Applications and Value of QFLS in Solar Photovoltaic Research

In solar photovoltaic material research, Quasi-Fermi Level Splitting (QFLS) has become a critical metric for analyzing device physics and optimizing performance. It quantifies the chemical potential difference of photogenerated charge carriers, directly revealing radiative and non-radiative recombination losses within materials. These losses determine the limits of solar cell open-circuit voltage (V_OC). 

Core Value of QFLS: Quantifying Energy Losses and Identifying Root Causes

Ideally, QFLS values should equal the device’s external V_OC. However, in actual solar cells, contact points, transport layers, and material defects cause electrochemical potential losses, resulting in actual V_OC being lower than theoretical QFLS. This mismatch between QFLS and V_OC is the source of voltage losses.

QFLS measurements directly quantify radiative and non-radiative recombination losses, particularly as non-radiative recombination causes QFLS to deviate from its radiative limit value. This provides a basis for identifying voltage loss origins—whether from bulk recombination within the material itself or interface issues.

Specific Case Studies

• Tin-Lead Perovskite Research: Professor Henry J. Snaith’s team at Oxford University and Professor Meng Li’s team at Henan University, in their study adding cysteine hydrochloride (CysHCl) [1], discovered through QFLS mapping (Figure 3e) that adding CysHCl enhanced QFLS values in tin-lead perovskite films with more uniform spatial distribution. Results indicated that CysHCl passivated material defects, reducing non-radiative recombination.
• Device Structure Optimization: Professor Zhongmin Zhou and Professor Fang Yue’s team at Qingdao University of Science and Technology studied enhanced electric field effects on QFLS deficit [2]. Figure 1d displays PLQY-measured QFLS data for different half-stacked devices, confirming that optimized perovskite/FPD structures can suppress QFLS deficit. This suppression mechanism is achieved through enhanced carrier separation and extraction efficiency.
• Rapid Detection Technology: Enlitech’s QFLS-Maper detection equipment can obtain QFLS visual maps within 3 seconds, rapidly displaying overall quasi-Fermi level distribution in materials, intuitively identifying material quality and defect distribution, accelerating material screening and optimization processes.

QFLS Guidance for Device Optimization and Material Selection

After QFLS quantification, researchers can identify voltage loss bottlenecks, guiding device design and material selection. It evaluates the impact of different transport layer materials and changes in film surface properties during different processing stages.

Transport Layer Optimization

Professor Stefaan De Wolf’s team at King Abdullah University of Science and Technology utilized QFLS mapping (Figure 3d–f) and QFLS distribution histograms (Figure 3g) [3] to compare QFLS values of narrow-bandgap perovskites on different ITO/SAM substrates. Results revealed the impact of different SAMs on QFLS distribution, providing guidance for hole transport layer optimization.

Recombination Mechanism Analysis

Professor Dieter Neher’s team at the University of Potsdam pointed out [4] that measuring QFLS can effectively evaluate the voltage potential of passivation molecules and charge transport layers. Research shows that even when V_OC saturates due to ion migration or electrode/transport layer interface recombination, QFLS still faithfully reflects the material’s own recombination mechanisms, making it a key indicator for evaluating intrinsic material quality.

Enlitech’s QFLS-Maper features rapid layered QFLS testing capabilities, enabling layer-by-layer analysis of how each material layer (such as HTL, electron transport layer ETL) affects overall component performance, helping researchers understand the quality of each material layer, thereby enabling real-time monitoring of material effects during preparation processes, significantly shortening development timelines.

Role of QFLS in Evaluating Charge Carrier Concentration and Recombination Dynamics

QFLS directly reflects charge carrier concentration and recombination rates. Through QFLS measurements, the impact of factors such as carrier lifetime and doping concentration on device performance can be determined.

Doping Concentration Studies

Professor Damilola Adeleye’s team at the University of Luxembourg conducted in-depth research on doping concentration effects on QFLS [5], estimating doping concentrations from QFLS and carrier lifetime measurements. (Figure 3a) in the literature clearly shows QFLS and carrier lifetime variations with Cu/In ratio and different growth temperatures. The research emphasized QFLS’s ability to directly reflect charge carrier concentration and recombination rates, helping researchers deeply understand carrier behavior and precisely control material properties.

Enlitech’s QFLS-Maper combines multi-modal functions including PLQY, EL-EQE, and in-situ PL, providing 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.

QFLS Assistance in Publishing Research Results

In academic publications, the application of QFLS data can significantly enhance paper persuasiveness and depth. It provides precise data for quantifying voltage losses and enables comparison of experimental results with theoretical efficiency limits, thereby highlighting research potential.

Theoretical Limit Analysis

Hichem Bencherif’s team at the University of Batna, Algeria, related photoluminescence quantum yield (PLQY) to QFLS through formulations [6], indicating that QFLS can define theoretical efficiency limits. (Figure 3e) in the literature displays QFLS results and distribution histograms for perovskite films, used to evaluate efficiency losses. This provides powerful evidence for reporting device potential approaching theoretical limits, demonstrating the advantages of 2D/3D heterostructures in efficiency improvement.

Aging Mechanism Research

Professor Christoph J. Brabec’s team at Friedrich-Alexander-Universität Erlangen-Nürnberg utilized QFLS (Figure 4a) to evaluate changes in non-radiative losses under different layer stacking [7], finding that bulk recombination is the main source of non-radiative losses in complete devices. Even with stable transport layers, bulk defect formation remains the primary factor affecting long-term stability. This quantitative QFLS analysis provides key data for deeply understanding device aging mechanisms, crucial for developing more stable perovskite solar cells.

Providing Universal Benchmarks and Experimental Result Comparability

QFLS, as an absolute, dimensional physical quantity, provides a universal benchmark, enabling research results from different compositions and laboratories to be compared on the same basis. This holds significant importance for academic exchange and research standardization.

Multi-Material Comparative Studies

Professor Henry J. Snaith and Professor Shuaifeng Hu’s team at Oxford University, UK, through QFLS mapping (Figure 3a) [8], evaluated the optoelectronic performance of Sn-Pb perovskite films prepared from different solutions. Research showed that PhA (phosphoric acid) treated films had more uniform QFLS distribution and higher average values, demonstrating the quality improvement effect of additives on materials. The introduction of such QFLS data enables objective comparison of different materials and processes.

Enlitech’s QFLS-Maper features multi-modal functions including QFLS, PLQY, and iV_oc, providing standardized and high-precision measurement data with PLQY sensitivity reaching 6 orders of magnitude (minimum 1E-4%), ensuring measurement accuracy and international acceptance, greatly benefiting paper publication and cross-laboratory data comparison.

Predicting Device Performance and Screening Materials

QFLS and its derived pseudo J-V curves can effectively predict device potential, enabling material screening before actual device manufacturing, significantly saving development costs and time.

Performance Prediction Technology

Professor René A. J. Janssen’s team at Eindhoven University of Technology, Netherlands, converted QFLS light intensity dependence data to pseudo J-V curves (Figure 4b), deriving pseudo fill factor (pFF) (Figure 4d) and pseudo power conversion efficiency (pPCE) (Figure 4e) [9].

Kessels and colleagues precisely measured quasi-Fermi level splitting (QFLS) values of perovskite films under different GlyHCl concentrations through experiments. They converted light intensity-dependent QFLS data to pseudo J-V curves, achieved through utilizing the proportional relationship between photocurrent density and light intensity, with QFLS serving as voltage.

From these pseudo J-V curves, the research team derived pseudo fill factor (pFF) and pseudo power conversion efficiency (pPCE). While these derived parameters don’t directly represent actual J-V performance of final devices (as they ignore charge transport losses), they can effectively exclude transport losses in device fabrication, more purely reflecting the intrinsic optoelectronic quality and potential of perovskite materials themselves.

GlyHCl Additive Effect Studies

Through in-depth analysis of QFLS and its derived parameters, Kessels and colleagues’ research clearly revealed the positive effects of GlyHCl on lead-tin-based perovskite materials:

• Suppressing non-radiative recombination: In perovskite films with 1-2 mol% GlyHCl addition, QFLS values improved from 886 meV to 898-900 meV
• Improving crystal quality: GlyHCl delayed Sn²⁺ oxidation in precursor solutions, improving perovskite grain size distribution and crystallization quality
• Extending carrier lifetime: Carrier half-life increased from 0.7 μs to 1.5 μs
• Enhancing device performance: V_OC improved from 0.69 V to 0.81 V, FF from 71% to 78%
• Enhancing stability: Adding 2 or 4 mol% GlyHCl significantly improved device stability under continuous illumination

Enlitech’s QFLS-Maper can predict material pseudo J-V curves within as fast as 2 minutes, evaluating material efficiency potential from a theoretical perspective, greatly shortening experimental cycles, and can visualize prediction results, allowing researchers to understand at a glance.

Validating Theoretical Models and Simulation Results

QFLS also plays an indispensable role in validating theoretical models and simulation results. Experimentally measured QFLS data can provide key experimental evidence for first-principles calculations (DFT) and drift-diffusion simulations, ensuring theoretical model accuracy.

Interface Engineering Validation

King Abdulaziz City for Science and Technology scholar Masaud Almalki and colleagues clearly stated in their research [10]: “Successful calibration of SCAPS models with experimental data demonstrates the critical role of surface recombination velocity in improving device efficiency. The use of long-chain alkylammonium salts indicates reduced surface recombination velocity, thereby mitigating V_OC-QFLS mismatch.”

They systematically introduced different chain-length alkylammonium iodides as surface passivation agents, using energy band diagrams to explain the relationship between QFLS and V_OC. Their simulation results were consistent with experimental observations, showing that passivation treatment can effectively suppress interface recombination and enhance QFLS (Figure 7a,b).

Device Modeling Validation

The US First Solar research team demonstrated comparisons between E-Solver simulator and SCAPS-1D simulator in band diagrams and QFLS/q–V_OC differences in Supporting Information [11], with highly consistent results and minimal error (Figure S1A,B and Figure S2A,B). Such comparisons prove QFLS’s importance in validating numerical simulation and theoretical calculation accuracy, providing reliable theoretical foundations for semiconductor device design.

First-Principles Calculations

Korean Institute of Technology scholars Yong-Hoon Kim with Juho Lee, Hyeonwoo Yeo and colleagues first reported extracting nanoscale junction QFLS profiles from first-principles calculations [12], exploring their correlation with charge transport under finite bias (Figure 4A,B). Through first-principles QFLS calculations, researchers can predict electrical behavior and voltage potential under different bias and excitation conditions before actual synthesis or manufacturing of new materials and devices. This shortens development cycles and enables systematic exploration of material design space.

Conclusion

QFLS and its mapping technology have become indispensable tools in solar photovoltaic research. They provide precise data for quantifying energy losses, guiding material selection, and optimizing device design, while playing key roles in academic publications. Through QFLS data, researchers can gain deeper understanding of intrinsic physical mechanisms in photovoltaic materials, validate theoretical models, and provide solid scientific foundations for developing more efficient and stable solar cells. The continued development and application of this technology will continue to drive advances in solar photovoltaic technology, contributing to 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. Angewandte Chemie 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., Ščajev, P., Farshchi, R., McReynolds, K., & Sankin, I. (2024, September 16). Understanding ERE and iV_OC 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.