Numerical Optimization of CsₓFA₁₋ₓPbI₃-Based Perovskite Solar Cells Using SCAPS-1D

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Alshehri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6894058/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a comprehensive numerical investigation of CsₓFA₁₋ₓPbI₃-based perovskite solar cells (PSCs) using the SCAPS-1D simulation software. A previously reported device architecture was first simulated to validate the model, yielding a power conversion efficiency (PCE) of 11.8%, closely matching experimental data. Following validation, key parameters were systematically optimized, including absorber thickness, defect density (Nt), and transport layers materials. The results indicate that an optimal absorber thickness in the range of 1000–1200 nm provides a balance between enhanced light absorption and efficient carrier collection. The N t in the absorber layer was found to have a critical impact on device performance, with substantial degradation observed for densities above 10 16 cm − 3 due to non-radiative recombination. Furthermore, alternative transport layers were evaluated. Among the seven electron transport layers (ETLs) and five hole transport layers (HTLs) examined, the combination of C₆₀ and NiO yielded the highest simulated PCE of 18.29%, attributed to favorable band alignment and reduced interfacial losses. These results emphasize the importance of optimizing both absorber quality and interfacial materials in designing high-efficiency PSCs. The insights gained from this work provide valuable guidelines for experimental efforts aimed at the development of next-generation perovskite solar cells. Physical sciences/Engineering/Mechanical engineering Physical sciences/Materials science/Materials for devices Physical sciences/Materials science/Materials for energy and catalysis Physical sciences/Materials science/Nanoscale materials Physical sciences/Energy science and technology/Energy harvesting Physical sciences/Energy science and technology/Renewable energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Perovskite solar cells (PSCs) have emerged as one of the most promising photovoltaic technologies over the last decade due to their remarkable PCE, low fabrication cost, and ease of processing. Since their inception, PSCs have achieved significant progress, with efficiencies exceeding 25%, placing them in direct competition with conventional silicon-based solar cells. Perovskite materials have attracted significant interest for next-generation solar energy applications due to their superior optoelectronic properties, including high absorption coefficients, tunable bandgaps, and long carrier diffusion lengths. Despite their impressive performance, further optimization of the device structure and reduction of loss mechanisms are required to realize their full potential. Among the different perovskite compositions studied, the mixed-cation perovskite CsₓFA₁₋ₓPbI₃ has demonstrated excellent thermal stability, a suitable bandgap for photovoltaic applications, and improved structural integrity, making it a promising candidate for efficient and stable perovskite solar cells. [ 1 , 2 ] . The combination of cesium (Cs⁺) and formamidinium (FA⁺) cations mitigates the instability often observed in purely organic perovskites, such as methylammonium lead iodide (MAPbI 3 ). Cs⁺ contributes to improved thermal stability and lattice rigidity, while FA⁺ enables the tuning of the bandgap [ 3 ] , which are ideal for single-junction solar cells. Additionally, CsₓFA₁₋ₓPbI₃ exhibits superior light absorption and charge transport properties, making it an excellent candidate for achieving high-efficiency devices [ 1 , 2 ] . Its stability under varying environmental conditions further addresses one of the key challenges in PSC development long-term operational durability. Recently, many approaches have been presented to improve the performance of CsPbI₃-based devices, including compositional engineering, surface passivation, and the incorporation of stabilizing additives to suppress defects and enhance structural stability [ 4 , 5 ] . These strategies have led to notable enhancements in both power conversion efficiency and operational stability, highlighting the versatility and promise of CsPbI₃ and its derivatives for photovoltaic applications. The device architecture of PSCs is a critical factor that influences overall performance, including efficiency and long-term stability. A typical device consists of a multilayer structure: a transparent conductive oxide (TCO) substrate, ETL, perovskite absorber layer, HTL, and metal back contact. The interface properties, carrier recombination rates, and layer thicknesses directly influence key parameters such as the open-circuit voltage (V OC ), short-circuit current density (J SC ), fill factor (FF), and overall PCE. Numerical simulation tools, such as SCAPS-1D (Solar Cell Capacitance Simulator) is commonly used as 1D simulation tool tailored for thin-film solar cells developed by the University of Ghent [ 6 ] . It provides a robust platform for studying the impact of various material and structural parameters on device performance. By simulating the device behavior, researchers can gain insight into performance limitations and investigate optimization strategies without the need for time-consuming and costly experimental fabrication. In this study, we focus on enhancing the performance of PSCs using SCAPS-1D simulations. To validate the accuracy of our simulation approach, we simulate a perovskite solar cell with the structure FTO/TiO₂/ CsₓFA₁₋ₓPbI₃/Spiro-OMeTAD/Al as reported experimentally in Ref [ 1 ] . This step ensures that our simulation parameters, including material properties and boundary conditions, are consistent with experimental results. Following this validation, we systematically investigate performance enhancement strategies by optimizing absorber thicknesses, defect densities of the absorber. Additionally, we explore alternative electron and hole transport layers to further improve device efficiency. To get reliable and realistic results. We avoid varying parameters which could influence other dependent parameters such as band gap, relative permittivity and electron affinities of the materials as demonstrated in Ref [ 7 ] . The outcomes of this study provide a comprehensive understanding of the factors influencing PSC performance and highlight potential pathways for achieving higher efficiencies. By utilizing numerical simulations, this study demonstrates that systematic optimization of device parameters can result in significant improvements in performance, offering a pathway toward more efficient and stable perovskite solar cells. 2. Methodology The SCAPS-1D simulation tool was developed to numerically solve the fundamental equations governing one-dimensional semiconductor devices, including Poisson’s equation, and the continuity equations for electrons and holes. The governing equations are expressed as follows equations: [ 8 ] \(\:\frac{d}{dx}(-\epsilon\:\left(x\right)\:\frac{d\psi\:}{dx})=q[p\left(x\right)-n\left(x\right)+{N}_{d}^{+}(x)-{N}_{a}^{-}(x\left)\right]\) (1) \(\:\frac{\partial\:{j}_{n}}{\partial\:x}=q\left({R}_{n}-G+\frac{\partial\:n}{\partial\:t}\right)\) (2) \(\:\frac{\partial\:{j}_{p}}{\partial\:x}=-q\left({R}_{p}-G+\frac{\partial\:p}{\partial\:t}\right)\) (3) Where, ψ represents the electrostatic potential, and ε is the permittivity of the medium. The symbols n and p refer to the concentrations of free electrons and holes, respectively. The terms \(\:{N}_{d}^{+}\) ​ and \(\:{N}_{a}^{-}\) denote the densities of ionized donors and acceptors, respectively. The quantities j n and j p ​represent the current densities of electrons and holes, while R n and R p ​indicate the net recombination rates of electrons and holes per unit volume, respectively. Finally, G denotes the generation rate of charge carriers per unit volume. The simulated structure is FTO/TiO₂/CsₓFA₁₋ₓPbI₃/Spiro-OMeTAD/Al. FTO acts as the transparent conductive oxide, TiO₂ as the ETL, CsₓFA₁₋ₓPbI₃ as the perovskite absorber, Spiro-OMeTAD as HTL, and Al as the back contact. Initial thicknesses were 50 nm (TiO₂), 300 nm (perovskite), and 200 nm (Spiro-OMeTAD), based on common experimental values. All material parameters, including bandgap, electron affinity, carrier mobility, and defect densities, were sourced from experimental literature to ensure realistic simulations. The temperature was set at 300 K with AM1.5G illumination (100 mW/cm²), and carrier thermal velocities were set to 1×10 7 cm/s. Both Shockley–Read–Hall and interface recombination mechanisms were included. Figures 1 (a) and (b) illustrate the device structure and energy band diagram. The alignment of energy levels supports efficient charge separation and reduced recombination losses. Tables 1 and 2 summarize the key parameters used in the SCAPS-1D simulations for various ETLs and their corresponding interface defects with the perovskite absorber, respectively. Table 3 presents the key parameters for the different HTLs considered in this study. These values were carefully selected based on reported experimental data and validated simulation studies to ensure physical accuracy and to prevent non-physical device behavior. The accuracy and consistency of these parameters are critical for obtaining reliable and meaningful simulation outcomes. Table 1 Material parameters of FTO, absorber and ETLs are taken from literature and simulation studies. Parameter FTO TiO₂ CsₓFA₁₋ₓPbI₃ PCBM ZnO C₆₀ SnO₂ CdS WO₃ Thickness [nm] 500 30 820 50 50 50 50 30 50 Band gap [eV] 3.5 3.1 1.72 2.0 3.3 1.7 3.6 2.4 2.6 Electron affinity [eV] 4 4 4 3.9 4 3.9 4 4.2 3.8 Relative permittivity (ε r ) 9 9 25 3.9 9 4.2 9 10 4.8 CB effective density of states [cm⁻³] 2.2×10 18 2×10 18 2.2×10 18 2.5×10 21 3.7×10 18 8×10 19 2.2×10 18 2.2×10 18 2.2×10 21 VB effective density of states [cm⁻³] 1.8×10 19 1.8×10 19 1.8×10 19 2.5×10 21 1.8×10 19 8×10 19 1.8×10 19 1.8×10 19 2.2×10 21 Electron mobility [cm²/V·s] 20 50 1 0.2 100 8×10⁻² 100 100 30 Hole mobility [cm²/V·s] 10 10 1 0.2 25 3.5×10⁻³ 25 25 30 Donor density ND [cm⁻³] 2×10 19 9×10 16 1×10 15 2.93×10 13 1×10 18 1×10 18 1×10 17 1×10 18 6.35×10 17 Acceptor density NA [cm⁻³] 0 0 1×10 17 0 0 0 0 0 0 defect density Nt [cm⁻³] 1×10 15 1×10 17 1×10 15 1×10 15 1×10 15 1×10 15 1×10 15 1×10 15 1×10 15 Radiative recombination coefficient [cm³/s] 0 0 1×10 − 10 0 0 0 0 0 0 Reference [ 8 ] [ 9 ] - [ 8 ] [ 8 ] [ 8 ] [ 8 ] [ 10 ] [ 11 ] Table 2 Optimized Interface Defect Parameters for ETL/CsFAPbI₃ Perovskite Solar Cells in SCAPS Simulations. Note. σₙ = electron capture cross-section; σₚ = hole capture cross-section; Eₜ = defect energy level relative to conduction band (EC); Nₜ = defect density. [ 9 , 12 ] ETL Defect Type σₙ (cm 2 ) σₚ (cm 2 ) Eₜ Position Nₜ (cm − 2 ) Distribution TiO 2 Donor 10 − 16 10 − 16 EC − 0.75 eV 10 10 Gaussian PCBM Neutral 10 − 17 10 − 14 EC − 0.2 eV 10 10 Single ZnO Donor 10⁻¹⁵ 10 − 14 EC − 0.3 eV 5×10 10 Gaussian C 60 Neutral 10 − 18 10 − 14 EC − 0.2 eV 10 10 Single SnO 2 Donor 10 − 16 10 − 15 EC − 0.4 eV 5×10 10 Gaussian CdS Donor 10 − 15 10 − 14 EC − 0.5 eV 10 11 Gaussian WO 3 Donor 10 − 16 10 − 13 EC − 0.3 eV 10 12 Gaussian Table 3 Material parameters for hole transport layers (HTLs). Parameters Spiro-OMeTAD NiO Cu 2 O GO CuI P3HT Thickness [nm] 300 50 50 100 50 200 Band gap [eV] 2.88 3.8 2.17 2.48 3.1 1.05 Electron affinity [eV] 2.05 1.46 3.2 2.4 2.1 3.9 Relative permittivity (ε r ) 3 10 7.11 10 6.5 3 CB effective density of states [cm⁻³] 2.2×10 18 2.8×10 19 2.1×10 17 2.2×10 18 2.8×10 19 1×10 20 VB effective density of states [cm⁻³] 1.8×10 19 1×10 19 1.1×10 19 1.8×10 19 1×10 19 1×10 20 Electron mobility [cm²/V·s] 2×10 − 4 12 200 26 1×10 2 1×10 − 4 Hole mobility [cm²/V·s] 2×10 − 4 2.8 80 123 4.3×10 1 1×10 − 4 Donor density ND [cm⁻³] 2×10 − 4 0 0 0 0 0 Acceptor density NA [cm⁻³] 2×10 − 4 10 21 10 18 2×10 18 10 18 10 18 defect density Nt [cm⁻³] 10 14 10 14 10 14 10 14 10 15 0 Radiative recombination coefficient [cm³/s] 0 2.3×10 − 9 0 0 0 0 Reference [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 17 ] [ 18 ] The absorption coefficient of Cs x FA 1−x PbI 3 was defined using the direct bandgap Tauc model: α(hν) = A(hν − Eg​) 2 with a bandgap of 1.72 eV. The simulated absorption spectrum is shown in Fig. 2 a. The series resistance (Rs) and shunt resistance (R shunt ) were set to match experimental values of 9.8 Ω·cm² and 833 Ω·cm² [ 1 ] , respectively, ensuring that the model reflects realistic operating conditions. Figure 2 b compares the simulated and experimental J–V characteristics for the CsₓFA₁₋ₓPbI₃-based perovskite solar cell, while Table 4 provides a quantitative comparison of key photovoltaic parameters, including V OC , J SC , FF, and PCE. The close agreement between the curves and values confirms the SCAPS-1D model's capability to replicate real device behavior. Both the simulation and experimental results yield a PCE of 11.8%, confirming the validity of the model in predicting overall device performance. However, minor discrepancies are observed in certain parameters. The simulated V OC is slightly higher (0.97 V) than the experimental value (0.96 V), which can be attributed to the idealized recombination assumptions in SCAPS-1D that do not account for non-radiative losses present in real devices. Similarly, the simulated J SC is 18.8 mA/cm², exceeding the experimental 17.7 mA/cm², which may result from the model assuming perfect charge extraction and ignoring optical losses. Conversely, the fill factor is lower in simulation (64.5%) compared to the experimental value (69%), possibly due to overestimated recombination losses or underestimated charge transport efficiency. While the PCE alignment confirms the model’s validity, these differences highlight the need for further refinement of recombination modeling, transport properties, and optical effects to improve predictive accuracy. Table 4 Comparison of experimental and simulated photovoltaic results for the CsₓFA₁₋ₓPbI₃-based solar cell. R s and R shunt were used as input values in the simulation. Experimental data from Ref [ 1 ] . Device type V OC [V] J SC [mA/cm 2 ] FF [%] R s [Ω.cm 2 ] R shunt [Ω.cm 2 ] PCE [%] Experiment 0.96 17.7 69 9.8 833 11.8 SCAPS Simulation 0.97 18.8 64.5 9.8 833 11.8 To investigate the influence of key design parameters, a series of simulations were performed by varying the absorber layer thickness (500–1500 nm), the N t in the range of 10 13 –10 19 cm − 3 , and by evaluating different ETL and HTL materials. 3. Results and Discussion 3.1 Effect of Absorber Layer Thickness on Device Performance: The absorber layer in perovskite solar cells (PSCs) plays a critical role in absorbing incident photons and facilitating the generation and transport of charge carriers. In this study, the effect of Cs x FA 1−x PbI 3 absorber thickness was systematically investigated over a range of 500–1500 nm. The objective was to determine an optimal thickness that provides a trade-off between enhanced light absorption and efficient carrier collection, thereby maximizing the PCE. The J–V characteristics (Fig. 3 a) and quantum efficiency (QE) spectra (Fig. 3 b) demonstrate that increasing the absorber thickness enhances light absorption across the visible spectrum, particularly between 450–750 nm. As a result, J SC (Fig. 3 d) rises from ~ 17.2 mA/cm² to ~ 20.0 mA/cm² with increasing thickness. This trend is primarily driven by the greater optical path length in thicker films, which leads to increased photogeneration of charge carriers. However, J SC begins to saturate beyond ~ 1200 nm due to the absorber reaching near-total absorption of incident photons at its band edge. The V OC (Fig. 3 c) also improves slightly with thickness, from 0.955 V at 500 nm to nearly 0.996 V at 1500 nm. This increase is attributed to a higher carrier density under illumination and reduced recombination at lower thickness. V OC is closely linked to the separation of quasi-Fermi levels, which improves as photogeneration increases. However, the modest rise in V OC suggests that recombination still occurs in thicker layers and begins to offset the gains from improved photogeneration. The FF (Fig. 3 e) initially increases with absorber thickness, reaching a maximum of approximately 64.6% at around 1000 nm, and then exhibits a slight decline at greater thicknesses. This trend reflects the balance between charge generation and transport processes. At lower thicknesses, the improvement in FF is attributed to enhanced charge generation and efficient carrier collection. However, when the absorber thickness exceeds the carrier diffusion length, photogenerated carriers, particularly those generated deep within the layer, may recombine before reaching the selective contacts. The longer path increases both bulk recombination and series resistance, ultimately lowering FF. The PCE (Fig. 3 f) combines the effects of Voc, Jsc, and FF. It increases from ~ 10.5% at 500 nm to ~ 12.7% at 1500 nm. However, similar to Jsc, the efficiency gains taper off beyond 1200 nm. This saturation reflects a fundamental limitation: while thicker absorbers collect more photons, carrier recombination losses become significant when the absorber thickness exceeds the diffusion length of electrons and holes, especially in the absence of perfect passivation. The increasing likelihood of recombination counteracts the benefits of enhanced light absorption. These results highlight the importance of achieving a balance between optical absorption and carrier extraction. An optimal absorber thickness in the range of 1000–1200 nm provides the best compromise, enabling sufficient photon absorption while remaining within the carrier diffusion length. Maintaining the thickness within this range enhances charge extraction efficiency and minimizes bulk recombination losses. These insights are in agreement with both theoretical and experimental studies, which have shown that perovskite layers exceeding the carrier diffusion length (typically around 1 µm in high-quality perovskite films) tend to exhibit reduced performance due to increased bulk recombination. This performance loss can be mitigated through the use of optimized charge transport layers or effective surface passivation strategies. For example, Stranks et al . [ 19 ] demonstrated that efficient carrier extraction in perovskite films is highly dependent on the diffusion length and that recombination dominates when carriers are required to travel beyond this characteristic distance. 3.2 Absorber defect variation: The performance of PSCs is highly sensitive to the N t within the absorber layer, as defects act as trap centers that facilitate non-radiative recombination. To understand this effect, a simulation study was conducted by varying the Nt of the Cs x FA 1−x PbI 3 absorber layer from 1×10 13 to 1×10 19 cm − 3 , while keeping the absorber layer thickness fixed at 820 nm. The corresponding trends in J–V behavior and photovoltaic parameters are presented in Figs. 4 a– 4 e. As shown in the J–V curves (Fig. 4 a), increasing N t leads to a significant deterioration in current output and fill factor. For low defect densities (≤ 10 15 cm − 3 ), the curves are sharp and the output current is nearly unaffected. However, for N t ≥ 10 17 cm − 3 , the current density decreases significantly and the device exhibits increased resistive behavior, indicative of severe recombination losses. The V OC (Fig. 4 b) remains nearly constant (~ 0.97 V) for N t up to 10 15 cm − 3 but begins to decline sharply beyond 10 16 cm − 3 , reaching below 0.70 V at N t = 10 19 cm − 3 . This reduction is due to increased Shockley–Read–Hall (SRH) recombination, which narrows the quasi-Fermi level splitting under illumination. Higher N t introduces mid-gap states that accelerate carrier recombination before they can be collected. The Jsc (Fig. 4 c) follows a similar trend, with only a slight decline at N t ≤ 10 15 cm − 3 , but significant degradation occurs at higher defect levels. Jsc drops from ~ 18.7 mA/cm 2 at low N t to just ~ 9 mA/cm 2 at N t = 10 19 cm − 3 . This is attributed to the reduced carrier lifetime and diffusion length in the presence of dense recombination centers, which limit the ability of photogenerated carriers to reach the electrodes. The FF (Fig. 4 d) exhibits a steep decline beyond N t = 10 16 cm − 3 . While FF remains above 60% at lower defect levels, it drops drastically to below 10% at N t = 10 17 cm − 3 , indicating that the internal recombination current dominates and prevents efficient extraction of power at the maximum power point. At this point, the diode behavior deviates from ideal, reflecting a poor-quality junction. Consequently, the overall PCE (Fig. 4 e) decreases significantly with increasing N t . For N t ≤ 10 15 cm − 3 , the efficiency remains above 11%, while for N t = 10 18 cm − 3 it drops to ~ 4.5%, and further down to ~ 2.5% at N t = 10 19 cm − 3 . This emphasizes the critical role of absorber quality in achieving high device performance. These results underscore that bulk trap density must be maintained below ~ 10 16 cm − 3 to preserve high V OC , J SC , and FF, and to avoid excessive recombination. This aligns with experimental reports that show high-efficiency perovskite devices typically require low defect densities (~ 10 14 –10 15 cm − 3 ) for long diffusion lengths and suppressed non-radiative losses [ 19 , 20 ] . 3.3 Effect of ETL and HTL Variation on Device Performance : The selection of both ETLs and HTLs significantly influences the charge extraction, interfacial recombination, and overall efficiency of PSCs. In this study, a systematic simulation was conducted using seven different ETL materials i.e. TiO₂, PCBM, ZnO, C₆₀, SnO₂, CdS, and WO₃ and five HTLs materials: Spiro-OMeTAD, NiO, Cu₂O, CuI, and P3HT, while maintaining the same perovskite absorber (CsₓFA₁₋ₓPbI₃, 820 nm). Material parameters are listed in Table 1 . Interface defects (Table 2 ) were customized for each material: metal oxide ETLs included donor-type defects (e.g., oxygen vacancies) with Gaussian energy distributions (FWHM = 0.1–0.15 eV), while organic ETLs (PCBM, C₆₀) and HTLs were modeled with neutral traps using discrete single-level distributions. These parameterizations, grounded in first-principles defect studies, enable a comprehensive comparison of interfacial behavior and performance trends across all ETL/HTL combinations. Figure 5 shows the simulated J–V characteristics of CsₓFA₁₋ₓPbI₃ - based solar cells, illustrating the strong influence of both ETL and HTL materials on device performance. In all subfigures, devices incorporating NiO or Cu₂O as HTLs consistently exhibit superior photovoltaic parameters- higher Voc, Jsc, and FF. This is attributed to better energy level alignment and more efficient hole extraction, resulting in reduced interfacial recombination. Among the ETLs, devices using C₆₀ (Fig. 5 d), ZnO (Fig. 5 c), and SnO ₂ (Fig. 5 e) demonstrate slightly improved performance compared to those using TiO₂ (Fig. 5 a), PCBM (Fig. 5 b), and CdS (Fig. 5 f). This is likely due to the higher electron mobility and more favorable conduction band alignment of C₆₀, ZnO, and SnO₂ with the perovskite layer. The best-performing combinations are observed in Fig. 5 d and 5 e, where C₆₀/NiO and SnO₂/NiO deliver the most efficient charge extraction with minimal recombination. Conversely, devices using P3HT or Spiro-OMeTAD show lower Voc and Jsc values across all ETLs, as seen in the orange and black curves in each panel. This performance drop is likely due to suboptimal energy level alignment and higher interfacial defect densities. Overall, the data across Figs. 5 a– 5 g highlight that device efficiency depends not just on the individual ETL or HTL, but on their synergistic interaction. Careful selection and pairing of ETL/HTL materials is thus critical for optimizing the performance of CsₓFA₁₋ₓPbI₃-based perovskite solar cells. The trends observed in the J–V curves of Fig. 5 are quantitatively confirmed by the performance parameters shown in Fig. 6 . In particular, the bar charts clearly show that devices incorporating NiO and Cu 2 O as HTLs consistently achieve higher PCEs, mainly driven by their superior V OC and FF. For example, in Fig. 6 d (C₆₀-based devices), the combinations with NiO and Cu 2 O yield the highest PCEs of 18.29% and 18.06%, respectively matching the peak performance curves seen in Fig. 5 d. Similarly, ZnO (Fig. 6 c) and SnO 2 (Fig. 6 e) show improved efficiencies compared to TiO₂ and CdS, especially when paired with NiO or Cu₂O, confirming their advantageous electron transport and interfacial alignment. On the other hand, devices employing P3HT and Spiro-OMeTAD consistently exhibit lower V OC and FF values across all ETLs, resulting in reduced PCSs. This trend is evident in the last two bars of each subplot in Fig. 6 . This supports the conclusion that these HTLs introduce either interfacial mismatches or higher defect densities that limit charge extraction efficiency. The combined analysis of Fig. 5 and Fig. 6 confirms that optimal device performance is not solely determined by a single transport layer, but rather by the energy level alignment between the ETL and HTL materials. These findings underscore the importance of energy level matching and defect management at both interfaces to minimize recombination losses and maximize charge extraction. Conclusion In this work, a detailed numerical analysis of CsₓFA₁₋ₓPbI₃-based perovskite solar cells was carried out using the SCAPS-1D simulation tool to explore various design parameters influencing device performance. The simulation model was first validated against experimental data, confirming its accuracy with a matched PCE of 11.8%. Subsequent parametric studies revealed that the absorber thickness plays a crucial role, with the optimal range identified between 1000 and 1200 nm, balancing light absorption and charge collection. Moreover, maintaining the N t of the absorber below 10 16 cm − 3 was shown to be essential to suppress non-radiative recombination and retain high photovoltaic output. A comprehensive comparison of seven ETLs and five HTLs demonstrated the significant impact of transport layers on overall device efficiency. Among all configurations, the C₆₀/NiO combination delivered the best performance, reaching a PCE of 18.29%, which is attributed to favorable energy level alignment and minimal interfacial recombination losses. These findings emphasize the importance of concurrently optimizing both the absorber layer properties and the interfacial layers in the development of high-performance perovskite solar cells. The results presented in this study offer valuable guidelines for experimental implementation and contribute to a deeper understanding of device physics through simulation-based design strategies. Declarations Competing interests The author declares no competing interests Additional information Requests for further details or materials related to this study should be directed to A.A Author Contribution A.A solely conducted the research, including the design and implementation of simulations, data interpretation, writing of the manuscript, and approval of the final version for submission. Acknowledgement The SCAPS-1D simulation software, developed by Dr. Marc Burgelman at the University of Gent, Belgium, is gratefully acknowledged by the author. 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Anand, M. N. Tripathi, N. Srivastava, A. K. Sharma, M. Yoshimura, L. Chang, R. N. Tiwari, Sci. Rep. 2023 , 13 , 18411. S. Hosseini, N. Delibaş, M. Bahramgour, A. T. Mashayekh, A. Niaie, Avrupa Bilim ve Teknol. Derg. 2021 , 121. S. D. Stranks, G. E. Eperon, G. Grancini, C. Menelaou, M. J. P. Alcocer, T. Leijtens, L. M. Herz, A. Petrozza, H. J. Snaith, Science (80-. ). 2013 , 342 , 341. D. W. de Quilettes, S. M. Vorpahl, S. D. Stranks, H. Nagaoka, G. E. Eperon, M. E. Ziffer, H. J. Snaith, D. S. Ginger, Science (80-. ). 2015 , 348 , 683. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6894058","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473558092,"identity":"e8bde39a-1a70-445e-8838-0cff158bfd40","order_by":0,"name":"Abdullah H. Alshehri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3Qv0vDQBTA8RcOMh1krVTMv/CCIBQJ/Vc8Ap2sFFycDiGQyeoasX/EuXXzhQd26R8QqEt2h06i4OCzQsHh/LEJ3nd6HPlweQcQCv3FeoAAEziSAWi9PfyW4Adp6l8TpX9CkpvSwTPak6Q/7Tif2xRJNSsNNvVe8nA/iS6QT3dmC+TxkjNHcXGogbNzn2mPETSSce0IeFxR5Egf9DVQ5COpkOgVrbl7J4PKDh0lT0Ls0EdQiNKojOsJiSoZSMdClPGRTHbhXWRTyy3NdMnFNcf7gxly4SN7q/K2ezyz5qoeqfXL3OaXi7Jr5ST3ri/RdtqsrDY//MX3n/K+UigUCv3r3gAdeFu2BqjIjgAAAABJRU5ErkJggg==","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"H.","lastName":"Alshehri","suffix":""}],"badges":[],"createdAt":"2025-06-14 12:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6894058/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6894058/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85189381,"identity":"6ae76600-1891-45bf-a56b-63b2e727c19b","added_by":"auto","created_at":"2025-06-23 08:39:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":316389,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Solar cell structure, (b) Energy band diagram for FTO/TiO₂ / CsₓFA₁₋ₓPbI₃ /Spiro-OMeTAD /Al solar cell.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/ea86e159a541d7018eae718e.png"},{"id":85189405,"identity":"7dc90312-7f26-458f-b32d-3fb7f03faba2","added_by":"auto","created_at":"2025-06-23 08:39:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":504071,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Simulated absorption of Cs\u003csub\u003ex\u003c/sub\u003eFA\u003csub\u003e1-x\u003c/sub\u003ePbI\u003csub\u003e3\u003c/sub\u003e. (b) Simulated vs. experimental J–V curves validating the SCAPS-1D model using data from Ref \u003csup\u003e[1]\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/e177aac1757e182efab6a124.png"},{"id":85189382,"identity":"89810193-6c76-42bb-8b26-58ff7f373a83","added_by":"auto","created_at":"2025-06-23 08:39:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1011543,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Cs\u003csub\u003ex\u003c/sub\u003eFA\u003csub\u003e1-x\u003c/sub\u003ePbI\u003csub\u003e3\u003c/sub\u003e absorber layer thickness (500–1500 nm) on device performance: a) J–V characteristics under, b) QE spectra, c) Voc, d) Jsc, e) FF, and f) PCE.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/e1ec851a57a94c97efe1e694.png"},{"id":85189406,"identity":"3786a5bd-35cd-4262-b67d-2f71499a494f","added_by":"auto","created_at":"2025-06-23 08:39:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":889015,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of absorber N\u003csub\u003et\u003c/sub\u003e on device performance for CsₓFA₁₋ₓPbI₃ at 820 nm thickness:(a) J–V characteristics; (b) Voc; (c) Jsc; (d) FF; (e) PCE.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/90181478e4bcae24dcf25056.png"},{"id":85189411,"identity":"4dc7346e-27b9-430c-b0a6-a572e8009390","added_by":"auto","created_at":"2025-06-23 08:39:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2077942,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated J–V curves of CsₓFA₁₋ₓPbI₃ solar cells with various ETL/HTL combinations. Figures (a–g) show results for different ETLs (TiO₂ to WO₃) paired with HTLs: Spiro (black), NiO (red), Cu₂O (blue), CuI (green), and P3HT (orange).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/9d6338e784e439508a8e8dbf.png"},{"id":85189384,"identity":"ce5ad564-0dd0-4f5a-a3be-32bc8f6671bf","added_by":"auto","created_at":"2025-06-23 08:39:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":975526,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of CsₓFA₁₋ₓPbI₃ on PSC characteristics for various HTLs with Au as back metal contact and ETLs: (a) TiO\u003csub\u003e2\u003c/sub\u003e, (b) PCBM (c) ZnO, (d) C\u003csub\u003e60\u003c/sub\u003e,(e) SnO\u003csub\u003e2\u003c/sub\u003e, and (f) CdS (g) WO\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/8696abe1341df8d1399ff252.png"},{"id":88428645,"identity":"e78ac99f-29d4-4c54-ac26-d6c91eef3162","added_by":"auto","created_at":"2025-08-06 10:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6687929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6894058/v1/c31f2b67-5077-4a4f-b1a2-49d4bc1aaa1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Numerical Optimization of CsₓFA₁₋ₓPbI₃-Based Perovskite Solar Cells Using SCAPS-1D","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePerovskite solar cells (PSCs) have emerged as one of the most promising photovoltaic technologies over the last decade due to their remarkable PCE, low fabrication cost, and ease of processing. Since their inception, PSCs have achieved significant progress, with efficiencies exceeding 25%, placing them in direct competition with conventional silicon-based solar cells. Perovskite materials have attracted significant interest for next-generation solar energy applications due to their superior optoelectronic properties, including high absorption coefficients, tunable bandgaps, and long carrier diffusion lengths. Despite their impressive performance, further optimization of the device structure and reduction of loss mechanisms are required to realize their full potential. Among the different perovskite compositions studied, the mixed-cation perovskite CsₓFA₁₋ₓPbI₃ has demonstrated excellent thermal stability, a suitable bandgap for photovoltaic applications, and improved structural integrity, making it a promising candidate for efficient and stable perovskite solar cells.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The combination of cesium (Cs⁺) and formamidinium (FA⁺) cations mitigates the instability often observed in purely organic perovskites, such as methylammonium lead iodide (MAPbI\u003csub\u003e3\u003c/sub\u003e). Cs⁺ contributes to improved thermal stability and lattice rigidity, while FA⁺ enables the tuning of the bandgap \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, which are ideal for single-junction solar cells. Additionally, CsₓFA₁₋ₓPbI₃ exhibits superior light absorption and charge transport properties, making it an excellent candidate for achieving high-efficiency devices\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Its stability under varying environmental conditions further addresses one of the key challenges in PSC development long-term operational durability. Recently, many approaches have been presented to improve the performance of CsPbI₃-based devices, including compositional engineering, surface passivation, and the incorporation of stabilizing additives to suppress defects and enhance structural stability\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. These strategies have led to notable enhancements in both power conversion efficiency and operational stability, highlighting the versatility and promise of CsPbI₃ and its derivatives for photovoltaic applications. The device architecture of PSCs is a critical factor that influences overall performance, including efficiency and long-term stability. A typical device consists of a multilayer structure: a transparent conductive oxide (TCO) substrate, ETL, perovskite absorber layer, HTL, and metal back contact. The interface properties, carrier recombination rates, and layer thicknesses directly influence key parameters such as the open-circuit voltage (V\u003csub\u003eOC\u003c/sub\u003e), short-circuit current density (J\u003csub\u003eSC\u003c/sub\u003e), fill factor (FF), and overall PCE. Numerical simulation tools, such as SCAPS-1D (Solar Cell Capacitance Simulator) is commonly used as 1D simulation tool tailored for thin-film solar cells developed by the University of Ghent\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. It provides a robust platform for studying the impact of various material and structural parameters on device performance. By simulating the device behavior, researchers can gain insight into performance limitations and investigate optimization strategies without the need for time-consuming and costly experimental fabrication.\u003c/p\u003e \u003cp\u003eIn this study, we focus on enhancing the performance of PSCs using SCAPS-1D simulations. To validate the accuracy of our simulation approach, we simulate a perovskite solar cell with the structure FTO/TiO₂/ CsₓFA₁₋ₓPbI₃/Spiro-OMeTAD/Al as reported experimentally in Ref\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This step ensures that our simulation parameters, including material properties and boundary conditions, are consistent with experimental results. Following this validation, we systematically investigate performance enhancement strategies by optimizing absorber thicknesses, defect densities of the absorber. Additionally, we explore alternative electron and hole transport layers to further improve device efficiency. To get reliable and realistic results. We avoid varying parameters which could influence other dependent parameters such as band gap, relative permittivity and electron affinities of the materials as demonstrated in Ref\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe outcomes of this study provide a comprehensive understanding of the factors influencing PSC performance and highlight potential pathways for achieving higher efficiencies. By utilizing numerical simulations, this study demonstrates that systematic optimization of device parameters can result in significant improvements in performance, offering a pathway toward more efficient and stable perovskite solar cells.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThe SCAPS-1D simulation tool was developed to numerically solve the fundamental equations governing one-dimensional semiconductor devices, including Poisson\u0026rsquo;s equation, and the continuity equations for electrons and holes. The governing equations are expressed as follows equations:\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{d}{dx}(-\\epsilon\\:\\left(x\\right)\\:\\frac{d\\psi\\:}{dx})=q[p\\left(x\\right)-n\\left(x\\right)+{N}_{d}^{+}(x)-{N}_{a}^{-}(x\\left)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:{j}_{n}}{\\partial\\:x}=q\\left({R}_{n}-G+\\frac{\\partial\\:n}{\\partial\\:t}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:{j}_{p}}{\\partial\\:x}=-q\\left({R}_{p}-G+\\frac{\\partial\\:p}{\\partial\\:t}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eψ\u003c/em\u003e represents the electrostatic potential, and \u003cem\u003eε\u003c/em\u003e is the permittivity of the medium. The symbols \u003cem\u003en\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e refer to the concentrations of free electrons and holes, respectively. The terms \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{d}^{+}\\)\u003c/span\u003e\u003c/span\u003e​ and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{a}^{-}\\)\u003c/span\u003e\u003c/span\u003e denote the densities of ionized donors and acceptors, respectively. The quantities \u003cem\u003ej\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ej\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e ​represent the current densities of electrons and holes, while \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e ​indicate the net recombination rates of electrons and holes per unit volume, respectively. Finally, \u003cem\u003eG\u003c/em\u003e denotes the generation rate of charge carriers per unit volume. The simulated structure is FTO/TiO₂/CsₓFA₁₋ₓPbI₃/Spiro-OMeTAD/Al. FTO acts as the transparent conductive oxide, TiO₂ as the ETL, CsₓFA₁₋ₓPbI₃ as the perovskite absorber, Spiro-OMeTAD as HTL, and Al as the back contact. Initial thicknesses were 50 nm (TiO₂), 300 nm (perovskite), and 200 nm (Spiro-OMeTAD), based on common experimental values. All material parameters, including bandgap, electron affinity, carrier mobility, and defect densities, were sourced from experimental literature to ensure realistic simulations. The temperature was set at 300 K with AM1.5G illumination (100 mW/cm\u0026sup2;), and carrier thermal velocities were set to 1\u0026times;10\u003csup\u003e7\u003c/sup\u003e cm/s. Both Shockley\u0026ndash;Read\u0026ndash;Hall and interface recombination mechanisms were included.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a) and (b) illustrate the device structure and energy band diagram. The alignment of energy levels supports efficient charge separation and reduced recombination losses. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the key parameters used in the SCAPS-1D simulations for various ETLs and their corresponding interface defects with the perovskite absorber, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the key parameters for the different HTLs considered in this study. These values were carefully selected based on reported experimental data and validated simulation studies to ensure physical accuracy and to prevent non-physical device behavior. The accuracy and consistency of these parameters are critical for obtaining reliable and meaningful simulation outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterial parameters of FTO, absorber and ETLs are taken from literature and simulation studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTiO₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCsₓFA₁₋ₓPbI₃\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePCBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZnO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC₆₀\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSnO₂\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCdS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWO₃\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness [nm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand gap [eV]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectron affinity [eV]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative permittivity (ε\u003csub\u003er\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB effective density of states [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026times;10\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVB effective density of states [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026times;10\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectron mobility [cm\u0026sup2;/V\u0026middot;s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u0026times;10⁻\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHole mobility [cm\u0026sup2;/V\u0026middot;s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor density ND [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026times;10\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.93\u0026times;10\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.35\u0026times;10\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptor density NA [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edefect density Nt [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiative recombination coefficient [cm\u0026sup3;/s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOptimized Interface Defect Parameters for ETL/CsFAPbI₃ Perovskite Solar Cells in SCAPS Simulations. Note.\u003c/em\u003e σₙ = electron capture cross-section; σₚ = hole capture cross-section; Eₜ = defect energy level relative to conduction band (EC); Nₜ = defect density.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσₙ (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eσₚ (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEₜ Position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNₜ (cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.75 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.2 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZnO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10⁻\u0026sup1;⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.3 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u0026times;10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e60\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.2 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.4 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u0026times;10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCdS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.5 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDonor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEC \u0026minus;\u0026thinsp;0.3 eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterial parameters for hole transport layers (HTLs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpiro-OMeTAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCu\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCuI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP3HT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness [nm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand gap [eV]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectron affinity [eV]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative permittivity (ε\u003csub\u003er\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB effective density of states [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026times;10\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVB effective density of states [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectron mobility [cm\u0026sup2;/V\u0026middot;s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHole mobility [cm\u0026sup2;/V\u0026middot;s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3\u0026times;10\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor density ND [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptor density NA [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026times;10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edefect density Nt [cm⁻\u0026sup3;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiative recombination coefficient [cm\u0026sup3;/s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe absorption coefficient of Cs\u003csub\u003ex\u003c/sub\u003eFA\u003csub\u003e1\u0026minus;x\u003c/sub\u003ePbI\u003csub\u003e3\u003c/sub\u003e was defined using the direct bandgap Tauc model: α(hν)\u0026thinsp;=\u0026thinsp;A(hν\u0026thinsp;\u0026minus;\u0026thinsp;Eg​)\u003csup\u003e2\u003c/sup\u003e with a bandgap of 1.72 eV. The simulated absorption spectrum is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003eThe series resistance (Rs) and shunt resistance (R\u003csub\u003eshunt\u003c/sub\u003e) were set to match experimental values of 9.8 Ω\u0026middot;cm\u0026sup2; and 833 Ω\u0026middot;cm\u0026sup2;\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, respectively, ensuring that the model reflects realistic operating conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb compares the simulated and experimental J\u0026ndash;V characteristics for the CsₓFA₁₋ₓPbI₃-based perovskite solar cell, while Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a quantitative comparison of key photovoltaic parameters, including V\u003csub\u003eOC\u003c/sub\u003e, J\u003csub\u003eSC\u003c/sub\u003e, FF, and PCE. The close agreement between the curves and values confirms the SCAPS-1D model's capability to replicate real device behavior. Both the simulation and experimental results yield a PCE of 11.8%, confirming the validity of the model in predicting overall device performance. However, minor discrepancies are observed in certain parameters. The simulated V\u003csub\u003eOC\u003c/sub\u003e is slightly higher (0.97 V) than the experimental value (0.96 V), which can be attributed to the idealized recombination assumptions in SCAPS-1D that do not account for non-radiative losses present in real devices. Similarly, the simulated J\u003csub\u003eSC\u003c/sub\u003e is 18.8 mA/cm\u0026sup2;, exceeding the experimental 17.7 mA/cm\u0026sup2;, which may result from the model assuming perfect charge extraction and ignoring optical losses. Conversely, the fill factor is lower in simulation (64.5%) compared to the experimental value (69%), possibly due to overestimated recombination losses or underestimated charge transport efficiency. While the PCE alignment confirms the model\u0026rsquo;s validity, these differences highlight the need for further refinement of recombination modeling, transport properties, and optical effects to improve predictive accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of experimental and simulated photovoltaic results for the CsₓFA₁₋ₓPbI₃-based solar cell. R\u003csub\u003es\u003c/sub\u003e and R\u003csub\u003eshunt\u003c/sub\u003e were used as input values in the simulation. Experimental data from Ref \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003csub\u003eOC\u003c/sub\u003e [V]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJ\u003csub\u003eSC\u003c/sub\u003e [mA/cm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFF [%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csub\u003es\u003c/sub\u003e [Ω.cm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csub\u003eshunt\u003c/sub\u003e [Ω.cm\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePCE [%]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCAPS Simulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo investigate the influence of key design parameters, a series of simulations were performed by varying the absorber layer thickness (500\u0026ndash;1500 nm), the N\u003csub\u003et\u003c/sub\u003e in the range of 10\u003csup\u003e13\u003c/sup\u003e\u0026ndash;10\u003csup\u003e19\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and by evaluating different ETL and HTL materials.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effect of Absorber Layer Thickness on Device Performance:\u003c/h2\u003e \u003cp\u003eThe absorber layer in perovskite solar cells (PSCs) plays a critical role in absorbing incident photons and facilitating the generation and transport of charge carriers. In this study, the effect of Cs\u003csub\u003ex\u003c/sub\u003eFA\u003csub\u003e1−x\u003c/sub\u003ePbI\u003csub\u003e3\u003c/sub\u003e absorber thickness was systematically investigated over a range of 500–1500 nm. The objective was to determine an optimal thickness that provides a trade-off between enhanced light absorption and efficient carrier collection, thereby maximizing the PCE.\u003c/p\u003e \u003cp\u003eThe J–V characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and quantum efficiency (QE) spectra (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) demonstrate that increasing the absorber thickness enhances light absorption across the visible spectrum, particularly between 450–750 nm. As a result, J\u003csub\u003eSC\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) rises from ~ 17.2 mA/cm² to ~ 20.0 mA/cm² with increasing thickness. This trend is primarily driven by the greater optical path length in thicker films, which leads to increased photogeneration of charge carriers. However, J\u003csub\u003eSC\u003c/sub\u003e begins to saturate beyond ~ 1200 nm due to the absorber reaching near-total absorption of incident photons at its band edge.\u003c/p\u003e \u003cp\u003eThe V\u003csub\u003eOC\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) also improves slightly with thickness, from 0.955 V at 500 nm to nearly 0.996 V at 1500 nm. This increase is attributed to a higher carrier density under illumination and reduced recombination at lower thickness. V\u003csub\u003eOC\u003c/sub\u003e is closely linked to the separation of quasi-Fermi levels, which improves as photogeneration increases. However, the modest rise in V\u003csub\u003eOC\u003c/sub\u003e suggests that recombination still occurs in thicker layers and begins to offset the gains from improved photogeneration.\u003c/p\u003e \u003cp\u003eThe FF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) initially increases with absorber thickness, reaching a maximum of approximately 64.6% at around 1000 nm, and then exhibits a slight decline at greater thicknesses. This trend reflects the balance between charge generation and transport processes. At lower thicknesses, the improvement in FF is attributed to enhanced charge generation and efficient carrier collection. However, when the absorber thickness exceeds the carrier diffusion length, photogenerated carriers, particularly those generated deep within the layer, may recombine before reaching the selective contacts. The longer path increases both bulk recombination and series resistance, ultimately lowering FF.\u003c/p\u003e \u003cp\u003eThe PCE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef) combines the effects of Voc, Jsc, and FF. It increases from ~ 10.5% at 500 nm to ~ 12.7% at 1500 nm. However, similar to Jsc, the efficiency gains taper off beyond 1200 nm. This saturation reflects a fundamental limitation: while thicker absorbers collect more photons, carrier recombination losses become significant when the absorber thickness exceeds the diffusion length of electrons and holes, especially in the absence of perfect passivation. The increasing likelihood of recombination counteracts the benefits of enhanced light absorption.\u003c/p\u003e \u003cp\u003eThese results highlight the importance of achieving a balance between optical absorption and carrier extraction. An optimal absorber thickness in the range of 1000–1200 nm provides the best compromise, enabling sufficient photon absorption while remaining within the carrier diffusion length. Maintaining the thickness within this range enhances charge extraction efficiency and minimizes bulk recombination losses.\u003c/p\u003e \u003cp\u003eThese insights are in agreement with both theoretical and experimental studies, which have shown that perovskite layers exceeding the carrier diffusion length (typically around 1 µm in high-quality perovskite films) tend to exhibit reduced performance due to increased bulk recombination. This performance loss can be mitigated through the use of optimized charge transport layers or effective surface passivation strategies. For example, Stranks \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e demonstrated that efficient carrier extraction in perovskite films is highly dependent on the diffusion length and that recombination dominates when carriers are required to travel beyond this characteristic distance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Absorber defect variation:\u003c/h2\u003e \u003cp\u003eThe performance of PSCs is highly sensitive to the N\u003csub\u003et\u003c/sub\u003e within the absorber layer, as defects act as trap centers that facilitate non-radiative recombination. To understand this effect, a simulation study was conducted by varying the Nt of the Cs\u003csub\u003ex\u003c/sub\u003eFA\u003csub\u003e1−x\u003c/sub\u003ePbI\u003csub\u003e3\u003c/sub\u003e absorber layer from 1×10\u003csup\u003e13\u003c/sup\u003e to 1×10\u003csup\u003e19\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, while keeping the absorber layer thickness fixed at 820 nm.\u003c/p\u003e \u003cp\u003eThe corresponding trends in J–V behavior and photovoltaic parameters are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea–\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee. As shown in the J–V curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), increasing N\u003csub\u003et\u003c/sub\u003e leads to a significant deterioration in current output and fill factor. For low defect densities (≤ 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e), the curves are sharp and the output current is nearly unaffected. However, for N\u003csub\u003et\u003c/sub\u003e ≥ 10\u003csup\u003e17\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, the current density decreases significantly and the device exhibits increased resistive behavior, indicative of severe recombination losses.\u003c/p\u003e \u003cp\u003eThe V\u003csub\u003eOC\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) remains nearly constant (~ 0.97 V) for N\u003csub\u003et\u003c/sub\u003e up to 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e but begins to decline sharply beyond 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, reaching below 0.70 V at N\u003csub\u003et\u003c/sub\u003e= 10\u003csup\u003e19\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e. This reduction is due to increased Shockley–Read–Hall (SRH) recombination, which narrows the quasi-Fermi level splitting under illumination. Higher N\u003csub\u003et\u003c/sub\u003e introduces mid-gap states that accelerate carrier recombination before they can be collected.\u003c/p\u003e \u003cp\u003eThe Jsc (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) follows a similar trend, with only a slight decline at N\u003csub\u003et\u003c/sub\u003e ≤ 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, but significant degradation occurs at higher defect levels. Jsc drops from ~ 18.7 mA/cm\u003csup\u003e2\u003c/sup\u003e at low N\u003csub\u003et\u003c/sub\u003e to just ~ 9 mA/cm\u003csup\u003e2\u003c/sup\u003e at N\u003csub\u003et\u003c/sub\u003e = 10\u003csup\u003e19\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e. This is attributed to the reduced carrier lifetime and diffusion length in the presence of dense recombination centers, which limit the ability of photogenerated carriers to reach the electrodes.\u003c/p\u003e \u003cp\u003eThe FF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) exhibits a steep decline beyond N\u003csub\u003et\u003c/sub\u003e = 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e. While FF remains above 60% at lower defect levels, it drops drastically to below 10% at N\u003csub\u003et\u003c/sub\u003e = 10\u003csup\u003e17\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, indicating that the internal recombination current dominates and prevents efficient extraction of power at the maximum power point. At this point, the diode behavior deviates from ideal, reflecting a poor-quality junction. Consequently, the overall PCE (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) decreases significantly with increasing N\u003csub\u003et\u003c/sub\u003e. For N\u003csub\u003et\u003c/sub\u003e ≤ 10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e, the efficiency remains above 11%, while for N\u003csub\u003et\u003c/sub\u003e = 10\u003csup\u003e18\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e it drops to ~ 4.5%, and further down to ~ 2.5% at N\u003csub\u003et\u003c/sub\u003e = 10\u003csup\u003e19\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e. This emphasizes the critical role of absorber quality in achieving high device performance. These results underscore that bulk trap density must be maintained below ~ 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e to preserve high V\u003csub\u003eOC\u003c/sub\u003e, J\u003csub\u003eSC\u003c/sub\u003e, and FF, and to avoid excessive recombination. This aligns with experimental reports that show high-efficiency perovskite devices typically require low defect densities (~ 10\u003csup\u003e14\u003c/sup\u003e–10\u003csup\u003e15\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e) for long diffusion lengths and suppressed non-radiative losses \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 Effect of ETL and HTL Variation on Device Performance\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe selection of both ETLs and HTLs significantly influences the charge extraction, interfacial recombination, and overall efficiency of PSCs. In this study, a systematic simulation was conducted using seven different ETL materials i.e. TiO₂, PCBM, ZnO, C₆₀, SnO₂, CdS, and WO₃ and five HTLs materials: Spiro-OMeTAD, NiO, Cu₂O, CuI, and P3HT, while maintaining the same perovskite absorber (CsₓFA₁₋ₓPbI₃, 820 nm). Material parameters are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Interface defects (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were customized for each material: metal oxide ETLs included donor-type defects (e.g., oxygen vacancies) with Gaussian energy distributions (FWHM = 0.1–0.15 eV), while organic ETLs (PCBM, C₆₀) and HTLs were modeled with neutral traps using discrete single-level distributions. These parameterizations, grounded in first-principles defect studies, enable a comprehensive comparison of interfacial behavior and performance trends across all ETL/HTL combinations.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the simulated J–V characteristics of CsₓFA₁₋ₓPbI₃\u003cb\u003e-\u003c/b\u003ebased solar cells, illustrating the strong influence of both ETL and HTL materials on device performance. In all subfigures, devices incorporating NiO or Cu₂O as HTLs consistently exhibit superior photovoltaic parameters- higher Voc, Jsc, and FF. This is attributed to better energy level alignment and more efficient hole extraction, resulting in reduced interfacial recombination. Among the ETLs, devices using C₆₀ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), ZnO (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), and SnO\u003cb\u003e₂\u003c/b\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) demonstrate slightly improved performance compared to those using TiO₂ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), PCBM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), and CdS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). This is likely due to the higher electron mobility and more favorable conduction band alignment of C₆₀, ZnO, and SnO₂ with the perovskite layer. The best-performing combinations are observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, where C₆₀/NiO and SnO₂/NiO deliver the most efficient charge extraction with minimal recombination. Conversely, devices using P3HT or Spiro-OMeTAD show lower Voc and Jsc values across all ETLs, as seen in the orange and black curves in each panel. This performance drop is likely due to suboptimal energy level alignment and higher interfacial defect densities. Overall, the data across Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea–\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg highlight that device efficiency depends not just on the individual ETL or HTL, but on their synergistic interaction. Careful selection and pairing of ETL/HTL materials is thus critical for optimizing the performance of CsₓFA₁₋ₓPbI₃-based perovskite solar cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe trends observed in the J–V curves of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are quantitatively confirmed by the performance parameters shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In particular, the bar charts clearly show that devices incorporating NiO and Cu\u003csub\u003e2\u003c/sub\u003eO as HTLs consistently achieve higher PCEs, mainly driven by their superior V\u003csub\u003eOC\u003c/sub\u003e and FF. For example, in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed (C₆₀-based devices), the combinations with NiO and Cu\u003csub\u003e2\u003c/sub\u003eO yield the highest PCEs of 18.29% and 18.06%, respectively matching the peak performance curves seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed. Similarly, ZnO (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) and SnO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) show improved efficiencies compared to TiO₂ and CdS, especially when paired with NiO or Cu₂O, confirming their advantageous electron transport and interfacial alignment.\u003c/p\u003e \u003cp\u003eOn the other hand, devices employing P3HT and Spiro-OMeTAD consistently exhibit lower V\u003csub\u003eOC\u003c/sub\u003e and FF values across all ETLs, resulting in reduced PCSs. This trend is evident in the last two bars of each subplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. This supports the conclusion that these HTLs introduce either interfacial mismatches or higher defect densities that limit charge extraction efficiency.\u003c/p\u003e \u003cp\u003eThe combined analysis of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e confirms that optimal device performance is not solely determined by a single transport layer, but rather by the energy level alignment between the ETL and HTL materials. These findings underscore the importance of energy level matching and defect management at both interfaces to minimize recombination losses and maximize charge extraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this work, a detailed numerical analysis of CsₓFA₁₋ₓPbI₃-based perovskite solar cells was carried out using the SCAPS-1D simulation tool to explore various design parameters influencing device performance. The simulation model was first validated against experimental data, confirming its accuracy with a matched PCE of 11.8%. Subsequent parametric studies revealed that the absorber thickness plays a crucial role, with the optimal range identified between 1000 and 1200 nm, balancing light absorption and charge collection. Moreover, maintaining the N\u003csub\u003et\u003c/sub\u003e of the absorber below 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e was shown to be essential to suppress non-radiative recombination and retain high photovoltaic output. A comprehensive comparison of seven ETLs and five HTLs demonstrated the significant impact of transport layers on overall device efficiency. Among all configurations, the C₆₀/NiO combination delivered the best performance, reaching a PCE of 18.29%, which is attributed to favorable energy level alignment and minimal interfacial recombination losses. These findings emphasize the importance of concurrently optimizing both the absorber layer properties and the interfacial layers in the development of high-performance perovskite solar cells. The results presented in this study offer valuable guidelines for experimental implementation and contribute to a deeper understanding of device physics through simulation-based design strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author declares no competing interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eRequests for further details or materials related to this study should be directed to A.A\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.A solely conducted the research, including the design and implementation of simulations, data interpretation, writing of the manuscript, and approval of the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe SCAPS-1D simulation software, developed by Dr. Marc Burgelman at the University of Gent, Belgium, is gratefully acknowledged by the author. The author extends his appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/01/31709).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data and materials used in this study are available from the author and will be provided to the journal upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eK. Ibrahim, A. Shahin, A. Jones, A. H. Alshehri, K. Mistry, M. D. Singh, F. Ye, J. Sanderson, M. Yavuz, K. P. Musselman, \u003cem\u003eSol. Energy\u003c/em\u003e \u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e224\u003c/em\u003e, 787.\u003c/li\u003e\n\u003cli\u003eW. H. Jeong, J. Ye, J. Kim, R. Xu, X. Shen, C.-Y. Chang, E. L. Quinn, M. H. Song, P. Nellist, H. J. Snaith, \u003cem\u003earXiv Prepr. arXiv2505.22817\u003c/em\u003e \u003cstrong\u003e2025\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eZ. Zheng, S. Wang, Y. Hu, Y. Rong, A. 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Ginger, \u003cem\u003eScience (80-. ).\u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e348\u003c/em\u003e, 683.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6894058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6894058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive numerical investigation of CsₓFA₁₋ₓPbI₃-based perovskite solar cells (PSCs) using the SCAPS-1D simulation software. A previously reported device architecture was first simulated to validate the model, yielding a power conversion efficiency (PCE) of 11.8%, closely matching experimental data. Following validation, key parameters were systematically optimized, including absorber thickness, defect density (Nt), and transport layers materials. The results indicate that an optimal absorber thickness in the range of 1000\u0026ndash;1200 nm provides a balance between enhanced light absorption and efficient carrier collection. The N\u003csub\u003et\u003c/sub\u003e in the absorber layer was found to have a critical impact on device performance, with substantial degradation observed for densities above 10\u003csup\u003e16\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e due to non-radiative recombination. Furthermore, alternative transport layers were evaluated. Among the seven electron transport layers (ETLs) and five hole transport layers (HTLs) examined, the combination of C₆₀ and NiO yielded the highest simulated PCE of 18.29%, attributed to favorable band alignment and reduced interfacial losses. These results emphasize the importance of optimizing both absorber quality and interfacial materials in designing high-efficiency PSCs. The insights gained from this work provide valuable guidelines for experimental efforts aimed at the development of next-generation perovskite solar cells.\u003c/p\u003e","manuscriptTitle":"Numerical Optimization of CsₓFA₁₋ₓPbI₃-Based Perovskite Solar Cells Using SCAPS-1D","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 08:38:48","doi":"10.21203/rs.3.rs-6894058/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"831340e2-4e24-428f-a877-df9e97ed7a40","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50291488,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":50291489,"name":"Physical sciences/Materials science/Materials for devices"},{"id":50291490,"name":"Physical sciences/Materials science/Materials for energy and catalysis"},{"id":50291491,"name":"Physical sciences/Materials science/Nanoscale materials"},{"id":50291492,"name":"Physical sciences/Energy science and technology/Energy harvesting"},{"id":50291493,"name":"Physical sciences/Energy science and technology/Renewable energy"}],"tags":[],"updatedAt":"2025-08-06T10:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 08:38:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6894058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6894058","identity":"rs-6894058","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}