within the wavelength range of 300–1100 nm, with the spectral resolution set to 1 nm and the scanning speed set to medium. Specifically, the photoluminescence quantum efficiency was measured using an integrating sphere-assisted fluorescence spectrometer on the same thin film sample. The excitation source was selected to be monochromatic light that matched the maximum absorption peak of the material. For benzodithiophene polymers, the excitation wavelength was set to 520–550 nm, and the excitation light intensity was controlled between 10 and 50 mW / cm². 2 To avoid photo-induced degradation, the thin film sample was placed in a nitrogen-protected sample chamber during the test to prevent oxygen and moisture from causing material performance degradation. The absolute photoluminescence quantum efficiency was calculated by comparing the ratio of the number of emitted photons to the number of absorbed photons.

[0027] Specifically, the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material were obtained under constant temperature conditions of 25±2℃, and at least three parallel measurements were performed on each sample to ensure the repeatability and statistical reliability of the test results.

[0028] The method for obtaining the absorption spectrum and photoluminescence quantum efficiency described in this application involves constructing a standardized test carrier for the optical response of a target photovoltaic material by preparing it as a thin film sample of a predetermined thickness; obtaining the absorption spectrum curve and characteristic absorption peak parameters by performing absorption spectral testing on the thin film sample, thus providing a basic spectral characteristic basis for subsequent photoluminescence testing; determining the excitation wavelength and excitation intensity parameters for photoluminescence testing based on the absorption spectrum curve and characteristic absorption peak parameters, thus optimizing the matching between excitation conditions and material absorption characteristics; obtaining the number of emitted photons and absorbed photons by performing photoluminescence testing on the thin film sample under predetermined environmental conditions, thus achieving quantitative characterization of the photoluminescence process; and calculating the photoluminescence quantum efficiency of the target photovoltaic material based on the number of emitted and absorbed photons, thereby achieving a highly consistent and repeatable quantitative evaluation of the radiative recombination characteristics of the photovoltaic material.

[0029] In some embodiments, the method for obtaining the electroluminescence quantum efficiency and external quantum efficiency curves is as follows: The prepared photovoltaic device was packaged, and an electroluminescence and external quantum efficiency testing environment was constructed under inert atmosphere and dark room conditions. Electroluminescence testing was performed on the packaged photovoltaic device to obtain electroluminescence spectrum and electrical characteristic data; The electroluminescence spectrum was corrected and calculated to obtain the electroluminescence quantum efficiency parameter; The external quantum efficiency of the packaged photovoltaic device was tested to obtain the raw data of the external quantum efficiency. The raw external quantum efficiency data is processed by dark current subtraction, spectral response correction and noise correction to obtain the target external quantum efficiency curve.

[0030] For example, the photovoltaic device adopts a forward structure of ITO / PEDOT:PSS / active layer / PDINO / Al or a reverse structure of ITO / ZnO / active layer / MoO3 / Ag; the inert atmosphere is nitrogen with a concentration greater than 99.99%.

[0031] For example, in the electroluminescence test, a precision source meter is used to inject forward current, and the current density is controlled between 0.1 and 100 mA / cm². 2 Within the range, the electroluminescence spectrum emitted by the photovoltaic device is collected using a photomultiplier tube coupled monochromator system, with the spectral acquisition range set to 600–1000 nm; by adjusting the injection current density, electroluminescence spectral data under different luminescence intensity conditions can be obtained.

[0032] In some embodiments, the electroluminescence quantum efficiency is obtained by integrating the electroluminescence spectrum; simultaneously, the current-voltage characteristic curve of the device is recorded, and parameters such as series resistance and parallel resistance are extracted to evaluate the rectification behavior and fill factor characteristics of the device.

[0033] In some embodiments, the luminescence intensity distribution is collected by rotating the sample stage at different emission angles for subsequent calculation of light extraction efficiency correction.

[0034] Furthermore, the electroluminescence measurement is performed in a dark room environment, with the test temperature controlled within the range of 25±2℃, in order to eliminate the influence of ambient light and temperature fluctuations on the test results.

[0035] For example, in acquiring the external quantum efficiency curve, a calibrated monochromatic light source system is used to perform spectral scanning on the photovoltaic device. The measurement wavelength range is 300–1100 nm, preferably extended to above 1100 nm for narrow bandgap materials. The spectral sampling interval is set to 5 nm to ensure data point density. During testing, the photovoltaic device is placed in a short-circuit state, and the incident monochromatic light is modulated by a chopper and then irradiates the active area of ​​the device, where the effective area is 0.04–0.09 cm². 2 The modulation frequency was set to 273Hz to work with the lock-in amplifier for high signal-to-noise ratio detection.

[0036] In the spectral response correction process, calibrated silicon photodiodes and germanium photodiodes were used as reference detectors in the visible and near-infrared bands, respectively, to ensure measurement accuracy across the entire spectrum. For the low-energy region of the external quantum efficiency curve, signal acquisition was performed by extending the integration time and using an indium gallium arsenide detector cooled by liquid nitrogen, achieving a minimum energy of 10. -6 External quantum efficiency detection at the order of magnitude.

[0037] The method for obtaining the electroluminescence quantum efficiency and external quantum efficiency curves described in this application involves encapsulating the fabricated photovoltaic device and constructing an electroluminescence and external quantum efficiency testing environment under inert atmosphere and dark chamber conditions, thereby effectively eliminating the influence of ambient oxygen, water vapor, and light interference on the test results. Electroluminescence testing is performed on the encapsulated photovoltaic device to obtain electroluminescence spectrum and electrical characteristic data, achieving the original characterization of the device's radiative recombination characteristics under electrical injection conditions. The electroluminescence quantum efficiency parameter is obtained by correcting and calculating the electroluminescence spectrum, thereby achieving a quantitative assessment of the device's radiative recombination efficiency. External quantum efficiency testing is performed on the encapsulated photovoltaic device to obtain raw external quantum efficiency data, achieving preliminary acquisition of the device's photoelectric conversion response characteristics. Based on this, dark current subtraction, spectral response correction, and noise correction are performed on the raw external quantum efficiency data to obtain the target external quantum efficiency curve, thereby significantly improving the accuracy and comparability of the external quantum efficiency data and providing a reliable data foundation for subsequent energy loss analysis.

[0038] In some embodiments, the intrinsic radiation loss, charge-transfer state radiation loss, and nonradiative recombination loss are determined as follows: Determining the optical band gap based on the absorption spectrum of the target photovoltaic material And calculate the radiation-limiting open-circuit voltage under preset solar spectrum conditions. ; Obtain the actual open-circuit voltage of the photovoltaic device And based on the radiation limit open circuit voltage Compared with the actual open circuit voltage The difference between them determines the total voltage drop. ; Based on optical band gap and radiation limit open circuit voltage The intrinsic radiation loss is obtained. ; Fitting analysis was performed on the low-energy end of the electroluminescence spectrum of photovoltaic devices to obtain the charge transfer state energy levels. And based on optical bandgap With charge transfer state energy level The difference is used to determine the radiation loss of the charge-transferred state. ; Calculation of nonradiative recombination loss based on electroluminescence quantum efficiency and satisfy the relation + + = .

[0039] In some embodiments, the formula for calculating intrinsic radiation loss is: ,in, It is the elementary charge; Charge-transfer state radiation loss The calculation formula is: ; The formula for calculating nonradiative recombination loss is: Where k is the Boltzmann constant; Absolute temperature; The quantum efficiency of electroluminescence.

[0040] The method for determining intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss described in this application establishes an ideal radiation reference by determining the optical bandgap based on the absorption spectrum and calculating the radiation-limiting open-circuit voltage under preset solar spectral conditions. It quantifies the overall loss by obtaining the actual open-circuit voltage and determining the total voltage loss based on the difference between the radiation-limiting open-circuit voltage and the actual open-circuit voltage. It characterizes the basic radiation loss by calculating the intrinsic radiation loss based on the optical bandgap and the radiation-limiting open-circuit voltage. It obtains the charge-transfer state energy level by fitting the low-energy end of the electroluminescence spectrum and determines the charge-transfer state radiation loss based on the difference between the optical bandgap and this energy level. It quantitatively characterizes the non-radiative recombination loss by calculating the non-radiative recombination loss based on the electroluminescence quantum efficiency. Based on this, the sum of the intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss is correlated with the total voltage loss, thereby achieving the decomposition and closed-loop consistency analysis of the photovoltaic device's voltage loss.

[0041] In some embodiments, the method for obtaining microstructure parameters is as follows: Based on the grazing-incidence wide-angle X-ray scattering data of the target photovoltaic material, the molecular packing distance, π-π packing distance and molecular orientation parameters were obtained by analysis. Based on the atomic force microscopy imaging results of the target photovoltaic material, the phase domain size distribution of the donor and acceptor was determined; Based on the cross-sectional imaging results of the transmission electron microscope of the target photovoltaic material, the grain structure and phase separation interface morphology parameters were determined. Based on the resonant soft X-ray scattering characterization results of the target photovoltaic material, the spatial distribution parameters of the components were determined, including the spatial distribution and interface orientation parameters of the donor, acceptor and third component. The microstructure parameters of the target photovoltaic material are obtained by comprehensively characterizing the molecular packing distance, π-π packing distance, molecular orientation parameters, phase domain size distribution, interface morphology parameters, and component spatial distribution parameters. The microstructure parameters include grain size, phase domain purity, interface roughness, molecular orientation characteristics, and packing density.

[0042] The method for obtaining the aforementioned microstructure parameters in this application characterizes the material's order at the molecular scale by analyzing molecular packing distance, π-π packing distance, and molecular orientation parameters based on grazing incidence wide-angle X-ray scattering data; it determines the donor and acceptor phase domain size distribution based on atomic force microscopy imaging results, achieving spatial-scale characterization of phase separation morphology; it determines grain structure and phase separation interface morphology parameters based on transmission electron microscopy cross-sectional imaging results, achieving fine analysis of bulk and interface structures; it determines component spatial distribution and interface orientation parameters based on resonant soft X-ray scattering characterization results, achieving spatial distribution characterization of multi-component systems; and based on this, it integrates molecular packing distance, π-π packing distance, molecular orientation parameters, phase domain size distribution, interface morphology parameters, and component spatial distribution parameters to obtain the microstructure parameters of the target photovoltaic material, thereby achieving multi-scale synergistic characterization of grain size, phase domain purity, interface roughness, molecular orientation characteristics, and packing density.

[0043] In some embodiments, the correlation between microstructure parameters and charge-transfer state radiation loss is established as follows: The correlation between microstructure parameters and charge-transfer state radiation loss was analyzed, and candidate structural parameters affecting charge-transfer state radiation loss were screened. The candidate structural parameters are parameterized to construct a unified microstructural parameter characterization system; Based on the microstructure parameter characterization system, an initial mathematical model is constructed between charge transfer state radiation loss and multiple microstructure parameters; Characteristic importance analysis of microstructural parameters was performed to screen out key structural parameters that significantly affect the radiation loss of charge-transfer states; The initial mathematical model was optimized and verified based on key structural parameters to obtain the optimized mathematical model. Based on the optimized mathematical model, the correlation between microstructure parameters and charge transfer state radiation loss was determined.

[0044] For example, the initial mathematical model between charge-transfer state radiation loss and multiple microstructure parameters is expressed by the following formula: , This represents the initial value of the charge transfer state radiation loss of the target photovoltaic material system under preset standard preparation conditions and reference microstructure. For the first One candidate microstructure parameter; No. The main effect coefficients of each candidate microstructure parameter represent the effect of that parameter changing alone on the microstructure. Intensity and direction of influence: For the first One candidate microstructure parameter; The interaction coefficient represents... and When both change simultaneously, the additional effects on the radiation loss of the charge-transfer state are: No. With the The product term of the candidate structural parameters represents the synergistic effect of the two candidate structural parameters. This represents the random error term of the initial mathematical model; The total number of candidate structure parameters; This indicates that only unique combinations of different candidate microstructure parameters are selected.

[0045] The method for establishing the correlation between microstructural parameters and charge-transfer state radiation loss described in this application involves analyzing the correlation between microstructural parameters and charge-transfer state radiation loss to screen candidate structural parameters that affect charge-transfer state radiation loss; constructing a unified microstructural parameter characterization system by parameterizing the candidate structural parameters to achieve standardized expression of multi-scale structural information; building an initial mathematical model between charge-transfer state radiation loss and multiple microstructural parameters based on the characterization system to achieve a preliminary mapping between structural factors and loss mechanisms; screening key structural parameters that significantly affect charge-transfer state radiation loss by performing feature importance analysis on each microstructural parameter; optimizing and verifying the initial mathematical model based on the key structural parameters to obtain an optimized mathematical model, improving the stability and accuracy of the correlation; and determining the correlation between microstructural parameters and charge-transfer state radiation loss based on the optimized mathematical model, thereby achieving a quantitative description of charge-transfer state radiation loss by structural features.

[0046] In some embodiments, the method for constructing the correlation between microstructure parameters and nonradiative recombination loss is as follows: Defect state tests were performed on the target photovoltaic material to obtain the trap state density and its energy level parameters; By correlating the microstructure parameters of the target photovoltaic material with the trap state density, the correspondence between defect states and microstructure parameters is obtained. Based on the density of trapped states and its energy level parameters, the carrier capture and release process is derived, and the equivalent carrier migration behavior parameters and recombination center trapping characteristic parameters are determined. A functional model was constructed to connect the nonradiative recombination loss with the trap state density, the equivalent carrier migration behavior parameters, and the recombination center trapping characteristic parameters, and the parameters were fitted. By introducing phase separation scale and molecular packing parameters, the function model is modified to obtain a modified model that characterizes the regulatory effect of microstructure on composite pathways. The modified model was verified and optimized based on numerical simulation, and the correlation between microstructure parameters and nonradiative recombination loss was obtained.

[0047] The functional model relating nonradiative recombination loss to the density of trapped states, equivalent carrier migration behavior parameters, and recombination center trapping characteristic parameters is expressed as follows: ,in, This is the baseline value for the inherent nonradiative recombination loss of the target photovoltaic material system under preset standard preparation conditions and with all influencing parameters in a reference state; Density of trap states; Equivalent carrier mobility; It is the reciprocal of the equivalent carrier mobility; For capturing feature parameters of the composite center; This is the contribution coefficient of the trap state density to the nonradiative loss; This is the contribution factor of the equivalent carrier mobility to non-radiative losses; The contribution coefficient of the composite center trapping characteristic parameter to the non-radiative loss; This represents the coupling effect coefficient between the trap state density and the equivalent carrier migration behavior parameters; express ; This represents the coupling influence coefficient between the equivalent carrier migration behavior parameters and the recombination center trapping characteristic parameters; This represents the random error term of the function model.

[0048] The method for constructing the correlation between microstructure parameters and nonradiative recombination loss described in this application involves: conducting defect state tests on the target photovoltaic material to obtain the density of trapped states and its energy level parameters, thereby characterizing the internal defect distribution characteristics of the material; establishing a correspondence between defect states and microstructure parameters through correlation analysis of microstructure parameters and the density of trapped states; deriving the carrier trapping and release process based on the density of trapped states and its energy level parameters to determine the equivalent carrier migration behavior parameters and recombination center trapping characteristic parameters, thus achieving an equivalent description of the recombination dynamics process; constructing a functional model between nonradiative recombination loss and the density of trapped states, the equivalent carrier migration behavior parameters, and the recombination center trapping characteristic parameters, and performing parameter fitting to achieve a quantitative characterization of nonradiative recombination behavior; modifying the functional model by introducing phase separation scale and molecular packing parameters to obtain a modified model characterizing the role of microstructure in regulating the recombination path; and verifying and optimizing the modified model based on numerical simulation to obtain the correlation between microstructure parameters and nonradiative recombination loss, thereby achieving a quantitative mapping of structural features to the nonradiative recombination mechanism.

[0049] In some embodiments, the method for extracting the Urbach energy to characterize the energy disorder of the active layer of a device is as follows: Data from the low-response range in the external quantum efficiency curve, located on the low-energy side of the absorption edge and corresponding to the optical bandgap of 50 to 200 millielectron volts, were selected. By performing a natural logarithmic transformation on the external quantum efficiency curve in the low response range, the relationship curve between the natural logarithm of the external quantum efficiency and the photon energy is obtained. The natural logarithm of the external quantum efficiency is used to filter out data points affected by measurement noise. Linear fitting was performed on the natural logarithm of the selected external quantum efficiency to obtain the slope of the linear region of the relationship curve; Based on the slope of the linear region, the Urbach energy, which characterizes the energy disorder of the material, is obtained.

[0050] The Urbach energy extraction method described in this application determines the exponential absorption tail state analysis interval by selecting low-response interval data from the outer quantum efficiency curve located on the low-energy side of the absorption edge, corresponding to a certain energy range below the optical bandgap. A natural logarithmic transformation is performed on the outer quantum efficiency curve in the low-response interval to construct a relationship curve between the logarithm of the outer quantum efficiency and the photon energy, achieving a linearized characterization of the exponential decay characteristic. The natural logarithmic data of the outer quantum efficiency are screened to eliminate data points affected by measurement noise, improving data reliability. Linear fitting is performed on the screened data to obtain the slope of the linear region of the relationship curve. Based on this, the Urbach energy is extracted from the slope of the linear region, thereby achieving a quantitative characterization of the energy disorder of the active layer of the material.

[0051] In some embodiments, the method for establishing the correlation between Urbach energy and nonradiative recombination loss is as follows: Temperature dependence analysis was performed on the Urbach energy under different temperature conditions, and the static Urbach energy was obtained by extrapolation. A comparative analysis was conducted on the Urbach energy of photovoltaic devices prepared under different process conditions to determine the influence of process parameters on energy disorder. Establish a functional relationship model between Urbach energy and the corresponding nonradiative recombination loss, and perform fitting analysis; Based on a functional relationship model, the quantitative correlation between Urbach energy and nonradiative recombination loss was determined.

[0052] The functional relationship model is expressed by the following formula: ,in, The initial value of the reference nonradiative recombination loss under the reference disorder condition; Urbach's energy influence coefficient represents the change in nonradiative loss caused by a unit increase in energy disorder. This represents the fitting error term of the functional relationship model; For Urbach energy.

[0053] The method for establishing the correlation between Urbach energy and nonradiative recombination loss described in this application eliminates the influence of thermal disturbance on the characterization of energy disorder by performing temperature dependence analysis on Urbach energy under different temperature conditions and extrapolating it to obtain static Urbach energy; by comparing and analyzing the Urbach energy of photovoltaic devices prepared under different process conditions, the influence of process parameters on the energy disorder of materials is clarified; by establishing a functional relationship model between Urbach energy and corresponding nonradiative recombination loss and performing fitting analysis, a preliminary mapping between the two is achieved; based on this, the quantitative correlation between Urbach energy and nonradiative recombination loss is determined based on the functional relationship model, thereby achieving a quantitative characterization of the influence of energy disorder on the nonradiative recombination process.

[0054] In some embodiments, the method for obtaining the carrier dynamic parameters is as follows: The target photovoltaic material was tested using femtosecond transient absorption spectroscopy to obtain transient dynamic curves at different detection wavelengths; The transient dynamic curves were analyzed to extract exciton lifetime, charge transfer time constant, and bimolecular recombination rate constant. Photovoltaic devices were tested using time-resolved photoluminescence spectroscopy to obtain decay kinetics information of radiative and non-radiative recombination processes, and parameters of different decay components were extracted by multi-exponential fitting. Impedance spectroscopy was used to test the frequency response of photovoltaic devices, and carrier lifetime, transport time and composite resistance parameters were extracted based on equivalent circuit model fitting. The space charge-confined current method was used to perform electrical tests on photovoltaic devices to obtain electron mobility and hole mobility parameters. The carrier dynamic parameters are obtained by comprehensively analyzing and verifying the exciton lifetime, charge transfer time constant, bimolecular recombination rate constant, decay component parameters, carrier lifetime, transport time, recombination resistance, electron mobility and hole mobility.

[0055] The method for obtaining the aforementioned carrier dynamics parameters in this application involves testing the target photovoltaic material using femtosecond transient absorption spectroscopy to obtain transient dynamic curves at different detection wavelengths, thereby characterizing the ultrafast evolution process of carriers; analyzing the transient dynamic curves to extract exciton lifetime, charge transfer time constant, and bimolecular recombination rate constant, achieving preliminary quantification of excited-state dynamic behavior; testing the photovoltaic device using time-resolved photoluminescence spectroscopy to obtain decay dynamics information of radiative and non-radiative recombination processes, and extracting different decay component parameters through multi-exponential fitting to achieve separation and characterization of recombination paths; and employing impedance spectroscopy to... Photovoltaic devices undergo frequency response testing, and carrier lifetime, transit time, and recombination resistance parameters are extracted based on equivalent circuit model fitting to achieve equivalent characterization of macroscopic transport and recombination characteristics. Electron mobility and hole mobility parameters are obtained through electrical testing using the space charge-limited current method to achieve quantitative characterization of carrier mobility. On this basis, exciton lifetime, charge transfer time constant, bimolecular recombination rate constant, decay component parameters, carrier lifetime, transit time, recombination resistance, and electron and hole mobility are comprehensively analyzed and their consistency verified to obtain carrier dynamic parameters, thereby achieving a unified characterization of multi-scale dynamic information.

[0056] In some embodiments, the method for establishing the mapping relationship between carrier dynamic parameters and charge-transfer state radiation loss and non-radiative recombination loss is as follows: A first initial theoretical model is established between the carrier dynamic parameters and the nonradiative recombination loss, and a second initial theoretical model is established between the carrier dynamic parameters and the charge-transfer state radiation loss. Based on the carrier dynamics parameters and the corresponding nonradiative recombination loss and charge-transfer state radiation loss, the first initial theoretical model and the second initial theoretical model are fitted and corrected to obtain the mapping relationship between the carrier dynamics parameters and the nonradiative recombination loss, as well as the mapping relationship between the carrier dynamics parameters and the charge-transfer state radiation loss.

[0057] The first initial theoretical model is expressed as follows: ,in, This represents the initial value of nonradiative recombination loss of the target photovoltaic material system caused by the inherent nonradiative recombination path of the material, under preset standard test conditions and reference carrier lifetime, reference recombination rate, reference transport time and reference mobility. For carrier lifetime; The composite rate constant; For carrier transport time; Equivalent mobility; This is the error term of the first initial theoretical model; This represents the influence coefficient of the reciprocal of carrier lifetime on nonradiative recombination loss, that is, the degree to which the shortening of carrier lifetime contributes to the increase in loss; It represents the influence coefficient of the recombination rate constant on nonradiative recombination loss, that is, the degree of increase in loss caused by the faster recombination process; This represents the influence coefficient of carrier transport time on nonradiative recombination loss, that is, the degree to which carrier transport is slower and residence time is longer, and the contribution to the increase in loss.

[0058] The second initial theoretical model is expressed as follows: ,in, This represents the initial value of charge transfer state radiation loss of the target photovoltaic material system caused by the material's inherent energy level matching relationship and interface charge transfer characteristics, under preset standard preparation conditions and reference charge transfer state lifetime, reference charge separation rate, reference recombination rate, and reference carrier migration equilibrium state. The lifetime of the charge-transfer state; It is the charge separation rate constant; The rate constant for recombination of opposite charges; The hole / electron mobility balance factor; This is the error term of the second initial theoretical model; This is the sensitivity coefficient of charge-transfer state lifetime change to charge-transfer state radiative loss; This represents the coefficient representing the influence of the reciprocal of the charge separation rate on the radiative loss of the charge-transferred state; This represents the coefficient indicating the influence of the reverse recombination rate on the radiative loss of the charge-transferred state; This represents the coefficient that indicates the influence of the electron / hole mobility balance on the radiative loss of the charge-transfer state.

[0059] The method for establishing the mapping relationship between the aforementioned carrier dynamics parameters and the radiation loss and non-radiative recombination loss of the charge-transfer state in this application establishes a theoretical description framework for different loss mechanisms by constructing a first initial theoretical model between carrier dynamics parameters and non-radiative recombination loss, and a second initial theoretical model between carrier dynamics parameters and radiation loss of the charge-transfer state. Based on the carrier dynamics parameters and corresponding non-radiative recombination loss and radiation loss data of the charge-transfer state, the first and second initial theoretical models are fitted and corrected to achieve experimental constraints and optimization of the model parameters. On this basis, the mapping relationship between carrier dynamics parameters and non-radiative recombination loss, and the mapping relationship between carrier dynamics parameters and radiation loss of the charge-transfer state are obtained, respectively, thereby achieving a quantitative correlation and unified characterization between carrier transport behavior and different energy loss mechanisms.

[0060] In some embodiments, the multidimensional energy loss evaluation model is constructed using a hierarchical architecture, including: The basic parameter layer is used to acquire raw measurement data, which includes absorption spectrum, photoluminescence quantum efficiency, electroluminescence spectrum and external quantum efficiency curve, and introduces donor-acceptor energy level matching degree, phase separation scale and molecular packing orientation information for the target photovoltaic material. The intermediate parameter layer is used to extract key parameters characterizing material properties based on the original measurement data. These key parameters include microstructure parameters, Urbach energy, and carrier dynamics parameters. The energy loss calculation layer is used to calculate intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss based on detailed equilibrium theory and the correlation between key parameters, and to constrain and correct the determination of charge-transfer state radiation loss and the measurement process of electroluminescence external quantum efficiency. The comprehensive evaluation layer is used to integrate intrinsic radiation loss, charge-transfer state radiation loss, non-radiative recombination loss and their dependence on material parameters, and to construct the energy loss decomposition results and their characterization spectra.

[0061] The multidimensional energy loss evaluation model described in this application is constructed using a hierarchical architecture, including a basic parameter layer, an intermediate parameter layer, an energy loss calculation layer, and a comprehensive evaluation layer. The basic parameter layer acquires raw measurement data such as absorption spectra, photoluminescence quantum efficiency, electroluminescence spectra, and external quantum efficiency curves. It also introduces donor-acceptor energy level matching, phase separation scale, and molecular packing orientation information for the target photovoltaic material, achieving unified acquisition of multi-source basic data. The intermediate parameter layer extracts key material characteristics such as microstructure parameters, Urbach energy, and carrier dynamics parameters based on the raw measurement data, achieving parameterized characterization of material properties. The energy loss calculation layer calculates intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss based on detailed equilibrium theory and the correlation between key parameters. It also constrains and corrects the determination of charge-transfer state radiation loss and the measurement process of electroluminescence and external quantum efficiency, thereby improving the consistency and accuracy of loss calculation. The comprehensive evaluation layer integrates various energy losses and their dependencies on material parameters, constructs energy loss decomposition results and characterization spectra, and achieves hierarchical analysis and overall evaluation of photovoltaic device energy loss.

[0062] In some embodiments, the method for determining the contribution of each parameter to each energy loss through sensitivity analysis is as follows: Determine the input parameters of the multidimensional energy loss evaluation model and set the value range of each input parameter; Based on the value range of each input parameter, a parameter sample set is generated using a Sobol sequence; Input the parameter sample set into the multidimensional energy loss evaluation model to calculate the corresponding intrinsic radiation loss, charge-transfer state radiation loss and non-radiative recombination loss. Based on the calculation results, the variance decomposition method is used to calculate the first-order Sobol sensitivity index and the total Sobol sensitivity index corresponding to each input parameter. Based on the first-order Sobol sensitivity index and the total Sobol sensitivity index, the contribution of each input parameter to intrinsic radiation loss, charge-transfer state radiation loss and nonradiative recombination loss is determined, and key parameters are identified. Local sensitivity analysis is performed on key parameters to obtain sensitivity analysis results, including the variation trends and optimization directions of intrinsic radiation loss, charge-transfer state radiation loss and non-radiative recombination loss relative to key parameters. Based on the sensitivity analysis results, the output ranks the contributions of each input parameter to intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss.

[0063] The method described in this application for determining the contribution of each parameter to each energy loss through sensitivity analysis achieves standardized definition of the analysis variable space by determining the input parameters of the multidimensional energy loss evaluation model and setting the value range of each input parameter; it achieves highly uniform global sampling by generating a parameter sample set using a Sobol sequence based on the value range of each input parameter; it calculates the corresponding intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss by inputting the parameter sample set into the multidimensional energy loss evaluation model, realizing the loss response mapping under different parameter combinations; and it calculates the variance of each input parameter based on the calculation results using a variance decomposition method. The system employs a first-order Sobol sensitivity index and a total Sobol sensitivity index to measure the global impact of each parameter on different energy losses. By determining the contribution of each input parameter to each energy loss based on the sensitivity indices and identifying key parameters, it screens the dominant influencing factors. Through local sensitivity analysis of key parameters, it obtains the changing trends and optimization directions of each energy loss relative to the key parameters, providing guidance for parameter regulation. Based on this, it outputs the ranking results of the contributions of each input parameter to intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss, thereby achieving quantitative classification and optimization path identification of the influence of multiple parameters on multiple loss mechanisms.

[0064] In some embodiments, the method for setting the preset threshold is as follows: The loss components of a predetermined number of high-efficiency photovoltaic materials covering different material systems are collected. The loss components include intrinsic radiation loss, charge transfer state radiation loss and nonradiative recombination loss, to obtain an energy loss dataset. Statistical distribution analysis was performed on the energy loss dataset to obtain the numerical range and contribution distribution characteristics of each loss component; Based on the numerical range and contribution distribution characteristics, the anomaly judgment threshold corresponding to each loss component is calculated, and the contribution threshold and absolute value threshold of each loss component are determined to obtain the threshold judgment parameters. Based on the contribution threshold and the absolute value threshold, the preset thresholds for intrinsic radiation loss, charge transfer state radiation loss and non-radiative recombination loss are determined respectively, and the initial preset thresholds are obtained. The efficiency level of the photovoltaic device prepared from the high-efficiency photovoltaic material is obtained, and the initial preset threshold is adjusted according to the efficiency level to obtain the corrected preset threshold. Based on the application scenario requirements, the modified preset threshold is adapted to the scenario to obtain the preset threshold.

[0065] The method for setting the aforementioned preset threshold in this application constructs an energy loss dataset by collecting intrinsic radiation loss, charge transfer state radiation loss, and non-radiative recombination loss data of high-efficiency photovoltaic materials covering different material systems, thereby achieving a unified characterization of loss distribution across multiple systems. Statistical distribution analysis of the energy loss dataset yields the numerical range and contribution distribution characteristics of each loss component, thus characterizing the statistical regularities of different loss mechanisms. Anomaly detection thresholds corresponding to each loss component are calculated based on the numerical range and contribution distribution characteristics, and contribution thresholds and absolute value thresholds are determined, forming a threshold detection parameter system. Initial preset thresholds for each loss component are determined based on the threshold detection parameters, thus defining the basic constraints for different loss mechanisms. The initial preset thresholds are adjusted in conjunction with the efficiency level of devices fabricated from high-efficiency photovoltaic materials, achieving adaptive matching between the thresholds and device performance levels. Based on this, the corrected preset thresholds are adapted to the application scenario requirements to obtain the final preset threshold, achieving dynamic threshold construction for different material systems and application needs.

[0066] Specifically, when intrinsic radiation loss is identified as the dominant limiting factor, recommended solutions for optimizing the material bandgap are output, including: The appropriate bandgap range is determined based on the target application scenario. For indoor photovoltaic applications, it is recommended to use wide bandgap materials with a bandgap of 1.8 to 2.0 eV to match the emission spectrum of fluorescent lamps or LED light sources. For outdoor photovoltaic applications, it is recommended to use narrow bandgap materials with a bandgap of 1.3 to 1.5 eV to improve the utilization efficiency of the solar spectrum. By adjusting the combination of donor and acceptor units, the intramolecular charge transfer intensity can be altered; by adjusting the side chain type, side chain length, and substitution position, the molecular packing morphology and energy level structure can be controlled to obtain the target bandgap material. When the photovoltaic device is a tandem organic photovoltaic cell, the bandgap combination of the front cell and the rear cell is determined based on the detailed equilibrium limit theory. It is recommended that the front cell use a bandgap material of 1.7 to 1.8 electron volts and the rear cell use a bandgap material of 1.2 to 1.3 electron volts to improve the theoretical conversion efficiency of the tandem device. A material sequence with bandgap varying within the target value ±0.1 eV was constructed, and the optimal bandgap range was determined through device performance testing; Based on the candidate material database, the search and matching is performed. After inputting the target band gap range, the corresponding polymer donor material, small molecule acceptor material or combination thereof is output to obtain the candidate material set. Feasibility assessments were conducted on each optimization scheme, and the intrinsic radiation loss was obtained by combining the corresponding efficiency improvement. The optimization suggestions are as follows.

[0067] Specifically, when charge-transfer state radiation loss is identified as the dominant limiting factor, recommended solutions for interface energy level modulation and morphology optimization are output, including: The energy level shift between the highest occupied molecular orbital (HOMO) energy level of the donor material and the lowest unoccupied molecular orbital (LUMO) energy level of the acceptor material is analyzed. When the energy level shift exceeds a preset threshold, it is recommended to select a combination of donor and acceptor materials with higher energy level matching to reduce the radiation loss of the interface charge transfer state. A ternary component modulation strategy is adopted, introducing a third component material between the host donor and host acceptor materials, so that its energy level is located between the host donor and host acceptor, forming a stepped energy level arrangement, thereby reducing the radiation loss of charge-transferred states. And maintain charge separation efficiency; Optimize the molecular orientation and packing morphology of the donor-acceptor interface by adjusting the processing solvent, additives and post-processing parameters to promote the formation of face-on orientation structures at the interface, thereby enhancing the charge transfer coupling strength and reducing the charge transfer state energy level. Materials with smaller Stokes shift and higher luminous efficiency are selected to reduce the energy difference between the absorption and emission states and reduce the radiation loss of the charge transfer state. An interface modification layer is set at the donor-acceptor interface, and the additional energy loss at the interface is reduced by adjusting the energy level position and interface dipole moment of the interface modification layer. In-situ spectroelectrochemical testing and electroluminescence-external quantum efficiency testing were conducted to obtain the position of charge-transfer state energy levels and oscillator strength parameters, providing experimental basis for interface optimization. The various optimization schemes are evaluated, and the corresponding process parameters and charge-transfer state radiation losses are output. The expected reduction was used to obtain optimization suggestions for charge-transfer state radiation loss.

[0068] Specifically, when non-radiative recombination loss is identified as the dominant limiting factor, optimized solutions for material purification, morphology control, and defect passivation are provided, including: Purification of organic photovoltaic raw materials involves removing low-molecular-weight impurities, residual catalysts, and byproducts through Soxhlet extraction, recrystallization, column chromatography, or sublimation processes. This improves the purity of donor and acceptor materials and reduces non-radiative recombination losses caused by impurities. Optimize thin film preparation process parameters, including solution concentration, spin coating speed, substrate temperature, annealing temperature and annealing time, and determine the process window through orthogonal experimental design or response surface analysis to reduce the density of defects in thin film structure. A defect passivation strategy is adopted to introduce passivation materials at the active layer interface, grain boundary or bulk region. By combining with uncoordinated atoms, polar groups or electron-deficient sites, the density of trapped states and the probability of carrier recombination are reduced. Grain coarsening is controlled by using thermal annealing or solvent vapor annealing to promote molecular rearrangement and grain growth, reduce the number of grain boundaries, and increase crystallization order, thereby reducing non-radiative recombination losses. ; By using mixed solvents or additive systems, the film formation rate and phase separation process can be controlled to promote molecular self-assembly into an ordered morphological structure and improve carrier transport performance. For polymer donor materials, controlling the molecular weight and molecular weight distribution can improve the crystallinity, continuity and morphological stability of the film, and reduce defect-induced recombination loss. The optimization effect was characterized by obtaining defect state density, carrier lifetime and recombination behavior parameters through photoluminescence lifetime testing, capacitance-voltage analysis, impedance spectroscopy testing and variable temperature electrical testing. Based on the characterization results, each optimization scheme is evaluated, and the corresponding process parameters and non-radiative recombination losses are output. The expected reduction was used to obtain optimization suggestions for non-radiative composite loss.

[0069] This application also aims to provide a photovoltaic material energy loss analysis system for implementing the aforementioned photovoltaic material energy loss analysis method, including: The spectral acquisition module is used to acquire the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device prepared based on the target photovoltaic material; The electrical testing module is used to obtain the carrier dynamics parameters and Urbach energy of photovoltaic devices, establish the mapping relationship between carrier dynamics parameters and charge transfer state radiation loss and non-radiative recombination loss, and the correlation relationship between Urbach energy and non-radiative recombination loss; The microstructure characterization module is used to obtain the microstructure parameters of the target photovoltaic material and establish the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state. The model building module is used to construct a multidimensional energy loss evaluation model, perform quantitative decomposition and sensitivity analysis on each energy loss, identify the dominant limiting factors, and output material optimization suggestions or a ranking of key limiting factors.

[0070] The photovoltaic material energy loss analysis system described in this application includes a spectral acquisition module, an electrical testing module, a microscopic characterization module, and a model building module. The spectral acquisition module acquires the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device fabricated based on the target photovoltaic material, thus acquiring basic photoelectric response data. The electrical testing module acquires the carrier dynamics parameters and Urbach energy of the photovoltaic device, and establishes the mapping relationship between the carrier dynamics parameters and the radiative and non-radiative recombination losses of the charge-transfer state, as well as the correlation between the Urbach energy and the non-radiative recombination loss, thus quantitatively characterizing the loss mechanism through dynamics and energy disorder. The microscopic characterization module acquires the microstructural parameters of the target photovoltaic material and establishes the correlation between them and the radiative and non-radiative recombination losses of the charge-transfer state, thus characterizing the loss mechanism through structural factors. The model building module constructs a multidimensional energy loss evaluation model, quantitatively decomposes each energy loss, conducts sensitivity analysis, identifies the dominant limiting factors, and outputs material optimization suggestions or a ranking of key limiting factors, thereby achieving multidimensional collaborative analysis and systematic evaluation of photovoltaic material energy loss.

[0071] To verify the effectiveness and accuracy of the photovoltaic material energy loss analysis method disclosed in this application in practical applications, the following uses a typical organic photovoltaic system PM6:L8-BO:BTP-eC9 ternary active layer material as an example.

[0072] The photovoltaic device fabricated using the PM6:L8-BO:BTP-eC9 system is used as the research object. This device adopts an ITO / PEDOT:PSS / active layer / PDINO / Al structure and operates under standard illumination conditions (AM1.5G, 100mW / cm²). 2 Under the following conditions, its initial photoelectric conversion efficiency was 17.6%, its open-circuit voltage was 0.89V, and its short-circuit current density was 25.3mA / cm². 2 The fill factor is 78.2%. To achieve a systematic analysis of the energy loss mechanism of this device, a multidimensional energy loss evaluation model was constructed based on the method of this application, and the device underwent full-process data acquisition, parameter extraction, and loss decomposition analysis.

[0073] First, the optical response characteristics of the material were obtained by measuring the absorption spectrum and photoluminescence quantum efficiency of the active layer thin film. Simultaneously, the electroluminescence quantum efficiency and external quantum efficiency curves of the corresponding photovoltaic device were measured to construct a basic parameter dataset. The test results show that the optical bandgap of this system is 1.38 eV, and the electroluminescence quantum efficiency is 1.2 × 10⁻⁶. -4 The external quantum efficiency reaches a peak of about 82% in the visible light region, providing basic data support for subsequent energy loss decomposition.

[0074] In the energy loss decomposition process, the radiation-limiting open-circuit voltage was calculated to be 1.12V based on detailed equilibrium theory. Combined with the measured open-circuit voltage of 0.89V, the total voltage loss was found to be 0.23eV. Further decomposition yielded the intrinsic radiation loss. The charge-transfer state radiation loss is 0.26 eV. The nonradiative recombination loss is 0.11 eV. The value was 0.12 eV, with non-radiative recombination loss accounting for the highest proportion of the total loss, initially indicating that it is the potential dominant limiting factor.

[0075] In terms of microstructure characterization, grazing-incidence wide-angle X-ray scattering and atomic force microscopy revealed a π-π packing distance of 3.62 Å, a phase domain size of approximately 18 nm, and an interface roughness of 1.8 nm. Further analysis established a correlation model between microstructure parameters and energy loss, showing that a larger phase domain size and higher interface disorder significantly enhance the nonradiative recombination pathway, thereby improving energy efficiency. .

[0076] Simultaneously, by fitting the exponential tail state to the low-energy end of the external quantum efficiency curve, the Urbach energy was extracted to be 28 meV, indicating a certain degree of energy disorder in the system. Based on the established correlation model between the Urbach energy and nonradiative recombination loss, the energy disorder pair was calculated. The contribution rate was approximately 31%, further verifying that energy disorder is an important factor affecting nonradiative losses.

[0077] In carrier dynamics analysis, transient absorption spectroscopy and impedance spectroscopy measurements yielded a carrier lifetime of approximately 1.8 ns and a recombination rate constant of 2.6 × 10⁻⁶. 6 s -1 The carrier mobility is 3.2 × 10⁻⁶. -4 cm 2 / V·s. Based on the mapping model between carrier dynamics parameters and energy loss, the analysis shows that low carrier mobility and high recombination rate are important reasons for the increase in nonradiative recombination loss.

[0078] Based on this, fundamental parameter data, microstructure parameters, Urbach energy, and carrier dynamics parameters were input into a multidimensional energy loss evaluation model, and the contribution of each parameter to different energy losses was calculated using the Sobol sensitivity analysis method. The results show that nonradiative recombination loss... The overall sensitivity index is the highest, with the trap state density, Urbach energy, and carrier mobility being the top three key influencing parameters, contributing a combined percentage of 68%, exceeding the preset threshold, thus triggering the judgment. The primary limiting factor is listed in the appendix. Figure 2 .

[0079] Based on the above analysis results, optimization suggestions for non-radiative recombination loss are automatically generated, including: further purifying raw materials to reduce the density of trapped states introduced by impurities; adjusting the phase domain size to the range of 10-15 nm by optimizing the solvent system and annealing process; introducing interface passivation molecules to reduce interface defect density; and improving carrier mobility through molecular structure regulation.

[0080] After improving the device process according to the optimization suggestions, the photovoltaic device was re-fabricated and tested. The results showed that the nonradiative recombination loss was reduced to 0.08 eV, the open-circuit voltage was increased to 0.94 V, and the photoelectric conversion efficiency was improved to 18.9%. Simultaneously, the Urbach energy decreased to 24 meV, indicating that the material's energy disorder was effectively improved. Please refer to the appendix for details. Figure 3 .

[0081] In subsequent stability tests, the optimized device was continuously run for 1000 hours and its performance degradation rate was found to be significantly reduced, which verified the effectiveness of the method in guiding material optimization and improving device performance.

[0082] In summary, the photovoltaic material energy loss analysis method proposed in this application, through multi-source parameter fusion modeling and sensitivity analysis, achieves quantitative decomposition and identification of the dominant mechanisms of intrinsic radiation loss, charge transfer state radiation loss, and non-radiative recombination loss. Compared with traditional single-parameter analysis methods, it has significant advantages in terms of the depth of loss mechanism analysis, the accuracy of key factor identification, and the ability to guide optimization paths, providing reliable technical support for the design of high-performance photovoltaic materials and device optimization.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing energy loss in photovoltaic materials, characterized in that, Includes the following steps: The absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device fabricated based on the target photovoltaic material, are obtained to construct a basic parameter dataset; Based on the basic parameter dataset, the intrinsic radiation loss, charge transfer state radiation loss, and nonradiative recombination loss of photovoltaic devices are determined. The microstructure parameters of the target photovoltaic material were obtained, and the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state was established. Based on the exponential absorption tail state at the low energy end of the external quantum efficiency curve, the Urbach energy characterizing the energy disorder of the active layer of the device is obtained, and the correlation between the Urbach energy and the nonradiative recombination loss is established. Obtain the carrier dynamics parameters of the photovoltaic device and establish the mapping relationship between the carrier dynamics parameters and the radiative loss and nonradiative recombination loss of the charge transfer state; Based on the basic parameter dataset, microstructure parameters, Urbach energy and carrier dynamics parameters and their correlation or mapping relationship with the corresponding energy loss, a multidimensional energy loss evaluation model is constructed. The method for obtaining microstructure parameters is as follows: Based on the grazing-incidence wide-angle X-ray scattering data of the target photovoltaic material, the molecular packing distance, π-π packing distance and molecular orientation parameters were obtained by analysis. Based on the atomic force microscopy imaging results of the target photovoltaic material, the phase domain size distribution of the donor and acceptor was determined; Based on the cross-sectional imaging results of the transmission electron microscope of the target photovoltaic material, the grain structure and phase separation interface morphology parameters were determined. Based on the resonant soft X-ray scattering characterization results of the target photovoltaic material, the spatial distribution parameters of the components were determined, including the spatial distribution and interface orientation parameters of the donor, acceptor and third component. Based on molecular packing distance, π-π packing distance, molecular orientation parameters, phase domain size distribution, interface morphology parameters and component spatial distribution parameters, the microstructure parameters of the target photovoltaic material are comprehensively characterized. The microstructure parameters include grain size, phase domain purity, interface roughness, molecular orientation characteristics and packing density. The method for establishing the correlation between microstructure parameters and charge transfer state radiation loss is as follows: The correlation between microstructure parameters and charge-transfer state radiation loss was analyzed, and candidate structural parameters affecting charge-transfer state radiation loss were screened. The candidate structural parameters are parameterized to construct a unified microstructural parameter characterization system; Based on the microstructure parameter characterization system, an initial mathematical model is constructed between charge transfer state radiation loss and multiple microstructure parameters; Characteristic importance analysis of microstructural parameters was performed to screen out key structural parameters that significantly affect the radiation loss of charge-transfer states; The initial mathematical model was optimized and verified based on key structural parameters to obtain the optimized mathematical model. Based on the optimized mathematical model, the correlation between microstructure parameters and charge transfer state radiation loss was determined; The method for constructing the correlation between microstructure parameters and nonradiative recombination loss is as follows: Defect state tests were performed on the target photovoltaic material to obtain the trap state density and its energy level parameters; By correlating the microstructure parameters of the target photovoltaic material with the trap state density, the correspondence between defect states and microstructure parameters is obtained. Based on the density of trapped states and its energy level parameters, the carrier capture and release process is derived, and the equivalent carrier migration behavior parameters and recombination center trapping characteristic parameters are determined. A functional model was constructed to connect the nonradiative recombination loss with the trap state density, the equivalent carrier migration behavior parameters, and the recombination center trapping characteristic parameters, and the parameters were fitted. By introducing phase separation scale and molecular packing parameters, the function model is modified to obtain a modified model that characterizes the regulatory effect of microstructure on composite pathways. The modified model was verified and optimized based on numerical simulation, and the correlation between microstructure parameters and nonradiative recombination loss was obtained.

2. The method for analyzing energy loss of photovoltaic materials according to claim 1, characterized in that, The methods for determining intrinsic radiation loss, charge-transfer state radiation loss, and nonradiative recombination loss are as follows: Determining the optical band gap based on the absorption spectrum of the target photovoltaic material And calculate the radiation-limiting open-circuit voltage under preset solar spectrum conditions. ; Obtain the actual open-circuit voltage of the photovoltaic device And based on the radiation limit open circuit voltage Compared with the actual open circuit voltage The difference between them determines the total voltage drop. ; Based on optical band gap and radiation limit open circuit voltage The intrinsic radiation loss is obtained. ; Fitting analysis was performed on the low-energy end of the electroluminescence spectrum of photovoltaic devices to obtain the charge transfer state energy levels. And based on optical bandgap With charge transfer state energy level The difference is used to determine the radiation loss of the charge-transferred state. ; Calculation of nonradiative recombination loss based on electroluminescence quantum efficiency and satisfy the relation + + = .

3. The method for analyzing energy loss in photovoltaic materials according to claim 1, characterized in that, The method for extracting the Urbach energy to characterize the energy disorder of the active layer of a device is as follows: Data from the low-response range in the external quantum efficiency curve, located on the low-energy side of the absorption edge and corresponding to the optical bandgap of 50 to 200 millielectron volts, were selected. By performing a natural logarithmic transformation on the external quantum efficiency curve in the low response range, the relationship curve between the natural logarithm of the external quantum efficiency and the photon energy is obtained. The natural logarithm of the external quantum efficiency is used to filter out data points affected by measurement noise. Linear fitting was performed on the natural logarithm of the selected external quantum efficiency to obtain the slope of the linear region of the relationship curve; Based on the slope of the linear region, the Urbach energy, which characterizes the energy disorder of the material, is obtained.

4. The method for analyzing energy loss of photovoltaic materials according to claim 1, characterized in that, The method for establishing the correlation between Urbach energy and nonradiative recombination loss is as follows: Temperature dependence analysis was performed on the Urbach energy under different temperature conditions, and the static Urbach energy was obtained by extrapolation. A comparative analysis was conducted on the Urbach energy of photovoltaic devices prepared under different process conditions to determine the influence of process parameters on energy disorder. Establish a functional relationship model between Urbach energy and the corresponding nonradiative recombination loss, and perform fitting analysis; Based on a functional relationship model, the quantitative correlation between Urbach energy and nonradiative recombination loss was determined.

5. The method for analyzing energy loss of photovoltaic materials according to claim 1, characterized in that, The method for establishing the mapping relationship between carrier dynamic parameters and radiative and nonradiative recombination losses in the charge-transfer state is as follows: A first initial theoretical model is established between the carrier dynamic parameters and the nonradiative recombination loss, and a second initial theoretical model is established between the carrier dynamic parameters and the charge-transfer state radiation loss. Based on the carrier dynamics parameters and the corresponding nonradiative recombination loss and charge-transfer state radiation loss, the first initial theoretical model and the second initial theoretical model are fitted and corrected to obtain the mapping relationship between the carrier dynamics parameters and the nonradiative recombination loss, as well as the mapping relationship between the carrier dynamics parameters and the charge-transfer state radiation loss.

6. The method for analyzing energy loss in photovoltaic materials according to claim 1, characterized in that, The multidimensional energy loss assessment model is constructed using a hierarchical architecture, including: The basic parameter layer is used to acquire raw measurement data, which includes absorption spectrum, photoluminescence quantum efficiency, electroluminescence spectrum and external quantum efficiency curve, and introduces donor-acceptor energy level matching degree, phase separation scale and molecular packing orientation information for the target photovoltaic material. The intermediate parameter layer is used to extract key parameters characterizing material properties based on the original measurement data. These key parameters include microstructure parameters, Urbach energy, and carrier dynamics parameters. The energy loss calculation layer is used to calculate intrinsic radiation loss, charge-transfer state radiation loss, and non-radiative recombination loss based on detailed equilibrium theory and the correlation between key parameters, and to constrain and correct the determination of charge-transfer state radiation loss and the measurement process of electroluminescence external quantum efficiency. The comprehensive evaluation layer is used to integrate intrinsic radiation loss, charge-transfer state radiation loss, non-radiative recombination loss and their dependence on material parameters, and to construct the energy loss decomposition results and their characterization spectra.

7. The method for analyzing energy loss of photovoltaic materials according to any one of claims 1-6, characterized in that, Methods for analyzing energy loss in photovoltaic materials also include: A multidimensional energy loss evaluation model was used to quantitatively decompose each energy loss, and sensitivity analysis was used to determine the contribution of each parameter to each energy loss. If the contribution of any energy loss exceeds a preset threshold, the energy loss is marked as the dominant limiting factor, and material optimization suggestions for the dominant limiting factor are output. If the contribution of each energy loss does not exceed the preset threshold, the energy loss distribution is determined to be balanced, and the energy loss distribution and the ranking of key limiting factors obtained based on parameter sensitivity analysis are output.

8. A photovoltaic material energy loss analysis system, used to implement the photovoltaic material energy loss analysis method as described in claim 7, characterized in that, include: The spectral acquisition module is used to acquire the absorption spectrum and photoluminescence quantum efficiency of the target photovoltaic material, as well as the electroluminescence quantum efficiency and external quantum efficiency curves of the photovoltaic device made from the target photovoltaic material; The electrical testing module is used to obtain the carrier dynamics parameters and Urbach energy of photovoltaic devices, establish the mapping relationship between carrier dynamics parameters and charge transfer state radiation loss and non-radiative recombination loss, and the correlation relationship between Urbach energy and non-radiative recombination loss; The microstructure characterization module is used to obtain the microstructure parameters of the target photovoltaic material and establish the correlation between the microstructure parameters and the radiative loss and nonradiative recombination loss of the charge transfer state. The model building module is used to construct a multidimensional energy loss evaluation model, perform quantitative decomposition and sensitivity analysis on each energy loss, identify the dominant limiting factors, and output material optimization suggestions or a ranking of key limiting factors.

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