QFLSApplication and value in solar photovoltaic research

In the research of solar photovoltaic materials, quasi Fermi level splitting(QFLS)It has become a key indicator for analyzing the physical and optimization performance of devices. It quantifies the chemical potential difference of photo generated charge carriers and directly reveals the radiative and non radiative recombination losses inside the material. These losses determine the open circuit voltage of solar cells(VOC)The limit.

QFLSCore Value: Quantifying Energy Loss and Identifying Root Causes

In an ideal situation,QFLSThe value should be consistent with the external components of the deviceVOCequal. However, in actual solar cells, contact points, transport layers, and material defects lead to electrochemical potential loss, resulting inVOCBelow theoryQFLSThis kind ofQFLSandVOCThe mismatch is the source of voltage loss.

QFLSMeasuring direct quantification of radiative and non radiative recombination losses, especially non radiative recombination can lead toQFLSDeviation from its radiation limit. This provides a basis for identifying the root cause of voltage loss——Is it the internal composite of the material itself or an interface issue.

Specific case analysis

Research on Tin Lead Perovskite：University of OxfordHenry J. SnaithProfessor and Professor Li Meng's team from Henan UniversityAdding cysteine hydrochloride(CysHCl)In the research[1], throughQFLS mapping(image3e)Discover, addCysHClPost tin lead perovskite thin filmQFLSThe value increases and the spatial distribution becomes more uniform. The results showCysHClPassivation of material defects reduces non radiative recombination.

Device structure optimization：Professor Zhou Zhongmin and Professor Yue Fang's team from Qingdao University of Science and TechnologyResearch on Enhancing Electric FieldsQFLSThe impact of deficit[2]. Picture1dDisplaying different semi stacked devicesPLQYmeasurementQFLSData confirms the optimization of perovskite/FPDStructure can suppressQFLSDeficit. This suppression mechanism is achieved by enhancing the efficiency of carrier separation and extraction.

Rapid detection technologyGuangyan TechnologyEnlitechQFLS-MaperTesting equipment can be used3Obtain within secondsQFLSVisual diagram, quickly presenting the overall quasi Fermi level distribution of materials, intuitively identifying the quality and defect distribution of materials, accelerating the material screening and optimization process.

QFLSGuiding role in device optimization and material selection

QFLSAfter quantification, researchers can identify voltage loss bottlenecks and guide device design and material selection. It assesses the influence of different transport layer materials, as well as the changes in surface properties of films at different treatment stages.

Transport layer optimization：King Abdullah University of Science and TechnologyStefaan De WolfProfessor team利用QFLS mapping(image3d–f)andQFLSDistribution histogram(image3g)[3]Comparing narrow bandgap perovskites in different contextsITO/SAMOn the substrateQFLSValue. The results revealed differentSAMtoQFLSThe influence of distribution provides guidance for optimizing the hole transport layer.

Composite mechanism analysis：University of PotsdamofDieter NeherProfessor teampoint out[4], measurementQFLSCan effectively evaluate the voltage potential of passivation molecules and charge transport layers. Research shows that even ifVOCDue to ion migration or electrode/The interface of the transport layer is composite and saturated,QFLSStill faithfully reflecting the composite mechanism of the material itself is a key indicator for evaluating the inherent quality of the material.

Guangyan TechnologyQFLS-MaperCapable of rapid layeringQFLSTesting function, capable of analyzing materials layer by layer (such as...)HTLElectronic transport layerETL）The impact on the overall component performance helps researchers understand the advantages and disadvantages of each layer of materials, and thus grasp the material effect in real-time during the material preparation process, greatly reducing the research and development time.

QFLSThe role of evaluating charge carrier concentration and recombination kinetics

QFLSDirectly reflecting the concentration and recombination rate of charge carriers. passQFLSMeasurement can determine the impact of factors such as carrier lifetime and doping concentration on device performance.

Study on doping concentration：University of LuxembourgDamilola AdeleyeProfessor teamDeeply studied the effect of doping concentration onQFLSthe impact of[5]And fromQFLSEstimate the doping concentration in the measurement of carrier lifetime. In the literature(image3a)Clearly demonstratedQFLSThe carrier lifetime varies withCu/InCompare and analyze the changes in growth temperature. The study emphasizedQFLSThe ability to directly reflect the concentration and recombination rate of charge carriers can help researchers gain a deeper understanding of carrier behavior and accurately regulate material properties.

Guangyan TechnologyQFLS-Mapercombined withPLQYTheEL-EQEandin-situ PLThe multimodal function provides comprehensive data support for carrier dynamics research, helping researchers accurately determine the impact of factors such as carrier lifetime and doping concentration on device performance.

QFLSAssistance in publishing research findings

In academic publications,QFLSThe use of data can significantly enhance the persuasiveness and depth of a paper. It provides precise data for quantifying voltage loss and can also compare experimental results with theoretical efficiency limits, highlighting the potential of research findings.

Theoretical limit analysis：University of Batna, AlgeriaHichem BencherifteamCalculate the photoluminescence quantum yield using a formula（PLQY）WithQFLSLinking up[6], pointing outQFLSTheoretical efficiency limits can be defined. In the literature(image3e)Presented the perovskite thin filmQFLSThe results and their distribution histograms are used to evaluate efficiency loss. This provides strong evidence for the potential of the reported device to approach its theoretical limit, proving that2D/3DThe advantages of heterostructures in improving efficiency.

Research on Aging Mechanism：Erlangen, Germany-Nuremberg UniversityChristoph J. BrabecProfessor team利用QFLS(image4a)Evaluate the changes in non radiative losses under different layer stacks[7]It was found that in vivo recombination is the main source of non radiative losses in the entire device. Even with a stable transport layer, the formation of internal defects remains the main factor affecting long-term stability. this kind ofQFLSThe quantitative analysis provides key data for a deeper understanding of the aging mechanism of devices, which is crucial for developing more stable perovskite solar cells.

Provide universal benchmarks and comparability of experimental results

QFLSAs an absolute and dimensional physical quantity, it provides a universal benchmark, allowing research results from different components and laboratories to be compared on the same benchmark. This is of great significance for academic exchange and standardization of research.

Comparative study of multiple materials：University of Oxford, UKHenry J. SnaithandShuaifeng HuProfessor teampassQFLS mapping(image3a)[8], evaluated the preparation of different solutionsSn-PbThe photoelectric properties of perovskite thin films. Research shows,PhAThe film treated with phosphoric acid has a more uniformQFLSThe distribution and higher average values demonstrate the improvement effect of additives on material quality. this type ofQFLSThe introduction of data enables objective comparison of the advantages and disadvantages of different materials and processes.

Guangyan TechnologyQFLS-MaperpossessQFLSThePLQYTheiVocMulti modal functionality provides standardized and high-precision measurement dataPLQYSensitivity can reach6An order of magnitude（1E-4%）This ensures measurement accuracy and international acceptance, greatly facilitating paper publication and cross laboratory data comparison.

Predicting device performance and screening materials

QFLSAnd its derivative pseudoJ-V (pseudo J-V)Curves can effectively predict the potential of devices and screen materials before actual device manufacturing, greatly saving research and development costs and time.

Performance prediction technology：Eindhoven University of Technology, NetherlandsRené A. J. JanssenProfessor teamTranslate into EnglishQFLSConversion of light intensity dependent data into pseudo dataJ-Vcurve(image4b)And derive a pseudo fill factor from it（pFF）(image4d)Pseudo power conversion efficiency（pPCE）(image4e)[9].

KesselsThrough experiments, scholars have accurately measured the performance of perovskite films in different environmentsGlyHClQuasi Fermi level splitting under concentration（QFLS）Value. They rely on the intensity of lightQFLSConvert data to pseudoJ-VCurve, this conversion is achieved by utilizing the proportional relationship between photocurrent density and light intensity, andQFLSImplemented as a voltage.

From these pseudoJ-VIn the curve, the research team derived a pseudo fill factor（pFF）Pseudo power conversion efficiency（pPCE）. Although these derived parameters do not directly represent the actual performance of the final deviceJ-VPerformance (because charge transfer loss is ignored), but it can effectively eliminate transfer loss in device preparation, and more purely reflect the inherent optoelectronic quality and potential of perovskite materials themselves.

GlyHClResearch on additive effects: ThroughQFLSIn depth analysis of its derived parameters,KesselsWaiting for scholars' research to clearly revealGlyHClPositive effects on lead tin based perovskite materials:

• Inhibit non radiative recombination: Adding1-2 mol% GlyHClIn the perovskite thin film,QFLSValue from886 meVupgrade to898-900 meV

• Improve crystal quality：GlyHClDelay in precursor solutionSn2+Oxidation improves the grain size distribution and crystal quality of perovskite

• Extend carrier lifetime: Half life of charge carriers from0.7 μsIncrease to1.5 μs

• Improve device performance：VOCfrom0.69 Vupgrade to0.81 V，FFfrom71%upgrade to78%

• Enhanced stability: Add2or4 mol% GlyHClCan significantly improve the stability of the device under continuous illumination

Guangyan TechnologyQFLS-MaperCan be the fastest2Predicting the pseudo accuracy of materials within minutesJ-VThe curve can evaluate the efficiency potential of materials from a theoretical perspective, greatly shorten the experimental cycle, and visualize the predicted results, allowing researchers to have a clear understanding at a glance.

Validate theoretical models and simulation results

experimentally measuredQFLSData can be used for first principles calculations（DFT）And drift diffusion（drift-diffusion）Simulations provide key experimental evidence to ensure the accuracy of theoretical models.

Interface engineering verification：Abdulaziz King Science and Technology City ScholarMasaud Almalkiwait for someoneClearly state in the research[10]: "SCAPSThe successful calibration of the model and experimental data demonstrates the crucial role of surface recombination velocity in improving device efficiency. The use of long-chain alkyl ammonium salts indicates a decrease in surface recombination rate, thereby reducingVOC-QFLSMismatch.

They systematically introduced alkyl ammonium iodides with different chain lengths as surface passivators and explained them using energy band diagramsQFLSandVOCThe simulation results are consistent with experimental observations, indicating that passivation treatment can effectively suppress interface recombination and enhanceQFLS(image7a,b).

Device modeling verification：the United StatesFirst Solarresearch teamInSupporting InformationDisplayed in the middleE-SolverSimulators andSCAPS-1DSimulators with graphics andQFLS/q–VOCComparison in terms of differences[11]The results are highly consistent with minimal error(imageS1A,BAnd the pictureS2A,B)This type of comparison proves thatQFLSThe importance of verifying the accuracy of numerical simulations and theoretical calculations provides a reliable theoretical basis for the design of semiconductor devices.

First principles calculations：Korea Institute of TechnologyYong-Hoon KimandJuho Lee, Hyeonwoo YeoWaiting for scholarsReported the extraction of nanoscale nodes from first principles calculationsQFLSsectional view[12]And explore its correlation with charge transfer under finite bias voltage(image4A,B)Using first principles calculationsQFLSResearchers can predict the electrical behavior and voltage potential of new materials and devices under different bias and excitation conditions before actually synthesizing or manufacturing them. This shortens the research and development cycle and enables systematic exploration of material design space.

Conclusion

QFLSand itsmappingTechnology has become a tool for solar photovoltaic research. It provides precise data for quantifying energy loss, guiding material selection, and optimizing device design, and plays a key role in academic publications. passQFLSData enables researchers to gain a deeper understanding of the intrinsic physical mechanisms of photovoltaic materials, validate theoretical models, and provide a solid scientific basis for developing more efficient and stable solar cells. The continuous development and application of this technology will continue to promote the progress of solar photovoltaic technology and contribute to the sustainable development of human society.

References

1. Hu, S., Sun, X., Liu, W., Gregori, L., Zhao, P., Pascual, J., Dallmann, A., Dasgupta, A., Yang, F., Li, G., Aldamasy, M., Turren-Cruz, S.-H., Flatken, M. A., Fu, S., Iwasaki, Y., Murdey, R., Hoell, A., Schorr, S., Albrecht, S., Yang, S., Abate, A., Wakamiya, A., De Angelis, F., Li, M., & Snaith, H. J. (2025). Accessing Metal-Containing Species in Tin–Lead Perovskite Precursor Solutions via Molecular Strategies Guided by the Hard–Soft Acid–Base Principle.Applied Chemistry International Edition,64(34), 202514010.

2. Cheng, J., Cao, H., Zhang, S., Shao, J., Yan, W., Peng, C., Yue, F., & Zhou, Z. (2024). Enhanced Electric Field Minimizing Quasi-Fermi Level Splitting Deficit for High-Performance Tin-Lead Perovskite Solar Cells.Advanced Materials,36(41), 202410298.

3. Zhumagali, S., Li, C., Marcinskas, M., Dally, P., Liu, Y., Ugur, E., Petoukhoff, C. E., Ghadiyali, M., Prasetio, A., Marengo, M., Pininti, A. R., Azmi, R., Schwingenschlögl, U., Laquai, F., Getautis, V., Malinauskas, T., Aydin, E., Sargent, E. H., & De Wolf, S. (2025). Efficient Narrow Bandgap Pb-Sn Perovskite Solar Cells Through Self-Assembled Hole Transport Layer with Ionic Head.Advanced Energy Materials,15(1), 202404617.

4. Caprioglio, P., Stolterfoht, M., Wolff, C. M., Unold, T., Rech, B., Albrecht, S., & Neher, D. (2019). On the Relation between the Open-Circuit Voltage and Quasi-Fermi Level Splitting in Efficient Perovskite Solar Cells.Advanced Energy Materials,9(37), 201901631.

5. Adeleye, D., Lomuscio, A., Sood, M., & Siebentritt, S. (2021). Lifetime, quasi-Fermi level splitting and doping concentration of Cu-rich CuInS2 absorbers.Materials Research Express,8(2), 025905.

6. Aouni, Q., Kouda, S., Batoo, K. M., Ijaz, M. F., Sahoo, G. S., Bhattarai, S., Sasikumar, P., & Bencherif, H. (2025). Achieving Quasi-Fermi level splitting near its radiative limit in efficient and stable 2D/3D perovskite solar Cells: Detailed balance model.Solar Energy,286, 113144.

7. Peng, Z., Wortmann, J., Hong, J., Zhou, S., Bornschlegl, A. J., Haffner-Schirmer, J., Le Corre, V. M., Heumüller, T., Osvet, A., Rand, B. P., Lüer, L., & Brabec, C. J. (2025). Locating Non-Radiative Recombination Losses and Understanding Their Impact on the Stability of Perovskite Solar Cells During Photo-Thermal Accelerated Ageing.Advanced Energy Materials,15(29), 202502787.

8. Hu, S., Wang, J., Zhao, P., Pascual, J., Wang, J., Rombach, F., Dasgupta, A., Liu, W., Truong, M. A., Zhu, H., Kober-Czerny, M., Drysdale, J. N., Smith, J. A., Yuan, Z., Aalbers, G. J. W., Schipper, N. R. M., Yao, J., Nakano, K., Turren-Cruz, S.-H., Dallmann, A., Christoforo, M. G., Ball, J. M., McMeekin, D. P., Zaininger, K.-A., Liu, Z., Noel, N. K., Tajima, K., Chen, W., Ehara, M., Janssen, R. A. J., Wakamiya, A., & Snaith, H. J. (2025). Steering perovskite precursor solutions for multijunction photovoltaics.Nature,639(7925), 93–101.

9. Kessels, L. M., Remmerswaal, W. H. M., van der Poll, L. M., Bellini, L., Bannenberg, L. J., Wienk, M. M., Savenije, T. J., & Janssen, R. A. J. (2024). Unraveling the Positive Effects of Glycine Hydrochloride on the Performance of Pb–Sn-Based Perovskite Solar Cells.Solar RRL,8(18), 202400506.

10. Almalki, I. S., Alanazi, T. I., Aldoghan, L., Aldossari, N., Almutawa, F., Alzahrani, R. A., Alenzi, S. M., Alzahrani, Y. A., Yafi, G. S., Almutairi, A., Aldukhail, A., Alharthi, B., Aljuwayr, A., Alghannam, F. S., Alanzi, A. Z., Alkhaldi, H., Alhajri, F., Alhumud, H. S., Alqarni, A. A., Alotaibi, M. H., AL-Saleem, N. K., Alkahtani, M., Alanazi, A. Q., & Almalki, M. (2025). Molecular Engineering of Alkylammonium Interfaces for Enhanced Efficiency in Perovskite Solar Cells.Solar RRL,9(14), 202500389.

11. Krasikov, D., Kuciauskas, D., S.Noajev, P., Farshchi, R., McReynolds, K., & Sankin, I. (2024, September 16).Understanding ERE and iVOC metrics for graded CdSeTe absorbers. Authorea.

12. Lee, J., Yeo, H., & Kim, Y.-H. (2020). Quasi-Fermi level splitting in nanoscale junctions from ab initio.Proceedings of the National Academy of Sciences,117(19), 10142–10148.